A 52-year-old man presented with bilateral weakness in all extremities. He noted the gradual onset of progressive muscle weakness 6 months prior to presentation. He reported generalized fatigue and difficulty with climbing stairs and carrying heavy objects.

Initial considerations of chronic weakness and fatigue are myopathy, polyneuropathy, medications, malignancy, endocrinopathies, human immunodeficiency virus (HIV), neuromuscular junction dysfunction, and central nervous system (CNS) disorders, such as amyotrophic lateral sclerosis (ALS) or multiple sclerosis (MS). Symmetrical muscle involvement and proximal weakness make myopathy most likely. Polyneuropathy, such as chronic inflammatory demyelinating polyneuropathy (CIDP), is less likely but still possible given the slowly progressive course. The use of medications that can cause myopathy should be explored, including colchicine, steroids, and statins. Gathering further history should focus on risk factors for HIV, as well as alcohol and illicit drug use. Malignancy can cause paraneoplastic myopathy. The review of systems should include symptoms of endocrinopathies, such as thyrotoxicosis and hypothyroidism. Fluctuations in weakness and dysphagia or ocular symptoms would suggest myasthenia gravis (MG). The time course and symmetrical weakness make a central disorder, such as ALS or MS, unlikely.

His past medical history was notable for pulmonary tuberculosis diagnosed at the age of 6 years, which was treated with hospitalization and an unknown medication regimen. He was not taking medications prior to this admission. His family history was significant for diabetes mellitus in both parents. He denied sick contacts. He was sexually active with his wife. He denied the use of tobacco and illicit drugs but endorsed alcohol consumption on a daily basis over the last 32 years. He reported no fluctuation in his symptoms, muscle or joint pains, rash, fevers, chills, diaphoresis, chest pain, dyspnea, abdominal pain, diarrhea, paresthesias, weight loss, or night sweats. He had never had a colonoscopy.

Painless progressive weakness of the limbs without sensory deficit is typical of a myopathy. Though CIDP can present with only motor weakness, the majority of patients have sensory symptoms, making this less likely. Although chronic alcohol abuse can cause myopathy, it seems less likely because other neurologic complications, such as sensory polyneuropathy or ataxia, would be expected. A review of systems does not suggest a thyroid disorder or malignancy, although this does not preclude an evaluation for both. The absence of fluctuations in weakness argues against MG. Though ALS, MG, MS, and CIDP are less likely, a neurologic exam is crucial in excluding them. The hallmark of ALS is upper motor neuron (UMN) and lower motor neuron signs in the absence of sensory symptoms and signs, while global hyporeflexia would be expected in CIDP, and fatigability on repeated power testing would be expected in MG. Neurologic findings disseminated in space (neuro-anatomically) would be expected in MS.

On physical examination, the patient had a temperature of 36.9°C, heart rate of 70 beats per minute, and regular respiratory rate of 10 breaths per minute, blood pressure 130/80 mmHg, and oxygen saturation 98% while breathing ambient air. Auscultation of the heart and lungs revealed normal findings. The abdomen was soft, nontender, and without masses or organomegaly. Neurologic examination disclosed bilateral symmetric upper and lower extremity weakness with positive Gower sign. Muscle strength scores of the bilateral biceps brachii, iliopsoas, and digitis extensor were between 4 and 5 without fatigability. Grasping power was impaired. Deep tendon reflexes were preserved, and there were no UMN signs. There was no tenderness to palpation in any muscle groups. Sensory testing was normal. Skin and lymph examinations were without abnormality. The rest of the physical examination was unremarkable.

Gower sign, characteristic of but not specific to muscular dystrophy, indicates proximal muscle weakness of lower extremities, wherein hands and arms are used to walk up the body into an upright position. The exam also reveals distal weakness as shown by reduced hand grasp. Symmetrical proximal weakness of all extremities without sensory deficits suggests a myopathic process, albeit one with some distal involvement. The absence of UMN signs argues against ALS, lack of fatigability argues against MG, and the absence of CNS or sensory deficits argues against MS.

 

Because myopathy is most likely, the next step would be to determine if this is an idiopathic inflammatory myopathy, such as polymyositis (PM) or dermatomyositis (DM), secondary inflammatory myopathy, or noninflammatory myopathy due to endocrinopathies. The time course is consistent with an inflammatory myopathy, such as PM or DM. Inclusion body myositis (IBM), another inflammatory myopathy, presents much more insidiously over years and tends to be asymmetric compared to PM. The absence of myalgia, arthralgia, rash, and gastrointestinal symptoms makes myopathy as a component of a connective tissue disease, such as systemic lupus erythematosus, or a mixed connective tissue disease unlikely. The next steps would be laboratory testing of muscle enzymes, complete blood count, biochemical profile, and antinuclear antibody (ANA).

Laboratory studies revealed a white blood cell count of 4460/mm³ with normal differential, hemoglobin 12.5 g/dL, and platelet count 345,000/mm³. Creatinine was 0.87 mg/dL, aspartate aminotransferase 61 IU/mL, alanine aminotransferase 45 IU/mL, and creatine kinase (CK) 529 U/L (normal range, 38-174 U/L). Other liver function enzymes were normal. Biochemistry studies disclosed normal sodium, potassium, glucose, calcium, and magnesium levels. Dipstick urinalysis revealed blood and protein, and the microscopic examination of urinary sediment was unremarkable without the presence of erythrocytes. Twenty-four-hour creatinine clearance was 106 mL/min (normal range, 97-137 mL/min). Chest radiography was unrevealing.

The modest increase in CK, evidence of myoglobinuria, and proteinuria can all occur with an inflammatory or metabolic myopathy. The combination of proximal and distal weakness, coupled with only a modestly elevated CK, makes IBM more likely than PM, as PM usually presents with proximal weakness and much higher CK values. Normal skin examination makes DM less likely, as skin manifestations are generally found at time of presentation. The onset of symptoms after age 50 and the patient being male also favor IBM, though a longer time course would be expected. Definitively distinguishing IBM from PM is important because treatment and prognosis differ.

Thyroid function and HIV testing should be obtained. ANA, more common in PM than in IBM, should be checked because these myopathies can be associated with other autoimmune diseases. Imaging is generally not essential, although magnetic resonance imaging (MRI) of the thighs may help to differentiate IBM from PM. Electromyography (EMG) should be done to determine the pattern of myopathy and select muscle biopsy sites.

Additional testing revealed a normal thyroid stimulating hormone level. HIV and ANA were negative. Serum aldolase level was 19 IU/L (normal range, 2.7-5.9 IU/L), myoglobin 277 ng/mL (normal range, 28-72 ng/mL), lactate dehydrogenase 416 IU/mL (normal range, 119-229 IU/mL), and C-reactive protein 0.32 mg/dL. An EMG revealed mild myogenic changes in all extremities. An MRI of the left brachial muscle revealed multiple scattered high-signal lesions.

The EMG and MRI findings are consistent with an inflammatory myopathy. The modest elevation in muscle enzymes and negative ANA are more consistent with IBM since most patients with PM or DM are ANA positive. Muscle biopsy can be very helpful in establishing the etiology of myopathy.

Given the concern for possible PM or DM, further imaging was obtained to assess for malignancy. Fluorodeoxyglucose (FDG) positron emission tomography (PET) and computerized axial tomography (CT) revealed multiple areas of linear uptake of FDG diffusely distributed along the bundles of systemic skeletal striated muscles (Figure 1). Gallium scintigraphy demonstrated intense uptake within the systemic skeletal striated muscles of all 4 extremities (Figure 2). There was no imaging evidence of malignancy.

Malignancy is associated with DM and PM in about 9% and 4% of patients, respectively. The common cancers associated with these conditions are adenocarcinomas of the ovary, cervix, lung, pancreas, and stomach. Most cancers are diagnosed around the time of myositis diagnosis, although they can precede or follow by years. Idiopathic IBM is not associated with cancer.

In idiopathic inflammatory myopathy, screening for cancer should consist of appropriate laboratory studies, chest radiography, and age-, sex-, and symptom-driven testing. FDG PET/CT is the most sensitive test for detecting occult cancer. The gallium scan positivity, though not specific, suggests possible sarcoid myopathy. Asymptomatic muscle involvement can be found histologically in up to 70% of patients with sarcoidosis, but symptomatic myopathy is uncommon. This patient has neither muscle pain nor evidence of thoracic sarcoidosis. Myopathy as an initial presentation of sarcoidosis is rare. Gallium scanning should be reserved for patients in whom muscle biopsy or other signs and symptoms suggest sarcoidosis.

Open surgical muscle biopsy of the left biceps brachii was performed. Light microscopic examination disclosed interstitial edema and noncaseating granulomas. Immunostaining revealed an increase in the number of cluster of differentiation (CD) 4+ T cells. Caseating granulomas and Langhans giant cells were not present (Figure 3).

The biopsy shows granulomatous myopathy (GM), suggestive of but not pathognomonic for sarcoid myopathy. GM can be found in other causes of inflammatory myopathies, including vasculitides, PM, DM, tuberculosis, inflammatory bowel disease, lymphoma, and MG. This patient has no symptoms, signs, laboratory, or radiologic evidence of any of the above conditions. Remaining possibilities include sarcoid chronic myopathy and idiopathic granulomatous myositis, but it is crucial to exclude all other etiologies. Serum antineutrophil cytoplasmic antibody (ANCA) should be checked, and biopsy specimens should be stained for acid-fast bacilli (AFB) and fungal elements. The gallium scan should be reviewed for salivary and lacrimal gland uptake (panda sign), which would be suggestive of sarcoidosis.

Tuberculin reaction and interferon-γ-release assay were negative. Staining for AFB and fungi was negative. ANCA, rheumatoid factor (RF), anti-Ro/SSA, anti-La/SSB, anti-Sm, anti-RNP, and anti-Jo-1 were all negative or unremarkable. Serum angiotensin converting enzyme (ACE) level was 155.6 U/L (normal range, 7-25 U/L). Twenty-four-hour urine analysis revealed calcium excretion of 517.7 mg/day (normal range, 58-450 mg/day), β2-microglobulin 69,627 ug/day (normal range, <254 ug/day), and N-acetyl-D-glucosamine 95.3 U/day (normal range, <5.1 U/day) with a normal creatinine clearance. Serum intact parathyroid hormone level (PTH) was 5 pg/mL (normal range, 10-65 pg/mL), and 25-hydroxyvitamin D level was 51.1 ng/mL (normal range, 30-80 ng/mL). A CT of the thorax revealed a small ground-glass density lesion in the left lower lobe but no hilar or mediastinal lymphadenopathy.

Negative ANCA, RF, and autoantibodies exclude systemic vasculitis and connective tissue disease as causes of GM. Hypercalciuria is suggestive of granulomatous production of calcitriol, which, in turn, suppresses PTH. Hypercalcemia is not common in patients with sarcoidosis, but hypercalciuria occurs frequently. Serum ACE is a marker associated with sarcoidosis, but its diagnostic and prognostic utility is unclear.

 

Though there is a concern for sarcoidosis, this diagnosis can only be confidently made by finding noncaseating granulomas on a background of compatible clinical and radiologic findings after alternate possible etiologies are excluded. The chest CT reveals a small ground-glass density lesion without hilar adenopathy. These findings, though not incompatible, are not typical for pulmonary sarcoidosis. Therefore, finding noncaeseating granulomas in a second organ system would point toward systemic sarcoidosis as a unifying diagnosis. Bronchoscopy with bronchoalveolar lavage (BAL) and transbronchial biopsy has a reasonable yield even in the absence of hilar adenopathy or typical parenchymal findings. A CD4/CD8 T-cell ratio of 2 or more on BAL provides supportive evidence for sarcoidosis.

It is reasonable to start empiric glucocorticoids for GM given that the AFB and fungal stains on histopathology are negative and that there is no evidence of lymphoma.

The patient underwent a bronchoscopy with BAL fluid, demonstrating 76% macrophages, 23.5% lymphocytes, and a CD4/CD8 T-cell ratio of 3.7. Culture of this fluid was negative for infection. The patient was diagnosed with sarcoidosis with the extrapulmonary manifestation of sarcoid myopathy. He underwent treatment with 1 mg/kg of prednisolone daily, which resulted in rapid decreases in serum CK and ACE levels as well as urine calcium excretion. He noted gradual improvement in his weakness over the ensuing 3 months. Also noted was the complete resolution of the uptake in systemic skeletal muscles on gallium scintigraphy (Figure 4). Eighteen months later, the patient is taking 7 mg of prednisolone daily and continues to be free of weakness.

The CD4/CD8 T-cell ratio greater than 2, combined with the absence of neutrophils and eosinophils on BAL, is helpful in distinguishing sarcoidosis from other pulmonary diseases. This patient’s inflammatory myopathy was revealed to be a rare initial manifestation of systemic sarcoidosis.

DISCUSSION

Weakness is a common symptom of muscle disorders such as myopathies and muscular dystrophy. Idiopathic inflammatory myopathies include PM, DM, and others.^1,2 These usually present with proximal-dominant muscle weakness, decreased endurance, and muscle inflammation. A diagnosis is made according to symptoms in combination with diagnostic examinations, including elevated serum CK levels, abnormal EMG findings, and histopathology of skeletal muscle biopsy specimens.

Sarcoidosis, a multisystem disorder of unknown etiology, is characterized histopathologically by noncaseating granulomas in affected organs.³ It typically affects young adults, with incidence peaking at 20 to 39 years of age. Although any organ may be involved, the disorder usually presents with 1 or more common abnormalities, including bilateral hilar lymphadenopathy, lung lesions, and skin and eye involvement. Musculoskeletal involvement is less common. It is estimated that skeletal muscle is involved in 50% to 80% of patients with sarcoidosis but is rarely symptomatic (0.5% to 2.5%).^4-6

In this patient, weakness was distributed in both proximal and distal muscles, yet proximal weakness is the most characteristic feature in PM and DM. Therefore, sarcoidosis should be considered in the differential diagnosis of idiopathic inflammatory myopathies, especially when weakness accompanies abnormalities in other organs typically affected by sarcoidosis.

Myoglobinuria often is observed in rhabdomyolysis and inflammatory myopathies, conditions that produce high levels of serum CK and myoglobin. Myoglobinuria, often accompanied by the elevation of urinary β2-microglobulin and N-acetyl-D-glucosamine levels, can induce tubulointerstitial damage, which leads to acute kidney injury. In this case, however, these abnormal kidney findings were observed without high levels of serum CK or myoglobin. This suggests the potential for other causes of tubulointerstitial damage, such as granulomatous interstitial nephritis in renal sarcoidosis.³

Another characteristic abnormality was the elevation of urinary calcium excretion, which indicated an underlying granulomatous disorder, such as mycobacterial infection, granulomatosis with polyangiitis, or sarcoidosis. In sarcoidosis, hypercalciuria occurs in 40% of patients, hypercalcemia in 11%, and renal calculi in 10%.^3,7 Hypercalciuria, for this patient, was important in arriving at the correct diagnosis after the gallium scan was obtained given the dearth of other typical features of sarcoidosis.

Although muscle biopsy is essential, imaging studies for idiopathic inflammatory myopathy are considered useful tools to narrow the differential diagnosis. The use of MRI of the skeletal muscle is helpful to both identify an adequate muscle for biopsy and demonstrate the pattern of affected muscles beyond clinical appearance, which aids in excluding, for example, muscular dystrophies.^8,9

FDG PET/CT is a very sensitive imaging modality used to detect neoplastic lesions and has been widely used to screen for occult neoplasms and detect metastases.^10-12 It is also useful for detecting inflammation in patients with osteomyelitis, metastatic infectious diseases, rheumatoid arthritis, vasculitis, inflammatory bowel diseases, fever of unknown origin, and sarcoidosis.^11,12 In PM and DM, however, the sensitivity of FDG PET/CT for detection of myositis is reportedly lower than that of EMG and MRI.¹³ Similarly, gallium scintigraphy is usually performed to examine the disease activity of interstitial pneumonia or to detect malignancy. Previous literature and this case show that the striking images of gallium scintigraphy and FDG PET/CT have utility, not only for detection of sarcoid myopathy but also for the evaluation of treatment efficacy.^14-17 Characteristic imaging findings on FDG PET/CT have been described as a “tiger man” appearance.¹⁷

For the treatment of sarcoid myopathy, systemic glucocorticoids are used for patients with symptomatic acute or chronic forms. The standard doses of prednisolone used for other forms of idiopathic inflammatory myopathies are usually administered.^3-6 In general, the response of acute sarcoid myopathy to glucocorticoid therapy is favorable, and the clinical course is usually benign. However, the course in chronic sarcoid myopathy can be unpredictable with exacerbations. Given the lack of randomized trials of this therapy and because glucocorticoids themselves can cause steroid-induced myopathy, they are not used for asymptomatic patients.

In the end, astute clinical thinking, deductive reasoning, and pattern recognition were all instrumental in making this strong diagnosis of weakness.

 

KEY TEACHING POINTS

• Proximal muscle–dominant weakness is the characteristic feature in inflammatory myopathies like PM and DM. Myopathy causing proximal and distal weakness is more characteristic of sarcoidosis, IBM, alcohol, and statins.
• Elevations of urinary Times New Romanβ2-microglobulin and N-acetyl-D-glucosamine are often observed in inflammatory muscle diseases because of myoglobin-induced tubulointerstitial damage. These findings may also be caused by other conditions that affect the tubules, such as lupus nephritis, Sjogren’s syndrome, or renal sarcoidosis.
• Hypercalciuria in a patient with myopathy could suggest an underlying granulomatous disorder, such as mycobacterial infection, granulomatosis with polyangiitis, or sarcoidosis.
• The striking uptake within systemic skeletal striated muscles on gallium scintigraphy and “tiger man” appearance on FDG PET/CT are characteristic features of acute sarcoid myopathy; these are not common in other inflammatory myopathies.

Disclosure

Drs. Sudo, Wada, Narita, Mba, and Houchens have no conflicts of interest to disclose.

A 52-year-old man presented with bilateral weakness in all extremities. He noted the gradual onset of progressive muscle weakness 6 months prior to presentation. He reported generalized fatigue and difficulty with climbing stairs and carrying heavy objects.

Initial considerations of chronic weakness and fatigue are myopathy, polyneuropathy, medications, malignancy, endocrinopathies, human immunodeficiency virus (HIV), neuromuscular junction dysfunction, and central nervous system (CNS) disorders, such as amyotrophic lateral sclerosis (ALS) or multiple sclerosis (MS). Symmetrical muscle involvement and proximal weakness make myopathy most likely. Polyneuropathy, such as chronic inflammatory demyelinating polyneuropathy (CIDP), is less likely but still possible given the slowly progressive course. The use of medications that can cause myopathy should be explored, including colchicine, steroids, and statins. Gathering further history should focus on risk factors for HIV, as well as alcohol and illicit drug use. Malignancy can cause paraneoplastic myopathy. The review of systems should include symptoms of endocrinopathies, such as thyrotoxicosis and hypothyroidism. Fluctuations in weakness and dysphagia or ocular symptoms would suggest myasthenia gravis (MG). The time course and symmetrical weakness make a central disorder, such as ALS or MS, unlikely.

His past medical history was notable for pulmonary tuberculosis diagnosed at the age of 6 years, which was treated with hospitalization and an unknown medication regimen. He was not taking medications prior to this admission. His family history was significant for diabetes mellitus in both parents. He denied sick contacts. He was sexually active with his wife. He denied the use of tobacco and illicit drugs but endorsed alcohol consumption on a daily basis over the last 32 years. He reported no fluctuation in his symptoms, muscle or joint pains, rash, fevers, chills, diaphoresis, chest pain, dyspnea, abdominal pain, diarrhea, paresthesias, weight loss, or night sweats. He had never had a colonoscopy.

Painless progressive weakness of the limbs without sensory deficit is typical of a myopathy. Though CIDP can present with only motor weakness, the majority of patients have sensory symptoms, making this less likely. Although chronic alcohol abuse can cause myopathy, it seems less likely because other neurologic complications, such as sensory polyneuropathy or ataxia, would be expected. A review of systems does not suggest a thyroid disorder or malignancy, although this does not preclude an evaluation for both. The absence of fluctuations in weakness argues against MG. Though ALS, MG, MS, and CIDP are less likely, a neurologic exam is crucial in excluding them. The hallmark of ALS is upper motor neuron (UMN) and lower motor neuron signs in the absence of sensory symptoms and signs, while global hyporeflexia would be expected in CIDP, and fatigability on repeated power testing would be expected in MG. Neurologic findings disseminated in space (neuro-anatomically) would be expected in MS.

On physical examination, the patient had a temperature of 36.9°C, heart rate of 70 beats per minute, and regular respiratory rate of 10 breaths per minute, blood pressure 130/80 mmHg, and oxygen saturation 98% while breathing ambient air. Auscultation of the heart and lungs revealed normal findings. The abdomen was soft, nontender, and without masses or organomegaly. Neurologic examination disclosed bilateral symmetric upper and lower extremity weakness with positive Gower sign. Muscle strength scores of the bilateral biceps brachii, iliopsoas, and digitis extensor were between 4 and 5 without fatigability. Grasping power was impaired. Deep tendon reflexes were preserved, and there were no UMN signs. There was no tenderness to palpation in any muscle groups. Sensory testing was normal. Skin and lymph examinations were without abnormality. The rest of the physical examination was unremarkable.

Gower sign, characteristic of but not specific to muscular dystrophy, indicates proximal muscle weakness of lower extremities, wherein hands and arms are used to walk up the body into an upright position. The exam also reveals distal weakness as shown by reduced hand grasp. Symmetrical proximal weakness of all extremities without sensory deficits suggests a myopathic process, albeit one with some distal involvement. The absence of UMN signs argues against ALS, lack of fatigability argues against MG, and the absence of CNS or sensory deficits argues against MS.

 

Because myopathy is most likely, the next step would be to determine if this is an idiopathic inflammatory myopathy, such as polymyositis (PM) or dermatomyositis (DM), secondary inflammatory myopathy, or noninflammatory myopathy due to endocrinopathies. The time course is consistent with an inflammatory myopathy, such as PM or DM. Inclusion body myositis (IBM), another inflammatory myopathy, presents much more insidiously over years and tends to be asymmetric compared to PM. The absence of myalgia, arthralgia, rash, and gastrointestinal symptoms makes myopathy as a component of a connective tissue disease, such as systemic lupus erythematosus, or a mixed connective tissue disease unlikely. The next steps would be laboratory testing of muscle enzymes, complete blood count, biochemical profile, and antinuclear antibody (ANA).

Laboratory studies revealed a white blood cell count of 4460/mm³ with normal differential, hemoglobin 12.5 g/dL, and platelet count 345,000/mm³. Creatinine was 0.87 mg/dL, aspartate aminotransferase 61 IU/mL, alanine aminotransferase 45 IU/mL, and creatine kinase (CK) 529 U/L (normal range, 38-174 U/L). Other liver function enzymes were normal. Biochemistry studies disclosed normal sodium, potassium, glucose, calcium, and magnesium levels. Dipstick urinalysis revealed blood and protein, and the microscopic examination of urinary sediment was unremarkable without the presence of erythrocytes. Twenty-four-hour creatinine clearance was 106 mL/min (normal range, 97-137 mL/min). Chest radiography was unrevealing.

The modest increase in CK, evidence of myoglobinuria, and proteinuria can all occur with an inflammatory or metabolic myopathy. The combination of proximal and distal weakness, coupled with only a modestly elevated CK, makes IBM more likely than PM, as PM usually presents with proximal weakness and much higher CK values. Normal skin examination makes DM less likely, as skin manifestations are generally found at time of presentation. The onset of symptoms after age 50 and the patient being male also favor IBM, though a longer time course would be expected. Definitively distinguishing IBM from PM is important because treatment and prognosis differ.

Thyroid function and HIV testing should be obtained. ANA, more common in PM than in IBM, should be checked because these myopathies can be associated with other autoimmune diseases. Imaging is generally not essential, although magnetic resonance imaging (MRI) of the thighs may help to differentiate IBM from PM. Electromyography (EMG) should be done to determine the pattern of myopathy and select muscle biopsy sites.

Additional testing revealed a normal thyroid stimulating hormone level. HIV and ANA were negative. Serum aldolase level was 19 IU/L (normal range, 2.7-5.9 IU/L), myoglobin 277 ng/mL (normal range, 28-72 ng/mL), lactate dehydrogenase 416 IU/mL (normal range, 119-229 IU/mL), and C-reactive protein 0.32 mg/dL. An EMG revealed mild myogenic changes in all extremities. An MRI of the left brachial muscle revealed multiple scattered high-signal lesions.

The EMG and MRI findings are consistent with an inflammatory myopathy. The modest elevation in muscle enzymes and negative ANA are more consistent with IBM since most patients with PM or DM are ANA positive. Muscle biopsy can be very helpful in establishing the etiology of myopathy.

Given the concern for possible PM or DM, further imaging was obtained to assess for malignancy. Fluorodeoxyglucose (FDG) positron emission tomography (PET) and computerized axial tomography (CT) revealed multiple areas of linear uptake of FDG diffusely distributed along the bundles of systemic skeletal striated muscles (Figure 1). Gallium scintigraphy demonstrated intense uptake within the systemic skeletal striated muscles of all 4 extremities (Figure 2). There was no imaging evidence of malignancy.

Malignancy is associated with DM and PM in about 9% and 4% of patients, respectively. The common cancers associated with these conditions are adenocarcinomas of the ovary, cervix, lung, pancreas, and stomach. Most cancers are diagnosed around the time of myositis diagnosis, although they can precede or follow by years. Idiopathic IBM is not associated with cancer.

In idiopathic inflammatory myopathy, screening for cancer should consist of appropriate laboratory studies, chest radiography, and age-, sex-, and symptom-driven testing. FDG PET/CT is the most sensitive test for detecting occult cancer. The gallium scan positivity, though not specific, suggests possible sarcoid myopathy. Asymptomatic muscle involvement can be found histologically in up to 70% of patients with sarcoidosis, but symptomatic myopathy is uncommon. This patient has neither muscle pain nor evidence of thoracic sarcoidosis. Myopathy as an initial presentation of sarcoidosis is rare. Gallium scanning should be reserved for patients in whom muscle biopsy or other signs and symptoms suggest sarcoidosis.

Open surgical muscle biopsy of the left biceps brachii was performed. Light microscopic examination disclosed interstitial edema and noncaseating granulomas. Immunostaining revealed an increase in the number of cluster of differentiation (CD) 4+ T cells. Caseating granulomas and Langhans giant cells were not present (Figure 3).

The biopsy shows granulomatous myopathy (GM), suggestive of but not pathognomonic for sarcoid myopathy. GM can be found in other causes of inflammatory myopathies, including vasculitides, PM, DM, tuberculosis, inflammatory bowel disease, lymphoma, and MG. This patient has no symptoms, signs, laboratory, or radiologic evidence of any of the above conditions. Remaining possibilities include sarcoid chronic myopathy and idiopathic granulomatous myositis, but it is crucial to exclude all other etiologies. Serum antineutrophil cytoplasmic antibody (ANCA) should be checked, and biopsy specimens should be stained for acid-fast bacilli (AFB) and fungal elements. The gallium scan should be reviewed for salivary and lacrimal gland uptake (panda sign), which would be suggestive of sarcoidosis.

Tuberculin reaction and interferon-γ-release assay were negative. Staining for AFB and fungi was negative. ANCA, rheumatoid factor (RF), anti-Ro/SSA, anti-La/SSB, anti-Sm, anti-RNP, and anti-Jo-1 were all negative or unremarkable. Serum angiotensin converting enzyme (ACE) level was 155.6 U/L (normal range, 7-25 U/L). Twenty-four-hour urine analysis revealed calcium excretion of 517.7 mg/day (normal range, 58-450 mg/day), β2-microglobulin 69,627 ug/day (normal range, <254 ug/day), and N-acetyl-D-glucosamine 95.3 U/day (normal range, <5.1 U/day) with a normal creatinine clearance. Serum intact parathyroid hormone level (PTH) was 5 pg/mL (normal range, 10-65 pg/mL), and 25-hydroxyvitamin D level was 51.1 ng/mL (normal range, 30-80 ng/mL). A CT of the thorax revealed a small ground-glass density lesion in the left lower lobe but no hilar or mediastinal lymphadenopathy.

Negative ANCA, RF, and autoantibodies exclude systemic vasculitis and connective tissue disease as causes of GM. Hypercalciuria is suggestive of granulomatous production of calcitriol, which, in turn, suppresses PTH. Hypercalcemia is not common in patients with sarcoidosis, but hypercalciuria occurs frequently. Serum ACE is a marker associated with sarcoidosis, but its diagnostic and prognostic utility is unclear.

 

Though there is a concern for sarcoidosis, this diagnosis can only be confidently made by finding noncaseating granulomas on a background of compatible clinical and radiologic findings after alternate possible etiologies are excluded. The chest CT reveals a small ground-glass density lesion without hilar adenopathy. These findings, though not incompatible, are not typical for pulmonary sarcoidosis. Therefore, finding noncaeseating granulomas in a second organ system would point toward systemic sarcoidosis as a unifying diagnosis. Bronchoscopy with bronchoalveolar lavage (BAL) and transbronchial biopsy has a reasonable yield even in the absence of hilar adenopathy or typical parenchymal findings. A CD4/CD8 T-cell ratio of 2 or more on BAL provides supportive evidence for sarcoidosis.

It is reasonable to start empiric glucocorticoids for GM given that the AFB and fungal stains on histopathology are negative and that there is no evidence of lymphoma.

The patient underwent a bronchoscopy with BAL fluid, demonstrating 76% macrophages, 23.5% lymphocytes, and a CD4/CD8 T-cell ratio of 3.7. Culture of this fluid was negative for infection. The patient was diagnosed with sarcoidosis with the extrapulmonary manifestation of sarcoid myopathy. He underwent treatment with 1 mg/kg of prednisolone daily, which resulted in rapid decreases in serum CK and ACE levels as well as urine calcium excretion. He noted gradual improvement in his weakness over the ensuing 3 months. Also noted was the complete resolution of the uptake in systemic skeletal muscles on gallium scintigraphy (Figure 4). Eighteen months later, the patient is taking 7 mg of prednisolone daily and continues to be free of weakness.

The CD4/CD8 T-cell ratio greater than 2, combined with the absence of neutrophils and eosinophils on BAL, is helpful in distinguishing sarcoidosis from other pulmonary diseases. This patient’s inflammatory myopathy was revealed to be a rare initial manifestation of systemic sarcoidosis.

DISCUSSION

Weakness is a common symptom of muscle disorders such as myopathies and muscular dystrophy. Idiopathic inflammatory myopathies include PM, DM, and others.^1,2 These usually present with proximal-dominant muscle weakness, decreased endurance, and muscle inflammation. A diagnosis is made according to symptoms in combination with diagnostic examinations, including elevated serum CK levels, abnormal EMG findings, and histopathology of skeletal muscle biopsy specimens.

Sarcoidosis, a multisystem disorder of unknown etiology, is characterized histopathologically by noncaseating granulomas in affected organs.³ It typically affects young adults, with incidence peaking at 20 to 39 years of age. Although any organ may be involved, the disorder usually presents with 1 or more common abnormalities, including bilateral hilar lymphadenopathy, lung lesions, and skin and eye involvement. Musculoskeletal involvement is less common. It is estimated that skeletal muscle is involved in 50% to 80% of patients with sarcoidosis but is rarely symptomatic (0.5% to 2.5%).^4-6

In this patient, weakness was distributed in both proximal and distal muscles, yet proximal weakness is the most characteristic feature in PM and DM. Therefore, sarcoidosis should be considered in the differential diagnosis of idiopathic inflammatory myopathies, especially when weakness accompanies abnormalities in other organs typically affected by sarcoidosis.

Myoglobinuria often is observed in rhabdomyolysis and inflammatory myopathies, conditions that produce high levels of serum CK and myoglobin. Myoglobinuria, often accompanied by the elevation of urinary β2-microglobulin and N-acetyl-D-glucosamine levels, can induce tubulointerstitial damage, which leads to acute kidney injury. In this case, however, these abnormal kidney findings were observed without high levels of serum CK or myoglobin. This suggests the potential for other causes of tubulointerstitial damage, such as granulomatous interstitial nephritis in renal sarcoidosis.³

Another characteristic abnormality was the elevation of urinary calcium excretion, which indicated an underlying granulomatous disorder, such as mycobacterial infection, granulomatosis with polyangiitis, or sarcoidosis. In sarcoidosis, hypercalciuria occurs in 40% of patients, hypercalcemia in 11%, and renal calculi in 10%.^3,7 Hypercalciuria, for this patient, was important in arriving at the correct diagnosis after the gallium scan was obtained given the dearth of other typical features of sarcoidosis.

Although muscle biopsy is essential, imaging studies for idiopathic inflammatory myopathy are considered useful tools to narrow the differential diagnosis. The use of MRI of the skeletal muscle is helpful to both identify an adequate muscle for biopsy and demonstrate the pattern of affected muscles beyond clinical appearance, which aids in excluding, for example, muscular dystrophies.^8,9

FDG PET/CT is a very sensitive imaging modality used to detect neoplastic lesions and has been widely used to screen for occult neoplasms and detect metastases.^10-12 It is also useful for detecting inflammation in patients with osteomyelitis, metastatic infectious diseases, rheumatoid arthritis, vasculitis, inflammatory bowel diseases, fever of unknown origin, and sarcoidosis.^11,12 In PM and DM, however, the sensitivity of FDG PET/CT for detection of myositis is reportedly lower than that of EMG and MRI.¹³ Similarly, gallium scintigraphy is usually performed to examine the disease activity of interstitial pneumonia or to detect malignancy. Previous literature and this case show that the striking images of gallium scintigraphy and FDG PET/CT have utility, not only for detection of sarcoid myopathy but also for the evaluation of treatment efficacy.^14-17 Characteristic imaging findings on FDG PET/CT have been described as a “tiger man” appearance.¹⁷

For the treatment of sarcoid myopathy, systemic glucocorticoids are used for patients with symptomatic acute or chronic forms. The standard doses of prednisolone used for other forms of idiopathic inflammatory myopathies are usually administered.^3-6 In general, the response of acute sarcoid myopathy to glucocorticoid therapy is favorable, and the clinical course is usually benign. However, the course in chronic sarcoid myopathy can be unpredictable with exacerbations. Given the lack of randomized trials of this therapy and because glucocorticoids themselves can cause steroid-induced myopathy, they are not used for asymptomatic patients.

In the end, astute clinical thinking, deductive reasoning, and pattern recognition were all instrumental in making this strong diagnosis of weakness.

 

KEY TEACHING POINTS

• Proximal muscle–dominant weakness is the characteristic feature in inflammatory myopathies like PM and DM. Myopathy causing proximal and distal weakness is more characteristic of sarcoidosis, IBM, alcohol, and statins.
• Elevations of urinary Times New Romanβ2-microglobulin and N-acetyl-D-glucosamine are often observed in inflammatory muscle diseases because of myoglobin-induced tubulointerstitial damage. These findings may also be caused by other conditions that affect the tubules, such as lupus nephritis, Sjogren’s syndrome, or renal sarcoidosis.
• Hypercalciuria in a patient with myopathy could suggest an underlying granulomatous disorder, such as mycobacterial infection, granulomatosis with polyangiitis, or sarcoidosis.
• The striking uptake within systemic skeletal striated muscles on gallium scintigraphy and “tiger man” appearance on FDG PET/CT are characteristic features of acute sarcoid myopathy; these are not common in other inflammatory myopathies.

Disclosure

Drs. Sudo, Wada, Narita, Mba, and Houchens have no conflicts of interest to disclose.

A 52-year-old man presented with bilateral weakness in all extremities. He noted the gradual onset of progressive muscle weakness 6 months prior to presentation. He reported generalized fatigue and difficulty with climbing stairs and carrying heavy objects.

Initial considerations of chronic weakness and fatigue are myopathy, polyneuropathy, medications, malignancy, endocrinopathies, human immunodeficiency virus (HIV), neuromuscular junction dysfunction, and central nervous system (CNS) disorders, such as amyotrophic lateral sclerosis (ALS) or multiple sclerosis (MS). Symmetrical muscle involvement and proximal weakness make myopathy most likely. Polyneuropathy, such as chronic inflammatory demyelinating polyneuropathy (CIDP), is less likely but still possible given the slowly progressive course. The use of medications that can cause myopathy should be explored, including colchicine, steroids, and statins. Gathering further history should focus on risk factors for HIV, as well as alcohol and illicit drug use. Malignancy can cause paraneoplastic myopathy. The review of systems should include symptoms of endocrinopathies, such as thyrotoxicosis and hypothyroidism. Fluctuations in weakness and dysphagia or ocular symptoms would suggest myasthenia gravis (MG). The time course and symmetrical weakness make a central disorder, such as ALS or MS, unlikely.

His past medical history was notable for pulmonary tuberculosis diagnosed at the age of 6 years, which was treated with hospitalization and an unknown medication regimen. He was not taking medications prior to this admission. His family history was significant for diabetes mellitus in both parents. He denied sick contacts. He was sexually active with his wife. He denied the use of tobacco and illicit drugs but endorsed alcohol consumption on a daily basis over the last 32 years. He reported no fluctuation in his symptoms, muscle or joint pains, rash, fevers, chills, diaphoresis, chest pain, dyspnea, abdominal pain, diarrhea, paresthesias, weight loss, or night sweats. He had never had a colonoscopy.

Painless progressive weakness of the limbs without sensory deficit is typical of a myopathy. Though CIDP can present with only motor weakness, the majority of patients have sensory symptoms, making this less likely. Although chronic alcohol abuse can cause myopathy, it seems less likely because other neurologic complications, such as sensory polyneuropathy or ataxia, would be expected. A review of systems does not suggest a thyroid disorder or malignancy, although this does not preclude an evaluation for both. The absence of fluctuations in weakness argues against MG. Though ALS, MG, MS, and CIDP are less likely, a neurologic exam is crucial in excluding them. The hallmark of ALS is upper motor neuron (UMN) and lower motor neuron signs in the absence of sensory symptoms and signs, while global hyporeflexia would be expected in CIDP, and fatigability on repeated power testing would be expected in MG. Neurologic findings disseminated in space (neuro-anatomically) would be expected in MS.

On physical examination, the patient had a temperature of 36.9°C, heart rate of 70 beats per minute, and regular respiratory rate of 10 breaths per minute, blood pressure 130/80 mmHg, and oxygen saturation 98% while breathing ambient air. Auscultation of the heart and lungs revealed normal findings. The abdomen was soft, nontender, and without masses or organomegaly. Neurologic examination disclosed bilateral symmetric upper and lower extremity weakness with positive Gower sign. Muscle strength scores of the bilateral biceps brachii, iliopsoas, and digitis extensor were between 4 and 5 without fatigability. Grasping power was impaired. Deep tendon reflexes were preserved, and there were no UMN signs. There was no tenderness to palpation in any muscle groups. Sensory testing was normal. Skin and lymph examinations were without abnormality. The rest of the physical examination was unremarkable.

Gower sign, characteristic of but not specific to muscular dystrophy, indicates proximal muscle weakness of lower extremities, wherein hands and arms are used to walk up the body into an upright position. The exam also reveals distal weakness as shown by reduced hand grasp. Symmetrical proximal weakness of all extremities without sensory deficits suggests a myopathic process, albeit one with some distal involvement. The absence of UMN signs argues against ALS, lack of fatigability argues against MG, and the absence of CNS or sensory deficits argues against MS.

 

Because myopathy is most likely, the next step would be to determine if this is an idiopathic inflammatory myopathy, such as polymyositis (PM) or dermatomyositis (DM), secondary inflammatory myopathy, or noninflammatory myopathy due to endocrinopathies. The time course is consistent with an inflammatory myopathy, such as PM or DM. Inclusion body myositis (IBM), another inflammatory myopathy, presents much more insidiously over years and tends to be asymmetric compared to PM. The absence of myalgia, arthralgia, rash, and gastrointestinal symptoms makes myopathy as a component of a connective tissue disease, such as systemic lupus erythematosus, or a mixed connective tissue disease unlikely. The next steps would be laboratory testing of muscle enzymes, complete blood count, biochemical profile, and antinuclear antibody (ANA).

Laboratory studies revealed a white blood cell count of 4460/mm³ with normal differential, hemoglobin 12.5 g/dL, and platelet count 345,000/mm³. Creatinine was 0.87 mg/dL, aspartate aminotransferase 61 IU/mL, alanine aminotransferase 45 IU/mL, and creatine kinase (CK) 529 U/L (normal range, 38-174 U/L). Other liver function enzymes were normal. Biochemistry studies disclosed normal sodium, potassium, glucose, calcium, and magnesium levels. Dipstick urinalysis revealed blood and protein, and the microscopic examination of urinary sediment was unremarkable without the presence of erythrocytes. Twenty-four-hour creatinine clearance was 106 mL/min (normal range, 97-137 mL/min). Chest radiography was unrevealing.

The modest increase in CK, evidence of myoglobinuria, and proteinuria can all occur with an inflammatory or metabolic myopathy. The combination of proximal and distal weakness, coupled with only a modestly elevated CK, makes IBM more likely than PM, as PM usually presents with proximal weakness and much higher CK values. Normal skin examination makes DM less likely, as skin manifestations are generally found at time of presentation. The onset of symptoms after age 50 and the patient being male also favor IBM, though a longer time course would be expected. Definitively distinguishing IBM from PM is important because treatment and prognosis differ.

Thyroid function and HIV testing should be obtained. ANA, more common in PM than in IBM, should be checked because these myopathies can be associated with other autoimmune diseases. Imaging is generally not essential, although magnetic resonance imaging (MRI) of the thighs may help to differentiate IBM from PM. Electromyography (EMG) should be done to determine the pattern of myopathy and select muscle biopsy sites.

Additional testing revealed a normal thyroid stimulating hormone level. HIV and ANA were negative. Serum aldolase level was 19 IU/L (normal range, 2.7-5.9 IU/L), myoglobin 277 ng/mL (normal range, 28-72 ng/mL), lactate dehydrogenase 416 IU/mL (normal range, 119-229 IU/mL), and C-reactive protein 0.32 mg/dL. An EMG revealed mild myogenic changes in all extremities. An MRI of the left brachial muscle revealed multiple scattered high-signal lesions.

The EMG and MRI findings are consistent with an inflammatory myopathy. The modest elevation in muscle enzymes and negative ANA are more consistent with IBM since most patients with PM or DM are ANA positive. Muscle biopsy can be very helpful in establishing the etiology of myopathy.

Given the concern for possible PM or DM, further imaging was obtained to assess for malignancy. Fluorodeoxyglucose (FDG) positron emission tomography (PET) and computerized axial tomography (CT) revealed multiple areas of linear uptake of FDG diffusely distributed along the bundles of systemic skeletal striated muscles (Figure 1). Gallium scintigraphy demonstrated intense uptake within the systemic skeletal striated muscles of all 4 extremities (Figure 2). There was no imaging evidence of malignancy.

Malignancy is associated with DM and PM in about 9% and 4% of patients, respectively. The common cancers associated with these conditions are adenocarcinomas of the ovary, cervix, lung, pancreas, and stomach. Most cancers are diagnosed around the time of myositis diagnosis, although they can precede or follow by years. Idiopathic IBM is not associated with cancer.

In idiopathic inflammatory myopathy, screening for cancer should consist of appropriate laboratory studies, chest radiography, and age-, sex-, and symptom-driven testing. FDG PET/CT is the most sensitive test for detecting occult cancer. The gallium scan positivity, though not specific, suggests possible sarcoid myopathy. Asymptomatic muscle involvement can be found histologically in up to 70% of patients with sarcoidosis, but symptomatic myopathy is uncommon. This patient has neither muscle pain nor evidence of thoracic sarcoidosis. Myopathy as an initial presentation of sarcoidosis is rare. Gallium scanning should be reserved for patients in whom muscle biopsy or other signs and symptoms suggest sarcoidosis.

Open surgical muscle biopsy of the left biceps brachii was performed. Light microscopic examination disclosed interstitial edema and noncaseating granulomas. Immunostaining revealed an increase in the number of cluster of differentiation (CD) 4+ T cells. Caseating granulomas and Langhans giant cells were not present (Figure 3).

The biopsy shows granulomatous myopathy (GM), suggestive of but not pathognomonic for sarcoid myopathy. GM can be found in other causes of inflammatory myopathies, including vasculitides, PM, DM, tuberculosis, inflammatory bowel disease, lymphoma, and MG. This patient has no symptoms, signs, laboratory, or radiologic evidence of any of the above conditions. Remaining possibilities include sarcoid chronic myopathy and idiopathic granulomatous myositis, but it is crucial to exclude all other etiologies. Serum antineutrophil cytoplasmic antibody (ANCA) should be checked, and biopsy specimens should be stained for acid-fast bacilli (AFB) and fungal elements. The gallium scan should be reviewed for salivary and lacrimal gland uptake (panda sign), which would be suggestive of sarcoidosis.

Tuberculin reaction and interferon-γ-release assay were negative. Staining for AFB and fungi was negative. ANCA, rheumatoid factor (RF), anti-Ro/SSA, anti-La/SSB, anti-Sm, anti-RNP, and anti-Jo-1 were all negative or unremarkable. Serum angiotensin converting enzyme (ACE) level was 155.6 U/L (normal range, 7-25 U/L). Twenty-four-hour urine analysis revealed calcium excretion of 517.7 mg/day (normal range, 58-450 mg/day), β2-microglobulin 69,627 ug/day (normal range, <254 ug/day), and N-acetyl-D-glucosamine 95.3 U/day (normal range, <5.1 U/day) with a normal creatinine clearance. Serum intact parathyroid hormone level (PTH) was 5 pg/mL (normal range, 10-65 pg/mL), and 25-hydroxyvitamin D level was 51.1 ng/mL (normal range, 30-80 ng/mL). A CT of the thorax revealed a small ground-glass density lesion in the left lower lobe but no hilar or mediastinal lymphadenopathy.

Negative ANCA, RF, and autoantibodies exclude systemic vasculitis and connective tissue disease as causes of GM. Hypercalciuria is suggestive of granulomatous production of calcitriol, which, in turn, suppresses PTH. Hypercalcemia is not common in patients with sarcoidosis, but hypercalciuria occurs frequently. Serum ACE is a marker associated with sarcoidosis, but its diagnostic and prognostic utility is unclear.

 

Though there is a concern for sarcoidosis, this diagnosis can only be confidently made by finding noncaseating granulomas on a background of compatible clinical and radiologic findings after alternate possible etiologies are excluded. The chest CT reveals a small ground-glass density lesion without hilar adenopathy. These findings, though not incompatible, are not typical for pulmonary sarcoidosis. Therefore, finding noncaeseating granulomas in a second organ system would point toward systemic sarcoidosis as a unifying diagnosis. Bronchoscopy with bronchoalveolar lavage (BAL) and transbronchial biopsy has a reasonable yield even in the absence of hilar adenopathy or typical parenchymal findings. A CD4/CD8 T-cell ratio of 2 or more on BAL provides supportive evidence for sarcoidosis.

It is reasonable to start empiric glucocorticoids for GM given that the AFB and fungal stains on histopathology are negative and that there is no evidence of lymphoma.

The patient underwent a bronchoscopy with BAL fluid, demonstrating 76% macrophages, 23.5% lymphocytes, and a CD4/CD8 T-cell ratio of 3.7. Culture of this fluid was negative for infection. The patient was diagnosed with sarcoidosis with the extrapulmonary manifestation of sarcoid myopathy. He underwent treatment with 1 mg/kg of prednisolone daily, which resulted in rapid decreases in serum CK and ACE levels as well as urine calcium excretion. He noted gradual improvement in his weakness over the ensuing 3 months. Also noted was the complete resolution of the uptake in systemic skeletal muscles on gallium scintigraphy (Figure 4). Eighteen months later, the patient is taking 7 mg of prednisolone daily and continues to be free of weakness.

The CD4/CD8 T-cell ratio greater than 2, combined with the absence of neutrophils and eosinophils on BAL, is helpful in distinguishing sarcoidosis from other pulmonary diseases. This patient’s inflammatory myopathy was revealed to be a rare initial manifestation of systemic sarcoidosis.

DISCUSSION

Weakness is a common symptom of muscle disorders such as myopathies and muscular dystrophy. Idiopathic inflammatory myopathies include PM, DM, and others.^1,2 These usually present with proximal-dominant muscle weakness, decreased endurance, and muscle inflammation. A diagnosis is made according to symptoms in combination with diagnostic examinations, including elevated serum CK levels, abnormal EMG findings, and histopathology of skeletal muscle biopsy specimens.

Sarcoidosis, a multisystem disorder of unknown etiology, is characterized histopathologically by noncaseating granulomas in affected organs.³ It typically affects young adults, with incidence peaking at 20 to 39 years of age. Although any organ may be involved, the disorder usually presents with 1 or more common abnormalities, including bilateral hilar lymphadenopathy, lung lesions, and skin and eye involvement. Musculoskeletal involvement is less common. It is estimated that skeletal muscle is involved in 50% to 80% of patients with sarcoidosis but is rarely symptomatic (0.5% to 2.5%).^4-6

In this patient, weakness was distributed in both proximal and distal muscles, yet proximal weakness is the most characteristic feature in PM and DM. Therefore, sarcoidosis should be considered in the differential diagnosis of idiopathic inflammatory myopathies, especially when weakness accompanies abnormalities in other organs typically affected by sarcoidosis.

Myoglobinuria often is observed in rhabdomyolysis and inflammatory myopathies, conditions that produce high levels of serum CK and myoglobin. Myoglobinuria, often accompanied by the elevation of urinary β2-microglobulin and N-acetyl-D-glucosamine levels, can induce tubulointerstitial damage, which leads to acute kidney injury. In this case, however, these abnormal kidney findings were observed without high levels of serum CK or myoglobin. This suggests the potential for other causes of tubulointerstitial damage, such as granulomatous interstitial nephritis in renal sarcoidosis.³

Another characteristic abnormality was the elevation of urinary calcium excretion, which indicated an underlying granulomatous disorder, such as mycobacterial infection, granulomatosis with polyangiitis, or sarcoidosis. In sarcoidosis, hypercalciuria occurs in 40% of patients, hypercalcemia in 11%, and renal calculi in 10%.^3,7 Hypercalciuria, for this patient, was important in arriving at the correct diagnosis after the gallium scan was obtained given the dearth of other typical features of sarcoidosis.

Although muscle biopsy is essential, imaging studies for idiopathic inflammatory myopathy are considered useful tools to narrow the differential diagnosis. The use of MRI of the skeletal muscle is helpful to both identify an adequate muscle for biopsy and demonstrate the pattern of affected muscles beyond clinical appearance, which aids in excluding, for example, muscular dystrophies.^8,9

FDG PET/CT is a very sensitive imaging modality used to detect neoplastic lesions and has been widely used to screen for occult neoplasms and detect metastases.^10-12 It is also useful for detecting inflammation in patients with osteomyelitis, metastatic infectious diseases, rheumatoid arthritis, vasculitis, inflammatory bowel diseases, fever of unknown origin, and sarcoidosis.^11,12 In PM and DM, however, the sensitivity of FDG PET/CT for detection of myositis is reportedly lower than that of EMG and MRI.¹³ Similarly, gallium scintigraphy is usually performed to examine the disease activity of interstitial pneumonia or to detect malignancy. Previous literature and this case show that the striking images of gallium scintigraphy and FDG PET/CT have utility, not only for detection of sarcoid myopathy but also for the evaluation of treatment efficacy.^14-17 Characteristic imaging findings on FDG PET/CT have been described as a “tiger man” appearance.¹⁷

For the treatment of sarcoid myopathy, systemic glucocorticoids are used for patients with symptomatic acute or chronic forms. The standard doses of prednisolone used for other forms of idiopathic inflammatory myopathies are usually administered.^3-6 In general, the response of acute sarcoid myopathy to glucocorticoid therapy is favorable, and the clinical course is usually benign. However, the course in chronic sarcoid myopathy can be unpredictable with exacerbations. Given the lack of randomized trials of this therapy and because glucocorticoids themselves can cause steroid-induced myopathy, they are not used for asymptomatic patients.

In the end, astute clinical thinking, deductive reasoning, and pattern recognition were all instrumental in making this strong diagnosis of weakness.

 

KEY TEACHING POINTS

• Proximal muscle–dominant weakness is the characteristic feature in inflammatory myopathies like PM and DM. Myopathy causing proximal and distal weakness is more characteristic of sarcoidosis, IBM, alcohol, and statins.
• Elevations of urinary Times New Romanβ2-microglobulin and N-acetyl-D-glucosamine are often observed in inflammatory muscle diseases because of myoglobin-induced tubulointerstitial damage. These findings may also be caused by other conditions that affect the tubules, such as lupus nephritis, Sjogren’s syndrome, or renal sarcoidosis.
• Hypercalciuria in a patient with myopathy could suggest an underlying granulomatous disorder, such as mycobacterial infection, granulomatosis with polyangiitis, or sarcoidosis.
• The striking uptake within systemic skeletal striated muscles on gallium scintigraphy and “tiger man” appearance on FDG PET/CT are characteristic features of acute sarcoid myopathy; these are not common in other inflammatory myopathies.

Disclosure

Drs. Sudo, Wada, Narita, Mba, and Houchens have no conflicts of interest to disclose.

Approximately a century ago, Francis Peabody taught that “the secret of the care of the patient is in caring for the patient.”¹ His advice remains true today. Despite the advent of novel diagnostic tests, technologically sophisticated interventional procedures, and life-saving medications, perhaps the most important skill a bedside clinician can use is the ability to connect with patients.

The literature on patient-physician interaction is vast^2-11 and generally indicates that exemplary bedside clinicians are able to interact well with patients by being competent, trustworthy, personable, empathetic, and effective communicators. “Etiquette-based medicine,” first proposed by Kahn,¹² emphasizes the importance of certain behaviors from physicians, such as introducing yourself and explaining your role, shaking hands, sitting down when speaking to patients, and asking open-ended questions.

Yet, improving patient-physician interactions remains necessary. A recent systematic review reported that almost half of the reviewed studies on the patient-physician relationship published between 2000 and 2014 conveyed the idea that the patient-physician relationship is deteriorating.¹³

As part of a broader study to understand the behaviors and approaches of exemplary inpatient attending physicians,^14-16 we examined how 12 carefully selected physicians interacted with their patients during inpatient teaching rounds.

METHODS

Overview

We conducted a multisite study using an exploratory, qualitative approach to inquiry, which has been described previously.^14-16 Our primary purpose was to study the attributes and behaviors of outstanding general medicine attendings in the setting of inpatient rounds. The focus of this article is on the attendings’ interactions with patients.

We used a modified snowball sampling approach¹⁷ to identify 12 exemplary physicians. First, we contacted individuals throughout the United States who were known to the principal investigator (S.S.) and asked for suggestions of excellent clinician educators (also referred to as attendings) for potential inclusion in the study. In addition to these personal contacts, other individuals unknown to the investigative team were contacted and asked to provide suggestions for attendings to include in the study. Specifically, the US News & World Report 2015 Top Medical Schools: Research Rankings,¹⁸ which are widely used to represent the best U.S. hospitals, were reviewed in an effort to identify attendings from a broad range of medical schools. Using this list, we identified other medical schools that were in the top 25 and were not already represented. We contacted the division chiefs of general internal (or hospital) medicine, chairs and chiefs of departments of internal medicine, and internal medicine residency program directors from these medical schools and asked for recommendations of attendings from both within and outside their institutions whom they considered to be great inpatient teachers.

This sampling method resulted in 59 potential participants. An internet search was conducted on each potential participant to obtain further information about the individuals and their institutions. Both personal characteristics (medical education, training, and educational awards) and organizational characteristics (geographic location, hospital size and affiliation, and patient population) were considered so that a variety of organizations and backgrounds were represented. Through this process, the list was narrowed to 16 attendings who were contacted to participate in the study, of which 12 agreed. The number of attendings examined was appropriate because saturation of metathemes can occur in as little as 6 interviews, and data saturation occurs at 12 interviews.¹⁹ The participants were asked to provide a list of their current learners (ie, residents and medical students) and 6 to 10 former learners to contact for interviews and focus groups.

Data Collection

Observations

Two researchers conducted the one-day site visits. One was a physician (S.S.) and the other a medical anthropologist (M.H.), and both have extensive experience in qualitative methods. The only exception was the site visit at the principal investigator’s own institution, which was conducted by the medical anthropologist and a nonpracticing physician who was unknown to the participants. The team structure varied slightly among different institutions but in general was composed of 1 attending, 1 senior medical resident, 1 to 2 interns, and approximately 2 medical students. Each site visit began with observing the attendings (n = 12) and current learners (n = 57) on morning rounds, which included their interactions with patients. These observations lasted approximately 2 to 3 hours. The observers took handwritten field notes, paying particular attention to group interactions, teaching approaches, and patient interactions. The observers stood outside the medical team circle and remained silent during rounds so as to be unobtrusive to the teams’ discussions. The observers discussed and compared their notes after each site visit.

 

Interviews and Focus Groups

The research team also conducted individual, semistructured interviews with the attendings (n = 12), focus groups with their current teams (n = 46), and interviews or focus groups with their former learners (n = 26). Current learners were asked open-ended questions about their roles on the teams, their opinions of the attendings, and the care the attendings provide to their patients. Because they were observed during rounds, the researchers asked for clarification about specific interactions observed during the teaching rounds. Depending on availability and location, former learners either participated in in-person focus groups or interviews on the day of the site visit, or in a later telephone interview. All interviews and focus groups were audio recorded and transcribed.

This study was deemed to be exempt from regulation by the University of Michigan Institutional Review Board. All participants were informed that their participation was completely voluntary and that they could refuse to answer any question.

Data Analysis

Data were analyzed using a thematic analysis approach,²⁰ which involves reading through the data to identify patterns (and create codes) that relate to behaviors, experiences, meanings, and activities. The patterns are then grouped into themes to help further explain the findings.²¹ The research team members (S.S. and M.H.) met after the first site visit and developed initial ideas about meanings and possible patterns. One team member (M.H.) read all the transcripts from the site visit and, based on the data, developed a codebook to be used for this study. This process was repeated after every site visit, and the coding definitions were refined as necessary. All transcripts were reviewed to apply any new codes when they developed. NVivo^® 10 software (QSR International, Melbourne, Australia) was used to assist with the qualitative data analysis.

To ensure consistency and identify relationships between codes, code reports listing all the data linked to a specific code were generated after all the field notes and transcripts were coded. Once verified, codes were grouped based on similarities and relationships into prominent themes related to physician-patient interactions by 2 team members (S.S. and M.H.), though all members reviewed them and concurred.

RESULTS

A total of 12 attending physicians participated (Table 1). The participants were from hospitals located throughout the U.S. and included both university-affiliated hospitals and Veterans Affairs medical centers. We observed the attending physicians interact with more than 100 patients, with 3 major patient interaction themes emerging. Table 2 lists key approaches for effective patient-physician interactions based on the study findings.

Care for the Patient’s Well-Being

The attendings we observed appeared to openly care for their patients’ well-being and were focused on the patients’ wants and needs. We noted that attendings were generally very attentive to the patients’ comfort. For example, we observed one attending sending the senior resident to find the patient’s nurse in order to obtain additional pain medications. The attending said to the patient several times, “I’m sorry you’re in so much pain.” When the team was leaving, she asked the intern to stay with the patient until the medications had been administered.

Learners noticed when an attending physician was especially skilled at demonstrating empathy and patient-centered care. While education on rounds was emphasized, patient connection was the priority. One learner described the following: “… he really is just so passionate about patient care and has so much empathy, really. And I will tell you, of all my favorite things about him, that is one of them...”

The attendings we observed could also be considered patient advocates, ensuring that patients received superb care. As one learner said about an attending who was attempting to have his patient listed for a liver transplant, “He is the biggest advocate for the patient that I have ever seen.” Regarding the balance between learning biomedical concepts and advocacy, another learner noted the following: “… there is always a teaching aspect, but he always makes sure that everything is taken care of for the patient…”

Building rapport creates and sustains bonds between people. Even though most of the attendings we observed primarily cared for hospitalized patients and had little long-term continuity with them, the attendings tended to take special care to talk with their patients about topics other than medicine to form a bond. This bonding between attending and patient was appreciated by learners. “Probably the most important thing I learned about patient care would be taking the time and really developing that relationship with patients,” said one of the former learners we interviewed. “There’s a question that he asks to a lot of our patients,” one learner told us, “especially our elderly patients, that [is], ‘What’s the most memorable moment in your life?’ So, he asks that question, and patient[s] open up and will share.”

The attendings often used touch to further solidify their relationships with their patients. We observed one attending who would touch her patients’ arms or knees when she was talking with them. Another attending would always shake the patient’s hand when leaving. Another attending would often lay his hand on the patient’s shoulder and help the patient sit up during the physical examination. Such humanistic behavior was noticed by learners. “She does a lot of comforting touch, particularly at the end of an exam,” said a current learner.

 

Consideration of the “Big Picture”

Our exemplary attendings kept the “big picture” (that is, the patient’s overall medical and social needs) in clear focus. They behaved in a way to ensure that the patients understood the key points of their care and explained so the patients and families could understand. A current learner said, “[The attending] really makes sure that the patient understands what’s going on. And she always asks them, ‘What do you understand, what do you know, how can we fill in any blanks?’ And that makes the patient really involved in their own care, which I think is important.” This reflection was supported by direct observations. Attendings posed the following questions at the conclusion of patient interactions: “Tell me what you know.” “Tell me what our plan is.” “What did the lung doctors tell you yesterday?” These questions, which have been termed “teach-back” and are crucial for health literacy, were not meant to quiz the patient but rather to ensure the patient and family understood the plan.

We noticed that the attendings effectively explained clinical details and the plan of care to the patient while avoiding medical jargon. The following is an example of one interaction with a patient: “You threw up and created a tear in the food tube. Air got from that into the middle of the chest, not into the lungs. Air isn’t normally there. If it is just air, the body will reabsorb [it]... But we worry about bacteria getting in with the air. We need to figure out if it is an infection. We’re still trying to figure it out. Hang in there with us.” One learner commented, “… since we do bedside presentations, he has a great way of translating our gibberish, basically, to real language the patient understands.”

Finally, the attendings anticipated what patients would need in the outpatient setting. We observed that attendings stressed what the next steps would be during transitions of care. As one learner put it, “But he also thinks ahead; what do they need as an outpatient?” Another current learner commented on how another attending always asked about the social situations of his patients stating, “And then there is the social part of it. So, he is very much interested [in] where do they live? What is their support system? So, I think it has been a very holistic approach to patient care.”

Respect for the Patient

The attendings we observed were steadfastly respectful toward patients. As one attending told us, “The patient’s room is sacred space, and it’s a privilege for us to be there. And if we don’t earn that privilege, then we don’t get to go there.” We observed that the attendings generally referred to the patient as Mr. or Ms. (last name) rather than the patient’s first name unless the patient insisted. We also noticed that many of the attendings would introduce the team members to the patients or ask each member to introduce himself or herself. They also tended to leave the room and patient the way they were found, for example, by pushing the patient’s bedside table so that it was back within his or her reach or placing socks back onto the patient’s feet.

We noted that many of our attendings used appropriate humor with patients and families. As one learner explained, “I think Dr. [attending] makes most of our patients laugh during rounds. I don’t know if you noticed, but he really puts a smile on their face[s] whenever he walks in. … Maybe it would catch them off guard the first day, but after that, they are so happy to see him.”

Finally, we noticed that several of our attendings made sure to meet the patient at eye level during discussions by either kneeling or sitting on a chair. One of the attendings put it this way: “That’s a horrible power dynamic when you’re an inpatient and you’re sick and someone’s standing over you telling you things, and I like to be able to make eye contact with people, and often times that requires me to kneel down or to sit on a stool or to sit on the bed. … I feel like you’re able to connect with the people in a much better way…” Learners viewed this behavior favorably. As one told us, “[The attending] gets down to their level and makes sure that all of their questions are answered. So that is one thing that other attendings don’t necessarily do.”

DISCUSSION

In our national, qualitative study of 12 exemplary attending physicians, we found that these clinicians generally exhibited the following behaviors with patients. First, they were personable and caring and made significant attempts to connect with their patients. This occasionally took the form of using touch to comfort patients. Second, they tended to seek the “big picture” and tried to understand what patients would need upon hospital discharge. They communicated plans clearly to patients and families and inquired if those plans were understood. Finally, they showed respect toward their patients without fail. Such respect took many forms but included leaving the patient and room exactly as they were found and speaking with patients at eye level.

 

Our findings are largely consistent with other key studies in this field. Not surprisingly, the attendings we observed adhered to the major suggestions that Branch and colleagues² put forth more than 15 years ago to improve the teaching of the humanistic dimension of the patient-physician relationship. Examples include greeting the patient, introducing team members and explaining each person’s role, asking open-ended questions, providing patient education, placing oneself at the same level as the patient, using appropriate touch, and being respectful. Weissmann et al.²² also found similar themes in their study of teaching physicians at 4 universities from 2003 to 2004. In that study, role-modeling was the primary method used by physician educators to teach the humanistic aspects of medical care, including nonverbal communication (eg, touch and eye contact), demonstration of respect, and building a personal connection with the patients.²²In a focus group-based study performed at a teaching hospital in Boston, Ramani and Orlander²³ concluded that both participating teachers and learners considered the patient’s bedside as a valuable venue to learn humanistic skills. Unfortunately, they also noted that there has been a decline in bedside teaching related to various factors, including documentation requirements and electronic medical records.²³ Our attendings all demonstrated the value of teaching at a patient’s bedside. Not only could physical examination skills be demonstrated but role-modeling of interpersonal skills could be observed by learners.

Block and colleagues²⁴ observed 29 interns in 732 patient encounters in 2 Baltimore training programs using Kahn’s “etiquette-based medicine” behaviors as a guide.¹² They found that interns introduced themselves 40% of the time, explained their role 37% of the time, touched patients on 65% of visits (including as part of the physical examination), asked open-ended questions 75% of the time, and sat down with patients during only 9% of visits.²⁴ Tackett et al.⁷ observed 24 hospitalists who collectively cared for 226 unique patients in 3 Baltimore-area hospitals. They found that each of the following behaviors was performed less than 30% of the time: explains role in care, shakes hand, and sits down.⁷ However, our attendings appeared to adhere to these behaviors to a much higher extent, though we did not quantify the interactions. This lends support to the notion that effective patient-physician interactions are the foundation of great teaching.

The attendings we observed (most of whom are inpatient based) tended to the contextual issues of the patients, such as their home environments and social support. Our exemplary physicians did what they could to ensure that patients received the appropriate follow-up care upon discharge.

Our study has important limitations. First, it was conducted in a limited number of US hospitals. The institutions represented were generally large, research-intensive, academic medical centers. Therefore, our findings may not apply to settings that are different from the hospitals studied. Second, our study included only 12 attendings and their learners, which may also limit the study’s generalizability. Third, we focused exclusively on teaching within general medicine rounds. Thus, our findings may not be generalizable to other subspecialties. Fourth, attendings were selected through a nonexhaustive method, increasing the potential for selection bias. However, the multisite design, the modified snowball sampling, and the inclusion of several types of institutions in the final participant pool introduced diversity to the final list. Former-learner responses were subject to recall bias. Finally, the study design is susceptible to observer bias. Attempts to reduce this included the diversity of the observers (ie, both a clinician and a nonclinician, the latter of whom was unfamiliar with medical education) and review of the data and coding by multiple research team members to ensure validity. Although we cannot discount the potential role of a Hawthorne effect on our data collection, the research team attempted to mitigate this by standing apart from the care teams and remaining unobtrusive during observations.

Limitations notwithstanding, we believe that our multisite study is important given the longstanding imperative to improve patient-physician interactions. We found empirical support for behaviors proposed by Branch and colleagues² and Kahn¹² in order to enhance these relationships. While others have studied attendings and their current learners,²² we add to the literature by also examining former learners’ perspectives on how the attendings’ teaching and role-modeling have created and sustained a lasting impact. The key findings of our national, qualitative study (care for the patient’s well-being, consideration of the “big picture,” and respect for the patient) can be readily adopted and honed by physicians to improve their interactions with hospitalized patients.

Acknowledgments

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Department of Veterans Affairs.

 

Funding

Dr. Saint provided funding for this study using a University of Michigan endowment.

Disclosure

The authors declare no conflicts of interest.

Approximately a century ago, Francis Peabody taught that “the secret of the care of the patient is in caring for the patient.”¹ His advice remains true today. Despite the advent of novel diagnostic tests, technologically sophisticated interventional procedures, and life-saving medications, perhaps the most important skill a bedside clinician can use is the ability to connect with patients.

The literature on patient-physician interaction is vast^2-11 and generally indicates that exemplary bedside clinicians are able to interact well with patients by being competent, trustworthy, personable, empathetic, and effective communicators. “Etiquette-based medicine,” first proposed by Kahn,¹² emphasizes the importance of certain behaviors from physicians, such as introducing yourself and explaining your role, shaking hands, sitting down when speaking to patients, and asking open-ended questions.

Yet, improving patient-physician interactions remains necessary. A recent systematic review reported that almost half of the reviewed studies on the patient-physician relationship published between 2000 and 2014 conveyed the idea that the patient-physician relationship is deteriorating.¹³

As part of a broader study to understand the behaviors and approaches of exemplary inpatient attending physicians,^14-16 we examined how 12 carefully selected physicians interacted with their patients during inpatient teaching rounds.

METHODS

Overview

We conducted a multisite study using an exploratory, qualitative approach to inquiry, which has been described previously.^14-16 Our primary purpose was to study the attributes and behaviors of outstanding general medicine attendings in the setting of inpatient rounds. The focus of this article is on the attendings’ interactions with patients.

We used a modified snowball sampling approach¹⁷ to identify 12 exemplary physicians. First, we contacted individuals throughout the United States who were known to the principal investigator (S.S.) and asked for suggestions of excellent clinician educators (also referred to as attendings) for potential inclusion in the study. In addition to these personal contacts, other individuals unknown to the investigative team were contacted and asked to provide suggestions for attendings to include in the study. Specifically, the US News & World Report 2015 Top Medical Schools: Research Rankings,¹⁸ which are widely used to represent the best U.S. hospitals, were reviewed in an effort to identify attendings from a broad range of medical schools. Using this list, we identified other medical schools that were in the top 25 and were not already represented. We contacted the division chiefs of general internal (or hospital) medicine, chairs and chiefs of departments of internal medicine, and internal medicine residency program directors from these medical schools and asked for recommendations of attendings from both within and outside their institutions whom they considered to be great inpatient teachers.

This sampling method resulted in 59 potential participants. An internet search was conducted on each potential participant to obtain further information about the individuals and their institutions. Both personal characteristics (medical education, training, and educational awards) and organizational characteristics (geographic location, hospital size and affiliation, and patient population) were considered so that a variety of organizations and backgrounds were represented. Through this process, the list was narrowed to 16 attendings who were contacted to participate in the study, of which 12 agreed. The number of attendings examined was appropriate because saturation of metathemes can occur in as little as 6 interviews, and data saturation occurs at 12 interviews.¹⁹ The participants were asked to provide a list of their current learners (ie, residents and medical students) and 6 to 10 former learners to contact for interviews and focus groups.

Data Collection

Observations

Two researchers conducted the one-day site visits. One was a physician (S.S.) and the other a medical anthropologist (M.H.), and both have extensive experience in qualitative methods. The only exception was the site visit at the principal investigator’s own institution, which was conducted by the medical anthropologist and a nonpracticing physician who was unknown to the participants. The team structure varied slightly among different institutions but in general was composed of 1 attending, 1 senior medical resident, 1 to 2 interns, and approximately 2 medical students. Each site visit began with observing the attendings (n = 12) and current learners (n = 57) on morning rounds, which included their interactions with patients. These observations lasted approximately 2 to 3 hours. The observers took handwritten field notes, paying particular attention to group interactions, teaching approaches, and patient interactions. The observers stood outside the medical team circle and remained silent during rounds so as to be unobtrusive to the teams’ discussions. The observers discussed and compared their notes after each site visit.

 

Interviews and Focus Groups

The research team also conducted individual, semistructured interviews with the attendings (n = 12), focus groups with their current teams (n = 46), and interviews or focus groups with their former learners (n = 26). Current learners were asked open-ended questions about their roles on the teams, their opinions of the attendings, and the care the attendings provide to their patients. Because they were observed during rounds, the researchers asked for clarification about specific interactions observed during the teaching rounds. Depending on availability and location, former learners either participated in in-person focus groups or interviews on the day of the site visit, or in a later telephone interview. All interviews and focus groups were audio recorded and transcribed.

This study was deemed to be exempt from regulation by the University of Michigan Institutional Review Board. All participants were informed that their participation was completely voluntary and that they could refuse to answer any question.

Data Analysis

Data were analyzed using a thematic analysis approach,²⁰ which involves reading through the data to identify patterns (and create codes) that relate to behaviors, experiences, meanings, and activities. The patterns are then grouped into themes to help further explain the findings.²¹ The research team members (S.S. and M.H.) met after the first site visit and developed initial ideas about meanings and possible patterns. One team member (M.H.) read all the transcripts from the site visit and, based on the data, developed a codebook to be used for this study. This process was repeated after every site visit, and the coding definitions were refined as necessary. All transcripts were reviewed to apply any new codes when they developed. NVivo^® 10 software (QSR International, Melbourne, Australia) was used to assist with the qualitative data analysis.

To ensure consistency and identify relationships between codes, code reports listing all the data linked to a specific code were generated after all the field notes and transcripts were coded. Once verified, codes were grouped based on similarities and relationships into prominent themes related to physician-patient interactions by 2 team members (S.S. and M.H.), though all members reviewed them and concurred.

RESULTS

A total of 12 attending physicians participated (Table 1). The participants were from hospitals located throughout the U.S. and included both university-affiliated hospitals and Veterans Affairs medical centers. We observed the attending physicians interact with more than 100 patients, with 3 major patient interaction themes emerging. Table 2 lists key approaches for effective patient-physician interactions based on the study findings.

Care for the Patient’s Well-Being

The attendings we observed appeared to openly care for their patients’ well-being and were focused on the patients’ wants and needs. We noted that attendings were generally very attentive to the patients’ comfort. For example, we observed one attending sending the senior resident to find the patient’s nurse in order to obtain additional pain medications. The attending said to the patient several times, “I’m sorry you’re in so much pain.” When the team was leaving, she asked the intern to stay with the patient until the medications had been administered.

Learners noticed when an attending physician was especially skilled at demonstrating empathy and patient-centered care. While education on rounds was emphasized, patient connection was the priority. One learner described the following: “… he really is just so passionate about patient care and has so much empathy, really. And I will tell you, of all my favorite things about him, that is one of them...”

The attendings we observed could also be considered patient advocates, ensuring that patients received superb care. As one learner said about an attending who was attempting to have his patient listed for a liver transplant, “He is the biggest advocate for the patient that I have ever seen.” Regarding the balance between learning biomedical concepts and advocacy, another learner noted the following: “… there is always a teaching aspect, but he always makes sure that everything is taken care of for the patient…”

Building rapport creates and sustains bonds between people. Even though most of the attendings we observed primarily cared for hospitalized patients and had little long-term continuity with them, the attendings tended to take special care to talk with their patients about topics other than medicine to form a bond. This bonding between attending and patient was appreciated by learners. “Probably the most important thing I learned about patient care would be taking the time and really developing that relationship with patients,” said one of the former learners we interviewed. “There’s a question that he asks to a lot of our patients,” one learner told us, “especially our elderly patients, that [is], ‘What’s the most memorable moment in your life?’ So, he asks that question, and patient[s] open up and will share.”

The attendings often used touch to further solidify their relationships with their patients. We observed one attending who would touch her patients’ arms or knees when she was talking with them. Another attending would always shake the patient’s hand when leaving. Another attending would often lay his hand on the patient’s shoulder and help the patient sit up during the physical examination. Such humanistic behavior was noticed by learners. “She does a lot of comforting touch, particularly at the end of an exam,” said a current learner.

 

Consideration of the “Big Picture”

Our exemplary attendings kept the “big picture” (that is, the patient’s overall medical and social needs) in clear focus. They behaved in a way to ensure that the patients understood the key points of their care and explained so the patients and families could understand. A current learner said, “[The attending] really makes sure that the patient understands what’s going on. And she always asks them, ‘What do you understand, what do you know, how can we fill in any blanks?’ And that makes the patient really involved in their own care, which I think is important.” This reflection was supported by direct observations. Attendings posed the following questions at the conclusion of patient interactions: “Tell me what you know.” “Tell me what our plan is.” “What did the lung doctors tell you yesterday?” These questions, which have been termed “teach-back” and are crucial for health literacy, were not meant to quiz the patient but rather to ensure the patient and family understood the plan.

We noticed that the attendings effectively explained clinical details and the plan of care to the patient while avoiding medical jargon. The following is an example of one interaction with a patient: “You threw up and created a tear in the food tube. Air got from that into the middle of the chest, not into the lungs. Air isn’t normally there. If it is just air, the body will reabsorb [it]... But we worry about bacteria getting in with the air. We need to figure out if it is an infection. We’re still trying to figure it out. Hang in there with us.” One learner commented, “… since we do bedside presentations, he has a great way of translating our gibberish, basically, to real language the patient understands.”

Finally, the attendings anticipated what patients would need in the outpatient setting. We observed that attendings stressed what the next steps would be during transitions of care. As one learner put it, “But he also thinks ahead; what do they need as an outpatient?” Another current learner commented on how another attending always asked about the social situations of his patients stating, “And then there is the social part of it. So, he is very much interested [in] where do they live? What is their support system? So, I think it has been a very holistic approach to patient care.”

Respect for the Patient

The attendings we observed were steadfastly respectful toward patients. As one attending told us, “The patient’s room is sacred space, and it’s a privilege for us to be there. And if we don’t earn that privilege, then we don’t get to go there.” We observed that the attendings generally referred to the patient as Mr. or Ms. (last name) rather than the patient’s first name unless the patient insisted. We also noticed that many of the attendings would introduce the team members to the patients or ask each member to introduce himself or herself. They also tended to leave the room and patient the way they were found, for example, by pushing the patient’s bedside table so that it was back within his or her reach or placing socks back onto the patient’s feet.

We noted that many of our attendings used appropriate humor with patients and families. As one learner explained, “I think Dr. [attending] makes most of our patients laugh during rounds. I don’t know if you noticed, but he really puts a smile on their face[s] whenever he walks in. … Maybe it would catch them off guard the first day, but after that, they are so happy to see him.”

Finally, we noticed that several of our attendings made sure to meet the patient at eye level during discussions by either kneeling or sitting on a chair. One of the attendings put it this way: “That’s a horrible power dynamic when you’re an inpatient and you’re sick and someone’s standing over you telling you things, and I like to be able to make eye contact with people, and often times that requires me to kneel down or to sit on a stool or to sit on the bed. … I feel like you’re able to connect with the people in a much better way…” Learners viewed this behavior favorably. As one told us, “[The attending] gets down to their level and makes sure that all of their questions are answered. So that is one thing that other attendings don’t necessarily do.”

DISCUSSION

In our national, qualitative study of 12 exemplary attending physicians, we found that these clinicians generally exhibited the following behaviors with patients. First, they were personable and caring and made significant attempts to connect with their patients. This occasionally took the form of using touch to comfort patients. Second, they tended to seek the “big picture” and tried to understand what patients would need upon hospital discharge. They communicated plans clearly to patients and families and inquired if those plans were understood. Finally, they showed respect toward their patients without fail. Such respect took many forms but included leaving the patient and room exactly as they were found and speaking with patients at eye level.

 

Our findings are largely consistent with other key studies in this field. Not surprisingly, the attendings we observed adhered to the major suggestions that Branch and colleagues² put forth more than 15 years ago to improve the teaching of the humanistic dimension of the patient-physician relationship. Examples include greeting the patient, introducing team members and explaining each person’s role, asking open-ended questions, providing patient education, placing oneself at the same level as the patient, using appropriate touch, and being respectful. Weissmann et al.²² also found similar themes in their study of teaching physicians at 4 universities from 2003 to 2004. In that study, role-modeling was the primary method used by physician educators to teach the humanistic aspects of medical care, including nonverbal communication (eg, touch and eye contact), demonstration of respect, and building a personal connection with the patients.²²In a focus group-based study performed at a teaching hospital in Boston, Ramani and Orlander²³ concluded that both participating teachers and learners considered the patient’s bedside as a valuable venue to learn humanistic skills. Unfortunately, they also noted that there has been a decline in bedside teaching related to various factors, including documentation requirements and electronic medical records.²³ Our attendings all demonstrated the value of teaching at a patient’s bedside. Not only could physical examination skills be demonstrated but role-modeling of interpersonal skills could be observed by learners.

Block and colleagues²⁴ observed 29 interns in 732 patient encounters in 2 Baltimore training programs using Kahn’s “etiquette-based medicine” behaviors as a guide.¹² They found that interns introduced themselves 40% of the time, explained their role 37% of the time, touched patients on 65% of visits (including as part of the physical examination), asked open-ended questions 75% of the time, and sat down with patients during only 9% of visits.²⁴ Tackett et al.⁷ observed 24 hospitalists who collectively cared for 226 unique patients in 3 Baltimore-area hospitals. They found that each of the following behaviors was performed less than 30% of the time: explains role in care, shakes hand, and sits down.⁷ However, our attendings appeared to adhere to these behaviors to a much higher extent, though we did not quantify the interactions. This lends support to the notion that effective patient-physician interactions are the foundation of great teaching.

The attendings we observed (most of whom are inpatient based) tended to the contextual issues of the patients, such as their home environments and social support. Our exemplary physicians did what they could to ensure that patients received the appropriate follow-up care upon discharge.

Our study has important limitations. First, it was conducted in a limited number of US hospitals. The institutions represented were generally large, research-intensive, academic medical centers. Therefore, our findings may not apply to settings that are different from the hospitals studied. Second, our study included only 12 attendings and their learners, which may also limit the study’s generalizability. Third, we focused exclusively on teaching within general medicine rounds. Thus, our findings may not be generalizable to other subspecialties. Fourth, attendings were selected through a nonexhaustive method, increasing the potential for selection bias. However, the multisite design, the modified snowball sampling, and the inclusion of several types of institutions in the final participant pool introduced diversity to the final list. Former-learner responses were subject to recall bias. Finally, the study design is susceptible to observer bias. Attempts to reduce this included the diversity of the observers (ie, both a clinician and a nonclinician, the latter of whom was unfamiliar with medical education) and review of the data and coding by multiple research team members to ensure validity. Although we cannot discount the potential role of a Hawthorne effect on our data collection, the research team attempted to mitigate this by standing apart from the care teams and remaining unobtrusive during observations.

Limitations notwithstanding, we believe that our multisite study is important given the longstanding imperative to improve patient-physician interactions. We found empirical support for behaviors proposed by Branch and colleagues² and Kahn¹² in order to enhance these relationships. While others have studied attendings and their current learners,²² we add to the literature by also examining former learners’ perspectives on how the attendings’ teaching and role-modeling have created and sustained a lasting impact. The key findings of our national, qualitative study (care for the patient’s well-being, consideration of the “big picture,” and respect for the patient) can be readily adopted and honed by physicians to improve their interactions with hospitalized patients.

Acknowledgments

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Department of Veterans Affairs.

 

Funding

Dr. Saint provided funding for this study using a University of Michigan endowment.

Disclosure

The authors declare no conflicts of interest.

Approximately a century ago, Francis Peabody taught that “the secret of the care of the patient is in caring for the patient.”¹ His advice remains true today. Despite the advent of novel diagnostic tests, technologically sophisticated interventional procedures, and life-saving medications, perhaps the most important skill a bedside clinician can use is the ability to connect with patients.

The literature on patient-physician interaction is vast^2-11 and generally indicates that exemplary bedside clinicians are able to interact well with patients by being competent, trustworthy, personable, empathetic, and effective communicators. “Etiquette-based medicine,” first proposed by Kahn,¹² emphasizes the importance of certain behaviors from physicians, such as introducing yourself and explaining your role, shaking hands, sitting down when speaking to patients, and asking open-ended questions.

Yet, improving patient-physician interactions remains necessary. A recent systematic review reported that almost half of the reviewed studies on the patient-physician relationship published between 2000 and 2014 conveyed the idea that the patient-physician relationship is deteriorating.¹³

As part of a broader study to understand the behaviors and approaches of exemplary inpatient attending physicians,^14-16 we examined how 12 carefully selected physicians interacted with their patients during inpatient teaching rounds.

METHODS

Overview

We conducted a multisite study using an exploratory, qualitative approach to inquiry, which has been described previously.^14-16 Our primary purpose was to study the attributes and behaviors of outstanding general medicine attendings in the setting of inpatient rounds. The focus of this article is on the attendings’ interactions with patients.

We used a modified snowball sampling approach¹⁷ to identify 12 exemplary physicians. First, we contacted individuals throughout the United States who were known to the principal investigator (S.S.) and asked for suggestions of excellent clinician educators (also referred to as attendings) for potential inclusion in the study. In addition to these personal contacts, other individuals unknown to the investigative team were contacted and asked to provide suggestions for attendings to include in the study. Specifically, the US News & World Report 2015 Top Medical Schools: Research Rankings,¹⁸ which are widely used to represent the best U.S. hospitals, were reviewed in an effort to identify attendings from a broad range of medical schools. Using this list, we identified other medical schools that were in the top 25 and were not already represented. We contacted the division chiefs of general internal (or hospital) medicine, chairs and chiefs of departments of internal medicine, and internal medicine residency program directors from these medical schools and asked for recommendations of attendings from both within and outside their institutions whom they considered to be great inpatient teachers.

This sampling method resulted in 59 potential participants. An internet search was conducted on each potential participant to obtain further information about the individuals and their institutions. Both personal characteristics (medical education, training, and educational awards) and organizational characteristics (geographic location, hospital size and affiliation, and patient population) were considered so that a variety of organizations and backgrounds were represented. Through this process, the list was narrowed to 16 attendings who were contacted to participate in the study, of which 12 agreed. The number of attendings examined was appropriate because saturation of metathemes can occur in as little as 6 interviews, and data saturation occurs at 12 interviews.¹⁹ The participants were asked to provide a list of their current learners (ie, residents and medical students) and 6 to 10 former learners to contact for interviews and focus groups.

Data Collection

Observations

Two researchers conducted the one-day site visits. One was a physician (S.S.) and the other a medical anthropologist (M.H.), and both have extensive experience in qualitative methods. The only exception was the site visit at the principal investigator’s own institution, which was conducted by the medical anthropologist and a nonpracticing physician who was unknown to the participants. The team structure varied slightly among different institutions but in general was composed of 1 attending, 1 senior medical resident, 1 to 2 interns, and approximately 2 medical students. Each site visit began with observing the attendings (n = 12) and current learners (n = 57) on morning rounds, which included their interactions with patients. These observations lasted approximately 2 to 3 hours. The observers took handwritten field notes, paying particular attention to group interactions, teaching approaches, and patient interactions. The observers stood outside the medical team circle and remained silent during rounds so as to be unobtrusive to the teams’ discussions. The observers discussed and compared their notes after each site visit.

 

Interviews and Focus Groups

The research team also conducted individual, semistructured interviews with the attendings (n = 12), focus groups with their current teams (n = 46), and interviews or focus groups with their former learners (n = 26). Current learners were asked open-ended questions about their roles on the teams, their opinions of the attendings, and the care the attendings provide to their patients. Because they were observed during rounds, the researchers asked for clarification about specific interactions observed during the teaching rounds. Depending on availability and location, former learners either participated in in-person focus groups or interviews on the day of the site visit, or in a later telephone interview. All interviews and focus groups were audio recorded and transcribed.

This study was deemed to be exempt from regulation by the University of Michigan Institutional Review Board. All participants were informed that their participation was completely voluntary and that they could refuse to answer any question.

Data Analysis

Data were analyzed using a thematic analysis approach,²⁰ which involves reading through the data to identify patterns (and create codes) that relate to behaviors, experiences, meanings, and activities. The patterns are then grouped into themes to help further explain the findings.²¹ The research team members (S.S. and M.H.) met after the first site visit and developed initial ideas about meanings and possible patterns. One team member (M.H.) read all the transcripts from the site visit and, based on the data, developed a codebook to be used for this study. This process was repeated after every site visit, and the coding definitions were refined as necessary. All transcripts were reviewed to apply any new codes when they developed. NVivo^® 10 software (QSR International, Melbourne, Australia) was used to assist with the qualitative data analysis.

To ensure consistency and identify relationships between codes, code reports listing all the data linked to a specific code were generated after all the field notes and transcripts were coded. Once verified, codes were grouped based on similarities and relationships into prominent themes related to physician-patient interactions by 2 team members (S.S. and M.H.), though all members reviewed them and concurred.

RESULTS

A total of 12 attending physicians participated (Table 1). The participants were from hospitals located throughout the U.S. and included both university-affiliated hospitals and Veterans Affairs medical centers. We observed the attending physicians interact with more than 100 patients, with 3 major patient interaction themes emerging. Table 2 lists key approaches for effective patient-physician interactions based on the study findings.

Care for the Patient’s Well-Being

The attendings we observed appeared to openly care for their patients’ well-being and were focused on the patients’ wants and needs. We noted that attendings were generally very attentive to the patients’ comfort. For example, we observed one attending sending the senior resident to find the patient’s nurse in order to obtain additional pain medications. The attending said to the patient several times, “I’m sorry you’re in so much pain.” When the team was leaving, she asked the intern to stay with the patient until the medications had been administered.

Learners noticed when an attending physician was especially skilled at demonstrating empathy and patient-centered care. While education on rounds was emphasized, patient connection was the priority. One learner described the following: “… he really is just so passionate about patient care and has so much empathy, really. And I will tell you, of all my favorite things about him, that is one of them...”

The attendings we observed could also be considered patient advocates, ensuring that patients received superb care. As one learner said about an attending who was attempting to have his patient listed for a liver transplant, “He is the biggest advocate for the patient that I have ever seen.” Regarding the balance between learning biomedical concepts and advocacy, another learner noted the following: “… there is always a teaching aspect, but he always makes sure that everything is taken care of for the patient…”

Building rapport creates and sustains bonds between people. Even though most of the attendings we observed primarily cared for hospitalized patients and had little long-term continuity with them, the attendings tended to take special care to talk with their patients about topics other than medicine to form a bond. This bonding between attending and patient was appreciated by learners. “Probably the most important thing I learned about patient care would be taking the time and really developing that relationship with patients,” said one of the former learners we interviewed. “There’s a question that he asks to a lot of our patients,” one learner told us, “especially our elderly patients, that [is], ‘What’s the most memorable moment in your life?’ So, he asks that question, and patient[s] open up and will share.”

The attendings often used touch to further solidify their relationships with their patients. We observed one attending who would touch her patients’ arms or knees when she was talking with them. Another attending would always shake the patient’s hand when leaving. Another attending would often lay his hand on the patient’s shoulder and help the patient sit up during the physical examination. Such humanistic behavior was noticed by learners. “She does a lot of comforting touch, particularly at the end of an exam,” said a current learner.

 

Consideration of the “Big Picture”

Our exemplary attendings kept the “big picture” (that is, the patient’s overall medical and social needs) in clear focus. They behaved in a way to ensure that the patients understood the key points of their care and explained so the patients and families could understand. A current learner said, “[The attending] really makes sure that the patient understands what’s going on. And she always asks them, ‘What do you understand, what do you know, how can we fill in any blanks?’ And that makes the patient really involved in their own care, which I think is important.” This reflection was supported by direct observations. Attendings posed the following questions at the conclusion of patient interactions: “Tell me what you know.” “Tell me what our plan is.” “What did the lung doctors tell you yesterday?” These questions, which have been termed “teach-back” and are crucial for health literacy, were not meant to quiz the patient but rather to ensure the patient and family understood the plan.

We noticed that the attendings effectively explained clinical details and the plan of care to the patient while avoiding medical jargon. The following is an example of one interaction with a patient: “You threw up and created a tear in the food tube. Air got from that into the middle of the chest, not into the lungs. Air isn’t normally there. If it is just air, the body will reabsorb [it]... But we worry about bacteria getting in with the air. We need to figure out if it is an infection. We’re still trying to figure it out. Hang in there with us.” One learner commented, “… since we do bedside presentations, he has a great way of translating our gibberish, basically, to real language the patient understands.”

Finally, the attendings anticipated what patients would need in the outpatient setting. We observed that attendings stressed what the next steps would be during transitions of care. As one learner put it, “But he also thinks ahead; what do they need as an outpatient?” Another current learner commented on how another attending always asked about the social situations of his patients stating, “And then there is the social part of it. So, he is very much interested [in] where do they live? What is their support system? So, I think it has been a very holistic approach to patient care.”

Respect for the Patient

The attendings we observed were steadfastly respectful toward patients. As one attending told us, “The patient’s room is sacred space, and it’s a privilege for us to be there. And if we don’t earn that privilege, then we don’t get to go there.” We observed that the attendings generally referred to the patient as Mr. or Ms. (last name) rather than the patient’s first name unless the patient insisted. We also noticed that many of the attendings would introduce the team members to the patients or ask each member to introduce himself or herself. They also tended to leave the room and patient the way they were found, for example, by pushing the patient’s bedside table so that it was back within his or her reach or placing socks back onto the patient’s feet.

We noted that many of our attendings used appropriate humor with patients and families. As one learner explained, “I think Dr. [attending] makes most of our patients laugh during rounds. I don’t know if you noticed, but he really puts a smile on their face[s] whenever he walks in. … Maybe it would catch them off guard the first day, but after that, they are so happy to see him.”

Finally, we noticed that several of our attendings made sure to meet the patient at eye level during discussions by either kneeling or sitting on a chair. One of the attendings put it this way: “That’s a horrible power dynamic when you’re an inpatient and you’re sick and someone’s standing over you telling you things, and I like to be able to make eye contact with people, and often times that requires me to kneel down or to sit on a stool or to sit on the bed. … I feel like you’re able to connect with the people in a much better way…” Learners viewed this behavior favorably. As one told us, “[The attending] gets down to their level and makes sure that all of their questions are answered. So that is one thing that other attendings don’t necessarily do.”

DISCUSSION

In our national, qualitative study of 12 exemplary attending physicians, we found that these clinicians generally exhibited the following behaviors with patients. First, they were personable and caring and made significant attempts to connect with their patients. This occasionally took the form of using touch to comfort patients. Second, they tended to seek the “big picture” and tried to understand what patients would need upon hospital discharge. They communicated plans clearly to patients and families and inquired if those plans were understood. Finally, they showed respect toward their patients without fail. Such respect took many forms but included leaving the patient and room exactly as they were found and speaking with patients at eye level.

 

Our findings are largely consistent with other key studies in this field. Not surprisingly, the attendings we observed adhered to the major suggestions that Branch and colleagues² put forth more than 15 years ago to improve the teaching of the humanistic dimension of the patient-physician relationship. Examples include greeting the patient, introducing team members and explaining each person’s role, asking open-ended questions, providing patient education, placing oneself at the same level as the patient, using appropriate touch, and being respectful. Weissmann et al.²² also found similar themes in their study of teaching physicians at 4 universities from 2003 to 2004. In that study, role-modeling was the primary method used by physician educators to teach the humanistic aspects of medical care, including nonverbal communication (eg, touch and eye contact), demonstration of respect, and building a personal connection with the patients.²²In a focus group-based study performed at a teaching hospital in Boston, Ramani and Orlander²³ concluded that both participating teachers and learners considered the patient’s bedside as a valuable venue to learn humanistic skills. Unfortunately, they also noted that there has been a decline in bedside teaching related to various factors, including documentation requirements and electronic medical records.²³ Our attendings all demonstrated the value of teaching at a patient’s bedside. Not only could physical examination skills be demonstrated but role-modeling of interpersonal skills could be observed by learners.

Block and colleagues²⁴ observed 29 interns in 732 patient encounters in 2 Baltimore training programs using Kahn’s “etiquette-based medicine” behaviors as a guide.¹² They found that interns introduced themselves 40% of the time, explained their role 37% of the time, touched patients on 65% of visits (including as part of the physical examination), asked open-ended questions 75% of the time, and sat down with patients during only 9% of visits.²⁴ Tackett et al.⁷ observed 24 hospitalists who collectively cared for 226 unique patients in 3 Baltimore-area hospitals. They found that each of the following behaviors was performed less than 30% of the time: explains role in care, shakes hand, and sits down.⁷ However, our attendings appeared to adhere to these behaviors to a much higher extent, though we did not quantify the interactions. This lends support to the notion that effective patient-physician interactions are the foundation of great teaching.

The attendings we observed (most of whom are inpatient based) tended to the contextual issues of the patients, such as their home environments and social support. Our exemplary physicians did what they could to ensure that patients received the appropriate follow-up care upon discharge.

Our study has important limitations. First, it was conducted in a limited number of US hospitals. The institutions represented were generally large, research-intensive, academic medical centers. Therefore, our findings may not apply to settings that are different from the hospitals studied. Second, our study included only 12 attendings and their learners, which may also limit the study’s generalizability. Third, we focused exclusively on teaching within general medicine rounds. Thus, our findings may not be generalizable to other subspecialties. Fourth, attendings were selected through a nonexhaustive method, increasing the potential for selection bias. However, the multisite design, the modified snowball sampling, and the inclusion of several types of institutions in the final participant pool introduced diversity to the final list. Former-learner responses were subject to recall bias. Finally, the study design is susceptible to observer bias. Attempts to reduce this included the diversity of the observers (ie, both a clinician and a nonclinician, the latter of whom was unfamiliar with medical education) and review of the data and coding by multiple research team members to ensure validity. Although we cannot discount the potential role of a Hawthorne effect on our data collection, the research team attempted to mitigate this by standing apart from the care teams and remaining unobtrusive during observations.

Limitations notwithstanding, we believe that our multisite study is important given the longstanding imperative to improve patient-physician interactions. We found empirical support for behaviors proposed by Branch and colleagues² and Kahn¹² in order to enhance these relationships. While others have studied attendings and their current learners,²² we add to the literature by also examining former learners’ perspectives on how the attendings’ teaching and role-modeling have created and sustained a lasting impact. The key findings of our national, qualitative study (care for the patient’s well-being, consideration of the “big picture,” and respect for the patient) can be readily adopted and honed by physicians to improve their interactions with hospitalized patients.

Acknowledgments

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Department of Veterans Affairs.

 

Funding

Dr. Saint provided funding for this study using a University of Michigan endowment.

Disclosure

The authors declare no conflicts of interest.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Syncope is a common cause of emergency department (ED) visits and hospitalizations. Echocardiogram is frequently used as a diagnostic tool in the evaluation of syncope, performed in 39%-91% of patients. The diagnostic yield of echocardiogram for detecting clinically important abnormalities in patients with a normal history, physical examination, and electrocardiogram (ECG), however, is extremely low. In contrast, echocardiograms performed on patients with syncope with a positive cardiac history, abnormal examination, and/or ECG identify an abnormality in up to 29% of cases, though these abnormalities are not always definitively the cause of symptoms. Recently updated clinical guidelines for syncope management from the American College of Cardiology now recommend echocardiogram only if initial history or examination suggests a cardiac etiology, or the ECG is abnormal. Universal echocardiography in patients with syncope exposes a significant number of patients to unnecessary testing and cost and does not represent evidence-based or high-value patient care.

CLINICAL SCENARIO

A 57-year-old woman presented to the ED after a syncopal episode. She had just eaten dinner when she slumped over and became unresponsive. Her husband estimated that she regained consciousness 30 seconds later and quickly returned to baseline mental status. She denied chest pain, shortness of breath, or palpitations. Her medical history included hypertension and hypothyroidism. Her medication regimen was unchanged.

Vital signs, including orthostatic blood pressures, were within normal ranges. A physical examination revealed regular heart sounds without murmur, rub, or gallop. ECG showed normal sinus rhythm, normal axis, and normal intervals. Chest radiograph, complete blood count, chemistry, pro-brain natriuretic peptide (pro-BNP), and troponin were within normal ranges.

BACKGROUND

Syncope, defined as “abrupt, transient, complete loss of consciousness, associated with inability to maintain postural tone, with rapid and spontaneous recovery,”¹ is a common clinical problem, accounting for 1% of ED visits in the United States.² As syncope has been shown to be associated with increased mortality,³ the primary goal of syncope evaluation is to identify modifiable underlying causes, particularly cardiac causes. Current guidelines recommend a complete history and physical, orthostatic blood pressure measurement, and ECG as the initial evaluation for syncope.¹ Echocardiogram is a frequent additional test, performed in 39%-91% of patients.^4-8

WHY YOU MAY THINK ECHOCARDIOGRAM IS HELPFUL

Echocardiogram may identify depressed ejection fraction, a risk factor for ventricular arrhythmias, along with structural causes of syncope, including aortic stenosis, pulmonary hypertension, and hypertrophic cardiomyopathy.⁹ Structural heart disease is the underlying etiology in about 3% of patients with syncope.¹⁰

Prior guidelines stated that “an echocardiogram is a helpful screening test if the history, physical examination, and ECG do not provide a diagnosis or if underlying heart disease is suspected.”¹¹ A separate guideline for the appropriate use of echocardiogram assigned a score of appropriateness on a 1-9 scale based on increasing indication.¹² Echocardiogram for syncope was scored a 7 in patients with “no other symptoms or signs of cardiovascular disease.”¹² Only 25%-40% of patients with syncope will have a cause identified after the history, physical examination, and ECG,^13,14 creating diagnostic uncertainty that often leads to further testing.

WHY ECHOCARDIOGRAM IS NOT NECESSARY IN ALL PATIENTS

Several studies have found that transthoracic echocardiogram has an extremely low diagnostic yield in patients with no cardiac history and a normal physical examination and ECG^4-8,15 (Table). A prospective study by Sarasin et al.¹⁵ identified 155 patients with unexplained syncope after an initial ED evaluation. All patients underwent echocardiogram, carotid massage, 24-hour Holter monitor, tilt-table testing, and electrophysiology testing if indicated. Patients were stratified by the presence of ECG abnormalities, defined as any arrhythmia or finding other than nonspecific ST and T wave abnormalities, or abnormal cardiac history, defined as documented coronary artery disease, valvular disease, or cardiomyopathy. None of the 67 patients with normal ECG and a negative cardiac history had findings on echocardiogram to explain syncope.

 

Recchia et al.⁴ performed a retrospective review of 128 patients admitted to a single center with syncope. Charts were reviewed for abnormal cardiac history, including coronary artery disease and congestive heart failure, and ECG abnormalities, defined as Q waves, any bundle branch block, ventricular ectopy/arrhythmia, supraventricular arrhythmia, or Mobitz II or higher atrioventricular block. Of the 38 patients with a normal cardiac history, examination, and ECG who underwent echocardiogram, none had findings that explained syncope.

Mendu et al.⁵ performed a single-center, retrospective study of the diagnostic yield of testing for syncope in 2106 consecutive patients older than 65 admitted over the course of 5 years. They retrospectively applied the San Francisco Syncope Rule (SFSR), which patients met if they had congestive heart failure, hematocrit <30%, abnormal ECG, shortness of breath, or systolic blood pressure <90 mm Hg. There were 821 patients (39%) who underwent echocardiogram. Among the 488 with no SFSR criteria, 10 patients (2%) had echocardiogram results that affected management, and 4 patients (1%) had results that helped determine the etiology of syncope.

Anderson et al. studied 323 syncope patients in a single ED observation unit over 18 months.⁶ Patients with high-risk features, including unstable vital signs, abnormal cardiac biomarkers, or ischemic ECG changes, were excluded from the unit. The initial ECG was considered abnormal if it contained arrhythmia, premature atrial or ventricular contractions, pacing, second- or third-degree heart block, or left bundle branch block. Of the 235 patients with a normal ECG who underwent echocardiogram, none had an abnormal study.

Chang et al.⁷ performed a retrospective review of 468 patients admitted with syncope at a single hospital. Charts were reviewed for ECG and echocardiogram results. Abnormal ECGs were defined as those containing arrhythmias, Q waves, ischemic changes, second- and third-degree heart block, paced rhythm, corrected QT interval (QTc) >500 ms, left bundle branch or bifasicular block, Brugada pattern, or abnormal axis. Among 321 patients with normal ECGs, echocardiograms were performed in 192. Eleven of those echocardiograms were abnormal: 3 demonstrated aortic stenosis in patients who already carried the diagnosis, and the other 8 abnormal echocardiograms revealed unexpected left ventricular ejection fractions <45% or other nonaortic valvular pathology. None of the findings were felt to be the cause of syncope.

Han et al.⁸ performed a retrospective cohort study of all syncope patients presenting to a single ED over the course of 1 year. Patients were stratified as high risk if they had chest pain, palpitations, a history of cardiac disease (defined as prior arrhythmia, heart failure, coronary artery disease, or structural heart disease), abnormal cardiac biomarkers, or an abnormal ECG (defined as sinus bradycardia, arrhythmia, premature beats, second- or third-degree heart block, ventricular hypertrophy, ischemic Q or ST changes, or abnormal QT interval). Patients with none of those symptoms or findings were considered low risk. Of those categorized as low risk (n = 115), 47 underwent echocardiogram, only 1 of which was abnormal.

Across studies, the percentage of patients with a normal cardiac history, examination, and ECG with new, significant abnormalities on echocardiogram was 0% in 3 studies (n = 340),^4,6,15 2% in 1 study (10/488 patients),⁵ 2.1% in 1 study (1/47 patients),⁸ and 4.2% in 1 study (8/192 patients).⁷ The 11 echocardiograms with significant findings in the studies by Mendu et al.⁵ and Han et al.⁸ were not further described. The 8 patients with abnormal echocardiograms reported by Chang et al.⁷ had depressed left ventricular ejection fraction or nonaortic valvular disease that did not represent a definitive etiology of their syncope. Given the cost of $1,000 to $2,220 per study,¹⁶ routine echocardiograms in patients with a normal history, examination, and ECG would thus require $60,000 to $132,000 in spending to find 1 new significant abnormality, which may be unrelated to the actual cause of syncope.

SITUATIONS IN WHICH ECHOCARDIOGRAM MAY BE HELPFUL

The diagnostic yield of echocardiogram is higher in patients with a positive cardiac history or abnormal ECG. In the prospective study by Sarasin et al.¹⁵ a total of 27% of patients with a positive cardiac history or abnormal ECG were found to have an ejection fraction less than or equal to 40%. Other studies reporting percentages of abnormal echocardiograms in patients with abnormal history, ECG, or examination found rates of 8% (26/333),⁵ 20% (7/35),⁶ 28% (27/97),⁸ and 29% (27/93).⁷ It should be noted that not all of these abnormalities were felt to be the cause of syncope. For example, Sarasin et al.¹⁵ reported that only half of the patients with newly identified depressed ejection fraction were diagnosed with arrhythmia-related syncope. Chang et al⁷ reported that 6 of the 27 patients (22%) with abnormal ECG and echocardiogram had the cause of syncope established by echocardiogram.

 

Finally, some syncope patients will have cardiac biomarkers sent in the ED. Han et al.⁸ found that among patients with syncope, those with abnormal versus normal echocardiogram were more likely to have elevated BNP (70% vs 23%) and troponin (36% vs 12.4%). Thus, obtaining an echocardiogram in patients with syncope and abnormal cardiac biomarkers may be reasonable. It should be noted, however, that while some studies have suggested a role for biomarkers in differentiating cardiac from noncardiac syncope,^17-20 current guidelines state that the usefulness of these tests is uncertain.¹

WHAT YOU SHOULD DO INSTEAD OF ECHOCARDIOGRAM FOR ALL PATIENTS

Clinicians should carefully screen patients with syncope for abnormal findings suggesting cardiac disease on history, physical examination, and ECG. Relevant cardiac history includes known coronary artery disease, valvular heart disease, arrhythmia, congestive heart failure, and risk factors for cardiac syncope (supplemental Appendix). The definition of abnormal ECG varies among studies, but abnormalities that should prompt an echocardiogram include arrhythmia, premature atrial or ventricular contractions, second- or third-degree heart block, sinus bradycardia, bundle branch or fascicular blocks, left ventricular hypertrophy, ischemic ST or T wave changes, Q waves, or a prolonged QTc interval. New guidelines from the American College of Cardiology state, “Routine cardiac imaging is not useful in the evaluation of patients with syncope unless cardiac etiology is suspected on the basis of an initial evaluation, including history, physical examination, or ECG.”¹

RECOMMENDATIONS

• All patients with syncope should receive a complete history, physical examination, orthostatic vital signs, and ECG.
• Perform echocardiogram on patients with syncope and a history of cardiac disease, examination suggestive of structural heart disease or congestive heart failure, or abnormal ECG.
• Echocardiogram may be reasonable in patients with syncope and abnormal cardiac biomarkers.

CONCLUSIONS

While commonly performed as part of syncope evaluations, echocardiogram has a very low diagnostic yield in patients with a normal history, physical, and ECG. The patient described in the initial case scenario would have an extremely low likelihood of having important diagnostic information found on echocardiogram.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Disclosure

The authors have no conflicts of interest relevant to this article.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Syncope is a common cause of emergency department (ED) visits and hospitalizations. Echocardiogram is frequently used as a diagnostic tool in the evaluation of syncope, performed in 39%-91% of patients. The diagnostic yield of echocardiogram for detecting clinically important abnormalities in patients with a normal history, physical examination, and electrocardiogram (ECG), however, is extremely low. In contrast, echocardiograms performed on patients with syncope with a positive cardiac history, abnormal examination, and/or ECG identify an abnormality in up to 29% of cases, though these abnormalities are not always definitively the cause of symptoms. Recently updated clinical guidelines for syncope management from the American College of Cardiology now recommend echocardiogram only if initial history or examination suggests a cardiac etiology, or the ECG is abnormal. Universal echocardiography in patients with syncope exposes a significant number of patients to unnecessary testing and cost and does not represent evidence-based or high-value patient care.

CLINICAL SCENARIO

A 57-year-old woman presented to the ED after a syncopal episode. She had just eaten dinner when she slumped over and became unresponsive. Her husband estimated that she regained consciousness 30 seconds later and quickly returned to baseline mental status. She denied chest pain, shortness of breath, or palpitations. Her medical history included hypertension and hypothyroidism. Her medication regimen was unchanged.

Vital signs, including orthostatic blood pressures, were within normal ranges. A physical examination revealed regular heart sounds without murmur, rub, or gallop. ECG showed normal sinus rhythm, normal axis, and normal intervals. Chest radiograph, complete blood count, chemistry, pro-brain natriuretic peptide (pro-BNP), and troponin were within normal ranges.

BACKGROUND

Syncope, defined as “abrupt, transient, complete loss of consciousness, associated with inability to maintain postural tone, with rapid and spontaneous recovery,”¹ is a common clinical problem, accounting for 1% of ED visits in the United States.² As syncope has been shown to be associated with increased mortality,³ the primary goal of syncope evaluation is to identify modifiable underlying causes, particularly cardiac causes. Current guidelines recommend a complete history and physical, orthostatic blood pressure measurement, and ECG as the initial evaluation for syncope.¹ Echocardiogram is a frequent additional test, performed in 39%-91% of patients.^4-8

WHY YOU MAY THINK ECHOCARDIOGRAM IS HELPFUL

Echocardiogram may identify depressed ejection fraction, a risk factor for ventricular arrhythmias, along with structural causes of syncope, including aortic stenosis, pulmonary hypertension, and hypertrophic cardiomyopathy.⁹ Structural heart disease is the underlying etiology in about 3% of patients with syncope.¹⁰

Prior guidelines stated that “an echocardiogram is a helpful screening test if the history, physical examination, and ECG do not provide a diagnosis or if underlying heart disease is suspected.”¹¹ A separate guideline for the appropriate use of echocardiogram assigned a score of appropriateness on a 1-9 scale based on increasing indication.¹² Echocardiogram for syncope was scored a 7 in patients with “no other symptoms or signs of cardiovascular disease.”¹² Only 25%-40% of patients with syncope will have a cause identified after the history, physical examination, and ECG,^13,14 creating diagnostic uncertainty that often leads to further testing.

WHY ECHOCARDIOGRAM IS NOT NECESSARY IN ALL PATIENTS

Several studies have found that transthoracic echocardiogram has an extremely low diagnostic yield in patients with no cardiac history and a normal physical examination and ECG^4-8,15 (Table). A prospective study by Sarasin et al.¹⁵ identified 155 patients with unexplained syncope after an initial ED evaluation. All patients underwent echocardiogram, carotid massage, 24-hour Holter monitor, tilt-table testing, and electrophysiology testing if indicated. Patients were stratified by the presence of ECG abnormalities, defined as any arrhythmia or finding other than nonspecific ST and T wave abnormalities, or abnormal cardiac history, defined as documented coronary artery disease, valvular disease, or cardiomyopathy. None of the 67 patients with normal ECG and a negative cardiac history had findings on echocardiogram to explain syncope.

 

Recchia et al.⁴ performed a retrospective review of 128 patients admitted to a single center with syncope. Charts were reviewed for abnormal cardiac history, including coronary artery disease and congestive heart failure, and ECG abnormalities, defined as Q waves, any bundle branch block, ventricular ectopy/arrhythmia, supraventricular arrhythmia, or Mobitz II or higher atrioventricular block. Of the 38 patients with a normal cardiac history, examination, and ECG who underwent echocardiogram, none had findings that explained syncope.

Mendu et al.⁵ performed a single-center, retrospective study of the diagnostic yield of testing for syncope in 2106 consecutive patients older than 65 admitted over the course of 5 years. They retrospectively applied the San Francisco Syncope Rule (SFSR), which patients met if they had congestive heart failure, hematocrit <30%, abnormal ECG, shortness of breath, or systolic blood pressure <90 mm Hg. There were 821 patients (39%) who underwent echocardiogram. Among the 488 with no SFSR criteria, 10 patients (2%) had echocardiogram results that affected management, and 4 patients (1%) had results that helped determine the etiology of syncope.

Anderson et al. studied 323 syncope patients in a single ED observation unit over 18 months.⁶ Patients with high-risk features, including unstable vital signs, abnormal cardiac biomarkers, or ischemic ECG changes, were excluded from the unit. The initial ECG was considered abnormal if it contained arrhythmia, premature atrial or ventricular contractions, pacing, second- or third-degree heart block, or left bundle branch block. Of the 235 patients with a normal ECG who underwent echocardiogram, none had an abnormal study.

Chang et al.⁷ performed a retrospective review of 468 patients admitted with syncope at a single hospital. Charts were reviewed for ECG and echocardiogram results. Abnormal ECGs were defined as those containing arrhythmias, Q waves, ischemic changes, second- and third-degree heart block, paced rhythm, corrected QT interval (QTc) >500 ms, left bundle branch or bifasicular block, Brugada pattern, or abnormal axis. Among 321 patients with normal ECGs, echocardiograms were performed in 192. Eleven of those echocardiograms were abnormal: 3 demonstrated aortic stenosis in patients who already carried the diagnosis, and the other 8 abnormal echocardiograms revealed unexpected left ventricular ejection fractions <45% or other nonaortic valvular pathology. None of the findings were felt to be the cause of syncope.

Han et al.⁸ performed a retrospective cohort study of all syncope patients presenting to a single ED over the course of 1 year. Patients were stratified as high risk if they had chest pain, palpitations, a history of cardiac disease (defined as prior arrhythmia, heart failure, coronary artery disease, or structural heart disease), abnormal cardiac biomarkers, or an abnormal ECG (defined as sinus bradycardia, arrhythmia, premature beats, second- or third-degree heart block, ventricular hypertrophy, ischemic Q or ST changes, or abnormal QT interval). Patients with none of those symptoms or findings were considered low risk. Of those categorized as low risk (n = 115), 47 underwent echocardiogram, only 1 of which was abnormal.

Across studies, the percentage of patients with a normal cardiac history, examination, and ECG with new, significant abnormalities on echocardiogram was 0% in 3 studies (n = 340),^4,6,15 2% in 1 study (10/488 patients),⁵ 2.1% in 1 study (1/47 patients),⁸ and 4.2% in 1 study (8/192 patients).⁷ The 11 echocardiograms with significant findings in the studies by Mendu et al.⁵ and Han et al.⁸ were not further described. The 8 patients with abnormal echocardiograms reported by Chang et al.⁷ had depressed left ventricular ejection fraction or nonaortic valvular disease that did not represent a definitive etiology of their syncope. Given the cost of $1,000 to $2,220 per study,¹⁶ routine echocardiograms in patients with a normal history, examination, and ECG would thus require $60,000 to $132,000 in spending to find 1 new significant abnormality, which may be unrelated to the actual cause of syncope.

SITUATIONS IN WHICH ECHOCARDIOGRAM MAY BE HELPFUL

The diagnostic yield of echocardiogram is higher in patients with a positive cardiac history or abnormal ECG. In the prospective study by Sarasin et al.¹⁵ a total of 27% of patients with a positive cardiac history or abnormal ECG were found to have an ejection fraction less than or equal to 40%. Other studies reporting percentages of abnormal echocardiograms in patients with abnormal history, ECG, or examination found rates of 8% (26/333),⁵ 20% (7/35),⁶ 28% (27/97),⁸ and 29% (27/93).⁷ It should be noted that not all of these abnormalities were felt to be the cause of syncope. For example, Sarasin et al.¹⁵ reported that only half of the patients with newly identified depressed ejection fraction were diagnosed with arrhythmia-related syncope. Chang et al⁷ reported that 6 of the 27 patients (22%) with abnormal ECG and echocardiogram had the cause of syncope established by echocardiogram.

 

Finally, some syncope patients will have cardiac biomarkers sent in the ED. Han et al.⁸ found that among patients with syncope, those with abnormal versus normal echocardiogram were more likely to have elevated BNP (70% vs 23%) and troponin (36% vs 12.4%). Thus, obtaining an echocardiogram in patients with syncope and abnormal cardiac biomarkers may be reasonable. It should be noted, however, that while some studies have suggested a role for biomarkers in differentiating cardiac from noncardiac syncope,^17-20 current guidelines state that the usefulness of these tests is uncertain.¹

WHAT YOU SHOULD DO INSTEAD OF ECHOCARDIOGRAM FOR ALL PATIENTS

Clinicians should carefully screen patients with syncope for abnormal findings suggesting cardiac disease on history, physical examination, and ECG. Relevant cardiac history includes known coronary artery disease, valvular heart disease, arrhythmia, congestive heart failure, and risk factors for cardiac syncope (supplemental Appendix). The definition of abnormal ECG varies among studies, but abnormalities that should prompt an echocardiogram include arrhythmia, premature atrial or ventricular contractions, second- or third-degree heart block, sinus bradycardia, bundle branch or fascicular blocks, left ventricular hypertrophy, ischemic ST or T wave changes, Q waves, or a prolonged QTc interval. New guidelines from the American College of Cardiology state, “Routine cardiac imaging is not useful in the evaluation of patients with syncope unless cardiac etiology is suspected on the basis of an initial evaluation, including history, physical examination, or ECG.”¹

RECOMMENDATIONS

• All patients with syncope should receive a complete history, physical examination, orthostatic vital signs, and ECG.
• Perform echocardiogram on patients with syncope and a history of cardiac disease, examination suggestive of structural heart disease or congestive heart failure, or abnormal ECG.
• Echocardiogram may be reasonable in patients with syncope and abnormal cardiac biomarkers.

CONCLUSIONS

While commonly performed as part of syncope evaluations, echocardiogram has a very low diagnostic yield in patients with a normal history, physical, and ECG. The patient described in the initial case scenario would have an extremely low likelihood of having important diagnostic information found on echocardiogram.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Disclosure

The authors have no conflicts of interest relevant to this article.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Syncope is a common cause of emergency department (ED) visits and hospitalizations. Echocardiogram is frequently used as a diagnostic tool in the evaluation of syncope, performed in 39%-91% of patients. The diagnostic yield of echocardiogram for detecting clinically important abnormalities in patients with a normal history, physical examination, and electrocardiogram (ECG), however, is extremely low. In contrast, echocardiograms performed on patients with syncope with a positive cardiac history, abnormal examination, and/or ECG identify an abnormality in up to 29% of cases, though these abnormalities are not always definitively the cause of symptoms. Recently updated clinical guidelines for syncope management from the American College of Cardiology now recommend echocardiogram only if initial history or examination suggests a cardiac etiology, or the ECG is abnormal. Universal echocardiography in patients with syncope exposes a significant number of patients to unnecessary testing and cost and does not represent evidence-based or high-value patient care.

CLINICAL SCENARIO

A 57-year-old woman presented to the ED after a syncopal episode. She had just eaten dinner when she slumped over and became unresponsive. Her husband estimated that she regained consciousness 30 seconds later and quickly returned to baseline mental status. She denied chest pain, shortness of breath, or palpitations. Her medical history included hypertension and hypothyroidism. Her medication regimen was unchanged.

Vital signs, including orthostatic blood pressures, were within normal ranges. A physical examination revealed regular heart sounds without murmur, rub, or gallop. ECG showed normal sinus rhythm, normal axis, and normal intervals. Chest radiograph, complete blood count, chemistry, pro-brain natriuretic peptide (pro-BNP), and troponin were within normal ranges.

BACKGROUND

Syncope, defined as “abrupt, transient, complete loss of consciousness, associated with inability to maintain postural tone, with rapid and spontaneous recovery,”¹ is a common clinical problem, accounting for 1% of ED visits in the United States.² As syncope has been shown to be associated with increased mortality,³ the primary goal of syncope evaluation is to identify modifiable underlying causes, particularly cardiac causes. Current guidelines recommend a complete history and physical, orthostatic blood pressure measurement, and ECG as the initial evaluation for syncope.¹ Echocardiogram is a frequent additional test, performed in 39%-91% of patients.^4-8

WHY YOU MAY THINK ECHOCARDIOGRAM IS HELPFUL

Echocardiogram may identify depressed ejection fraction, a risk factor for ventricular arrhythmias, along with structural causes of syncope, including aortic stenosis, pulmonary hypertension, and hypertrophic cardiomyopathy.⁹ Structural heart disease is the underlying etiology in about 3% of patients with syncope.¹⁰

Prior guidelines stated that “an echocardiogram is a helpful screening test if the history, physical examination, and ECG do not provide a diagnosis or if underlying heart disease is suspected.”¹¹ A separate guideline for the appropriate use of echocardiogram assigned a score of appropriateness on a 1-9 scale based on increasing indication.¹² Echocardiogram for syncope was scored a 7 in patients with “no other symptoms or signs of cardiovascular disease.”¹² Only 25%-40% of patients with syncope will have a cause identified after the history, physical examination, and ECG,^13,14 creating diagnostic uncertainty that often leads to further testing.

WHY ECHOCARDIOGRAM IS NOT NECESSARY IN ALL PATIENTS

Several studies have found that transthoracic echocardiogram has an extremely low diagnostic yield in patients with no cardiac history and a normal physical examination and ECG^4-8,15 (Table). A prospective study by Sarasin et al.¹⁵ identified 155 patients with unexplained syncope after an initial ED evaluation. All patients underwent echocardiogram, carotid massage, 24-hour Holter monitor, tilt-table testing, and electrophysiology testing if indicated. Patients were stratified by the presence of ECG abnormalities, defined as any arrhythmia or finding other than nonspecific ST and T wave abnormalities, or abnormal cardiac history, defined as documented coronary artery disease, valvular disease, or cardiomyopathy. None of the 67 patients with normal ECG and a negative cardiac history had findings on echocardiogram to explain syncope.

 

Recchia et al.⁴ performed a retrospective review of 128 patients admitted to a single center with syncope. Charts were reviewed for abnormal cardiac history, including coronary artery disease and congestive heart failure, and ECG abnormalities, defined as Q waves, any bundle branch block, ventricular ectopy/arrhythmia, supraventricular arrhythmia, or Mobitz II or higher atrioventricular block. Of the 38 patients with a normal cardiac history, examination, and ECG who underwent echocardiogram, none had findings that explained syncope.

Mendu et al.⁵ performed a single-center, retrospective study of the diagnostic yield of testing for syncope in 2106 consecutive patients older than 65 admitted over the course of 5 years. They retrospectively applied the San Francisco Syncope Rule (SFSR), which patients met if they had congestive heart failure, hematocrit <30%, abnormal ECG, shortness of breath, or systolic blood pressure <90 mm Hg. There were 821 patients (39%) who underwent echocardiogram. Among the 488 with no SFSR criteria, 10 patients (2%) had echocardiogram results that affected management, and 4 patients (1%) had results that helped determine the etiology of syncope.

Anderson et al. studied 323 syncope patients in a single ED observation unit over 18 months.⁶ Patients with high-risk features, including unstable vital signs, abnormal cardiac biomarkers, or ischemic ECG changes, were excluded from the unit. The initial ECG was considered abnormal if it contained arrhythmia, premature atrial or ventricular contractions, pacing, second- or third-degree heart block, or left bundle branch block. Of the 235 patients with a normal ECG who underwent echocardiogram, none had an abnormal study.

Chang et al.⁷ performed a retrospective review of 468 patients admitted with syncope at a single hospital. Charts were reviewed for ECG and echocardiogram results. Abnormal ECGs were defined as those containing arrhythmias, Q waves, ischemic changes, second- and third-degree heart block, paced rhythm, corrected QT interval (QTc) >500 ms, left bundle branch or bifasicular block, Brugada pattern, or abnormal axis. Among 321 patients with normal ECGs, echocardiograms were performed in 192. Eleven of those echocardiograms were abnormal: 3 demonstrated aortic stenosis in patients who already carried the diagnosis, and the other 8 abnormal echocardiograms revealed unexpected left ventricular ejection fractions <45% or other nonaortic valvular pathology. None of the findings were felt to be the cause of syncope.

Han et al.⁸ performed a retrospective cohort study of all syncope patients presenting to a single ED over the course of 1 year. Patients were stratified as high risk if they had chest pain, palpitations, a history of cardiac disease (defined as prior arrhythmia, heart failure, coronary artery disease, or structural heart disease), abnormal cardiac biomarkers, or an abnormal ECG (defined as sinus bradycardia, arrhythmia, premature beats, second- or third-degree heart block, ventricular hypertrophy, ischemic Q or ST changes, or abnormal QT interval). Patients with none of those symptoms or findings were considered low risk. Of those categorized as low risk (n = 115), 47 underwent echocardiogram, only 1 of which was abnormal.

Across studies, the percentage of patients with a normal cardiac history, examination, and ECG with new, significant abnormalities on echocardiogram was 0% in 3 studies (n = 340),^4,6,15 2% in 1 study (10/488 patients),⁵ 2.1% in 1 study (1/47 patients),⁸ and 4.2% in 1 study (8/192 patients).⁷ The 11 echocardiograms with significant findings in the studies by Mendu et al.⁵ and Han et al.⁸ were not further described. The 8 patients with abnormal echocardiograms reported by Chang et al.⁷ had depressed left ventricular ejection fraction or nonaortic valvular disease that did not represent a definitive etiology of their syncope. Given the cost of $1,000 to $2,220 per study,¹⁶ routine echocardiograms in patients with a normal history, examination, and ECG would thus require $60,000 to $132,000 in spending to find 1 new significant abnormality, which may be unrelated to the actual cause of syncope.

SITUATIONS IN WHICH ECHOCARDIOGRAM MAY BE HELPFUL

The diagnostic yield of echocardiogram is higher in patients with a positive cardiac history or abnormal ECG. In the prospective study by Sarasin et al.¹⁵ a total of 27% of patients with a positive cardiac history or abnormal ECG were found to have an ejection fraction less than or equal to 40%. Other studies reporting percentages of abnormal echocardiograms in patients with abnormal history, ECG, or examination found rates of 8% (26/333),⁵ 20% (7/35),⁶ 28% (27/97),⁸ and 29% (27/93).⁷ It should be noted that not all of these abnormalities were felt to be the cause of syncope. For example, Sarasin et al.¹⁵ reported that only half of the patients with newly identified depressed ejection fraction were diagnosed with arrhythmia-related syncope. Chang et al⁷ reported that 6 of the 27 patients (22%) with abnormal ECG and echocardiogram had the cause of syncope established by echocardiogram.

 

Finally, some syncope patients will have cardiac biomarkers sent in the ED. Han et al.⁸ found that among patients with syncope, those with abnormal versus normal echocardiogram were more likely to have elevated BNP (70% vs 23%) and troponin (36% vs 12.4%). Thus, obtaining an echocardiogram in patients with syncope and abnormal cardiac biomarkers may be reasonable. It should be noted, however, that while some studies have suggested a role for biomarkers in differentiating cardiac from noncardiac syncope,^17-20 current guidelines state that the usefulness of these tests is uncertain.¹

WHAT YOU SHOULD DO INSTEAD OF ECHOCARDIOGRAM FOR ALL PATIENTS

Clinicians should carefully screen patients with syncope for abnormal findings suggesting cardiac disease on history, physical examination, and ECG. Relevant cardiac history includes known coronary artery disease, valvular heart disease, arrhythmia, congestive heart failure, and risk factors for cardiac syncope (supplemental Appendix). The definition of abnormal ECG varies among studies, but abnormalities that should prompt an echocardiogram include arrhythmia, premature atrial or ventricular contractions, second- or third-degree heart block, sinus bradycardia, bundle branch or fascicular blocks, left ventricular hypertrophy, ischemic ST or T wave changes, Q waves, or a prolonged QTc interval. New guidelines from the American College of Cardiology state, “Routine cardiac imaging is not useful in the evaluation of patients with syncope unless cardiac etiology is suspected on the basis of an initial evaluation, including history, physical examination, or ECG.”¹

RECOMMENDATIONS

• All patients with syncope should receive a complete history, physical examination, orthostatic vital signs, and ECG.
• Perform echocardiogram on patients with syncope and a history of cardiac disease, examination suggestive of structural heart disease or congestive heart failure, or abnormal ECG.
• Echocardiogram may be reasonable in patients with syncope and abnormal cardiac biomarkers.

CONCLUSIONS

While commonly performed as part of syncope evaluations, echocardiogram has a very low diagnostic yield in patients with a normal history, physical, and ECG. The patient described in the initial case scenario would have an extremely low likelihood of having important diagnostic information found on echocardiogram.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Disclosure

The authors have no conflicts of interest relevant to this article.

Sepsis affects over 1 million Americans annually, resulting in significant morbidity, mortality, and costs for hospitalized patients.^1-4 There is an increasing interest in policy-oriented approaches to improving sepsis care at both the state and national levels.^5,6 The most prominent policy is the Centers for Medicare and Medicaid Services (CMS) Sepsis CMS Core (SEP-1) program, which was formally implemented in October 2015; the program mandates that hospitals report their compliance with a variety of sepsis treatment processes (Table 1). Academic quality experts generally applaud the increased attention to sepsis but are concerned that the measure’s design and specifications advance beyond the existing evidence base.^7,8 However, remarkably little is known about how front-line hospital quality officials perceive the program and how they are responding or not responding, to the new requirements. This knowledge gap is a critical barrier to evaluating the program’s practical impact on sepsis treatment and outcomes.

We therefore sought to understand hospital stakeholders’ perceptions of the SEP-1 program in general as well as their characterization of their local hospitals’ responses to the program. We were specifically interested in obtaining a focused perspective on the policy and hospitals’ responses to the policy rather than individual physicians’ attitudes regarding sepsis care protocols, which are complex and may be independent from the policy itself.⁹ We used a qualitative research approach designed to generate both a deep and broad understanding of how hospitals are responding to SEP-1 requirements, including the resources required to implement their responses.

METHODS

Study Design, Setting, and Subjects

We conducted a qualitative study by using semistructured telephone interviews with hospital quality officers in the United States. We targeted hospital quality officers because they are in a position to provide overarching insights into hospitals’ perceptions of and responses to the SEP-1 program. We enrolled quality officers at general, short-stay, nonfederal acute care hospitals because those are the hospitals to which the SEP-1 program applies. We generated a stratified random sample of hospitals by using 2013 data from Medicare’s Healthcare Cost and Reporting Information System (HCRIS) database.¹⁰ We stratified by size (greater than or less than 200 total beds), teaching status (presence or absence of any resident physician trainees), and ownership (for-profit vs nonprofit), creating 8 mutually exclusive strata. This sampling frame was designed to ensure representativeness from a broad range of hospital types, not to enable comparisons across hospital types, which is outside the scope of qualitative research.

Within strata, we contacted hospitals in a random order by phone using the primary number listed in the HCRIS database. We asked the hospital operator to connect us to the chief quality officer or an appropriate alternative hospital administrator with knowledge of hospital quality-improvement activities. We limited participation to 1 respondent per hospital. We did not offer any specific incentives for participation.

The study was approved by the University of Pittsburgh Institutional Review Board with a waiver of signed informed consent.

Data Collection

Interviews were conducted by a trained research coordinator between February 2016 and October 2016. Interviews were conducted concurrently with data analysis by using a constant comparison approach.¹¹ The constant comparison approach involves the iterative refinement of themes by comparing the existing themes to new data as they emerge during successive interviews. We chose a constant comparison approach because we wanted to systematically describe hospital responses to SEP-1 rather than specifically test individual hypotheses.¹¹ As is typical in qualitative research, we did not set the sample size a priori but instead continued the interviews until we achieved thematic saturation.^12,13

The interview script included a mix of directed and open-ended questions about respondents’ perspectives of and hospital responses to the SEP-1 program. The questions covered the following 4 domains: hospitals’ sepsis quality-improvement initiatives before and after the Medicare reporting program, reception of the hospital responses, the approach to data abstraction and reporting, and the overall impressions of the program and its impact.^6-8,14 We allowed for updates and revisions of the interview guide as necessary to explore any new content and emergent themes. We piloted the interview guide on 2 hospital quality officers at our institution and then revised its structure again after interviews with the initial 6 hospitals. The complete final interview guide is available in the supplemental digital content.

 

Analysis

Interviews were audio recorded, transcribed, and loaded onto a secure server. We used NVivo 11 (QSR International, Cambridge, Massachusetts) for coding and analysis. We iteratively reviewed and thematically analyzed the transcripts for structural content and emergent themes, consistent with established qualitative methods.¹⁵ Three investigators reviewed the initial 20 transcripts and developed the codebook through iterative discussion and consensus. The codes were then organized into themes and subthemes. Subsequently, 1 investigator coded the remaining transcripts. The results are presented as a series of key themes supported by direct quotes from the interviews.

RESULTS

Sample Description

We performed 29 interviews prior to achieving thematic saturation. Each of the 8 strata from the sampling frame was represented by at least 3 hospitals. Hospitals in the final sample were diverse in total bed size, intensive care unit bed capacity, teaching status, and ownership (Table 2). The median interview length was 25 minutes (interquartile range, 20-32 minutes). Respondents included 6 quality coordinators, 6 quality managers, and 11 quality directors, with the remainder holding a variety of other quality-related titles. Most respondents worked in hospital quality departments, although 4 were affiliated with individual clinical departments (eg, emergency medicine and/or critical care services). Of the 9 respondents who reported their professional training, 8 were registered nurses. Eleven respondents reported participating in measure abstraction.

Perspectives on SEP-1

Respondents’ general perspectives on the SEP-1 program are outlined in Table 3, with several key themes emerging. Foremost was the sheer complexity of the measure compounded by its reliance on time-stamped clinical documentation, and in particular, the physical reassessment in individual medical notes. Respondents expressed frustration with the “all-or-none” approach to declaring sepsis treatment a “success,” which they noted was unfair and difficult to justify to their local clinicians. In part because of the time and effort required to comply with the measure and report results to CMS, several respondents noted that the measure is a uniquely burdensome addition to an already-crowded landscape of hospital quality programs. Despite the resources required to adhere to the measures’ standards and report results to CMS, respondents expressed a belief that the increased attention to sepsis is driving positive changes in hospital care and leading to improved patient outcomes.

Responses to SEP-1

Respondents identified several specific ways in which their hospitals responded to the SEP-1 mandate (Table 4), including investments in measurement, planning and coordinating sepsis-specific quality-improvement activities, improving the early identification of patients with sepsis, improving sepsis treatment and measure compliance, and addressing negative attitudes towards the implementation of the SEP-1 program.

Efforts to Collect Data for SEP-1 Reporting

Respondents reported challenges in reliably and validly measuring and reporting data for the SEP-1 program. First, patient identification and the measurement of treatment processes depends largely on manual medical record review, which is subject to variation across coders. This presents a particular challenge because the clinical definition of sepsis itself is in evolution,¹ creating the possibility that treating physicians could identify a given patient as having sepsis or septic shock based on the most up-to-date definitions but not based on the measure’s specifications or vice versa. Second, each case requires up to an hour of manual medical record review and patients who develop sepsis during prolonged hospitalizations can require several hours or more, which is an unprecedented length of time to spend abstracting data for a single measure.

In addressing these measurement challenges, investment in human resources is the rule. No respondent reported automating abstraction of all the SEP-1 data elements, underscoring concerns regarding the measurement burden of the SEP-1 program.^7,8,14 Rather, hospitals with sufficient financial resources frequently employ full-time data abstractors and individuals responsible for ongoing performance feedback, which facilitates the iterative revision of sepsis quality-improvement initiatives. In contrast, hospitals with fewer resources often rely on contracts with third-party vendors, which delays reporting and complicates efforts to use the data for individualized performance improvement.

Efforts to Coordinate Hospital Responses Across Care Teams

Complying with the measure involves the longitudinal coordination of multiple care teams across different units, so planning and executing local hospital responses required interdepartmental and multidisciplinary stakeholder involvement. Respondents were uncertain about the ideal strategy to coordinate these quality-improvement efforts, yielding iterative changes to electronic health records (EHRs), education programs, and data collection methods. This “learning by doing” is necessary because no prior CMS quality measure is as complex as SEP-1 or as varied in the sources of data required to measure and report the results. By requiring hospitals to improve coordination of care throughout the hospital, SEP-1 presents a quality-improvement and measurement challenge that may ultimately drive innovation and better patient care.

 

Efforts to Improve Sepsis Diagnosis

Several hospitals are implementing sepsis screening and alerts to speed sepsis recognition and meet the measure’s time-sensitive treatment requirements. An example of a less-intensive alert is one hospital’s lowering of the threshold for lactate values that are viewed as “critical” (and thus requiring notification of the bedside clinician). Examples of more resource-intensive alerts included electronic screening for vital sign abnormalities that trigger bedside assessment for infection as well as nurse-driven manual sepsis screening tools.

Frequently, these more intensive efforts faced barriers to successful implementation related to the broader issues of performance measurement rather than the specifics of SEP-1. EHRs generally lacked built-in electronic screening capacity, and few hospitals had the resources required for customized EHR modification. Manual screening required nurses to spend time away from direct patient care. For both electronic and manual screening, respondents expressed concern about how these new alerts would fit into a care landscape already inundated with alerts, alarms, and care notifications.^16,17

Efforts to Improve Sepsis Treatment

Many hospitals are implementing sepsis-specific treatment protocols and order sets designed to help meet SEP-1 treatment specifications. In hospitals and health systems with preexisting sepsis quality-improvement efforts, SEP-1 stimulated adaptation and acceleration of their efforts; in hospitals without preexisting sepsis-specific quality improvement, SEP-1 inspired de novo program development and implementation. These programs were wide ranging. Several hospitals implemented a process by which an initially elevated lactate value automates an order for a repeat lactate level, facilitating an assessment of the clinical response to treatment. Other examples include triggers for sepsis-specific treatment protocols and checklists that bedside nurses can begin without initial physician oversight. In 1 hospital, sepsis alerts triggered by emergency medical first responders initiate responses prior to hospital arrival in a manner analogous to prehospital alerts for myocardial infarction and stroke.^18,19

Efforts to implement these protocols encountered several common challenges. Physicians were often resistant to adopting inflexible treatment rules that did not allow them to tailor therapies to individual patients. Furthermore, even protocols and order sets that worked in 1 setting did not necessarily generalize throughout the hospital or health system, reflecting the difficulty in implementing a highly specified measure across diverse treatment environments.

Efforts to Manage Clinician Attitudes Toward SEP-1 Implementation

In addition to addressing clinicians’ behaviors, hospitals sought to address stakeholders’ attitudes when those attitudes created barriers to SEP-1 implementation. First, hospitals frequently faced a lack of buy-in from clinicians who were resistant to the idea of protocolized care in general and who were specifically skeptical that initiatives designed to increase clinical documentation would drive improvements in patient-centered outcomes. Second, respondents had to confront a hierarchical hospital culture, which manifests not only in clinical care, but also in the quality-improvement infrastructure. Many respondents reported that physicians were more receptive to performance feedback from fellow physicians rather than nonphysician quality administrators.

Respondents described a range of approaches to counteract these attitudes. First, hospitals deployed department- and profession-specific “champions” to provide peer-to-peer performance feedback supported by data demonstrating a link between process improvements and patient outcomes. Second, many respondents noted that the addition of new clinical staff, who were often younger and more receptive to new initiatives, could alter a hospital’s quality culture; in smaller hospitals, just a few individuals could significantly alter the dynamic. Finally, when other efforts failed, some respondents indicated that top-down administrative support could persuade resistant individuals to change their approach. However, this solution worked best with employed physicians and was less effective with independent physician groups without direct financial ties to hospital performance. These efforts to overcome negative attitudes toward SEP-1 implementation required individuals’ time and energy, leading to frustration at times and adding to the resources required to comply with the program.

Planning for the Future of SEP-1

Respondents anticipate that performance of the SEP-1 measure will eventually become publicly reported and incorporated into value-based purchasing calculations. Hospitals are therefore seeking greater interaction with CMS as it makes iterative revisions to the measure because respondents expect that their hospitals’ level of performance, rather than just the act of participating, will affect hospital finances. Respondents expressed a desire for more live, interactive educational sessions with CMS moving forward, rather than limiting the opportunities for clarification to online comment forums or statements elsewhere in the public record. In addition, respondents hope that public reporting and pay-for-performance could be delayed to allow more time to work out the “kinks” in measurement and reporting.

DISCUSSION

We conducted semistructured telephone interviews with quality officers in U.S. hospitals in order to understand hospitals’ perceptions of and responses to Medicare’s SEP-1 sepsis quality-reporting program. Hospitals are struggling with the program’s complexity and investing considerable resources in order to iteratively revise their responses to the program. However, they generally believe that the program is bringing much-needed attention to sepsis diagnosis and treatment. These findings have several implications for the SEP-1 measure in particular and for hospital-based quality measurement and pay-for-performance policies in general.

 

First, we demonstrate that SEP-1 consistently requires a substantial investment of resources from hospitals already struggling under the weight of numerous local, state, and national quality-reporting and improvement programs.^14,20,21 In aggregate, these programs can stretch hospitals’ resources to their limit. Respondents universally reported that the SEP-1 program is requiring dedicated staff to meet the data abstraction and reporting requirements as well as multicomponent quality-improvement initiatives. In the absence of well-established roadmaps for improving sepsis care, these sepsis quality-improvement efforts require experimentation and iterative revision, which can contribute to fatigue and frustration among quality officers and clinical staff. This process of innovation inherently involves successes, failures, and the risk of harm and opportunity costs that strain hospital resources.

Second, our study indicates how SEP-1 could exacerbate existing inequalities in our health system. Sepsis incidence and mortality are already higher in medically underserved regions.²² Given the resources required to respond to the SEP-1 program, optimal performance may be beyond the reach of smaller hospitals, or even larger hospitals, whose resources are already stretched to their limits. Public reporting and pay-for-performance can be adisadvantage to hospitals caring for underserved populations.^23,24 To the extent that responding to sepsis-oriented public policy requires resources that certain hospitals cannot access, these policies could exacerbate existing health disparities.

Third, our findings highlight some specific ways that CMS could revise the SEP-1 program to better meet the needs of hospitals and improve outcomes for patients with sepsis. Primarily, although the program’s current specifications take an “all-or-none” approach to treatment success, a more flexible approach, such as a weighted score or composite measure that combines processes and outcomes,^25,26 could allow hospitals to focus their efforts on those components of the bundle with the strongest evidence for improved patient outcomes.²⁷ Second, policy makers need to reconcile the 2 existing clinical definitions for sepsis.^1,28 CMS has already stated its plans to retain the preexisting sepsis definition,²⁹ but this does not change the reality that frontline providers and quality officials face different, and at times conflicting, clinical definitions while caring for patients. Finally, current implementation challenges may support a delay in moving the measure toward public reporting and pay-for-performance. Hospitals are already responding to the measure in a substantial way, providing an opportunity for early quantitative evaluations of the program’s impact that could inform evidence-based revisions to the measure.

Our study has several limitations. First, by interviewing only individual quality officers within each hospital, it is possible that our findings were not representative of the perspectives of other individuals within their hospitals or the hospital as a whole; indeed, to the extent that quality officers “buy in” to quality measurement and reporting, their perspectives on SEP-1 may skew more positive than other hospital staff. Our respondents represented individuals from a range of positions within the quality infrastructure, whereas “hospital quality leaders” are often chief executive officers, chief medical officers, or vice presidents for quality.³⁰ However, by virtue of our purposive sampling approach, we included respondents from a broad range of hospitals and found similar themes across these respondents, supporting the internal validity of our findings. Second, as is inherent in interview-based research, we cannot verify that respondents’ reports of hospital responses to SEP-1 match the actual changes implemented “on the ground.” We are reassured, however, by the fact that many of the perspectives and quality-improvement changes that respondents described align with the opinions and suggestions of academic quality experts, which are informed by clinical experience.^6-8 Third, while respondents believe that hospital responses to SEP-1 are contributing to improvements in treatment and outcomes, we do not yet have robust objective data to support this opinion or to evaluate the association between quality officers’ perspectives and hospital performance. A quantitative evaluation of the clinical impact of SEP-1, as well as the relationship between hospital performance and quality officers’ perspectives on the measure, are important areas for future research.

CONCLUSIONS

In a qualitative study of hospital responses to Medicare’s SEP-1 program, we found that hospitals are implementing changes across a variety of domains and in ways that consistently require dedicated resources. Giving hospitals the flexibility to focus on treatment processes with the most direct impact on patient-centered outcomes might enhance the program’s effectiveness. Future work should quantify the program’s impact and develop novel approaches to data abstraction and quality improvement.

Disclosure

Aside from federal funding, the authors have no conflicts of interest to disclose. The authors received funding from the National Institutes of Health (IJB, F32HL132461) (JMK, K24HL133444). This work was submitted as an abstract to the 2017 American Thoracic Society International Conference, May 2017.

 

Sepsis affects over 1 million Americans annually, resulting in significant morbidity, mortality, and costs for hospitalized patients.^1-4 There is an increasing interest in policy-oriented approaches to improving sepsis care at both the state and national levels.^5,6 The most prominent policy is the Centers for Medicare and Medicaid Services (CMS) Sepsis CMS Core (SEP-1) program, which was formally implemented in October 2015; the program mandates that hospitals report their compliance with a variety of sepsis treatment processes (Table 1). Academic quality experts generally applaud the increased attention to sepsis but are concerned that the measure’s design and specifications advance beyond the existing evidence base.^7,8 However, remarkably little is known about how front-line hospital quality officials perceive the program and how they are responding or not responding, to the new requirements. This knowledge gap is a critical barrier to evaluating the program’s practical impact on sepsis treatment and outcomes.

We therefore sought to understand hospital stakeholders’ perceptions of the SEP-1 program in general as well as their characterization of their local hospitals’ responses to the program. We were specifically interested in obtaining a focused perspective on the policy and hospitals’ responses to the policy rather than individual physicians’ attitudes regarding sepsis care protocols, which are complex and may be independent from the policy itself.⁹ We used a qualitative research approach designed to generate both a deep and broad understanding of how hospitals are responding to SEP-1 requirements, including the resources required to implement their responses.

METHODS

Study Design, Setting, and Subjects

We conducted a qualitative study by using semistructured telephone interviews with hospital quality officers in the United States. We targeted hospital quality officers because they are in a position to provide overarching insights into hospitals’ perceptions of and responses to the SEP-1 program. We enrolled quality officers at general, short-stay, nonfederal acute care hospitals because those are the hospitals to which the SEP-1 program applies. We generated a stratified random sample of hospitals by using 2013 data from Medicare’s Healthcare Cost and Reporting Information System (HCRIS) database.¹⁰ We stratified by size (greater than or less than 200 total beds), teaching status (presence or absence of any resident physician trainees), and ownership (for-profit vs nonprofit), creating 8 mutually exclusive strata. This sampling frame was designed to ensure representativeness from a broad range of hospital types, not to enable comparisons across hospital types, which is outside the scope of qualitative research.

Within strata, we contacted hospitals in a random order by phone using the primary number listed in the HCRIS database. We asked the hospital operator to connect us to the chief quality officer or an appropriate alternative hospital administrator with knowledge of hospital quality-improvement activities. We limited participation to 1 respondent per hospital. We did not offer any specific incentives for participation.

The study was approved by the University of Pittsburgh Institutional Review Board with a waiver of signed informed consent.

Data Collection

Interviews were conducted by a trained research coordinator between February 2016 and October 2016. Interviews were conducted concurrently with data analysis by using a constant comparison approach.¹¹ The constant comparison approach involves the iterative refinement of themes by comparing the existing themes to new data as they emerge during successive interviews. We chose a constant comparison approach because we wanted to systematically describe hospital responses to SEP-1 rather than specifically test individual hypotheses.¹¹ As is typical in qualitative research, we did not set the sample size a priori but instead continued the interviews until we achieved thematic saturation.^12,13

The interview script included a mix of directed and open-ended questions about respondents’ perspectives of and hospital responses to the SEP-1 program. The questions covered the following 4 domains: hospitals’ sepsis quality-improvement initiatives before and after the Medicare reporting program, reception of the hospital responses, the approach to data abstraction and reporting, and the overall impressions of the program and its impact.^6-8,14 We allowed for updates and revisions of the interview guide as necessary to explore any new content and emergent themes. We piloted the interview guide on 2 hospital quality officers at our institution and then revised its structure again after interviews with the initial 6 hospitals. The complete final interview guide is available in the supplemental digital content.

 

Analysis

Interviews were audio recorded, transcribed, and loaded onto a secure server. We used NVivo 11 (QSR International, Cambridge, Massachusetts) for coding and analysis. We iteratively reviewed and thematically analyzed the transcripts for structural content and emergent themes, consistent with established qualitative methods.¹⁵ Three investigators reviewed the initial 20 transcripts and developed the codebook through iterative discussion and consensus. The codes were then organized into themes and subthemes. Subsequently, 1 investigator coded the remaining transcripts. The results are presented as a series of key themes supported by direct quotes from the interviews.

RESULTS

Sample Description

We performed 29 interviews prior to achieving thematic saturation. Each of the 8 strata from the sampling frame was represented by at least 3 hospitals. Hospitals in the final sample were diverse in total bed size, intensive care unit bed capacity, teaching status, and ownership (Table 2). The median interview length was 25 minutes (interquartile range, 20-32 minutes). Respondents included 6 quality coordinators, 6 quality managers, and 11 quality directors, with the remainder holding a variety of other quality-related titles. Most respondents worked in hospital quality departments, although 4 were affiliated with individual clinical departments (eg, emergency medicine and/or critical care services). Of the 9 respondents who reported their professional training, 8 were registered nurses. Eleven respondents reported participating in measure abstraction.

Perspectives on SEP-1

Respondents’ general perspectives on the SEP-1 program are outlined in Table 3, with several key themes emerging. Foremost was the sheer complexity of the measure compounded by its reliance on time-stamped clinical documentation, and in particular, the physical reassessment in individual medical notes. Respondents expressed frustration with the “all-or-none” approach to declaring sepsis treatment a “success,” which they noted was unfair and difficult to justify to their local clinicians. In part because of the time and effort required to comply with the measure and report results to CMS, several respondents noted that the measure is a uniquely burdensome addition to an already-crowded landscape of hospital quality programs. Despite the resources required to adhere to the measures’ standards and report results to CMS, respondents expressed a belief that the increased attention to sepsis is driving positive changes in hospital care and leading to improved patient outcomes.

Responses to SEP-1

Respondents identified several specific ways in which their hospitals responded to the SEP-1 mandate (Table 4), including investments in measurement, planning and coordinating sepsis-specific quality-improvement activities, improving the early identification of patients with sepsis, improving sepsis treatment and measure compliance, and addressing negative attitudes towards the implementation of the SEP-1 program.

Efforts to Collect Data for SEP-1 Reporting

Respondents reported challenges in reliably and validly measuring and reporting data for the SEP-1 program. First, patient identification and the measurement of treatment processes depends largely on manual medical record review, which is subject to variation across coders. This presents a particular challenge because the clinical definition of sepsis itself is in evolution,¹ creating the possibility that treating physicians could identify a given patient as having sepsis or septic shock based on the most up-to-date definitions but not based on the measure’s specifications or vice versa. Second, each case requires up to an hour of manual medical record review and patients who develop sepsis during prolonged hospitalizations can require several hours or more, which is an unprecedented length of time to spend abstracting data for a single measure.

In addressing these measurement challenges, investment in human resources is the rule. No respondent reported automating abstraction of all the SEP-1 data elements, underscoring concerns regarding the measurement burden of the SEP-1 program.^7,8,14 Rather, hospitals with sufficient financial resources frequently employ full-time data abstractors and individuals responsible for ongoing performance feedback, which facilitates the iterative revision of sepsis quality-improvement initiatives. In contrast, hospitals with fewer resources often rely on contracts with third-party vendors, which delays reporting and complicates efforts to use the data for individualized performance improvement.

Efforts to Coordinate Hospital Responses Across Care Teams

Complying with the measure involves the longitudinal coordination of multiple care teams across different units, so planning and executing local hospital responses required interdepartmental and multidisciplinary stakeholder involvement. Respondents were uncertain about the ideal strategy to coordinate these quality-improvement efforts, yielding iterative changes to electronic health records (EHRs), education programs, and data collection methods. This “learning by doing” is necessary because no prior CMS quality measure is as complex as SEP-1 or as varied in the sources of data required to measure and report the results. By requiring hospitals to improve coordination of care throughout the hospital, SEP-1 presents a quality-improvement and measurement challenge that may ultimately drive innovation and better patient care.

 

Efforts to Improve Sepsis Diagnosis

Several hospitals are implementing sepsis screening and alerts to speed sepsis recognition and meet the measure’s time-sensitive treatment requirements. An example of a less-intensive alert is one hospital’s lowering of the threshold for lactate values that are viewed as “critical” (and thus requiring notification of the bedside clinician). Examples of more resource-intensive alerts included electronic screening for vital sign abnormalities that trigger bedside assessment for infection as well as nurse-driven manual sepsis screening tools.

Frequently, these more intensive efforts faced barriers to successful implementation related to the broader issues of performance measurement rather than the specifics of SEP-1. EHRs generally lacked built-in electronic screening capacity, and few hospitals had the resources required for customized EHR modification. Manual screening required nurses to spend time away from direct patient care. For both electronic and manual screening, respondents expressed concern about how these new alerts would fit into a care landscape already inundated with alerts, alarms, and care notifications.^16,17

Efforts to Improve Sepsis Treatment

Many hospitals are implementing sepsis-specific treatment protocols and order sets designed to help meet SEP-1 treatment specifications. In hospitals and health systems with preexisting sepsis quality-improvement efforts, SEP-1 stimulated adaptation and acceleration of their efforts; in hospitals without preexisting sepsis-specific quality improvement, SEP-1 inspired de novo program development and implementation. These programs were wide ranging. Several hospitals implemented a process by which an initially elevated lactate value automates an order for a repeat lactate level, facilitating an assessment of the clinical response to treatment. Other examples include triggers for sepsis-specific treatment protocols and checklists that bedside nurses can begin without initial physician oversight. In 1 hospital, sepsis alerts triggered by emergency medical first responders initiate responses prior to hospital arrival in a manner analogous to prehospital alerts for myocardial infarction and stroke.^18,19

Efforts to implement these protocols encountered several common challenges. Physicians were often resistant to adopting inflexible treatment rules that did not allow them to tailor therapies to individual patients. Furthermore, even protocols and order sets that worked in 1 setting did not necessarily generalize throughout the hospital or health system, reflecting the difficulty in implementing a highly specified measure across diverse treatment environments.

Efforts to Manage Clinician Attitudes Toward SEP-1 Implementation

In addition to addressing clinicians’ behaviors, hospitals sought to address stakeholders’ attitudes when those attitudes created barriers to SEP-1 implementation. First, hospitals frequently faced a lack of buy-in from clinicians who were resistant to the idea of protocolized care in general and who were specifically skeptical that initiatives designed to increase clinical documentation would drive improvements in patient-centered outcomes. Second, respondents had to confront a hierarchical hospital culture, which manifests not only in clinical care, but also in the quality-improvement infrastructure. Many respondents reported that physicians were more receptive to performance feedback from fellow physicians rather than nonphysician quality administrators.

Respondents described a range of approaches to counteract these attitudes. First, hospitals deployed department- and profession-specific “champions” to provide peer-to-peer performance feedback supported by data demonstrating a link between process improvements and patient outcomes. Second, many respondents noted that the addition of new clinical staff, who were often younger and more receptive to new initiatives, could alter a hospital’s quality culture; in smaller hospitals, just a few individuals could significantly alter the dynamic. Finally, when other efforts failed, some respondents indicated that top-down administrative support could persuade resistant individuals to change their approach. However, this solution worked best with employed physicians and was less effective with independent physician groups without direct financial ties to hospital performance. These efforts to overcome negative attitudes toward SEP-1 implementation required individuals’ time and energy, leading to frustration at times and adding to the resources required to comply with the program.

Planning for the Future of SEP-1

Respondents anticipate that performance of the SEP-1 measure will eventually become publicly reported and incorporated into value-based purchasing calculations. Hospitals are therefore seeking greater interaction with CMS as it makes iterative revisions to the measure because respondents expect that their hospitals’ level of performance, rather than just the act of participating, will affect hospital finances. Respondents expressed a desire for more live, interactive educational sessions with CMS moving forward, rather than limiting the opportunities for clarification to online comment forums or statements elsewhere in the public record. In addition, respondents hope that public reporting and pay-for-performance could be delayed to allow more time to work out the “kinks” in measurement and reporting.

DISCUSSION

We conducted semistructured telephone interviews with quality officers in U.S. hospitals in order to understand hospitals’ perceptions of and responses to Medicare’s SEP-1 sepsis quality-reporting program. Hospitals are struggling with the program’s complexity and investing considerable resources in order to iteratively revise their responses to the program. However, they generally believe that the program is bringing much-needed attention to sepsis diagnosis and treatment. These findings have several implications for the SEP-1 measure in particular and for hospital-based quality measurement and pay-for-performance policies in general.

 

First, we demonstrate that SEP-1 consistently requires a substantial investment of resources from hospitals already struggling under the weight of numerous local, state, and national quality-reporting and improvement programs.^14,20,21 In aggregate, these programs can stretch hospitals’ resources to their limit. Respondents universally reported that the SEP-1 program is requiring dedicated staff to meet the data abstraction and reporting requirements as well as multicomponent quality-improvement initiatives. In the absence of well-established roadmaps for improving sepsis care, these sepsis quality-improvement efforts require experimentation and iterative revision, which can contribute to fatigue and frustration among quality officers and clinical staff. This process of innovation inherently involves successes, failures, and the risk of harm and opportunity costs that strain hospital resources.

Second, our study indicates how SEP-1 could exacerbate existing inequalities in our health system. Sepsis incidence and mortality are already higher in medically underserved regions.²² Given the resources required to respond to the SEP-1 program, optimal performance may be beyond the reach of smaller hospitals, or even larger hospitals, whose resources are already stretched to their limits. Public reporting and pay-for-performance can be adisadvantage to hospitals caring for underserved populations.^23,24 To the extent that responding to sepsis-oriented public policy requires resources that certain hospitals cannot access, these policies could exacerbate existing health disparities.

Third, our findings highlight some specific ways that CMS could revise the SEP-1 program to better meet the needs of hospitals and improve outcomes for patients with sepsis. Primarily, although the program’s current specifications take an “all-or-none” approach to treatment success, a more flexible approach, such as a weighted score or composite measure that combines processes and outcomes,^25,26 could allow hospitals to focus their efforts on those components of the bundle with the strongest evidence for improved patient outcomes.²⁷ Second, policy makers need to reconcile the 2 existing clinical definitions for sepsis.^1,28 CMS has already stated its plans to retain the preexisting sepsis definition,²⁹ but this does not change the reality that frontline providers and quality officials face different, and at times conflicting, clinical definitions while caring for patients. Finally, current implementation challenges may support a delay in moving the measure toward public reporting and pay-for-performance. Hospitals are already responding to the measure in a substantial way, providing an opportunity for early quantitative evaluations of the program’s impact that could inform evidence-based revisions to the measure.

Our study has several limitations. First, by interviewing only individual quality officers within each hospital, it is possible that our findings were not representative of the perspectives of other individuals within their hospitals or the hospital as a whole; indeed, to the extent that quality officers “buy in” to quality measurement and reporting, their perspectives on SEP-1 may skew more positive than other hospital staff. Our respondents represented individuals from a range of positions within the quality infrastructure, whereas “hospital quality leaders” are often chief executive officers, chief medical officers, or vice presidents for quality.³⁰ However, by virtue of our purposive sampling approach, we included respondents from a broad range of hospitals and found similar themes across these respondents, supporting the internal validity of our findings. Second, as is inherent in interview-based research, we cannot verify that respondents’ reports of hospital responses to SEP-1 match the actual changes implemented “on the ground.” We are reassured, however, by the fact that many of the perspectives and quality-improvement changes that respondents described align with the opinions and suggestions of academic quality experts, which are informed by clinical experience.^6-8 Third, while respondents believe that hospital responses to SEP-1 are contributing to improvements in treatment and outcomes, we do not yet have robust objective data to support this opinion or to evaluate the association between quality officers’ perspectives and hospital performance. A quantitative evaluation of the clinical impact of SEP-1, as well as the relationship between hospital performance and quality officers’ perspectives on the measure, are important areas for future research.

CONCLUSIONS

In a qualitative study of hospital responses to Medicare’s SEP-1 program, we found that hospitals are implementing changes across a variety of domains and in ways that consistently require dedicated resources. Giving hospitals the flexibility to focus on treatment processes with the most direct impact on patient-centered outcomes might enhance the program’s effectiveness. Future work should quantify the program’s impact and develop novel approaches to data abstraction and quality improvement.

Disclosure

Aside from federal funding, the authors have no conflicts of interest to disclose. The authors received funding from the National Institutes of Health (IJB, F32HL132461) (JMK, K24HL133444). This work was submitted as an abstract to the 2017 American Thoracic Society International Conference, May 2017.

 

Sepsis affects over 1 million Americans annually, resulting in significant morbidity, mortality, and costs for hospitalized patients.^1-4 There is an increasing interest in policy-oriented approaches to improving sepsis care at both the state and national levels.^5,6 The most prominent policy is the Centers for Medicare and Medicaid Services (CMS) Sepsis CMS Core (SEP-1) program, which was formally implemented in October 2015; the program mandates that hospitals report their compliance with a variety of sepsis treatment processes (Table 1). Academic quality experts generally applaud the increased attention to sepsis but are concerned that the measure’s design and specifications advance beyond the existing evidence base.^7,8 However, remarkably little is known about how front-line hospital quality officials perceive the program and how they are responding or not responding, to the new requirements. This knowledge gap is a critical barrier to evaluating the program’s practical impact on sepsis treatment and outcomes.

We therefore sought to understand hospital stakeholders’ perceptions of the SEP-1 program in general as well as their characterization of their local hospitals’ responses to the program. We were specifically interested in obtaining a focused perspective on the policy and hospitals’ responses to the policy rather than individual physicians’ attitudes regarding sepsis care protocols, which are complex and may be independent from the policy itself.⁹ We used a qualitative research approach designed to generate both a deep and broad understanding of how hospitals are responding to SEP-1 requirements, including the resources required to implement their responses.

METHODS

Study Design, Setting, and Subjects

We conducted a qualitative study by using semistructured telephone interviews with hospital quality officers in the United States. We targeted hospital quality officers because they are in a position to provide overarching insights into hospitals’ perceptions of and responses to the SEP-1 program. We enrolled quality officers at general, short-stay, nonfederal acute care hospitals because those are the hospitals to which the SEP-1 program applies. We generated a stratified random sample of hospitals by using 2013 data from Medicare’s Healthcare Cost and Reporting Information System (HCRIS) database.¹⁰ We stratified by size (greater than or less than 200 total beds), teaching status (presence or absence of any resident physician trainees), and ownership (for-profit vs nonprofit), creating 8 mutually exclusive strata. This sampling frame was designed to ensure representativeness from a broad range of hospital types, not to enable comparisons across hospital types, which is outside the scope of qualitative research.

Within strata, we contacted hospitals in a random order by phone using the primary number listed in the HCRIS database. We asked the hospital operator to connect us to the chief quality officer or an appropriate alternative hospital administrator with knowledge of hospital quality-improvement activities. We limited participation to 1 respondent per hospital. We did not offer any specific incentives for participation.

The study was approved by the University of Pittsburgh Institutional Review Board with a waiver of signed informed consent.

Data Collection

Interviews were conducted by a trained research coordinator between February 2016 and October 2016. Interviews were conducted concurrently with data analysis by using a constant comparison approach.¹¹ The constant comparison approach involves the iterative refinement of themes by comparing the existing themes to new data as they emerge during successive interviews. We chose a constant comparison approach because we wanted to systematically describe hospital responses to SEP-1 rather than specifically test individual hypotheses.¹¹ As is typical in qualitative research, we did not set the sample size a priori but instead continued the interviews until we achieved thematic saturation.^12,13

The interview script included a mix of directed and open-ended questions about respondents’ perspectives of and hospital responses to the SEP-1 program. The questions covered the following 4 domains: hospitals’ sepsis quality-improvement initiatives before and after the Medicare reporting program, reception of the hospital responses, the approach to data abstraction and reporting, and the overall impressions of the program and its impact.^6-8,14 We allowed for updates and revisions of the interview guide as necessary to explore any new content and emergent themes. We piloted the interview guide on 2 hospital quality officers at our institution and then revised its structure again after interviews with the initial 6 hospitals. The complete final interview guide is available in the supplemental digital content.

 

Analysis

Interviews were audio recorded, transcribed, and loaded onto a secure server. We used NVivo 11 (QSR International, Cambridge, Massachusetts) for coding and analysis. We iteratively reviewed and thematically analyzed the transcripts for structural content and emergent themes, consistent with established qualitative methods.¹⁵ Three investigators reviewed the initial 20 transcripts and developed the codebook through iterative discussion and consensus. The codes were then organized into themes and subthemes. Subsequently, 1 investigator coded the remaining transcripts. The results are presented as a series of key themes supported by direct quotes from the interviews.

RESULTS

Sample Description

We performed 29 interviews prior to achieving thematic saturation. Each of the 8 strata from the sampling frame was represented by at least 3 hospitals. Hospitals in the final sample were diverse in total bed size, intensive care unit bed capacity, teaching status, and ownership (Table 2). The median interview length was 25 minutes (interquartile range, 20-32 minutes). Respondents included 6 quality coordinators, 6 quality managers, and 11 quality directors, with the remainder holding a variety of other quality-related titles. Most respondents worked in hospital quality departments, although 4 were affiliated with individual clinical departments (eg, emergency medicine and/or critical care services). Of the 9 respondents who reported their professional training, 8 were registered nurses. Eleven respondents reported participating in measure abstraction.

Perspectives on SEP-1

Respondents’ general perspectives on the SEP-1 program are outlined in Table 3, with several key themes emerging. Foremost was the sheer complexity of the measure compounded by its reliance on time-stamped clinical documentation, and in particular, the physical reassessment in individual medical notes. Respondents expressed frustration with the “all-or-none” approach to declaring sepsis treatment a “success,” which they noted was unfair and difficult to justify to their local clinicians. In part because of the time and effort required to comply with the measure and report results to CMS, several respondents noted that the measure is a uniquely burdensome addition to an already-crowded landscape of hospital quality programs. Despite the resources required to adhere to the measures’ standards and report results to CMS, respondents expressed a belief that the increased attention to sepsis is driving positive changes in hospital care and leading to improved patient outcomes.

Responses to SEP-1

Respondents identified several specific ways in which their hospitals responded to the SEP-1 mandate (Table 4), including investments in measurement, planning and coordinating sepsis-specific quality-improvement activities, improving the early identification of patients with sepsis, improving sepsis treatment and measure compliance, and addressing negative attitudes towards the implementation of the SEP-1 program.

Efforts to Collect Data for SEP-1 Reporting

Respondents reported challenges in reliably and validly measuring and reporting data for the SEP-1 program. First, patient identification and the measurement of treatment processes depends largely on manual medical record review, which is subject to variation across coders. This presents a particular challenge because the clinical definition of sepsis itself is in evolution,¹ creating the possibility that treating physicians could identify a given patient as having sepsis or septic shock based on the most up-to-date definitions but not based on the measure’s specifications or vice versa. Second, each case requires up to an hour of manual medical record review and patients who develop sepsis during prolonged hospitalizations can require several hours or more, which is an unprecedented length of time to spend abstracting data for a single measure.

In addressing these measurement challenges, investment in human resources is the rule. No respondent reported automating abstraction of all the SEP-1 data elements, underscoring concerns regarding the measurement burden of the SEP-1 program.^7,8,14 Rather, hospitals with sufficient financial resources frequently employ full-time data abstractors and individuals responsible for ongoing performance feedback, which facilitates the iterative revision of sepsis quality-improvement initiatives. In contrast, hospitals with fewer resources often rely on contracts with third-party vendors, which delays reporting and complicates efforts to use the data for individualized performance improvement.

Efforts to Coordinate Hospital Responses Across Care Teams

Complying with the measure involves the longitudinal coordination of multiple care teams across different units, so planning and executing local hospital responses required interdepartmental and multidisciplinary stakeholder involvement. Respondents were uncertain about the ideal strategy to coordinate these quality-improvement efforts, yielding iterative changes to electronic health records (EHRs), education programs, and data collection methods. This “learning by doing” is necessary because no prior CMS quality measure is as complex as SEP-1 or as varied in the sources of data required to measure and report the results. By requiring hospitals to improve coordination of care throughout the hospital, SEP-1 presents a quality-improvement and measurement challenge that may ultimately drive innovation and better patient care.

 

Efforts to Improve Sepsis Diagnosis

Several hospitals are implementing sepsis screening and alerts to speed sepsis recognition and meet the measure’s time-sensitive treatment requirements. An example of a less-intensive alert is one hospital’s lowering of the threshold for lactate values that are viewed as “critical” (and thus requiring notification of the bedside clinician). Examples of more resource-intensive alerts included electronic screening for vital sign abnormalities that trigger bedside assessment for infection as well as nurse-driven manual sepsis screening tools.

Frequently, these more intensive efforts faced barriers to successful implementation related to the broader issues of performance measurement rather than the specifics of SEP-1. EHRs generally lacked built-in electronic screening capacity, and few hospitals had the resources required for customized EHR modification. Manual screening required nurses to spend time away from direct patient care. For both electronic and manual screening, respondents expressed concern about how these new alerts would fit into a care landscape already inundated with alerts, alarms, and care notifications.^16,17

Efforts to Improve Sepsis Treatment

Many hospitals are implementing sepsis-specific treatment protocols and order sets designed to help meet SEP-1 treatment specifications. In hospitals and health systems with preexisting sepsis quality-improvement efforts, SEP-1 stimulated adaptation and acceleration of their efforts; in hospitals without preexisting sepsis-specific quality improvement, SEP-1 inspired de novo program development and implementation. These programs were wide ranging. Several hospitals implemented a process by which an initially elevated lactate value automates an order for a repeat lactate level, facilitating an assessment of the clinical response to treatment. Other examples include triggers for sepsis-specific treatment protocols and checklists that bedside nurses can begin without initial physician oversight. In 1 hospital, sepsis alerts triggered by emergency medical first responders initiate responses prior to hospital arrival in a manner analogous to prehospital alerts for myocardial infarction and stroke.^18,19

Efforts to implement these protocols encountered several common challenges. Physicians were often resistant to adopting inflexible treatment rules that did not allow them to tailor therapies to individual patients. Furthermore, even protocols and order sets that worked in 1 setting did not necessarily generalize throughout the hospital or health system, reflecting the difficulty in implementing a highly specified measure across diverse treatment environments.

Efforts to Manage Clinician Attitudes Toward SEP-1 Implementation

In addition to addressing clinicians’ behaviors, hospitals sought to address stakeholders’ attitudes when those attitudes created barriers to SEP-1 implementation. First, hospitals frequently faced a lack of buy-in from clinicians who were resistant to the idea of protocolized care in general and who were specifically skeptical that initiatives designed to increase clinical documentation would drive improvements in patient-centered outcomes. Second, respondents had to confront a hierarchical hospital culture, which manifests not only in clinical care, but also in the quality-improvement infrastructure. Many respondents reported that physicians were more receptive to performance feedback from fellow physicians rather than nonphysician quality administrators.

Respondents described a range of approaches to counteract these attitudes. First, hospitals deployed department- and profession-specific “champions” to provide peer-to-peer performance feedback supported by data demonstrating a link between process improvements and patient outcomes. Second, many respondents noted that the addition of new clinical staff, who were often younger and more receptive to new initiatives, could alter a hospital’s quality culture; in smaller hospitals, just a few individuals could significantly alter the dynamic. Finally, when other efforts failed, some respondents indicated that top-down administrative support could persuade resistant individuals to change their approach. However, this solution worked best with employed physicians and was less effective with independent physician groups without direct financial ties to hospital performance. These efforts to overcome negative attitudes toward SEP-1 implementation required individuals’ time and energy, leading to frustration at times and adding to the resources required to comply with the program.

Planning for the Future of SEP-1

Respondents anticipate that performance of the SEP-1 measure will eventually become publicly reported and incorporated into value-based purchasing calculations. Hospitals are therefore seeking greater interaction with CMS as it makes iterative revisions to the measure because respondents expect that their hospitals’ level of performance, rather than just the act of participating, will affect hospital finances. Respondents expressed a desire for more live, interactive educational sessions with CMS moving forward, rather than limiting the opportunities for clarification to online comment forums or statements elsewhere in the public record. In addition, respondents hope that public reporting and pay-for-performance could be delayed to allow more time to work out the “kinks” in measurement and reporting.

DISCUSSION

We conducted semistructured telephone interviews with quality officers in U.S. hospitals in order to understand hospitals’ perceptions of and responses to Medicare’s SEP-1 sepsis quality-reporting program. Hospitals are struggling with the program’s complexity and investing considerable resources in order to iteratively revise their responses to the program. However, they generally believe that the program is bringing much-needed attention to sepsis diagnosis and treatment. These findings have several implications for the SEP-1 measure in particular and for hospital-based quality measurement and pay-for-performance policies in general.

 

First, we demonstrate that SEP-1 consistently requires a substantial investment of resources from hospitals already struggling under the weight of numerous local, state, and national quality-reporting and improvement programs.^14,20,21 In aggregate, these programs can stretch hospitals’ resources to their limit. Respondents universally reported that the SEP-1 program is requiring dedicated staff to meet the data abstraction and reporting requirements as well as multicomponent quality-improvement initiatives. In the absence of well-established roadmaps for improving sepsis care, these sepsis quality-improvement efforts require experimentation and iterative revision, which can contribute to fatigue and frustration among quality officers and clinical staff. This process of innovation inherently involves successes, failures, and the risk of harm and opportunity costs that strain hospital resources.

Second, our study indicates how SEP-1 could exacerbate existing inequalities in our health system. Sepsis incidence and mortality are already higher in medically underserved regions.²² Given the resources required to respond to the SEP-1 program, optimal performance may be beyond the reach of smaller hospitals, or even larger hospitals, whose resources are already stretched to their limits. Public reporting and pay-for-performance can be adisadvantage to hospitals caring for underserved populations.^23,24 To the extent that responding to sepsis-oriented public policy requires resources that certain hospitals cannot access, these policies could exacerbate existing health disparities.

Third, our findings highlight some specific ways that CMS could revise the SEP-1 program to better meet the needs of hospitals and improve outcomes for patients with sepsis. Primarily, although the program’s current specifications take an “all-or-none” approach to treatment success, a more flexible approach, such as a weighted score or composite measure that combines processes and outcomes,^25,26 could allow hospitals to focus their efforts on those components of the bundle with the strongest evidence for improved patient outcomes.²⁷ Second, policy makers need to reconcile the 2 existing clinical definitions for sepsis.^1,28 CMS has already stated its plans to retain the preexisting sepsis definition,²⁹ but this does not change the reality that frontline providers and quality officials face different, and at times conflicting, clinical definitions while caring for patients. Finally, current implementation challenges may support a delay in moving the measure toward public reporting and pay-for-performance. Hospitals are already responding to the measure in a substantial way, providing an opportunity for early quantitative evaluations of the program’s impact that could inform evidence-based revisions to the measure.

Our study has several limitations. First, by interviewing only individual quality officers within each hospital, it is possible that our findings were not representative of the perspectives of other individuals within their hospitals or the hospital as a whole; indeed, to the extent that quality officers “buy in” to quality measurement and reporting, their perspectives on SEP-1 may skew more positive than other hospital staff. Our respondents represented individuals from a range of positions within the quality infrastructure, whereas “hospital quality leaders” are often chief executive officers, chief medical officers, or vice presidents for quality.³⁰ However, by virtue of our purposive sampling approach, we included respondents from a broad range of hospitals and found similar themes across these respondents, supporting the internal validity of our findings. Second, as is inherent in interview-based research, we cannot verify that respondents’ reports of hospital responses to SEP-1 match the actual changes implemented “on the ground.” We are reassured, however, by the fact that many of the perspectives and quality-improvement changes that respondents described align with the opinions and suggestions of academic quality experts, which are informed by clinical experience.^6-8 Third, while respondents believe that hospital responses to SEP-1 are contributing to improvements in treatment and outcomes, we do not yet have robust objective data to support this opinion or to evaluate the association between quality officers’ perspectives and hospital performance. A quantitative evaluation of the clinical impact of SEP-1, as well as the relationship between hospital performance and quality officers’ perspectives on the measure, are important areas for future research.

CONCLUSIONS

In a qualitative study of hospital responses to Medicare’s SEP-1 program, we found that hospitals are implementing changes across a variety of domains and in ways that consistently require dedicated resources. Giving hospitals the flexibility to focus on treatment processes with the most direct impact on patient-centered outcomes might enhance the program’s effectiveness. Future work should quantify the program’s impact and develop novel approaches to data abstraction and quality improvement.

Disclosure

Aside from federal funding, the authors have no conflicts of interest to disclose. The authors received funding from the National Institutes of Health (IJB, F32HL132461) (JMK, K24HL133444). This work was submitted as an abstract to the 2017 American Thoracic Society International Conference, May 2017.

 

Patient sign-outs are defined as the transition of patient care that includes the transfer of information, task accountability, and personal responsibility between providers.^1-3 The adoption of mnemonics as a memory aid has been used to improve the transfer of patient information between providers.⁴ In the transfer of task accountability, providers transfer follow-up tasks to on-call or coverage providers and ensure that directives are understood. Joint task accountability is enhanced through collaborative giving and cross-checking of information received through assertive questioning to detect errors, and it also enables the receiver to codevelop an understanding of a patient’s condition.^5-8 In the transfer of personal responsibility for the primary team’s patients, the provision of anticipatory guidance enables the coverage provider to have prospective information about potential, upcoming issues to facilitate care plans.⁶ Enabling coverage providers to anticipate overnight events helps them exercise responsibility for patients who are under their temporary care.²

The Accreditation Council for Graduate Medical Education requires residency programs to provide formal instruction on sign-outs.⁹ Yet, variability across training programs exists,^8,10 with training emphasis on the transfer of information over accountability or responsibility.¹¹ Previous studies have demonstrated the efficacy of sign-out training, such as the illness severity, patient summary, action list, situation awareness and contingency planning, and synthesis by reviewer (I-PASS) bundle.³ Yet, participation is far from 100% because the I-PASS bundle requires in-person workshops, e-learning platforms, organizational change campaigns, and faculty participation,¹² involving resource and time commitments that few programs can afford. To address this issue, we seek to compare resource-efficient, knowledge-based, skill-based, compliance-based, and learner-initiated sign-out training pedagogies. We focused on the evening sign-out because it is a high-risk period when care for inpatients is transferred to smaller coverage intern teams.

METHODS

Setting and Study Design

A prospective, randomized cohort trial of 4 training interventions was conducted at an internal medicine residency program at a Mid-Atlantic, academic, tertiary-care hospital with 1192 inpatient beds. The 52 interns admitted to the program were randomly assigned to 4 firms caring for up to 25 inpatients on each floor of the hospital. The case mix faced by each firm was similar because patients were randomly assigned to firms based on bed availability. Teams of 5 interns in each firm worked in 5-day duty cycles, during which each intern rotated as a night cover for his or her firm. Interns remain in their firm throughout their residency. Sign-outs were conducted face to face with a computer. Receivers printed sign-out sheets populated with patient information and took notes when senders communicated information from the computer. The hospital’s institutional review board approved this study.

Interventions

The firms were randomly assigned to 1 of 4 one-hour quality-improvement training interventions delivered at the same time and day in November 2014 at each firm’s office, located on different floors of the hospital. There was virtually no cross-talk among the firms in the first year, which ensured the integrity of the cohort randomization and interventions. Faculty from an affiliated business school of the academic center worked with attending physicians to train the firms.

All interventions took 1 hour at noontime. Firm 1 (the control) received a didactic lecture on sign-out, which participants heard during orientation. Repeating that lecture reinforced their knowledge of sign-outs. Firm 2 was trained on the I-PASS mnemonic with a predictable progression of information elements to transfer.^3,12 Interns role-played 3 scenarios to practice sign-out.³ They received skills feedback and a debriefing to link I-PASS with information elements to transfer. Firm 3 was dealt a policy mandate by the interns’ attending physician to perform specific tasks at sign-out. Senders were to provide the night cover with to-do tasks, and receivers were to actively discuss and verify these tasks to ensure task accountability.¹³ Firm 4 was trained on a Plan-Do-Study-Act (PDSA) protocol to identify and solve perceived barriers to sign-outs. Firm 4 agreed to solve the problem of the lack of care plans by the day team to the night cover. An ad hoc team in Firm 4 refined, pilot tested, and rolled out the solution within a month. Its protocol emphasized information on anticipated changes in patient status, providing contingency plans and their rationale as well as discussions to clarify care plans. Details of the 4 interventions are shown in the Table.

 

Data Collection Process

Eight trained senior residents, recruited by the last author (S.V.D.), volunteered to observe 10 evening sign-outs in each firm 1 month prior to the intervention and another 10 nights 4 months after training. Observations were standardized with a sign-out checklist developed from the literature review and the Joint Commission’s 2006 National Patient Safety Goal 2E that followed the Situation, Background, Assessment, and Recommendation communication structure with opportunities for questioning and information verification.^14,15 Observers indicated “1” for each of the 17 sign-out elements in the checklist they observed, as shown in the supporting Table. Observers did not have supervisory relationships with the interns. Occasionally, the pairs of observers were different depending on their availability.

Outcomes

We measured improvements in sign-out quality by the mean percentage differences for each of the 3 dimensions of sign-out, as well as a multidimensional measure of sign-out comprising the 3 dimensions for each firm in 2 ways: (1) pre- and postintervention, and (2) vis-à-vis the control group postintervention.

Statistical Analysis

We factor analyzed the 17 sign-out elements using principal components analysis with varimax rotation to confirm their groupings within the 3 dimensions of sign-out using Statistical Package for the Social Sciences (SPSS) version 24 (IBM, North Castle, NY). We calculated the mean percentage differences and used Student t tests to evaluate statistical differences at P < 0.05.

RESULTS

Five hundred and sixty-three patient sign-outs were observed prior to the training interventions (κ = 0.646), and 620 patient sign-outs were observed after the interventions (κ = 0.648). Kappa values derived from SPSS were within acceptable interrater agreement ranges. Factor analysis of the 17 sign-out elements yielded 3 factors that we named patient information, task accountability, and responsibility, as shown in the supporting Table.

The supporting Figure reports 2 sets of results. The line graphs show the pre- and postintervention differences for each firm while the bar charts show the postintervention differences between each firm vis-à-vis the control group on sign-out dimensions. The line graphs indicate the greatest improvements in patient information, task accountability, and responsibility for the I-PASS, policy mandate, and PDSA groups, respectively. Mandate and PDSA groups reported low relative scores on sign-out dimensions that were not the foci of their training while the didactics group scored around 0 pre- and postintervention. I-PASS had the highest improvement on the multidimensional measure of sign-out quality but was not significantly different from the PDSA group at P < 0.05 (see supporting Figure for the calculations). The bar charts indicate that all groups vis-à-vis the control had higher improvements in task accountability, responsibility, and the multidimensional measure of sign-out quality. I-PASS vis-à-vis the control had the highest improvement but was not statistically different from the PDSA at P < 0.05. No sentinel events were reported during the entire study period.

DISCUSSION

The results indicated that after only 1 hour of training, skill-based, compliance-based, and learner-initiated sign-out training improved sign-out quality beyond knowledge-based didactics even though the number of sign-out elements taught in the latter 2 was lower than in the didactics group. Different training emphases influenced different dimensions of sign-out quality so that training interns to focus on task accountability or responsibility led to improvements in those dimensions only. The lower scores in other dimensions suggest potential risks in sign-out quality from focusing attention on 1 dimension at the expense of other dimensions. I-PASS, which covered the most sign-out elements and utilized 5 facilitators, led to the best overall improvement in sign-out quality, which is consistent with previous studies.^3,12 We demonstrated that only 1 hour of training on the I-PASS mnemonics using video, role-playing, and feedback led to significant improvements. This approach is portable and easily applied to any program. Potential improvements in I-PASS training could be obtained by emphasizing task accountability and responsibility because the mandate and PDSA groups obtained higher scores than the I-PASS group in these dimensions.

Limitations

We measured sign-out quality in the evening at this site because it was at greatest risk for errors. Future studies should consider daytime sign-outs, interunit handoffs, and other hospital settings, such as community or rural hospitals and nonacute patient settings, to ascertain generalizability. Data were collected from observations, so Hawthorne effects may introduce bias. However, we believe that using a standardized checklist, a control group, and assessing relative changes minimized this risk. Although we observed almost 1200 patient sign-outs over 80 shift changes, we were not able to observe every intern in every firm. Finally, no sentinel events were reported during the study period, and we did not include other measures of clinical outcomes, which represent an opportunity for future researchers to test which specific sign-out elements or dimensions are related to clinical outcomes or are relevant to specific patient types.

 

CONCLUSION

The results of this study indicate that 1 hour of formal training can improve sign-out quality. Program directors should consider including I-PASS with additional focus on task accountability and personal responsibility in their sign-out training plans.

Disclosure

The authors have nothing to disclose.

Patient sign-outs are defined as the transition of patient care that includes the transfer of information, task accountability, and personal responsibility between providers.^1-3 The adoption of mnemonics as a memory aid has been used to improve the transfer of patient information between providers.⁴ In the transfer of task accountability, providers transfer follow-up tasks to on-call or coverage providers and ensure that directives are understood. Joint task accountability is enhanced through collaborative giving and cross-checking of information received through assertive questioning to detect errors, and it also enables the receiver to codevelop an understanding of a patient’s condition.^5-8 In the transfer of personal responsibility for the primary team’s patients, the provision of anticipatory guidance enables the coverage provider to have prospective information about potential, upcoming issues to facilitate care plans.⁶ Enabling coverage providers to anticipate overnight events helps them exercise responsibility for patients who are under their temporary care.²

The Accreditation Council for Graduate Medical Education requires residency programs to provide formal instruction on sign-outs.⁹ Yet, variability across training programs exists,^8,10 with training emphasis on the transfer of information over accountability or responsibility.¹¹ Previous studies have demonstrated the efficacy of sign-out training, such as the illness severity, patient summary, action list, situation awareness and contingency planning, and synthesis by reviewer (I-PASS) bundle.³ Yet, participation is far from 100% because the I-PASS bundle requires in-person workshops, e-learning platforms, organizational change campaigns, and faculty participation,¹² involving resource and time commitments that few programs can afford. To address this issue, we seek to compare resource-efficient, knowledge-based, skill-based, compliance-based, and learner-initiated sign-out training pedagogies. We focused on the evening sign-out because it is a high-risk period when care for inpatients is transferred to smaller coverage intern teams.

METHODS

Setting and Study Design

A prospective, randomized cohort trial of 4 training interventions was conducted at an internal medicine residency program at a Mid-Atlantic, academic, tertiary-care hospital with 1192 inpatient beds. The 52 interns admitted to the program were randomly assigned to 4 firms caring for up to 25 inpatients on each floor of the hospital. The case mix faced by each firm was similar because patients were randomly assigned to firms based on bed availability. Teams of 5 interns in each firm worked in 5-day duty cycles, during which each intern rotated as a night cover for his or her firm. Interns remain in their firm throughout their residency. Sign-outs were conducted face to face with a computer. Receivers printed sign-out sheets populated with patient information and took notes when senders communicated information from the computer. The hospital’s institutional review board approved this study.

Interventions

The firms were randomly assigned to 1 of 4 one-hour quality-improvement training interventions delivered at the same time and day in November 2014 at each firm’s office, located on different floors of the hospital. There was virtually no cross-talk among the firms in the first year, which ensured the integrity of the cohort randomization and interventions. Faculty from an affiliated business school of the academic center worked with attending physicians to train the firms.

All interventions took 1 hour at noontime. Firm 1 (the control) received a didactic lecture on sign-out, which participants heard during orientation. Repeating that lecture reinforced their knowledge of sign-outs. Firm 2 was trained on the I-PASS mnemonic with a predictable progression of information elements to transfer.^3,12 Interns role-played 3 scenarios to practice sign-out.³ They received skills feedback and a debriefing to link I-PASS with information elements to transfer. Firm 3 was dealt a policy mandate by the interns’ attending physician to perform specific tasks at sign-out. Senders were to provide the night cover with to-do tasks, and receivers were to actively discuss and verify these tasks to ensure task accountability.¹³ Firm 4 was trained on a Plan-Do-Study-Act (PDSA) protocol to identify and solve perceived barriers to sign-outs. Firm 4 agreed to solve the problem of the lack of care plans by the day team to the night cover. An ad hoc team in Firm 4 refined, pilot tested, and rolled out the solution within a month. Its protocol emphasized information on anticipated changes in patient status, providing contingency plans and their rationale as well as discussions to clarify care plans. Details of the 4 interventions are shown in the Table.

 

Data Collection Process

Eight trained senior residents, recruited by the last author (S.V.D.), volunteered to observe 10 evening sign-outs in each firm 1 month prior to the intervention and another 10 nights 4 months after training. Observations were standardized with a sign-out checklist developed from the literature review and the Joint Commission’s 2006 National Patient Safety Goal 2E that followed the Situation, Background, Assessment, and Recommendation communication structure with opportunities for questioning and information verification.^14,15 Observers indicated “1” for each of the 17 sign-out elements in the checklist they observed, as shown in the supporting Table. Observers did not have supervisory relationships with the interns. Occasionally, the pairs of observers were different depending on their availability.

Outcomes

We measured improvements in sign-out quality by the mean percentage differences for each of the 3 dimensions of sign-out, as well as a multidimensional measure of sign-out comprising the 3 dimensions for each firm in 2 ways: (1) pre- and postintervention, and (2) vis-à-vis the control group postintervention.

Statistical Analysis

We factor analyzed the 17 sign-out elements using principal components analysis with varimax rotation to confirm their groupings within the 3 dimensions of sign-out using Statistical Package for the Social Sciences (SPSS) version 24 (IBM, North Castle, NY). We calculated the mean percentage differences and used Student t tests to evaluate statistical differences at P < 0.05.

RESULTS

Five hundred and sixty-three patient sign-outs were observed prior to the training interventions (κ = 0.646), and 620 patient sign-outs were observed after the interventions (κ = 0.648). Kappa values derived from SPSS were within acceptable interrater agreement ranges. Factor analysis of the 17 sign-out elements yielded 3 factors that we named patient information, task accountability, and responsibility, as shown in the supporting Table.

The supporting Figure reports 2 sets of results. The line graphs show the pre- and postintervention differences for each firm while the bar charts show the postintervention differences between each firm vis-à-vis the control group on sign-out dimensions. The line graphs indicate the greatest improvements in patient information, task accountability, and responsibility for the I-PASS, policy mandate, and PDSA groups, respectively. Mandate and PDSA groups reported low relative scores on sign-out dimensions that were not the foci of their training while the didactics group scored around 0 pre- and postintervention. I-PASS had the highest improvement on the multidimensional measure of sign-out quality but was not significantly different from the PDSA group at P < 0.05 (see supporting Figure for the calculations). The bar charts indicate that all groups vis-à-vis the control had higher improvements in task accountability, responsibility, and the multidimensional measure of sign-out quality. I-PASS vis-à-vis the control had the highest improvement but was not statistically different from the PDSA at P < 0.05. No sentinel events were reported during the entire study period.

DISCUSSION

The results indicated that after only 1 hour of training, skill-based, compliance-based, and learner-initiated sign-out training improved sign-out quality beyond knowledge-based didactics even though the number of sign-out elements taught in the latter 2 was lower than in the didactics group. Different training emphases influenced different dimensions of sign-out quality so that training interns to focus on task accountability or responsibility led to improvements in those dimensions only. The lower scores in other dimensions suggest potential risks in sign-out quality from focusing attention on 1 dimension at the expense of other dimensions. I-PASS, which covered the most sign-out elements and utilized 5 facilitators, led to the best overall improvement in sign-out quality, which is consistent with previous studies.^3,12 We demonstrated that only 1 hour of training on the I-PASS mnemonics using video, role-playing, and feedback led to significant improvements. This approach is portable and easily applied to any program. Potential improvements in I-PASS training could be obtained by emphasizing task accountability and responsibility because the mandate and PDSA groups obtained higher scores than the I-PASS group in these dimensions.

Limitations

We measured sign-out quality in the evening at this site because it was at greatest risk for errors. Future studies should consider daytime sign-outs, interunit handoffs, and other hospital settings, such as community or rural hospitals and nonacute patient settings, to ascertain generalizability. Data were collected from observations, so Hawthorne effects may introduce bias. However, we believe that using a standardized checklist, a control group, and assessing relative changes minimized this risk. Although we observed almost 1200 patient sign-outs over 80 shift changes, we were not able to observe every intern in every firm. Finally, no sentinel events were reported during the study period, and we did not include other measures of clinical outcomes, which represent an opportunity for future researchers to test which specific sign-out elements or dimensions are related to clinical outcomes or are relevant to specific patient types.

 

CONCLUSION

The results of this study indicate that 1 hour of formal training can improve sign-out quality. Program directors should consider including I-PASS with additional focus on task accountability and personal responsibility in their sign-out training plans.

Disclosure

The authors have nothing to disclose.

Patient sign-outs are defined as the transition of patient care that includes the transfer of information, task accountability, and personal responsibility between providers.^1-3 The adoption of mnemonics as a memory aid has been used to improve the transfer of patient information between providers.⁴ In the transfer of task accountability, providers transfer follow-up tasks to on-call or coverage providers and ensure that directives are understood. Joint task accountability is enhanced through collaborative giving and cross-checking of information received through assertive questioning to detect errors, and it also enables the receiver to codevelop an understanding of a patient’s condition.^5-8 In the transfer of personal responsibility for the primary team’s patients, the provision of anticipatory guidance enables the coverage provider to have prospective information about potential, upcoming issues to facilitate care plans.⁶ Enabling coverage providers to anticipate overnight events helps them exercise responsibility for patients who are under their temporary care.²

The Accreditation Council for Graduate Medical Education requires residency programs to provide formal instruction on sign-outs.⁹ Yet, variability across training programs exists,^8,10 with training emphasis on the transfer of information over accountability or responsibility.¹¹ Previous studies have demonstrated the efficacy of sign-out training, such as the illness severity, patient summary, action list, situation awareness and contingency planning, and synthesis by reviewer (I-PASS) bundle.³ Yet, participation is far from 100% because the I-PASS bundle requires in-person workshops, e-learning platforms, organizational change campaigns, and faculty participation,¹² involving resource and time commitments that few programs can afford. To address this issue, we seek to compare resource-efficient, knowledge-based, skill-based, compliance-based, and learner-initiated sign-out training pedagogies. We focused on the evening sign-out because it is a high-risk period when care for inpatients is transferred to smaller coverage intern teams.

METHODS

Setting and Study Design

A prospective, randomized cohort trial of 4 training interventions was conducted at an internal medicine residency program at a Mid-Atlantic, academic, tertiary-care hospital with 1192 inpatient beds. The 52 interns admitted to the program were randomly assigned to 4 firms caring for up to 25 inpatients on each floor of the hospital. The case mix faced by each firm was similar because patients were randomly assigned to firms based on bed availability. Teams of 5 interns in each firm worked in 5-day duty cycles, during which each intern rotated as a night cover for his or her firm. Interns remain in their firm throughout their residency. Sign-outs were conducted face to face with a computer. Receivers printed sign-out sheets populated with patient information and took notes when senders communicated information from the computer. The hospital’s institutional review board approved this study.

Interventions

The firms were randomly assigned to 1 of 4 one-hour quality-improvement training interventions delivered at the same time and day in November 2014 at each firm’s office, located on different floors of the hospital. There was virtually no cross-talk among the firms in the first year, which ensured the integrity of the cohort randomization and interventions. Faculty from an affiliated business school of the academic center worked with attending physicians to train the firms.

All interventions took 1 hour at noontime. Firm 1 (the control) received a didactic lecture on sign-out, which participants heard during orientation. Repeating that lecture reinforced their knowledge of sign-outs. Firm 2 was trained on the I-PASS mnemonic with a predictable progression of information elements to transfer.^3,12 Interns role-played 3 scenarios to practice sign-out.³ They received skills feedback and a debriefing to link I-PASS with information elements to transfer. Firm 3 was dealt a policy mandate by the interns’ attending physician to perform specific tasks at sign-out. Senders were to provide the night cover with to-do tasks, and receivers were to actively discuss and verify these tasks to ensure task accountability.¹³ Firm 4 was trained on a Plan-Do-Study-Act (PDSA) protocol to identify and solve perceived barriers to sign-outs. Firm 4 agreed to solve the problem of the lack of care plans by the day team to the night cover. An ad hoc team in Firm 4 refined, pilot tested, and rolled out the solution within a month. Its protocol emphasized information on anticipated changes in patient status, providing contingency plans and their rationale as well as discussions to clarify care plans. Details of the 4 interventions are shown in the Table.

 

Data Collection Process

Eight trained senior residents, recruited by the last author (S.V.D.), volunteered to observe 10 evening sign-outs in each firm 1 month prior to the intervention and another 10 nights 4 months after training. Observations were standardized with a sign-out checklist developed from the literature review and the Joint Commission’s 2006 National Patient Safety Goal 2E that followed the Situation, Background, Assessment, and Recommendation communication structure with opportunities for questioning and information verification.^14,15 Observers indicated “1” for each of the 17 sign-out elements in the checklist they observed, as shown in the supporting Table. Observers did not have supervisory relationships with the interns. Occasionally, the pairs of observers were different depending on their availability.

Outcomes

We measured improvements in sign-out quality by the mean percentage differences for each of the 3 dimensions of sign-out, as well as a multidimensional measure of sign-out comprising the 3 dimensions for each firm in 2 ways: (1) pre- and postintervention, and (2) vis-à-vis the control group postintervention.

Statistical Analysis

We factor analyzed the 17 sign-out elements using principal components analysis with varimax rotation to confirm their groupings within the 3 dimensions of sign-out using Statistical Package for the Social Sciences (SPSS) version 24 (IBM, North Castle, NY). We calculated the mean percentage differences and used Student t tests to evaluate statistical differences at P < 0.05.

RESULTS

Five hundred and sixty-three patient sign-outs were observed prior to the training interventions (κ = 0.646), and 620 patient sign-outs were observed after the interventions (κ = 0.648). Kappa values derived from SPSS were within acceptable interrater agreement ranges. Factor analysis of the 17 sign-out elements yielded 3 factors that we named patient information, task accountability, and responsibility, as shown in the supporting Table.

The supporting Figure reports 2 sets of results. The line graphs show the pre- and postintervention differences for each firm while the bar charts show the postintervention differences between each firm vis-à-vis the control group on sign-out dimensions. The line graphs indicate the greatest improvements in patient information, task accountability, and responsibility for the I-PASS, policy mandate, and PDSA groups, respectively. Mandate and PDSA groups reported low relative scores on sign-out dimensions that were not the foci of their training while the didactics group scored around 0 pre- and postintervention. I-PASS had the highest improvement on the multidimensional measure of sign-out quality but was not significantly different from the PDSA group at P < 0.05 (see supporting Figure for the calculations). The bar charts indicate that all groups vis-à-vis the control had higher improvements in task accountability, responsibility, and the multidimensional measure of sign-out quality. I-PASS vis-à-vis the control had the highest improvement but was not statistically different from the PDSA at P < 0.05. No sentinel events were reported during the entire study period.

DISCUSSION

The results indicated that after only 1 hour of training, skill-based, compliance-based, and learner-initiated sign-out training improved sign-out quality beyond knowledge-based didactics even though the number of sign-out elements taught in the latter 2 was lower than in the didactics group. Different training emphases influenced different dimensions of sign-out quality so that training interns to focus on task accountability or responsibility led to improvements in those dimensions only. The lower scores in other dimensions suggest potential risks in sign-out quality from focusing attention on 1 dimension at the expense of other dimensions. I-PASS, which covered the most sign-out elements and utilized 5 facilitators, led to the best overall improvement in sign-out quality, which is consistent with previous studies.^3,12 We demonstrated that only 1 hour of training on the I-PASS mnemonics using video, role-playing, and feedback led to significant improvements. This approach is portable and easily applied to any program. Potential improvements in I-PASS training could be obtained by emphasizing task accountability and responsibility because the mandate and PDSA groups obtained higher scores than the I-PASS group in these dimensions.

Limitations

We measured sign-out quality in the evening at this site because it was at greatest risk for errors. Future studies should consider daytime sign-outs, interunit handoffs, and other hospital settings, such as community or rural hospitals and nonacute patient settings, to ascertain generalizability. Data were collected from observations, so Hawthorne effects may introduce bias. However, we believe that using a standardized checklist, a control group, and assessing relative changes minimized this risk. Although we observed almost 1200 patient sign-outs over 80 shift changes, we were not able to observe every intern in every firm. Finally, no sentinel events were reported during the study period, and we did not include other measures of clinical outcomes, which represent an opportunity for future researchers to test which specific sign-out elements or dimensions are related to clinical outcomes or are relevant to specific patient types.

 

CONCLUSION

The results of this study indicate that 1 hour of formal training can improve sign-out quality. Program directors should consider including I-PASS with additional focus on task accountability and personal responsibility in their sign-out training plans.

Disclosure

The authors have nothing to disclose.

Health literacy (HL), defined as patients’ ability to understand health information and make health decisions,¹ is a prevalent problem in the outpatient and inpatient settings.^2,3 In both settings, low HL has adverse implications for self-care including interpreting health labels⁴ and taking medications correctly.⁵ Among outpatient cohorts, HL has been associated with worse outcomes and acute care utilization.⁶ Associations with low HL include increased hospitalizations,⁷ rehospitalizations,^8,9 emergency department visits,¹⁰ and decreased preventative care use.¹¹ Among the elderly, low HL is associated with increased mortality¹² and decreased self-perception of health.¹³

A systematic review revealed that most high-quality HL outcome studies were conducted in the outpatient setting.⁶ There have been very few studies assessing effects of low HL in an acute-care setting.^7,14 These studies have evaluated postdischarge outcomes, including admissions or readmissions,^7-9 and medication knowledge.¹⁴ To the best of our knowledge, there are no studies evaluating associations between HL and hospital length of stay (LOS).

LOS has received much attention as providers and payers focus more on resource utilization and eliminating adverse effects of prolonged hospitalization.¹⁵ LOS is multifactorial, depending on clinical characteristics like disease severity, as well as on sociocultural, demographic, and geographic factors.¹⁶ Despite evidence that LOS reductions translate into improved resource allocation and potentially fewer complications, there remains a tension between the appropriate LOS and one that is too short for a given condition.¹⁷

Because low HL is associated with inefficient resource utilization, we hypothesized that low HL would be associated with increased LOS after controlling for illness severity. Our objectives were to evaluate the association between low HL and LOS and whether such an association was modified by illness severity and sociodemographics.

METHODS

Study Design, Setting, Participants

An in-hospital, cohort study design of patients who were admitted or transferred to the general medicine service at the University of Chicago between October 2012 and November 2015 and screened for inclusion as part of a large, ongoing study of inpatient care quality was conducted.¹⁸ Exclusion criteria included observation status, age under 18 years, non-English speaking, and repeat participants. Those who died during hospitalization or whose discharge status was missing were excluded because the primary goal was to examine the association of HL and time to discharge, which could not be evaluated among those who died. We excluded participants with LOS >30 days to limit overly influential effects of extreme outliers (1% of the population).

Variables

HL was screened using the Brief Health Literacy Screen (BHLS), a validated, 3-question verbal survey not requiring adequate visual acuity to assess HL.^19,20 The 3 questions are as follows: (1) “How confident are you filling out medical forms by yourself?”, (2) “How often do you have someone help you read hospital materials?”, and (3) “How often do you have problems learning about your medical condition because of difficulty understanding written information?” Responses to the questions were scored on a 5-point Likert scale in which higher scores corresponded to higher HL.^21,22 The scores for each of the 3 questions were summed to yield a range between 3 and 15. On the individual questions, prior work has demonstrated improved test performance with a cutoff of ≤3, which corresponds to a response of “some of the time” or “somewhat”; therefore, when the 3 questions were summed together, scores of ≤9 were considered indicative of low HL.^21,23

For severity of illness adjustment, we used relative weights derived from the 3M (3M, Maplewood, MN) All Patient Refined Diagnosis Related Groups (APR-DRG) classification system, which uses administrative data to classify the severity. The APR-DRG system assigns each admission to a DRG based on principal diagnosis; for each DRG, patients are then subdivided into 4 severity classes based on age, comorbidity, and interactions between these variables and the admitting diagnosis.²⁴ Using the base DRG and severity score, the system assigns relative weights that reflect differences in expected hospital resource utilization.

LOS was derived from hospital administrative data and counted from the date of admission to the hospital. Participants who were discharged on the day of admission were counted as having an LOS of 1. Insurance status (Medicare, Medicaid, no payer, private) also was obtained from administrative data. Age, sex (male or female), education (junior high or less, some high school, high school graduate, some college, college graduate, postgraduate), and race (black/African American, white, Asian or Pacific Islander [including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guam/Chamorro, Samoan, other Pacific], American Indian or Alaskan Native, multiple race) were obtained from administrative data based on information provided by the patient. Participants with missing data on any of the sociodemographic variables or on the APR-DRG score were excluded from the analysis.

 

Statistical Analysis

χ² and 2-tailed t tests were used to compare categorical and continuous variables, respectively. Multivariate linear regressions were employed to measure associations between the independent variables (HL, illness severity, race, gender, education, and insurance status) and the dependent variable, LOS. Independent variables were chosen for clinical significance and retained in the model regardless of statistical significance. The adjusted R² values of models with and without the HL variable included were reported to provide information on the contribution of HL to the overall model.

Because LOS was observed to be right skewed and residuals of the untransformed regression were observed to be non-normally distributed, the decision was made to natural log transform LOS, which is consistent with previous hospital LOS studies.¹⁶ Regression coefficients and confidence intervals were then transformed into percentage estimates using the following equation: 100(e^β–1). Adjusted R² was reported for the transformed regression.

The APR-DRG relative weight was treated as a continuous variable. Sociodemographic variables were dichotomized as follows: female vs male; high school graduates vs not; African American vs not; Medicaid/no payer vs Medicare/private payer. Age was not included in the multivariate model because it has been incorporated into the weighted APR-DRG illness severity scores.

Each of the sociodemographic variables and the APR-DRG score were examined for effect modification via the same multivariate linear equation described above, with the addition of an interaction term. A separate regression was performed with an interaction term between age (dichotomized at ≥65) and HL to investigate whether age modified the association between HL and LOS. Finally, we explored whether effects were isolated to long vs short LOS by dividing the sample based on the mean LOS (≥6 days) and performing separate multivariate comparisons.

Sensitivity analyses were performed to exclude those with LOS greater than the 90th percentile and those with APR-DRG score greater than the 90th percentile; age was added to the model as a continuous variable to evaluate whether the illness severity score fully adjusted for the effects of age on LOS. Furthermore, we compared the participants with missing data to those with complete data across both dependent and independent variables. Alpha was set at 0.05; analyses were performed using Stata Version 14 (Stata, College Station, TX).

RESULTS

A total of 5983 participants met inclusion criteria and completed the HL assessment; of these participants, 75 (1%) died during hospitalization, 9 (0.2%) had missing discharge status, and 79 (1%) had LOS >30 days. Two hundred eighty (5%) were missing data on sociodemographic variables or APR-DRG score. Of the remaining (n = 5540), the mean age was 57 years (standard deviation [SD] = 19 years), over half of participants were female (57%), and the majority were African American (73%) and had graduated from high school (81%). The sample was divided into those with private insurance (25%), those with Medicare (46%), and those with Medicaid (26%); 2% had no payer. The mean APR-DRG score was 1.3 (SD = 1.2), and the scores ranged from 0.3 to 15.8.

On the BHLS screen for HL, 20% (1104/5540) had inadequate HL. Participants with low HL had higher weighted illness severity scores (average 1.4 vs 1.3; P = 0.003). Participants with low HL were also more likely to be 65 or older (55% vs 33%; P < 0.001), non-high school graduates (35% vs 15%; P < 0.001), and African American (78% vs 72%; P < 0.001), and to have Medicare or private insurance (75% vs 71%; P = 0.02). There was no significant difference with respect to gender (54% male vs 57% female; P = 0.1)

The mean and median LOS were 6 ± 5 days and 4 days (interquartile range 2-7 days), respectively. Those with low HL had a longer average LOS (6.0 vs 5.4 days; P < 0.001). In multivariate analysis controlling for APR-DRG score, gender, education, race, and insurance status, low HL was associated with an 11.1% longer LOS (95% CI, 6.1-16.1; P < 0.001; Table 1). The adjusted R² value for the regression was 25.0% including HL and 24.7% with HL excluded. Additionally, being African American (P < 0.001) and having Medicaid or no insurance (P < 0.001) were associated with a shorter LOS in multivariate analysis (Table 1). The association of HL and LOS in multivariate modeling remained significant among participants with LOS <6 days (10.2%; 95% CI, 5.6%-14.9%; P < 0.001), but not among participants with LOS ≥6 days (0.4%; 95% CI, −3.6% to 4.4%; P = 0.8).

Neither age ≥65 (P = 0.4) nor illness severity score (P = 0.5) significantly modified the effect of HL on LOS. However, the effect of HL on hospital LOS was significantly modified by gender (P = 0.02). Among men, low HL was associated with a 17.8% longer LOS (95% CI, 10.0%-25.7%; P < 0.001), but among women, low HL was associated with only a 7.7% longer LOS (95% CI, 1.9%-13.5%; P = 0.009). Among the remaining demographics, high school graduation status (P = 0.4), being African American (P = 0.6), and insurance status (P = 0.2) did not significantly modify the effect of HL on LOS. In sensitivity analysis, excluding participants with LOS above the 90th percentile of 12 days and excluding participants with illness severity scores above the 90th percentile, low HL was still associated with longer LOS (P < 0.001 and P = 0.001, respectively; Table 2). In the final sensitivity analysis, although age remained a significant predictor of longer LOS after controlling for illness severity (0.2% increase per year, 95% CI, 0.1%-0.3%; P < 0.001), low HL nevertheless remained significantly associated with longer LOS (P < 0.001; Table 2).

Finally, we compared the group with missing data (n = 280) to the group with complete data (n = 5540). The participants with missing data were more likely to have low HL (31% [86/280] vs 20%; P < 0.001) and to have Medicare or private insurance (82% [177/217] vs 72%; P = 0.002); however, they were not more likely to be 65 or older (40% [112/280] vs 37%; P = 0.3), high school graduates (88% [113/129] vs 81%; P = 0.06), African American (69% [177/256] vs 73%; P = 0.1), or female (57% [158/279] vs 57%; P = 1), nor were they more likely to have longer LOS (5.7 [n = 280] vs 5.5 days; P = 0.6) or higher illness severity scores (1.3 [n = 231] vs 1.3; P = 0.7).

 

DISCUSSION

To our knowledge, this study is the first to evaluate the association between low HL and an important in-hospital outcome measure, hospital LOS. We found that low HL was associated with a longer hospital LOS, a result which remained significant when controlling for severity of illness and sociodemographic variables and when testing the model for sensitivity to the highest values of LOS and illness severity. Additionally, the association of HL with LOS appeared concentrated among participants with shorter LOS. Relative to other predictors, the contribution of HL to the overall LOS model was small, as evidenced by the change in adjusted R² values with HL excluded.

Among the covariates, only gender modified the association between HL and LOS; the findings suggested that men were more susceptible to the effect of low HL on increased LOS. Illness severity and other sociodemographics, including age ≥65, did not appear to modify the association. We also found that being African American and having Medicaid or no insurance were associated with a significantly shorter LOS in multivariate analysis.

Previous work suggested that the adverse health effects of low HL may be mediated through several pathways, including health knowledge, self-efficacy, health skills, and illness stigma.^25-27 The finding of a small but significant relationship between HL and LOS was not surprising given these known associations; nevertheless, there may be an additional patient-dependent effect of low HL on LOS not discovered here. For instance, patients with poor health knowledge and self-efficacy might stay in the hospital longer if they or their providers do not feel comfortable with their self-care ability.

This finding may be useful in developing hospital-based interventions. HL-specific interventions, several of which have been tested in the inpatient setting,^14,28,29 have shown promise toward improving health knowledge,³⁰ disease severity,³¹ and health resource utilization.³²

Those with low HL may lack the self-efficacy to participate in discharge planning; in fact, previous work has related low HL to posthospital readmissions.^8,9 Conversely, patients with low HL might struggle to engage in the inpatient milieu, advocating for shorter LOS if they feel alienated by the inpatient experience.

These possibilities show that LOS is a complex measure shown to depend on patient-level characteristics and on provider-based, geographical, and sociocultural factors.^16,33 With these forces at play, additional effects of lower levels of HL may be lost without phenotyping patients by both level of HL and related characteristics, such as self-efficacy, health skills, and stigma. By gathering these additional data, future work should explore whether subpopulations of patients with low HL may be at risk for too-short vs too-long hospital admissions.

For instance, in this study, both race and Medicaid insurance were associated with shorter LOS. Being African American was associated with shorter LOS in our study but has been found to be associated with longer LOS in another study specifically focused on diabetes.³⁴ Prior findings found uninsured patients have shorter LOS.³⁵ Therefore, these findings in our study are difficult to explain without further work to understand whether there are health disparities in the way patients are cared for during hospitalization that may shorten or lengthen their LOS because of factors outside of their clinical need.

The finding that gender modified the effect of low HL on LOS was unexpected. There were similar proportions of men and women with low HL. There is evidence to support that women make the majority of health decisions for themselves and their familes³⁶; therefore, there may be unmeasured aspects of HL that provide an advantage for female vs male inpatients. Furthermore, omitted confounders, such as social support, may not fully capture potential gender-related differences. Future work is needed to understand the role of gender in relationship to HL and LOS.

Limitations of this study include its observational, single-centered design with information derived from administrative data; positive and negative confounding cannot be ruled out. For instance, we did not control for complex aspects affecting LOS, such as discharge disposition and goals of care (eg, aggressive care after discharge vs hospice). To address this limitation, multivariate analyses were performed, which were adjusted for illness severity scores and took into account both comorbidity and severity of the current illness. Additionally, although it is important to study such populations, our largely urban, minority sample is not representative of the U.S. population, and within our large sample, there were participants with missing data who had lower HL on average, although this group represented only 5% of the sample. Finally, different HL tools have noncomplete concordance, which has been seen when comparing the BHLS with more objective tools.^20,37 Furthermore, certain in-hospital clinical scenarios (eg, recent stroke or prolonged intensive care unit stay) may present unique challenges in establishing a baseline HL level. However, the BHLS was used in this study because of its greater feasibility.

In conclusion, this study is the first to evaluate the relationship between low HL and LOS. The findings suggest that HL may play a role in shaping outcomes in the inpatient setting and that targeting interventions toward screened patients may be a pathway toward mitigating adverse effects. Our findings need to be replicated in larger, more representative samples, and further work understanding subpopulations within the low HL population is needed. Future work should measure this association in diverse inpatient settings (eg, psychiatric, surgical, and specialty), in addition to assessing associations between HL and other important in-hospital outcome measures, including mortality and discharge disposition.

 

Acknowledgments

The authors thank the Hospitalist Project team for their assistance with data collection. The authors especially thank Chuanhong Liao and Ashley Snyder for assistance with statistical analyses; Andrea Flores, Ainoa Coltri, and Tom Best for their assistance with data management. The authors would also like to thank Nicole Twu for her help with preparing and editing the manuscript.

Disclosures

Dr. Jaffee was supported by a Calvin Fentress Research Fellowship and NIH R25MH094612. Dr. Press was supported by a career development award (NHLBI K23HL118151). This work was also supported by a seed grant from the Center for Health Administration Studies. All other authors declare no conflicts of interest.

Health literacy (HL), defined as patients’ ability to understand health information and make health decisions,¹ is a prevalent problem in the outpatient and inpatient settings.^2,3 In both settings, low HL has adverse implications for self-care including interpreting health labels⁴ and taking medications correctly.⁵ Among outpatient cohorts, HL has been associated with worse outcomes and acute care utilization.⁶ Associations with low HL include increased hospitalizations,⁷ rehospitalizations,^8,9 emergency department visits,¹⁰ and decreased preventative care use.¹¹ Among the elderly, low HL is associated with increased mortality¹² and decreased self-perception of health.¹³

A systematic review revealed that most high-quality HL outcome studies were conducted in the outpatient setting.⁶ There have been very few studies assessing effects of low HL in an acute-care setting.^7,14 These studies have evaluated postdischarge outcomes, including admissions or readmissions,^7-9 and medication knowledge.¹⁴ To the best of our knowledge, there are no studies evaluating associations between HL and hospital length of stay (LOS).

LOS has received much attention as providers and payers focus more on resource utilization and eliminating adverse effects of prolonged hospitalization.¹⁵ LOS is multifactorial, depending on clinical characteristics like disease severity, as well as on sociocultural, demographic, and geographic factors.¹⁶ Despite evidence that LOS reductions translate into improved resource allocation and potentially fewer complications, there remains a tension between the appropriate LOS and one that is too short for a given condition.¹⁷

Because low HL is associated with inefficient resource utilization, we hypothesized that low HL would be associated with increased LOS after controlling for illness severity. Our objectives were to evaluate the association between low HL and LOS and whether such an association was modified by illness severity and sociodemographics.

METHODS

Study Design, Setting, Participants

An in-hospital, cohort study design of patients who were admitted or transferred to the general medicine service at the University of Chicago between October 2012 and November 2015 and screened for inclusion as part of a large, ongoing study of inpatient care quality was conducted.¹⁸ Exclusion criteria included observation status, age under 18 years, non-English speaking, and repeat participants. Those who died during hospitalization or whose discharge status was missing were excluded because the primary goal was to examine the association of HL and time to discharge, which could not be evaluated among those who died. We excluded participants with LOS >30 days to limit overly influential effects of extreme outliers (1% of the population).

Variables

HL was screened using the Brief Health Literacy Screen (BHLS), a validated, 3-question verbal survey not requiring adequate visual acuity to assess HL.^19,20 The 3 questions are as follows: (1) “How confident are you filling out medical forms by yourself?”, (2) “How often do you have someone help you read hospital materials?”, and (3) “How often do you have problems learning about your medical condition because of difficulty understanding written information?” Responses to the questions were scored on a 5-point Likert scale in which higher scores corresponded to higher HL.^21,22 The scores for each of the 3 questions were summed to yield a range between 3 and 15. On the individual questions, prior work has demonstrated improved test performance with a cutoff of ≤3, which corresponds to a response of “some of the time” or “somewhat”; therefore, when the 3 questions were summed together, scores of ≤9 were considered indicative of low HL.^21,23

For severity of illness adjustment, we used relative weights derived from the 3M (3M, Maplewood, MN) All Patient Refined Diagnosis Related Groups (APR-DRG) classification system, which uses administrative data to classify the severity. The APR-DRG system assigns each admission to a DRG based on principal diagnosis; for each DRG, patients are then subdivided into 4 severity classes based on age, comorbidity, and interactions between these variables and the admitting diagnosis.²⁴ Using the base DRG and severity score, the system assigns relative weights that reflect differences in expected hospital resource utilization.

LOS was derived from hospital administrative data and counted from the date of admission to the hospital. Participants who were discharged on the day of admission were counted as having an LOS of 1. Insurance status (Medicare, Medicaid, no payer, private) also was obtained from administrative data. Age, sex (male or female), education (junior high or less, some high school, high school graduate, some college, college graduate, postgraduate), and race (black/African American, white, Asian or Pacific Islander [including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guam/Chamorro, Samoan, other Pacific], American Indian or Alaskan Native, multiple race) were obtained from administrative data based on information provided by the patient. Participants with missing data on any of the sociodemographic variables or on the APR-DRG score were excluded from the analysis.

 

Statistical Analysis

χ² and 2-tailed t tests were used to compare categorical and continuous variables, respectively. Multivariate linear regressions were employed to measure associations between the independent variables (HL, illness severity, race, gender, education, and insurance status) and the dependent variable, LOS. Independent variables were chosen for clinical significance and retained in the model regardless of statistical significance. The adjusted R² values of models with and without the HL variable included were reported to provide information on the contribution of HL to the overall model.

Because LOS was observed to be right skewed and residuals of the untransformed regression were observed to be non-normally distributed, the decision was made to natural log transform LOS, which is consistent with previous hospital LOS studies.¹⁶ Regression coefficients and confidence intervals were then transformed into percentage estimates using the following equation: 100(e^β–1). Adjusted R² was reported for the transformed regression.

The APR-DRG relative weight was treated as a continuous variable. Sociodemographic variables were dichotomized as follows: female vs male; high school graduates vs not; African American vs not; Medicaid/no payer vs Medicare/private payer. Age was not included in the multivariate model because it has been incorporated into the weighted APR-DRG illness severity scores.

Each of the sociodemographic variables and the APR-DRG score were examined for effect modification via the same multivariate linear equation described above, with the addition of an interaction term. A separate regression was performed with an interaction term between age (dichotomized at ≥65) and HL to investigate whether age modified the association between HL and LOS. Finally, we explored whether effects were isolated to long vs short LOS by dividing the sample based on the mean LOS (≥6 days) and performing separate multivariate comparisons.

Sensitivity analyses were performed to exclude those with LOS greater than the 90th percentile and those with APR-DRG score greater than the 90th percentile; age was added to the model as a continuous variable to evaluate whether the illness severity score fully adjusted for the effects of age on LOS. Furthermore, we compared the participants with missing data to those with complete data across both dependent and independent variables. Alpha was set at 0.05; analyses were performed using Stata Version 14 (Stata, College Station, TX).

RESULTS

A total of 5983 participants met inclusion criteria and completed the HL assessment; of these participants, 75 (1%) died during hospitalization, 9 (0.2%) had missing discharge status, and 79 (1%) had LOS >30 days. Two hundred eighty (5%) were missing data on sociodemographic variables or APR-DRG score. Of the remaining (n = 5540), the mean age was 57 years (standard deviation [SD] = 19 years), over half of participants were female (57%), and the majority were African American (73%) and had graduated from high school (81%). The sample was divided into those with private insurance (25%), those with Medicare (46%), and those with Medicaid (26%); 2% had no payer. The mean APR-DRG score was 1.3 (SD = 1.2), and the scores ranged from 0.3 to 15.8.

On the BHLS screen for HL, 20% (1104/5540) had inadequate HL. Participants with low HL had higher weighted illness severity scores (average 1.4 vs 1.3; P = 0.003). Participants with low HL were also more likely to be 65 or older (55% vs 33%; P < 0.001), non-high school graduates (35% vs 15%; P < 0.001), and African American (78% vs 72%; P < 0.001), and to have Medicare or private insurance (75% vs 71%; P = 0.02). There was no significant difference with respect to gender (54% male vs 57% female; P = 0.1)

The mean and median LOS were 6 ± 5 days and 4 days (interquartile range 2-7 days), respectively. Those with low HL had a longer average LOS (6.0 vs 5.4 days; P < 0.001). In multivariate analysis controlling for APR-DRG score, gender, education, race, and insurance status, low HL was associated with an 11.1% longer LOS (95% CI, 6.1-16.1; P < 0.001; Table 1). The adjusted R² value for the regression was 25.0% including HL and 24.7% with HL excluded. Additionally, being African American (P < 0.001) and having Medicaid or no insurance (P < 0.001) were associated with a shorter LOS in multivariate analysis (Table 1). The association of HL and LOS in multivariate modeling remained significant among participants with LOS <6 days (10.2%; 95% CI, 5.6%-14.9%; P < 0.001), but not among participants with LOS ≥6 days (0.4%; 95% CI, −3.6% to 4.4%; P = 0.8).

Neither age ≥65 (P = 0.4) nor illness severity score (P = 0.5) significantly modified the effect of HL on LOS. However, the effect of HL on hospital LOS was significantly modified by gender (P = 0.02). Among men, low HL was associated with a 17.8% longer LOS (95% CI, 10.0%-25.7%; P < 0.001), but among women, low HL was associated with only a 7.7% longer LOS (95% CI, 1.9%-13.5%; P = 0.009). Among the remaining demographics, high school graduation status (P = 0.4), being African American (P = 0.6), and insurance status (P = 0.2) did not significantly modify the effect of HL on LOS. In sensitivity analysis, excluding participants with LOS above the 90th percentile of 12 days and excluding participants with illness severity scores above the 90th percentile, low HL was still associated with longer LOS (P < 0.001 and P = 0.001, respectively; Table 2). In the final sensitivity analysis, although age remained a significant predictor of longer LOS after controlling for illness severity (0.2% increase per year, 95% CI, 0.1%-0.3%; P < 0.001), low HL nevertheless remained significantly associated with longer LOS (P < 0.001; Table 2).

Finally, we compared the group with missing data (n = 280) to the group with complete data (n = 5540). The participants with missing data were more likely to have low HL (31% [86/280] vs 20%; P < 0.001) and to have Medicare or private insurance (82% [177/217] vs 72%; P = 0.002); however, they were not more likely to be 65 or older (40% [112/280] vs 37%; P = 0.3), high school graduates (88% [113/129] vs 81%; P = 0.06), African American (69% [177/256] vs 73%; P = 0.1), or female (57% [158/279] vs 57%; P = 1), nor were they more likely to have longer LOS (5.7 [n = 280] vs 5.5 days; P = 0.6) or higher illness severity scores (1.3 [n = 231] vs 1.3; P = 0.7).

 

DISCUSSION

To our knowledge, this study is the first to evaluate the association between low HL and an important in-hospital outcome measure, hospital LOS. We found that low HL was associated with a longer hospital LOS, a result which remained significant when controlling for severity of illness and sociodemographic variables and when testing the model for sensitivity to the highest values of LOS and illness severity. Additionally, the association of HL with LOS appeared concentrated among participants with shorter LOS. Relative to other predictors, the contribution of HL to the overall LOS model was small, as evidenced by the change in adjusted R² values with HL excluded.

Among the covariates, only gender modified the association between HL and LOS; the findings suggested that men were more susceptible to the effect of low HL on increased LOS. Illness severity and other sociodemographics, including age ≥65, did not appear to modify the association. We also found that being African American and having Medicaid or no insurance were associated with a significantly shorter LOS in multivariate analysis.

Previous work suggested that the adverse health effects of low HL may be mediated through several pathways, including health knowledge, self-efficacy, health skills, and illness stigma.^25-27 The finding of a small but significant relationship between HL and LOS was not surprising given these known associations; nevertheless, there may be an additional patient-dependent effect of low HL on LOS not discovered here. For instance, patients with poor health knowledge and self-efficacy might stay in the hospital longer if they or their providers do not feel comfortable with their self-care ability.

This finding may be useful in developing hospital-based interventions. HL-specific interventions, several of which have been tested in the inpatient setting,^14,28,29 have shown promise toward improving health knowledge,³⁰ disease severity,³¹ and health resource utilization.³²

Those with low HL may lack the self-efficacy to participate in discharge planning; in fact, previous work has related low HL to posthospital readmissions.^8,9 Conversely, patients with low HL might struggle to engage in the inpatient milieu, advocating for shorter LOS if they feel alienated by the inpatient experience.

These possibilities show that LOS is a complex measure shown to depend on patient-level characteristics and on provider-based, geographical, and sociocultural factors.^16,33 With these forces at play, additional effects of lower levels of HL may be lost without phenotyping patients by both level of HL and related characteristics, such as self-efficacy, health skills, and stigma. By gathering these additional data, future work should explore whether subpopulations of patients with low HL may be at risk for too-short vs too-long hospital admissions.

For instance, in this study, both race and Medicaid insurance were associated with shorter LOS. Being African American was associated with shorter LOS in our study but has been found to be associated with longer LOS in another study specifically focused on diabetes.³⁴ Prior findings found uninsured patients have shorter LOS.³⁵ Therefore, these findings in our study are difficult to explain without further work to understand whether there are health disparities in the way patients are cared for during hospitalization that may shorten or lengthen their LOS because of factors outside of their clinical need.

The finding that gender modified the effect of low HL on LOS was unexpected. There were similar proportions of men and women with low HL. There is evidence to support that women make the majority of health decisions for themselves and their familes³⁶; therefore, there may be unmeasured aspects of HL that provide an advantage for female vs male inpatients. Furthermore, omitted confounders, such as social support, may not fully capture potential gender-related differences. Future work is needed to understand the role of gender in relationship to HL and LOS.

Limitations of this study include its observational, single-centered design with information derived from administrative data; positive and negative confounding cannot be ruled out. For instance, we did not control for complex aspects affecting LOS, such as discharge disposition and goals of care (eg, aggressive care after discharge vs hospice). To address this limitation, multivariate analyses were performed, which were adjusted for illness severity scores and took into account both comorbidity and severity of the current illness. Additionally, although it is important to study such populations, our largely urban, minority sample is not representative of the U.S. population, and within our large sample, there were participants with missing data who had lower HL on average, although this group represented only 5% of the sample. Finally, different HL tools have noncomplete concordance, which has been seen when comparing the BHLS with more objective tools.^20,37 Furthermore, certain in-hospital clinical scenarios (eg, recent stroke or prolonged intensive care unit stay) may present unique challenges in establishing a baseline HL level. However, the BHLS was used in this study because of its greater feasibility.

In conclusion, this study is the first to evaluate the relationship between low HL and LOS. The findings suggest that HL may play a role in shaping outcomes in the inpatient setting and that targeting interventions toward screened patients may be a pathway toward mitigating adverse effects. Our findings need to be replicated in larger, more representative samples, and further work understanding subpopulations within the low HL population is needed. Future work should measure this association in diverse inpatient settings (eg, psychiatric, surgical, and specialty), in addition to assessing associations between HL and other important in-hospital outcome measures, including mortality and discharge disposition.

 

Acknowledgments

The authors thank the Hospitalist Project team for their assistance with data collection. The authors especially thank Chuanhong Liao and Ashley Snyder for assistance with statistical analyses; Andrea Flores, Ainoa Coltri, and Tom Best for their assistance with data management. The authors would also like to thank Nicole Twu for her help with preparing and editing the manuscript.

Disclosures

Dr. Jaffee was supported by a Calvin Fentress Research Fellowship and NIH R25MH094612. Dr. Press was supported by a career development award (NHLBI K23HL118151). This work was also supported by a seed grant from the Center for Health Administration Studies. All other authors declare no conflicts of interest.

Health literacy (HL), defined as patients’ ability to understand health information and make health decisions,¹ is a prevalent problem in the outpatient and inpatient settings.^2,3 In both settings, low HL has adverse implications for self-care including interpreting health labels⁴ and taking medications correctly.⁵ Among outpatient cohorts, HL has been associated with worse outcomes and acute care utilization.⁶ Associations with low HL include increased hospitalizations,⁷ rehospitalizations,^8,9 emergency department visits,¹⁰ and decreased preventative care use.¹¹ Among the elderly, low HL is associated with increased mortality¹² and decreased self-perception of health.¹³

A systematic review revealed that most high-quality HL outcome studies were conducted in the outpatient setting.⁶ There have been very few studies assessing effects of low HL in an acute-care setting.^7,14 These studies have evaluated postdischarge outcomes, including admissions or readmissions,^7-9 and medication knowledge.¹⁴ To the best of our knowledge, there are no studies evaluating associations between HL and hospital length of stay (LOS).

LOS has received much attention as providers and payers focus more on resource utilization and eliminating adverse effects of prolonged hospitalization.¹⁵ LOS is multifactorial, depending on clinical characteristics like disease severity, as well as on sociocultural, demographic, and geographic factors.¹⁶ Despite evidence that LOS reductions translate into improved resource allocation and potentially fewer complications, there remains a tension between the appropriate LOS and one that is too short for a given condition.¹⁷

Because low HL is associated with inefficient resource utilization, we hypothesized that low HL would be associated with increased LOS after controlling for illness severity. Our objectives were to evaluate the association between low HL and LOS and whether such an association was modified by illness severity and sociodemographics.

METHODS

Study Design, Setting, Participants

An in-hospital, cohort study design of patients who were admitted or transferred to the general medicine service at the University of Chicago between October 2012 and November 2015 and screened for inclusion as part of a large, ongoing study of inpatient care quality was conducted.¹⁸ Exclusion criteria included observation status, age under 18 years, non-English speaking, and repeat participants. Those who died during hospitalization or whose discharge status was missing were excluded because the primary goal was to examine the association of HL and time to discharge, which could not be evaluated among those who died. We excluded participants with LOS >30 days to limit overly influential effects of extreme outliers (1% of the population).

Variables

HL was screened using the Brief Health Literacy Screen (BHLS), a validated, 3-question verbal survey not requiring adequate visual acuity to assess HL.^19,20 The 3 questions are as follows: (1) “How confident are you filling out medical forms by yourself?”, (2) “How often do you have someone help you read hospital materials?”, and (3) “How often do you have problems learning about your medical condition because of difficulty understanding written information?” Responses to the questions were scored on a 5-point Likert scale in which higher scores corresponded to higher HL.^21,22 The scores for each of the 3 questions were summed to yield a range between 3 and 15. On the individual questions, prior work has demonstrated improved test performance with a cutoff of ≤3, which corresponds to a response of “some of the time” or “somewhat”; therefore, when the 3 questions were summed together, scores of ≤9 were considered indicative of low HL.^21,23

For severity of illness adjustment, we used relative weights derived from the 3M (3M, Maplewood, MN) All Patient Refined Diagnosis Related Groups (APR-DRG) classification system, which uses administrative data to classify the severity. The APR-DRG system assigns each admission to a DRG based on principal diagnosis; for each DRG, patients are then subdivided into 4 severity classes based on age, comorbidity, and interactions between these variables and the admitting diagnosis.²⁴ Using the base DRG and severity score, the system assigns relative weights that reflect differences in expected hospital resource utilization.

LOS was derived from hospital administrative data and counted from the date of admission to the hospital. Participants who were discharged on the day of admission were counted as having an LOS of 1. Insurance status (Medicare, Medicaid, no payer, private) also was obtained from administrative data. Age, sex (male or female), education (junior high or less, some high school, high school graduate, some college, college graduate, postgraduate), and race (black/African American, white, Asian or Pacific Islander [including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guam/Chamorro, Samoan, other Pacific], American Indian or Alaskan Native, multiple race) were obtained from administrative data based on information provided by the patient. Participants with missing data on any of the sociodemographic variables or on the APR-DRG score were excluded from the analysis.

 

Statistical Analysis

χ² and 2-tailed t tests were used to compare categorical and continuous variables, respectively. Multivariate linear regressions were employed to measure associations between the independent variables (HL, illness severity, race, gender, education, and insurance status) and the dependent variable, LOS. Independent variables were chosen for clinical significance and retained in the model regardless of statistical significance. The adjusted R² values of models with and without the HL variable included were reported to provide information on the contribution of HL to the overall model.

Because LOS was observed to be right skewed and residuals of the untransformed regression were observed to be non-normally distributed, the decision was made to natural log transform LOS, which is consistent with previous hospital LOS studies.¹⁶ Regression coefficients and confidence intervals were then transformed into percentage estimates using the following equation: 100(e^β–1). Adjusted R² was reported for the transformed regression.

The APR-DRG relative weight was treated as a continuous variable. Sociodemographic variables were dichotomized as follows: female vs male; high school graduates vs not; African American vs not; Medicaid/no payer vs Medicare/private payer. Age was not included in the multivariate model because it has been incorporated into the weighted APR-DRG illness severity scores.

Each of the sociodemographic variables and the APR-DRG score were examined for effect modification via the same multivariate linear equation described above, with the addition of an interaction term. A separate regression was performed with an interaction term between age (dichotomized at ≥65) and HL to investigate whether age modified the association between HL and LOS. Finally, we explored whether effects were isolated to long vs short LOS by dividing the sample based on the mean LOS (≥6 days) and performing separate multivariate comparisons.

Sensitivity analyses were performed to exclude those with LOS greater than the 90th percentile and those with APR-DRG score greater than the 90th percentile; age was added to the model as a continuous variable to evaluate whether the illness severity score fully adjusted for the effects of age on LOS. Furthermore, we compared the participants with missing data to those with complete data across both dependent and independent variables. Alpha was set at 0.05; analyses were performed using Stata Version 14 (Stata, College Station, TX).

RESULTS

A total of 5983 participants met inclusion criteria and completed the HL assessment; of these participants, 75 (1%) died during hospitalization, 9 (0.2%) had missing discharge status, and 79 (1%) had LOS >30 days. Two hundred eighty (5%) were missing data on sociodemographic variables or APR-DRG score. Of the remaining (n = 5540), the mean age was 57 years (standard deviation [SD] = 19 years), over half of participants were female (57%), and the majority were African American (73%) and had graduated from high school (81%). The sample was divided into those with private insurance (25%), those with Medicare (46%), and those with Medicaid (26%); 2% had no payer. The mean APR-DRG score was 1.3 (SD = 1.2), and the scores ranged from 0.3 to 15.8.

On the BHLS screen for HL, 20% (1104/5540) had inadequate HL. Participants with low HL had higher weighted illness severity scores (average 1.4 vs 1.3; P = 0.003). Participants with low HL were also more likely to be 65 or older (55% vs 33%; P < 0.001), non-high school graduates (35% vs 15%; P < 0.001), and African American (78% vs 72%; P < 0.001), and to have Medicare or private insurance (75% vs 71%; P = 0.02). There was no significant difference with respect to gender (54% male vs 57% female; P = 0.1)

The mean and median LOS were 6 ± 5 days and 4 days (interquartile range 2-7 days), respectively. Those with low HL had a longer average LOS (6.0 vs 5.4 days; P < 0.001). In multivariate analysis controlling for APR-DRG score, gender, education, race, and insurance status, low HL was associated with an 11.1% longer LOS (95% CI, 6.1-16.1; P < 0.001; Table 1). The adjusted R² value for the regression was 25.0% including HL and 24.7% with HL excluded. Additionally, being African American (P < 0.001) and having Medicaid or no insurance (P < 0.001) were associated with a shorter LOS in multivariate analysis (Table 1). The association of HL and LOS in multivariate modeling remained significant among participants with LOS <6 days (10.2%; 95% CI, 5.6%-14.9%; P < 0.001), but not among participants with LOS ≥6 days (0.4%; 95% CI, −3.6% to 4.4%; P = 0.8).

Neither age ≥65 (P = 0.4) nor illness severity score (P = 0.5) significantly modified the effect of HL on LOS. However, the effect of HL on hospital LOS was significantly modified by gender (P = 0.02). Among men, low HL was associated with a 17.8% longer LOS (95% CI, 10.0%-25.7%; P < 0.001), but among women, low HL was associated with only a 7.7% longer LOS (95% CI, 1.9%-13.5%; P = 0.009). Among the remaining demographics, high school graduation status (P = 0.4), being African American (P = 0.6), and insurance status (P = 0.2) did not significantly modify the effect of HL on LOS. In sensitivity analysis, excluding participants with LOS above the 90th percentile of 12 days and excluding participants with illness severity scores above the 90th percentile, low HL was still associated with longer LOS (P < 0.001 and P = 0.001, respectively; Table 2). In the final sensitivity analysis, although age remained a significant predictor of longer LOS after controlling for illness severity (0.2% increase per year, 95% CI, 0.1%-0.3%; P < 0.001), low HL nevertheless remained significantly associated with longer LOS (P < 0.001; Table 2).

Finally, we compared the group with missing data (n = 280) to the group with complete data (n = 5540). The participants with missing data were more likely to have low HL (31% [86/280] vs 20%; P < 0.001) and to have Medicare or private insurance (82% [177/217] vs 72%; P = 0.002); however, they were not more likely to be 65 or older (40% [112/280] vs 37%; P = 0.3), high school graduates (88% [113/129] vs 81%; P = 0.06), African American (69% [177/256] vs 73%; P = 0.1), or female (57% [158/279] vs 57%; P = 1), nor were they more likely to have longer LOS (5.7 [n = 280] vs 5.5 days; P = 0.6) or higher illness severity scores (1.3 [n = 231] vs 1.3; P = 0.7).

 

DISCUSSION

To our knowledge, this study is the first to evaluate the association between low HL and an important in-hospital outcome measure, hospital LOS. We found that low HL was associated with a longer hospital LOS, a result which remained significant when controlling for severity of illness and sociodemographic variables and when testing the model for sensitivity to the highest values of LOS and illness severity. Additionally, the association of HL with LOS appeared concentrated among participants with shorter LOS. Relative to other predictors, the contribution of HL to the overall LOS model was small, as evidenced by the change in adjusted R² values with HL excluded.

Among the covariates, only gender modified the association between HL and LOS; the findings suggested that men were more susceptible to the effect of low HL on increased LOS. Illness severity and other sociodemographics, including age ≥65, did not appear to modify the association. We also found that being African American and having Medicaid or no insurance were associated with a significantly shorter LOS in multivariate analysis.

Previous work suggested that the adverse health effects of low HL may be mediated through several pathways, including health knowledge, self-efficacy, health skills, and illness stigma.^25-27 The finding of a small but significant relationship between HL and LOS was not surprising given these known associations; nevertheless, there may be an additional patient-dependent effect of low HL on LOS not discovered here. For instance, patients with poor health knowledge and self-efficacy might stay in the hospital longer if they or their providers do not feel comfortable with their self-care ability.

This finding may be useful in developing hospital-based interventions. HL-specific interventions, several of which have been tested in the inpatient setting,^14,28,29 have shown promise toward improving health knowledge,³⁰ disease severity,³¹ and health resource utilization.³²

Those with low HL may lack the self-efficacy to participate in discharge planning; in fact, previous work has related low HL to posthospital readmissions.^8,9 Conversely, patients with low HL might struggle to engage in the inpatient milieu, advocating for shorter LOS if they feel alienated by the inpatient experience.

These possibilities show that LOS is a complex measure shown to depend on patient-level characteristics and on provider-based, geographical, and sociocultural factors.^16,33 With these forces at play, additional effects of lower levels of HL may be lost without phenotyping patients by both level of HL and related characteristics, such as self-efficacy, health skills, and stigma. By gathering these additional data, future work should explore whether subpopulations of patients with low HL may be at risk for too-short vs too-long hospital admissions.

For instance, in this study, both race and Medicaid insurance were associated with shorter LOS. Being African American was associated with shorter LOS in our study but has been found to be associated with longer LOS in another study specifically focused on diabetes.³⁴ Prior findings found uninsured patients have shorter LOS.³⁵ Therefore, these findings in our study are difficult to explain without further work to understand whether there are health disparities in the way patients are cared for during hospitalization that may shorten or lengthen their LOS because of factors outside of their clinical need.

The finding that gender modified the effect of low HL on LOS was unexpected. There were similar proportions of men and women with low HL. There is evidence to support that women make the majority of health decisions for themselves and their familes³⁶; therefore, there may be unmeasured aspects of HL that provide an advantage for female vs male inpatients. Furthermore, omitted confounders, such as social support, may not fully capture potential gender-related differences. Future work is needed to understand the role of gender in relationship to HL and LOS.

Limitations of this study include its observational, single-centered design with information derived from administrative data; positive and negative confounding cannot be ruled out. For instance, we did not control for complex aspects affecting LOS, such as discharge disposition and goals of care (eg, aggressive care after discharge vs hospice). To address this limitation, multivariate analyses were performed, which were adjusted for illness severity scores and took into account both comorbidity and severity of the current illness. Additionally, although it is important to study such populations, our largely urban, minority sample is not representative of the U.S. population, and within our large sample, there were participants with missing data who had lower HL on average, although this group represented only 5% of the sample. Finally, different HL tools have noncomplete concordance, which has been seen when comparing the BHLS with more objective tools.^20,37 Furthermore, certain in-hospital clinical scenarios (eg, recent stroke or prolonged intensive care unit stay) may present unique challenges in establishing a baseline HL level. However, the BHLS was used in this study because of its greater feasibility.

In conclusion, this study is the first to evaluate the relationship between low HL and LOS. The findings suggest that HL may play a role in shaping outcomes in the inpatient setting and that targeting interventions toward screened patients may be a pathway toward mitigating adverse effects. Our findings need to be replicated in larger, more representative samples, and further work understanding subpopulations within the low HL population is needed. Future work should measure this association in diverse inpatient settings (eg, psychiatric, surgical, and specialty), in addition to assessing associations between HL and other important in-hospital outcome measures, including mortality and discharge disposition.

 

Acknowledgments

The authors thank the Hospitalist Project team for their assistance with data collection. The authors especially thank Chuanhong Liao and Ashley Snyder for assistance with statistical analyses; Andrea Flores, Ainoa Coltri, and Tom Best for their assistance with data management. The authors would also like to thank Nicole Twu for her help with preparing and editing the manuscript.

Disclosures

Dr. Jaffee was supported by a Calvin Fentress Research Fellowship and NIH R25MH094612. Dr. Press was supported by a career development award (NHLBI K23HL118151). This work was also supported by a seed grant from the Center for Health Administration Studies. All other authors declare no conflicts of interest.

Evidence suggests that troponin-only testing is the superior strategy to diagnose acute myocardial infarction (AMI).¹ Because of this, in February 2015, the Choosing Wisely^® campaign issued a recommendation to use troponin I or T to diagnose AMI, and not to test for myoglobin or creatine kinase-MB (CK-MB).² This recommendation was in line with guidelines from the American Heart Association and the American College of Cardiology, which recommended that myoglobin and CK-MB are not useful and offer no benefit for the diagnosis of acute coronary syndrome.³ Some institutions have developed interventions to promote troponin-only testing, reporting substantial cost savings and no negative consequences.^4,5

Despite these successes, it is likely that institutions vary with respect to the adoption of the Choosing Wisely^® troponin-only testing recommendation.⁶ Implementing this recommendation requires both promoting clinician behavior change and a strong institutional culture of high-value care.⁷ Understanding the variation across institutions of troponin-only testing could inform how to promote high-value care recommendations nationwide. We aimed to describe patterns of troponin, myoglobin, and CK-MB testing in a sample of academic teaching hospitals before and after the Choosing Wisely^® recommendation.

METHODS

Troponin, myoglobin, and CK-MB ordering data were extracted from Vizient’s (formerly University HealthSystem Consortium, Chicago, IL) Clinical Database/Resource Manager (CDB/RM^®) for all patients with a principal discharge diagnosis of AMI at all hospitals reporting all 36 months from the fourth quarter of 2013 through the third quarter of 2016. This period includes time both before and after the Choosing Wisely^® recommendation, which was released in the first quarter of 2015. Vizient’s CDB/RM contains ordering data for 300 academic medical centers and their affiliated hospitals and includes the discharge diagnoses for patients cared for by these institutions. Only patients with a principal discharge diagnosis of AMI were included because the Choosing Wisely^® recommendation is specific with regard to troponin-only testing for the diagnosis of AMI. Patients with a principal diagnosis code for subcategories of myocardial ischemia (eg, stable angina, unstable angina) were not included because of the large number of diagnosis codes for these subcategories (more than 100 in the International Classification of Diseases, Ninth Revision and the International Classification of Diseases, Tenth Revision) and because the variation in their use across institutions within the dataset limited the utility of using these codes to consistently and accurately identify patients with myocardial ischemia. Moreover, the diagnosis of AMI encompasses the subcategories of myocardial ischemia.⁸

Hospital rates of ordering cardiac biomarkers (troponin-only or troponin and myoglobin/CK-MB) were determined overall for the entire study period and for each quarter of the study period based on the total patients with a discharge diagnosis of AMI. For each quarter of the 12 study quarters, all the hospitals were divided into tertiles based on their rate of troponin-only testing per discharge diagnosis of AMI. Hospitals were then classified into 3 groups based on their tertile ranking over the full 12 study quarters. The first group included hospitals whose rate of troponin-only testing placed them in the top tertile for each and all quarters throughout the study period. The second group included hospitals whose troponin-only testing rate placed them in the bottom tertile for each and all quarters throughout the study period. The third group included hospitals whose troponin-only testing rate each quarter led to either an increase or decrease in their tertile ranking throughout the study period. χ² tests were used to test for bivariate associations among hospitals based on their rate of troponin-only testing and hospital size (number of beds), their regional geographic location, the volume of AMI patients seen at the hospital, whether the primary physician during the hospitalization was a cardiologist or other provider, and the hospitals’ quality ratings. Quality rating was based on an internal Vizient rating and the “Best Hospitals for Cardiology and Heart Surgery Rankings” as published in the US News & World Report.⁹ The Vizient quality rating is based on a composite score that combines scores from the domains of quality (hospital quality incentive scores), safety (patient safety indicators), patient-centeredness (Hospital Consumer Assessment of Healthcare Providers and Systems Hospital Survey), and equity (distribution of care by race/ethnicity, gender, and age). Simple slopes were calculated to determine the rate of change in troponin-only testing for each study quarter, and Student t tests were used to compare the rates of change of these simple slopes across study quarters.

 

RESULTS

Of the 300 hospitals in Vizient’s CDB/RM, 91 (30%, 91/300) had full reporting of data throughout the study period. These hospitals had a total of 106,954 inpatient discharges with a principal diagnosis of AMI during the study period. The overall rates of troponin-only testing for AMI discharges by hospital varied from 0% to 87.4% (Figure 1). The mean rate of troponin-only testing across all patients with a discharge diagnosis of AMI was 29.2% at the start of the study (fourth quarter of 2013) and 53.5% at the end of the study (third quarter 2016; Supplemental Figure). Nineteen hospitals (21%, 19/91; 27,973 discharges) had high rates of troponin-only testing for AMI and were in the top tertile of all hospitals throughout the study period. Thirty-four hospitals (37%, 34/91; 35,080 discharges) ordered both troponin and myoglobin/CK-MB tests to diagnose AMI, and they were in the bottom tertile of all hospitals throughout the study period. In the 38 hospitals (42%, 38/91; 43,090 discharges) that were not in the top or bottom tertile for all study quarters, the rate of troponin-only testing for AMI increased at each hospital during each quarter of the study period (Table).

Pattern of Troponin-Only Testing by Hospital Size

Of the hospitals in the top tertile of troponin-only testing throughout the study period, the majority had ≥500 beds (13/19), but the highest rate of troponin-only testing was in hospitals that had <250 beds (n = 4, troponin-only testing rate of 82/100 patients). Additionally, in hospitals that improved their troponin-only testing during the study period, hospitals that had <500 beds had higher rates of troponin-only testing than did hospitals with ≥500 beds. The differences in the rates of troponin-only testing across the 3 groups of hospitals and hospital size were statistically significant (P < 0.0001; Table).

Pattern of Troponin-Only Testing by Geographic Region

The rate of troponin-only testing also varied and was statistically significantly different when comparing the 3 groups of hospitals across geographic regions of the country (P < 0.0001). Of the hospitals in the top tertile of troponin-only testing throughout the study period, the majority were in the Midwest (n = 6) and Mid-Atlantic (n = 5) regions. However, the rate of troponin-only testing for AMI in this group was highest in hospitals in the West (86/100 patients) and/or Southeast (75/100 patients) regions, although this rate was based on a small number of hospitals in these geographic areas (n = 1 in the West, n = 2 in the Southeast). Of hospitals in the bottom tertile of troponin-only testing throughout the study period, the majority were in the Mid-Atlantic region (n = 10). Hospitals that increased their troponin-only testing during the study period were predominantly in the Midwest (n = 12) and Mid-Atlantic regions (n = 11; Table), with the hospitals in the Midwest having the highest rate of troponin-only testing in this group.

Pattern of Troponin-Only Testing by Volume of AMI Patients

Of the hospitals in the top tertile of troponin-only testing during the study period, the majority cared for ≥1500 AMI patients (n = 9), but interestingly, among these hospitals, those caring for a smaller volume of AMI patients all had higher rates of troponin-only testing per 100 patients (P < 0.0001; Table). There was no other obvious pattern of troponin-only testing based on the volume of AMI patients cared for in hospitals in either the bottom tertile of troponin-only testing or hospitals that improved troponin-only testing during the study period.

Pattern of Troponin-Only Testing by Physician Type

Of the hospitals in the top tertile of troponin-only testing throughout the study period, those where a cardiologist cared for patients with AMI had higher rates of troponin-only testing (71/100 patients) than did hospitals where patients were cared for by a noncardiologist (60/100 patients). However, of the hospitals that improved their troponin-only testing during the study period, higher rates of troponin-only testing were seen in hospitals where patients were cared for by a noncardiologist (48/100 patients) compared with patients cared for by a cardiologist (34/100 patients; Table). These differences in hospital rates of troponin-only testing during the study period based on physician type were statistically significant (P < 0.0001; Table).

Pattern of Troponin-Only Testing by Quality Rating

Hospitals that were in the top tertile of troponin-only testing and were rated highly by Vizient’s quality rating or recognized as a top hospital by the US News & World Report had higher rates of troponin-only testing per 100 patients than did hospitals in the top tertile that were not ranked highly by Vizient’s quality rating or recognized as a top hospital by the US News & World Report. However, the majority of hospitals in the top tertile of troponin-only testing were not rated highly by Vizient (n = 15) or recognized as a top hospital by the US News & World Report (n = 16). The large majority of hospitals in the bottom tertile of troponin-only testing were not recognized as high-quality hospitals by Vizient (n = 32) or the US News & World Report (n = 31). Of the hospitals that improved their troponin-only testing during the study period, the majority were not recognized as high-quality hospitals by Vizient (n = 33) or the US News & World Report (n = 36), but among this group, those hospitals recognized by Vizient as high quality (n = 5) had the highest rate of troponin-only testing (57/100 patients). The differences in the rate of troponin-only testing across the different groups of hospitals and quality ratings were statistically significant (P < 0.0001; Table).

 

The Effect of Choosing Wisely^® on Troponin-Only Testing

While in many institutions the rates of troponin-only testing were increasing before the Choosing Wisely^® recommendation was released in 2015, the release of the recommendation was associated with a significant increase in the rate of troponin-only testing in the institutions that were in the bottom tertile of troponin-only testing prior to the release of the recommendation but moved to the top tertile after the release of the recommendation (n = 5). The slope percentage of the rate of change of the 5 hospitals that went from the bottom tertile to the top tertile after the release of the Choosing Wisely^® recommendation was 5.7%. Additionally, the Choosing Wisely^® recommendation was associated with an accelerated rate of troponin-only testing in hospitals moving from the bottom tertile before the release of the recommendation to the middle tertile after the recommendation (n = 15; slope = 3.2%) and in hospitals moving from the middle tertile before the release of the recommendation to the top tertile after (n = 6; slope = 2.4%) (Figure 2). For all of these hospitals (n = 26), the increased rate of troponin-only testing in the study quarter after the Choosing Wisely^® recommendation was statistically significantly higher and different from the rate of troponin-only testing in all other study quarters, except for the period between 2014 quarter 3 and quarter 4 (P = 0.08), the period between 2015 quarter 2 and quarter 3 (P = 0.18), and 2015 quarter 3 and quarter 4 (P = 0.06), where the effect did not quite reach statistical significance (Figure 3).

DISCUSSION

In a broad sample of academic teaching hospitals, there was an overall increase in the rate of troponin-only testing starting from the fourth quarter of 2013 through the third quarter of 2016. However, there was wide variation in the adoption of troponin-only testing for AMI across institutions. Our study identified several high-performing hospitals where the rate of troponin-only testing was high prior to and after the Choosing Wisely^® troponin-only recommendation. Additionally, we identified several poor-performing hospitals, which even after the release of the Choosing Wisely® recommendation continue to order both troponin and myoglobin/CK-MB tests for the diagnosis of AMI. Lastly, we identified several hospitals in which the release of the Choosing Wisely® recommendation was associated with a significant increase in the rate of troponin-only testing for the diagnosis of AMI. 
The high-performing hospitals in our sample that were in the top tertile of troponin-only testing throughout the study period are “early adopters,” having already instituted troponin-only testing before the release of the Choosing Wisely^® troponin-only recommendation. These hospitals vary in size, geographic region of the country, volume of AMI patients cared for, whether AMI patients are cared for by a cardiologist or other provider, and quality rating. Interestingly, in these hospitals, AMI patients admitted under the care of a cardiologist had higher rates of troponin-only testing than when admitted under another physician type. This is perhaps not surprising given that cardiologists would be the most likely to be aware of the data supporting troponin-only testing prior to the Choosing Wisely^® recommendation and the most likely to institute interventions to promote troponin-only testing and disseminate this knowledge across their institution. These institutions and their practice of troponin-only testing before the Choosing Wisely^® recommendation represent the idea of positive deviance,¹⁰ whereby they had identified troponin-only testing as a superior strategy and instituted successful initiatives to reduce the use of unnecessary myoglobin and CK-MB testing before their peer hospitals and the release of the Choosing Wisely^® recommendation. Further efforts to explore and understand the additional factors that define the hospitals that had high rates of troponin-only testing prior to the Choosing Wisely^® recommendation may be helpful to understanding the necessary culture and institutional factors that can promote high-value care.

In the hospitals that demonstrated increasing adoption of troponin-only testing, there are several interesting patterns. First, among these hospitals, smaller hospitals tended to have higher overall rates of troponin-only testing per 100 patients than larger hospitals. Additionally, the hospitals with the highest rates were located in the Midwest region. These hospitals may be learning from and following the high-performing institutions observed in our data that are also located in the Midwest. Additionally, among the hospitals that significantly increased their rate of troponin-only testing, we see that the Choosing Wisely^® recommendation appeared to facilitate accelerated adoption of troponin-only testing. In these institutions, it is likely that the impact of Choosing Wisely^® was significant because there was attention to high-value care and already an existing movement underway to institute such high-value practices. For example, natural champions, leadership, infrastructure, and a supportive culture may all be prerequisites for Choosing Wisely^® recommendations to become institutionally adopted.

Lastly, in the hospitals that have continued to order myoglobin and CK-MB, future work is needed to understand and overcome barriers to adopting high-value care practices.

There are several limitations to this study. First, because this was an observational study, we cannot prove a causal relationship between the Choosing Wisely^® recommendation and the increased rates of troponin-only testing. Additionally, the Vizient CDB/RM contains reporting data for a limited number of academic medical centers only, and therefore, these results may not represent practices at nonacademic or even other academic medical centers. Our study only included patients with a principal discharge diagnosis of AMI because the Choosing Wisely^® recommendation to order troponin-only is specific for diagnosing patients with AMI. However, it is possible that the Choosing Wisely^® recommendation also has affected provider ordering in patients with diagnoses such as chest pain or angina, and these affects would not be captured in our study. Lastly, because instituting high-value care practices take time, our follow-up time may not have been long enough to capture improvement in troponin-only testing at institutions responding to and attempting to adhere to the Choosing Wisely^® recommendation to order troponin-only testing for patients with AMI.

 

Disclosure 

No other individuals besides the authors contributed to this work. This project was not funded or supported by any external grant or agency. Dr. Prochaska’s institute received funding from the Agency for Research Healthcare and Quality for a K12 Career Development Grant (AHRQ K12 HS023007) outside the submitted work. Dr. Hohmann and Dr Modes have nothing to disclose. Dr. Arora receives financial compensation as a member of the Board of Directors for the American Board of Internal Medicine and has received grant funding from the ABIM Foundation. She also receives royalties from McGraw Hill.

Evidence suggests that troponin-only testing is the superior strategy to diagnose acute myocardial infarction (AMI).¹ Because of this, in February 2015, the Choosing Wisely^® campaign issued a recommendation to use troponin I or T to diagnose AMI, and not to test for myoglobin or creatine kinase-MB (CK-MB).² This recommendation was in line with guidelines from the American Heart Association and the American College of Cardiology, which recommended that myoglobin and CK-MB are not useful and offer no benefit for the diagnosis of acute coronary syndrome.³ Some institutions have developed interventions to promote troponin-only testing, reporting substantial cost savings and no negative consequences.^4,5

Despite these successes, it is likely that institutions vary with respect to the adoption of the Choosing Wisely^® troponin-only testing recommendation.⁶ Implementing this recommendation requires both promoting clinician behavior change and a strong institutional culture of high-value care.⁷ Understanding the variation across institutions of troponin-only testing could inform how to promote high-value care recommendations nationwide. We aimed to describe patterns of troponin, myoglobin, and CK-MB testing in a sample of academic teaching hospitals before and after the Choosing Wisely^® recommendation.

METHODS

Troponin, myoglobin, and CK-MB ordering data were extracted from Vizient’s (formerly University HealthSystem Consortium, Chicago, IL) Clinical Database/Resource Manager (CDB/RM^®) for all patients with a principal discharge diagnosis of AMI at all hospitals reporting all 36 months from the fourth quarter of 2013 through the third quarter of 2016. This period includes time both before and after the Choosing Wisely^® recommendation, which was released in the first quarter of 2015. Vizient’s CDB/RM contains ordering data for 300 academic medical centers and their affiliated hospitals and includes the discharge diagnoses for patients cared for by these institutions. Only patients with a principal discharge diagnosis of AMI were included because the Choosing Wisely^® recommendation is specific with regard to troponin-only testing for the diagnosis of AMI. Patients with a principal diagnosis code for subcategories of myocardial ischemia (eg, stable angina, unstable angina) were not included because of the large number of diagnosis codes for these subcategories (more than 100 in the International Classification of Diseases, Ninth Revision and the International Classification of Diseases, Tenth Revision) and because the variation in their use across institutions within the dataset limited the utility of using these codes to consistently and accurately identify patients with myocardial ischemia. Moreover, the diagnosis of AMI encompasses the subcategories of myocardial ischemia.⁸

Hospital rates of ordering cardiac biomarkers (troponin-only or troponin and myoglobin/CK-MB) were determined overall for the entire study period and for each quarter of the study period based on the total patients with a discharge diagnosis of AMI. For each quarter of the 12 study quarters, all the hospitals were divided into tertiles based on their rate of troponin-only testing per discharge diagnosis of AMI. Hospitals were then classified into 3 groups based on their tertile ranking over the full 12 study quarters. The first group included hospitals whose rate of troponin-only testing placed them in the top tertile for each and all quarters throughout the study period. The second group included hospitals whose troponin-only testing rate placed them in the bottom tertile for each and all quarters throughout the study period. The third group included hospitals whose troponin-only testing rate each quarter led to either an increase or decrease in their tertile ranking throughout the study period. χ² tests were used to test for bivariate associations among hospitals based on their rate of troponin-only testing and hospital size (number of beds), their regional geographic location, the volume of AMI patients seen at the hospital, whether the primary physician during the hospitalization was a cardiologist or other provider, and the hospitals’ quality ratings. Quality rating was based on an internal Vizient rating and the “Best Hospitals for Cardiology and Heart Surgery Rankings” as published in the US News & World Report.⁹ The Vizient quality rating is based on a composite score that combines scores from the domains of quality (hospital quality incentive scores), safety (patient safety indicators), patient-centeredness (Hospital Consumer Assessment of Healthcare Providers and Systems Hospital Survey), and equity (distribution of care by race/ethnicity, gender, and age). Simple slopes were calculated to determine the rate of change in troponin-only testing for each study quarter, and Student t tests were used to compare the rates of change of these simple slopes across study quarters.

 

RESULTS

Of the 300 hospitals in Vizient’s CDB/RM, 91 (30%, 91/300) had full reporting of data throughout the study period. These hospitals had a total of 106,954 inpatient discharges with a principal diagnosis of AMI during the study period. The overall rates of troponin-only testing for AMI discharges by hospital varied from 0% to 87.4% (Figure 1). The mean rate of troponin-only testing across all patients with a discharge diagnosis of AMI was 29.2% at the start of the study (fourth quarter of 2013) and 53.5% at the end of the study (third quarter 2016; Supplemental Figure). Nineteen hospitals (21%, 19/91; 27,973 discharges) had high rates of troponin-only testing for AMI and were in the top tertile of all hospitals throughout the study period. Thirty-four hospitals (37%, 34/91; 35,080 discharges) ordered both troponin and myoglobin/CK-MB tests to diagnose AMI, and they were in the bottom tertile of all hospitals throughout the study period. In the 38 hospitals (42%, 38/91; 43,090 discharges) that were not in the top or bottom tertile for all study quarters, the rate of troponin-only testing for AMI increased at each hospital during each quarter of the study period (Table).

Pattern of Troponin-Only Testing by Hospital Size

Of the hospitals in the top tertile of troponin-only testing throughout the study period, the majority had ≥500 beds (13/19), but the highest rate of troponin-only testing was in hospitals that had <250 beds (n = 4, troponin-only testing rate of 82/100 patients). Additionally, in hospitals that improved their troponin-only testing during the study period, hospitals that had <500 beds had higher rates of troponin-only testing than did hospitals with ≥500 beds. The differences in the rates of troponin-only testing across the 3 groups of hospitals and hospital size were statistically significant (P < 0.0001; Table).

Pattern of Troponin-Only Testing by Geographic Region

The rate of troponin-only testing also varied and was statistically significantly different when comparing the 3 groups of hospitals across geographic regions of the country (P < 0.0001). Of the hospitals in the top tertile of troponin-only testing throughout the study period, the majority were in the Midwest (n = 6) and Mid-Atlantic (n = 5) regions. However, the rate of troponin-only testing for AMI in this group was highest in hospitals in the West (86/100 patients) and/or Southeast (75/100 patients) regions, although this rate was based on a small number of hospitals in these geographic areas (n = 1 in the West, n = 2 in the Southeast). Of hospitals in the bottom tertile of troponin-only testing throughout the study period, the majority were in the Mid-Atlantic region (n = 10). Hospitals that increased their troponin-only testing during the study period were predominantly in the Midwest (n = 12) and Mid-Atlantic regions (n = 11; Table), with the hospitals in the Midwest having the highest rate of troponin-only testing in this group.

Pattern of Troponin-Only Testing by Volume of AMI Patients

Of the hospitals in the top tertile of troponin-only testing during the study period, the majority cared for ≥1500 AMI patients (n = 9), but interestingly, among these hospitals, those caring for a smaller volume of AMI patients all had higher rates of troponin-only testing per 100 patients (P < 0.0001; Table). There was no other obvious pattern of troponin-only testing based on the volume of AMI patients cared for in hospitals in either the bottom tertile of troponin-only testing or hospitals that improved troponin-only testing during the study period.

Pattern of Troponin-Only Testing by Physician Type

Of the hospitals in the top tertile of troponin-only testing throughout the study period, those where a cardiologist cared for patients with AMI had higher rates of troponin-only testing (71/100 patients) than did hospitals where patients were cared for by a noncardiologist (60/100 patients). However, of the hospitals that improved their troponin-only testing during the study period, higher rates of troponin-only testing were seen in hospitals where patients were cared for by a noncardiologist (48/100 patients) compared with patients cared for by a cardiologist (34/100 patients; Table). These differences in hospital rates of troponin-only testing during the study period based on physician type were statistically significant (P < 0.0001; Table).

Pattern of Troponin-Only Testing by Quality Rating

Hospitals that were in the top tertile of troponin-only testing and were rated highly by Vizient’s quality rating or recognized as a top hospital by the US News & World Report had higher rates of troponin-only testing per 100 patients than did hospitals in the top tertile that were not ranked highly by Vizient’s quality rating or recognized as a top hospital by the US News & World Report. However, the majority of hospitals in the top tertile of troponin-only testing were not rated highly by Vizient (n = 15) or recognized as a top hospital by the US News & World Report (n = 16). The large majority of hospitals in the bottom tertile of troponin-only testing were not recognized as high-quality hospitals by Vizient (n = 32) or the US News & World Report (n = 31). Of the hospitals that improved their troponin-only testing during the study period, the majority were not recognized as high-quality hospitals by Vizient (n = 33) or the US News & World Report (n = 36), but among this group, those hospitals recognized by Vizient as high quality (n = 5) had the highest rate of troponin-only testing (57/100 patients). The differences in the rate of troponin-only testing across the different groups of hospitals and quality ratings were statistically significant (P < 0.0001; Table).

 

The Effect of Choosing Wisely^® on Troponin-Only Testing

While in many institutions the rates of troponin-only testing were increasing before the Choosing Wisely^® recommendation was released in 2015, the release of the recommendation was associated with a significant increase in the rate of troponin-only testing in the institutions that were in the bottom tertile of troponin-only testing prior to the release of the recommendation but moved to the top tertile after the release of the recommendation (n = 5). The slope percentage of the rate of change of the 5 hospitals that went from the bottom tertile to the top tertile after the release of the Choosing Wisely^® recommendation was 5.7%. Additionally, the Choosing Wisely^® recommendation was associated with an accelerated rate of troponin-only testing in hospitals moving from the bottom tertile before the release of the recommendation to the middle tertile after the recommendation (n = 15; slope = 3.2%) and in hospitals moving from the middle tertile before the release of the recommendation to the top tertile after (n = 6; slope = 2.4%) (Figure 2). For all of these hospitals (n = 26), the increased rate of troponin-only testing in the study quarter after the Choosing Wisely^® recommendation was statistically significantly higher and different from the rate of troponin-only testing in all other study quarters, except for the period between 2014 quarter 3 and quarter 4 (P = 0.08), the period between 2015 quarter 2 and quarter 3 (P = 0.18), and 2015 quarter 3 and quarter 4 (P = 0.06), where the effect did not quite reach statistical significance (Figure 3).

DISCUSSION

In a broad sample of academic teaching hospitals, there was an overall increase in the rate of troponin-only testing starting from the fourth quarter of 2013 through the third quarter of 2016. However, there was wide variation in the adoption of troponin-only testing for AMI across institutions. Our study identified several high-performing hospitals where the rate of troponin-only testing was high prior to and after the Choosing Wisely^® troponin-only recommendation. Additionally, we identified several poor-performing hospitals, which even after the release of the Choosing Wisely® recommendation continue to order both troponin and myoglobin/CK-MB tests for the diagnosis of AMI. Lastly, we identified several hospitals in which the release of the Choosing Wisely® recommendation was associated with a significant increase in the rate of troponin-only testing for the diagnosis of AMI. 
The high-performing hospitals in our sample that were in the top tertile of troponin-only testing throughout the study period are “early adopters,” having already instituted troponin-only testing before the release of the Choosing Wisely^® troponin-only recommendation. These hospitals vary in size, geographic region of the country, volume of AMI patients cared for, whether AMI patients are cared for by a cardiologist or other provider, and quality rating. Interestingly, in these hospitals, AMI patients admitted under the care of a cardiologist had higher rates of troponin-only testing than when admitted under another physician type. This is perhaps not surprising given that cardiologists would be the most likely to be aware of the data supporting troponin-only testing prior to the Choosing Wisely^® recommendation and the most likely to institute interventions to promote troponin-only testing and disseminate this knowledge across their institution. These institutions and their practice of troponin-only testing before the Choosing Wisely^® recommendation represent the idea of positive deviance,¹⁰ whereby they had identified troponin-only testing as a superior strategy and instituted successful initiatives to reduce the use of unnecessary myoglobin and CK-MB testing before their peer hospitals and the release of the Choosing Wisely^® recommendation. Further efforts to explore and understand the additional factors that define the hospitals that had high rates of troponin-only testing prior to the Choosing Wisely^® recommendation may be helpful to understanding the necessary culture and institutional factors that can promote high-value care.

In the hospitals that demonstrated increasing adoption of troponin-only testing, there are several interesting patterns. First, among these hospitals, smaller hospitals tended to have higher overall rates of troponin-only testing per 100 patients than larger hospitals. Additionally, the hospitals with the highest rates were located in the Midwest region. These hospitals may be learning from and following the high-performing institutions observed in our data that are also located in the Midwest. Additionally, among the hospitals that significantly increased their rate of troponin-only testing, we see that the Choosing Wisely^® recommendation appeared to facilitate accelerated adoption of troponin-only testing. In these institutions, it is likely that the impact of Choosing Wisely^® was significant because there was attention to high-value care and already an existing movement underway to institute such high-value practices. For example, natural champions, leadership, infrastructure, and a supportive culture may all be prerequisites for Choosing Wisely^® recommendations to become institutionally adopted.

Lastly, in the hospitals that have continued to order myoglobin and CK-MB, future work is needed to understand and overcome barriers to adopting high-value care practices.

There are several limitations to this study. First, because this was an observational study, we cannot prove a causal relationship between the Choosing Wisely^® recommendation and the increased rates of troponin-only testing. Additionally, the Vizient CDB/RM contains reporting data for a limited number of academic medical centers only, and therefore, these results may not represent practices at nonacademic or even other academic medical centers. Our study only included patients with a principal discharge diagnosis of AMI because the Choosing Wisely^® recommendation to order troponin-only is specific for diagnosing patients with AMI. However, it is possible that the Choosing Wisely^® recommendation also has affected provider ordering in patients with diagnoses such as chest pain or angina, and these affects would not be captured in our study. Lastly, because instituting high-value care practices take time, our follow-up time may not have been long enough to capture improvement in troponin-only testing at institutions responding to and attempting to adhere to the Choosing Wisely^® recommendation to order troponin-only testing for patients with AMI.

 

Disclosure 

No other individuals besides the authors contributed to this work. This project was not funded or supported by any external grant or agency. Dr. Prochaska’s institute received funding from the Agency for Research Healthcare and Quality for a K12 Career Development Grant (AHRQ K12 HS023007) outside the submitted work. Dr. Hohmann and Dr Modes have nothing to disclose. Dr. Arora receives financial compensation as a member of the Board of Directors for the American Board of Internal Medicine and has received grant funding from the ABIM Foundation. She also receives royalties from McGraw Hill.

Evidence suggests that troponin-only testing is the superior strategy to diagnose acute myocardial infarction (AMI).¹ Because of this, in February 2015, the Choosing Wisely^® campaign issued a recommendation to use troponin I or T to diagnose AMI, and not to test for myoglobin or creatine kinase-MB (CK-MB).² This recommendation was in line with guidelines from the American Heart Association and the American College of Cardiology, which recommended that myoglobin and CK-MB are not useful and offer no benefit for the diagnosis of acute coronary syndrome.³ Some institutions have developed interventions to promote troponin-only testing, reporting substantial cost savings and no negative consequences.^4,5

Despite these successes, it is likely that institutions vary with respect to the adoption of the Choosing Wisely^® troponin-only testing recommendation.⁶ Implementing this recommendation requires both promoting clinician behavior change and a strong institutional culture of high-value care.⁷ Understanding the variation across institutions of troponin-only testing could inform how to promote high-value care recommendations nationwide. We aimed to describe patterns of troponin, myoglobin, and CK-MB testing in a sample of academic teaching hospitals before and after the Choosing Wisely^® recommendation.

METHODS

Troponin, myoglobin, and CK-MB ordering data were extracted from Vizient’s (formerly University HealthSystem Consortium, Chicago, IL) Clinical Database/Resource Manager (CDB/RM^®) for all patients with a principal discharge diagnosis of AMI at all hospitals reporting all 36 months from the fourth quarter of 2013 through the third quarter of 2016. This period includes time both before and after the Choosing Wisely^® recommendation, which was released in the first quarter of 2015. Vizient’s CDB/RM contains ordering data for 300 academic medical centers and their affiliated hospitals and includes the discharge diagnoses for patients cared for by these institutions. Only patients with a principal discharge diagnosis of AMI were included because the Choosing Wisely^® recommendation is specific with regard to troponin-only testing for the diagnosis of AMI. Patients with a principal diagnosis code for subcategories of myocardial ischemia (eg, stable angina, unstable angina) were not included because of the large number of diagnosis codes for these subcategories (more than 100 in the International Classification of Diseases, Ninth Revision and the International Classification of Diseases, Tenth Revision) and because the variation in their use across institutions within the dataset limited the utility of using these codes to consistently and accurately identify patients with myocardial ischemia. Moreover, the diagnosis of AMI encompasses the subcategories of myocardial ischemia.⁸

Hospital rates of ordering cardiac biomarkers (troponin-only or troponin and myoglobin/CK-MB) were determined overall for the entire study period and for each quarter of the study period based on the total patients with a discharge diagnosis of AMI. For each quarter of the 12 study quarters, all the hospitals were divided into tertiles based on their rate of troponin-only testing per discharge diagnosis of AMI. Hospitals were then classified into 3 groups based on their tertile ranking over the full 12 study quarters. The first group included hospitals whose rate of troponin-only testing placed them in the top tertile for each and all quarters throughout the study period. The second group included hospitals whose troponin-only testing rate placed them in the bottom tertile for each and all quarters throughout the study period. The third group included hospitals whose troponin-only testing rate each quarter led to either an increase or decrease in their tertile ranking throughout the study period. χ² tests were used to test for bivariate associations among hospitals based on their rate of troponin-only testing and hospital size (number of beds), their regional geographic location, the volume of AMI patients seen at the hospital, whether the primary physician during the hospitalization was a cardiologist or other provider, and the hospitals’ quality ratings. Quality rating was based on an internal Vizient rating and the “Best Hospitals for Cardiology and Heart Surgery Rankings” as published in the US News & World Report.⁹ The Vizient quality rating is based on a composite score that combines scores from the domains of quality (hospital quality incentive scores), safety (patient safety indicators), patient-centeredness (Hospital Consumer Assessment of Healthcare Providers and Systems Hospital Survey), and equity (distribution of care by race/ethnicity, gender, and age). Simple slopes were calculated to determine the rate of change in troponin-only testing for each study quarter, and Student t tests were used to compare the rates of change of these simple slopes across study quarters.

 

RESULTS

Of the 300 hospitals in Vizient’s CDB/RM, 91 (30%, 91/300) had full reporting of data throughout the study period. These hospitals had a total of 106,954 inpatient discharges with a principal diagnosis of AMI during the study period. The overall rates of troponin-only testing for AMI discharges by hospital varied from 0% to 87.4% (Figure 1). The mean rate of troponin-only testing across all patients with a discharge diagnosis of AMI was 29.2% at the start of the study (fourth quarter of 2013) and 53.5% at the end of the study (third quarter 2016; Supplemental Figure). Nineteen hospitals (21%, 19/91; 27,973 discharges) had high rates of troponin-only testing for AMI and were in the top tertile of all hospitals throughout the study period. Thirty-four hospitals (37%, 34/91; 35,080 discharges) ordered both troponin and myoglobin/CK-MB tests to diagnose AMI, and they were in the bottom tertile of all hospitals throughout the study period. In the 38 hospitals (42%, 38/91; 43,090 discharges) that were not in the top or bottom tertile for all study quarters, the rate of troponin-only testing for AMI increased at each hospital during each quarter of the study period (Table).

Pattern of Troponin-Only Testing by Hospital Size

Of the hospitals in the top tertile of troponin-only testing throughout the study period, the majority had ≥500 beds (13/19), but the highest rate of troponin-only testing was in hospitals that had <250 beds (n = 4, troponin-only testing rate of 82/100 patients). Additionally, in hospitals that improved their troponin-only testing during the study period, hospitals that had <500 beds had higher rates of troponin-only testing than did hospitals with ≥500 beds. The differences in the rates of troponin-only testing across the 3 groups of hospitals and hospital size were statistically significant (P < 0.0001; Table).

Pattern of Troponin-Only Testing by Geographic Region

The rate of troponin-only testing also varied and was statistically significantly different when comparing the 3 groups of hospitals across geographic regions of the country (P < 0.0001). Of the hospitals in the top tertile of troponin-only testing throughout the study period, the majority were in the Midwest (n = 6) and Mid-Atlantic (n = 5) regions. However, the rate of troponin-only testing for AMI in this group was highest in hospitals in the West (86/100 patients) and/or Southeast (75/100 patients) regions, although this rate was based on a small number of hospitals in these geographic areas (n = 1 in the West, n = 2 in the Southeast). Of hospitals in the bottom tertile of troponin-only testing throughout the study period, the majority were in the Mid-Atlantic region (n = 10). Hospitals that increased their troponin-only testing during the study period were predominantly in the Midwest (n = 12) and Mid-Atlantic regions (n = 11; Table), with the hospitals in the Midwest having the highest rate of troponin-only testing in this group.

Pattern of Troponin-Only Testing by Volume of AMI Patients

Of the hospitals in the top tertile of troponin-only testing during the study period, the majority cared for ≥1500 AMI patients (n = 9), but interestingly, among these hospitals, those caring for a smaller volume of AMI patients all had higher rates of troponin-only testing per 100 patients (P < 0.0001; Table). There was no other obvious pattern of troponin-only testing based on the volume of AMI patients cared for in hospitals in either the bottom tertile of troponin-only testing or hospitals that improved troponin-only testing during the study period.

Pattern of Troponin-Only Testing by Physician Type

Of the hospitals in the top tertile of troponin-only testing throughout the study period, those where a cardiologist cared for patients with AMI had higher rates of troponin-only testing (71/100 patients) than did hospitals where patients were cared for by a noncardiologist (60/100 patients). However, of the hospitals that improved their troponin-only testing during the study period, higher rates of troponin-only testing were seen in hospitals where patients were cared for by a noncardiologist (48/100 patients) compared with patients cared for by a cardiologist (34/100 patients; Table). These differences in hospital rates of troponin-only testing during the study period based on physician type were statistically significant (P < 0.0001; Table).

Pattern of Troponin-Only Testing by Quality Rating

Hospitals that were in the top tertile of troponin-only testing and were rated highly by Vizient’s quality rating or recognized as a top hospital by the US News & World Report had higher rates of troponin-only testing per 100 patients than did hospitals in the top tertile that were not ranked highly by Vizient’s quality rating or recognized as a top hospital by the US News & World Report. However, the majority of hospitals in the top tertile of troponin-only testing were not rated highly by Vizient (n = 15) or recognized as a top hospital by the US News & World Report (n = 16). The large majority of hospitals in the bottom tertile of troponin-only testing were not recognized as high-quality hospitals by Vizient (n = 32) or the US News & World Report (n = 31). Of the hospitals that improved their troponin-only testing during the study period, the majority were not recognized as high-quality hospitals by Vizient (n = 33) or the US News & World Report (n = 36), but among this group, those hospitals recognized by Vizient as high quality (n = 5) had the highest rate of troponin-only testing (57/100 patients). The differences in the rate of troponin-only testing across the different groups of hospitals and quality ratings were statistically significant (P < 0.0001; Table).

 

The Effect of Choosing Wisely^® on Troponin-Only Testing

While in many institutions the rates of troponin-only testing were increasing before the Choosing Wisely^® recommendation was released in 2015, the release of the recommendation was associated with a significant increase in the rate of troponin-only testing in the institutions that were in the bottom tertile of troponin-only testing prior to the release of the recommendation but moved to the top tertile after the release of the recommendation (n = 5). The slope percentage of the rate of change of the 5 hospitals that went from the bottom tertile to the top tertile after the release of the Choosing Wisely^® recommendation was 5.7%. Additionally, the Choosing Wisely^® recommendation was associated with an accelerated rate of troponin-only testing in hospitals moving from the bottom tertile before the release of the recommendation to the middle tertile after the recommendation (n = 15; slope = 3.2%) and in hospitals moving from the middle tertile before the release of the recommendation to the top tertile after (n = 6; slope = 2.4%) (Figure 2). For all of these hospitals (n = 26), the increased rate of troponin-only testing in the study quarter after the Choosing Wisely^® recommendation was statistically significantly higher and different from the rate of troponin-only testing in all other study quarters, except for the period between 2014 quarter 3 and quarter 4 (P = 0.08), the period between 2015 quarter 2 and quarter 3 (P = 0.18), and 2015 quarter 3 and quarter 4 (P = 0.06), where the effect did not quite reach statistical significance (Figure 3).

DISCUSSION

In a broad sample of academic teaching hospitals, there was an overall increase in the rate of troponin-only testing starting from the fourth quarter of 2013 through the third quarter of 2016. However, there was wide variation in the adoption of troponin-only testing for AMI across institutions. Our study identified several high-performing hospitals where the rate of troponin-only testing was high prior to and after the Choosing Wisely^® troponin-only recommendation. Additionally, we identified several poor-performing hospitals, which even after the release of the Choosing Wisely® recommendation continue to order both troponin and myoglobin/CK-MB tests for the diagnosis of AMI. Lastly, we identified several hospitals in which the release of the Choosing Wisely® recommendation was associated with a significant increase in the rate of troponin-only testing for the diagnosis of AMI. 
The high-performing hospitals in our sample that were in the top tertile of troponin-only testing throughout the study period are “early adopters,” having already instituted troponin-only testing before the release of the Choosing Wisely^® troponin-only recommendation. These hospitals vary in size, geographic region of the country, volume of AMI patients cared for, whether AMI patients are cared for by a cardiologist or other provider, and quality rating. Interestingly, in these hospitals, AMI patients admitted under the care of a cardiologist had higher rates of troponin-only testing than when admitted under another physician type. This is perhaps not surprising given that cardiologists would be the most likely to be aware of the data supporting troponin-only testing prior to the Choosing Wisely^® recommendation and the most likely to institute interventions to promote troponin-only testing and disseminate this knowledge across their institution. These institutions and their practice of troponin-only testing before the Choosing Wisely^® recommendation represent the idea of positive deviance,¹⁰ whereby they had identified troponin-only testing as a superior strategy and instituted successful initiatives to reduce the use of unnecessary myoglobin and CK-MB testing before their peer hospitals and the release of the Choosing Wisely^® recommendation. Further efforts to explore and understand the additional factors that define the hospitals that had high rates of troponin-only testing prior to the Choosing Wisely^® recommendation may be helpful to understanding the necessary culture and institutional factors that can promote high-value care.

In the hospitals that demonstrated increasing adoption of troponin-only testing, there are several interesting patterns. First, among these hospitals, smaller hospitals tended to have higher overall rates of troponin-only testing per 100 patients than larger hospitals. Additionally, the hospitals with the highest rates were located in the Midwest region. These hospitals may be learning from and following the high-performing institutions observed in our data that are also located in the Midwest. Additionally, among the hospitals that significantly increased their rate of troponin-only testing, we see that the Choosing Wisely^® recommendation appeared to facilitate accelerated adoption of troponin-only testing. In these institutions, it is likely that the impact of Choosing Wisely^® was significant because there was attention to high-value care and already an existing movement underway to institute such high-value practices. For example, natural champions, leadership, infrastructure, and a supportive culture may all be prerequisites for Choosing Wisely^® recommendations to become institutionally adopted.

Lastly, in the hospitals that have continued to order myoglobin and CK-MB, future work is needed to understand and overcome barriers to adopting high-value care practices.

There are several limitations to this study. First, because this was an observational study, we cannot prove a causal relationship between the Choosing Wisely^® recommendation and the increased rates of troponin-only testing. Additionally, the Vizient CDB/RM contains reporting data for a limited number of academic medical centers only, and therefore, these results may not represent practices at nonacademic or even other academic medical centers. Our study only included patients with a principal discharge diagnosis of AMI because the Choosing Wisely^® recommendation to order troponin-only is specific for diagnosing patients with AMI. However, it is possible that the Choosing Wisely^® recommendation also has affected provider ordering in patients with diagnoses such as chest pain or angina, and these affects would not be captured in our study. Lastly, because instituting high-value care practices take time, our follow-up time may not have been long enough to capture improvement in troponin-only testing at institutions responding to and attempting to adhere to the Choosing Wisely^® recommendation to order troponin-only testing for patients with AMI.

 

Disclosure 

No other individuals besides the authors contributed to this work. This project was not funded or supported by any external grant or agency. Dr. Prochaska’s institute received funding from the Agency for Research Healthcare and Quality for a K12 Career Development Grant (AHRQ K12 HS023007) outside the submitted work. Dr. Hohmann and Dr Modes have nothing to disclose. Dr. Arora receives financial compensation as a member of the Board of Directors for the American Board of Internal Medicine and has received grant funding from the ABIM Foundation. She also receives royalties from McGraw Hill.

Intravascular ultrasonography (IVUS) has been used for the past 20 years to measure atheromatous plaque in patients with coronary artery disease. The total volume of atherosclerosis in a coronary artery segment can be calculated using IVUS. A rotating transducer produces an image of a single, cross-sectional slice of the artery from which the atheroma area is calculated. A motorized device is used to withdraw the catheter, obtaining a series of cross-sectional slices at 1-mm intervals. The atheroma area for each slice is summated to obtain the total volume of atherosclerosis in the artery.

IVUS has demonstrated that statins slow the progression or even induce regression of coronary atherosclerosis in proportion to the degree of reduction in low-density lipoprotein cholesterol (LDL-C).^1–4 No LDL-C-lowering therapy other than statins has shown regression of atherosclerosis in a trial using IVUS. The lowest LDL-C achieved in prior trials using statins was about 60 mg/dL.^1,3 While this is very low, lower levels have not previously been explored.

Proprotein convertase subtilisin–kexin type 9 (PCSK9) inhibitors, a new class of drugs, are injectable, fully human monoclonal antibodies that inactivate the PCSK9 protein. PCSK9 inhibitors have been shown to lower LDL-C incrementally when added to statins, achieving very low LDL-C levels.^5,6 However, no data exist describing the effect of low LDL-C levels reached using PCSK9 inhibitors on the progression of atherosclerosis.

THE GLAGOV TRIAL

Based on information from reference 7.

The Global Assessment of Plaque Regression With a PCSK9 Antibody as Measured by Intravascular Ultrasound (GLAGOV) trial assessed the effect of PCSK9 inhibitor therapy on coronary atheroma.⁷ The primary end point was the change in percent atheroma volume (PAV) after treatment, and secondary end points were the change in total atheroma volume and percent of patients with atheroma regression. This randomized, double-blind, placebo-controlled study included 968 patients with symptomatic coronary artery disease and other high-risk features from 197 centers around the world. Patients had a coronary angiogram with a vessel that contained an intermediate stenosis and received statin therapy for at least 4 weeks and had LDL-C levels greater than 80 mg/dL or 60 to 80 mg/dL with additional high-risk features. Following IVUS, patients were randomized for 18 months of treatment with either a statin alone or a statin plus a monthly injection of the PCSK9 inhibitor evolocumab. At the end of treatment, IVUS was performed in the same artery that we imaged at the beginning of the study (Figure 1).

Table 1 shows the patients’ baseline demographic features and statin use. The average age of patients was 60 and almost all were on statin therapy, with most taking high levels of high-intensity statins. Baseline LDL-C was very good at 92 mg/dL to 93 mg/dL, a level that would be considered good control by contemporary standards.

RESULTS

LDL-C levels

After 18 months of treatment, patients receiving statin monotherapy had a mean LDL-C of 93 mg/dL, which was essentially unchanged from the start of the study. Patients receiving statin therapy with the addition of the PCSK9 inhibitor evolocumab had a mean LDL-C of 36.6 mg/dL and a trough level of 29 mg/dL 2 weeks after dosing (Figure 2). To our knowledge, these are the lowest LDL-C levels that have ever been achieved in a major trial at the time.

 

 

Change in percent atheroma volume

Based on information from reference 7.

With respect to the primary end point of change in PAV, patients on statin monotherapy had neither progression nor regression, and the percent change from baseline was not statistically significant (Figure 3). However, patients receiving the addition of the PCSK9 inhibitor had a statistically significant change in PAV of –0.95% (P < .001); they had less plaque at the end of the 18-month trial than at the start.

Polynomial regression analysis was used to evaluate the relationship between the achieved LDL-C levels and the rate of atheroma progression. Starting at an LDL-C of 110 mg/dL to 20 mg/dL, there was a linear relationship between lower LDL-C and less atheroma progression (Figure 4). This striking relationship was a uniform benefit across the full population and held for virtually every subgroup including by age, sex, baseline non-high-density lipoprotein cholesterol, diabetes presence or absence, and intensity of statin therapy.

Total atheroma volume and percent of patients with atheroma regression

The secondary end point measuring the total atheroma volume in the coronaries showed no change in total volume of atherosclerotic plaque in the statin monotherapy group and a decrease in the statin plus evolocumab group.

Based on information from reference 7.

An additional secondary end point was the percent of patients with atheroma regression, defined as any decrease in total atheroma volume or PAV. The percent of patients with total atheroma volume regression was greater in the statin plus evolocumab group (61.5%) than in the monotherapy group (48.9%; P < .001). PAV regression was also greater in patients in the statin plus evolocumab group (64%) compared with patients in the statin monotherapy group (47%; P < .001) (Figure 5). It is important to note that atheroma regression cannot occur in all patients, as other factors drive atherosclerotic disease, but the high percentage of patients with manifest coronary disease experiencing regression in this study is encouraging.

Patients with LDL-C < 70 mg/dL

A subgroup of patients had a baseline LDL-C below 70 mg/dL, the lowest level recommended by guideline. Patients in this subgroup who received statin monotherapy remained at a mean LDL-C of 70 mg/dL whereas patients on statin plus evolocumab achieved a mean LDL-C of 24 mg/dL with a mean 2-week post-dosing trough level of 15 mg/dL, an unbelievably low level of LDL-C. In this subgroup, 81% of patients receiving statin plus evolocumab had atheroma regression, compared with 48% of patients in the statin monotherapy group. The percent of patients with atheroma regression in this subgroup of patients with low LDL-C at baseline was twice that seen in the larger study population (33% vs 17%), revealing profound levels of regression in patients treated with dual therapy.

 

 

Safety

Many people have expressed concerns about adverse effects of very low cholesterol levels. While this study was too small to evaluate morbidity and mortality, the rates of death, nonfatal myocardial infarction, nonfatal stroke, hospitalization for unstable angina, and coronary vascularization trended in a favorable direction (Table 2). Essentially, no safety findings of any significance were reported in patients treated to these extremely low LDL-C levels.

Limitations

Like all trials, this one has limitations. The population is very select: these are patients with clinically indicated angiogram, not a primary prevention population. Some study participants dropped out, which is always a limitation. And of course, this is a surrogate measure; it is a measure of disease activity, not a measure of morbidity and mortality. Morbidity and mortality data for this new class of drugs should be available in about a year, though this study suggests that those data will be favorable.

CONCLUSION

High LDL-C is universally accepted as a factor in the formation of arterial plaque and atherosclerosis. Statin therapy reduces LDL-C levels to slow or induce regression of coronary atherosclerosis in proportion to the magnitude of LDL-C reduction as measured by IVUS. However, the question of how far to reduce lipid levels has evolved over the last 4 decades. In the 1970s, a normal total cholesterol was < 300 mg/dL. More recent data that suggest optimal LDL-C levels for patients with coronary artery disease may be much lower than commonly achieved.

In this study, in patients with symptomatic coronary artery disease, treatment with statins and the addition of the PCSK9 inhibitor evolocumab achieved mean LDL-C levels of 36.6 mg/dL, produced atheroma regression with a mean change in PAV of about 1% (P < .001), induced regression in a greater percentage of patients, and showed incremental benefit for treatment of LDL-C down to as low as 20 mg/dL. The GLAGOV trial provides intriguing evidence that clinical benefits may extend to LDL-C levels as low as 20 mg/dL; however, the sample size of the trial was modest, providing limited power for safety assessments.

Since this presentation, the Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk (FOURIER) trial achieved a median LDL-C of 30 mg/dL and reduced risk of cardiovascular events in patients with atherosclerotic cardiovascular disease treated with evolocumab added to statin therapy.⁸ Additional large outcomes trials of PCSK9 inhibitors and their role in reducing LDL-C and regression of coronary atheroma and atherosclerosis are eagerly awaited.

Intravascular ultrasonography (IVUS) has been used for the past 20 years to measure atheromatous plaque in patients with coronary artery disease. The total volume of atherosclerosis in a coronary artery segment can be calculated using IVUS. A rotating transducer produces an image of a single, cross-sectional slice of the artery from which the atheroma area is calculated. A motorized device is used to withdraw the catheter, obtaining a series of cross-sectional slices at 1-mm intervals. The atheroma area for each slice is summated to obtain the total volume of atherosclerosis in the artery.

IVUS has demonstrated that statins slow the progression or even induce regression of coronary atherosclerosis in proportion to the degree of reduction in low-density lipoprotein cholesterol (LDL-C).^1–4 No LDL-C-lowering therapy other than statins has shown regression of atherosclerosis in a trial using IVUS. The lowest LDL-C achieved in prior trials using statins was about 60 mg/dL.^1,3 While this is very low, lower levels have not previously been explored.

Proprotein convertase subtilisin–kexin type 9 (PCSK9) inhibitors, a new class of drugs, are injectable, fully human monoclonal antibodies that inactivate the PCSK9 protein. PCSK9 inhibitors have been shown to lower LDL-C incrementally when added to statins, achieving very low LDL-C levels.^5,6 However, no data exist describing the effect of low LDL-C levels reached using PCSK9 inhibitors on the progression of atherosclerosis.

THE GLAGOV TRIAL

Based on information from reference 7.

The Global Assessment of Plaque Regression With a PCSK9 Antibody as Measured by Intravascular Ultrasound (GLAGOV) trial assessed the effect of PCSK9 inhibitor therapy on coronary atheroma.⁷ The primary end point was the change in percent atheroma volume (PAV) after treatment, and secondary end points were the change in total atheroma volume and percent of patients with atheroma regression. This randomized, double-blind, placebo-controlled study included 968 patients with symptomatic coronary artery disease and other high-risk features from 197 centers around the world. Patients had a coronary angiogram with a vessel that contained an intermediate stenosis and received statin therapy for at least 4 weeks and had LDL-C levels greater than 80 mg/dL or 60 to 80 mg/dL with additional high-risk features. Following IVUS, patients were randomized for 18 months of treatment with either a statin alone or a statin plus a monthly injection of the PCSK9 inhibitor evolocumab. At the end of treatment, IVUS was performed in the same artery that we imaged at the beginning of the study (Figure 1).

Table 1 shows the patients’ baseline demographic features and statin use. The average age of patients was 60 and almost all were on statin therapy, with most taking high levels of high-intensity statins. Baseline LDL-C was very good at 92 mg/dL to 93 mg/dL, a level that would be considered good control by contemporary standards.

RESULTS

LDL-C levels

After 18 months of treatment, patients receiving statin monotherapy had a mean LDL-C of 93 mg/dL, which was essentially unchanged from the start of the study. Patients receiving statin therapy with the addition of the PCSK9 inhibitor evolocumab had a mean LDL-C of 36.6 mg/dL and a trough level of 29 mg/dL 2 weeks after dosing (Figure 2). To our knowledge, these are the lowest LDL-C levels that have ever been achieved in a major trial at the time.

 

 

Change in percent atheroma volume

Based on information from reference 7.

With respect to the primary end point of change in PAV, patients on statin monotherapy had neither progression nor regression, and the percent change from baseline was not statistically significant (Figure 3). However, patients receiving the addition of the PCSK9 inhibitor had a statistically significant change in PAV of –0.95% (P < .001); they had less plaque at the end of the 18-month trial than at the start.

Polynomial regression analysis was used to evaluate the relationship between the achieved LDL-C levels and the rate of atheroma progression. Starting at an LDL-C of 110 mg/dL to 20 mg/dL, there was a linear relationship between lower LDL-C and less atheroma progression (Figure 4). This striking relationship was a uniform benefit across the full population and held for virtually every subgroup including by age, sex, baseline non-high-density lipoprotein cholesterol, diabetes presence or absence, and intensity of statin therapy.

Total atheroma volume and percent of patients with atheroma regression

The secondary end point measuring the total atheroma volume in the coronaries showed no change in total volume of atherosclerotic plaque in the statin monotherapy group and a decrease in the statin plus evolocumab group.

Based on information from reference 7.

An additional secondary end point was the percent of patients with atheroma regression, defined as any decrease in total atheroma volume or PAV. The percent of patients with total atheroma volume regression was greater in the statin plus evolocumab group (61.5%) than in the monotherapy group (48.9%; P < .001). PAV regression was also greater in patients in the statin plus evolocumab group (64%) compared with patients in the statin monotherapy group (47%; P < .001) (Figure 5). It is important to note that atheroma regression cannot occur in all patients, as other factors drive atherosclerotic disease, but the high percentage of patients with manifest coronary disease experiencing regression in this study is encouraging.

Patients with LDL-C < 70 mg/dL

A subgroup of patients had a baseline LDL-C below 70 mg/dL, the lowest level recommended by guideline. Patients in this subgroup who received statin monotherapy remained at a mean LDL-C of 70 mg/dL whereas patients on statin plus evolocumab achieved a mean LDL-C of 24 mg/dL with a mean 2-week post-dosing trough level of 15 mg/dL, an unbelievably low level of LDL-C. In this subgroup, 81% of patients receiving statin plus evolocumab had atheroma regression, compared with 48% of patients in the statin monotherapy group. The percent of patients with atheroma regression in this subgroup of patients with low LDL-C at baseline was twice that seen in the larger study population (33% vs 17%), revealing profound levels of regression in patients treated with dual therapy.

 

 

Safety

Many people have expressed concerns about adverse effects of very low cholesterol levels. While this study was too small to evaluate morbidity and mortality, the rates of death, nonfatal myocardial infarction, nonfatal stroke, hospitalization for unstable angina, and coronary vascularization trended in a favorable direction (Table 2). Essentially, no safety findings of any significance were reported in patients treated to these extremely low LDL-C levels.

Limitations

Like all trials, this one has limitations. The population is very select: these are patients with clinically indicated angiogram, not a primary prevention population. Some study participants dropped out, which is always a limitation. And of course, this is a surrogate measure; it is a measure of disease activity, not a measure of morbidity and mortality. Morbidity and mortality data for this new class of drugs should be available in about a year, though this study suggests that those data will be favorable.

CONCLUSION

High LDL-C is universally accepted as a factor in the formation of arterial plaque and atherosclerosis. Statin therapy reduces LDL-C levels to slow or induce regression of coronary atherosclerosis in proportion to the magnitude of LDL-C reduction as measured by IVUS. However, the question of how far to reduce lipid levels has evolved over the last 4 decades. In the 1970s, a normal total cholesterol was < 300 mg/dL. More recent data that suggest optimal LDL-C levels for patients with coronary artery disease may be much lower than commonly achieved.

In this study, in patients with symptomatic coronary artery disease, treatment with statins and the addition of the PCSK9 inhibitor evolocumab achieved mean LDL-C levels of 36.6 mg/dL, produced atheroma regression with a mean change in PAV of about 1% (P < .001), induced regression in a greater percentage of patients, and showed incremental benefit for treatment of LDL-C down to as low as 20 mg/dL. The GLAGOV trial provides intriguing evidence that clinical benefits may extend to LDL-C levels as low as 20 mg/dL; however, the sample size of the trial was modest, providing limited power for safety assessments.

Since this presentation, the Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk (FOURIER) trial achieved a median LDL-C of 30 mg/dL and reduced risk of cardiovascular events in patients with atherosclerotic cardiovascular disease treated with evolocumab added to statin therapy.⁸ Additional large outcomes trials of PCSK9 inhibitors and their role in reducing LDL-C and regression of coronary atheroma and atherosclerosis are eagerly awaited.

Intravascular ultrasonography (IVUS) has been used for the past 20 years to measure atheromatous plaque in patients with coronary artery disease. The total volume of atherosclerosis in a coronary artery segment can be calculated using IVUS. A rotating transducer produces an image of a single, cross-sectional slice of the artery from which the atheroma area is calculated. A motorized device is used to withdraw the catheter, obtaining a series of cross-sectional slices at 1-mm intervals. The atheroma area for each slice is summated to obtain the total volume of atherosclerosis in the artery.

IVUS has demonstrated that statins slow the progression or even induce regression of coronary atherosclerosis in proportion to the degree of reduction in low-density lipoprotein cholesterol (LDL-C).^1–4 No LDL-C-lowering therapy other than statins has shown regression of atherosclerosis in a trial using IVUS. The lowest LDL-C achieved in prior trials using statins was about 60 mg/dL.^1,3 While this is very low, lower levels have not previously been explored.

Proprotein convertase subtilisin–kexin type 9 (PCSK9) inhibitors, a new class of drugs, are injectable, fully human monoclonal antibodies that inactivate the PCSK9 protein. PCSK9 inhibitors have been shown to lower LDL-C incrementally when added to statins, achieving very low LDL-C levels.^5,6 However, no data exist describing the effect of low LDL-C levels reached using PCSK9 inhibitors on the progression of atherosclerosis.

THE GLAGOV TRIAL

Based on information from reference 7.

The Global Assessment of Plaque Regression With a PCSK9 Antibody as Measured by Intravascular Ultrasound (GLAGOV) trial assessed the effect of PCSK9 inhibitor therapy on coronary atheroma.⁷ The primary end point was the change in percent atheroma volume (PAV) after treatment, and secondary end points were the change in total atheroma volume and percent of patients with atheroma regression. This randomized, double-blind, placebo-controlled study included 968 patients with symptomatic coronary artery disease and other high-risk features from 197 centers around the world. Patients had a coronary angiogram with a vessel that contained an intermediate stenosis and received statin therapy for at least 4 weeks and had LDL-C levels greater than 80 mg/dL or 60 to 80 mg/dL with additional high-risk features. Following IVUS, patients were randomized for 18 months of treatment with either a statin alone or a statin plus a monthly injection of the PCSK9 inhibitor evolocumab. At the end of treatment, IVUS was performed in the same artery that we imaged at the beginning of the study (Figure 1).

Table 1 shows the patients’ baseline demographic features and statin use. The average age of patients was 60 and almost all were on statin therapy, with most taking high levels of high-intensity statins. Baseline LDL-C was very good at 92 mg/dL to 93 mg/dL, a level that would be considered good control by contemporary standards.

RESULTS

LDL-C levels

After 18 months of treatment, patients receiving statin monotherapy had a mean LDL-C of 93 mg/dL, which was essentially unchanged from the start of the study. Patients receiving statin therapy with the addition of the PCSK9 inhibitor evolocumab had a mean LDL-C of 36.6 mg/dL and a trough level of 29 mg/dL 2 weeks after dosing (Figure 2). To our knowledge, these are the lowest LDL-C levels that have ever been achieved in a major trial at the time.

 

 

Change in percent atheroma volume

Based on information from reference 7.

With respect to the primary end point of change in PAV, patients on statin monotherapy had neither progression nor regression, and the percent change from baseline was not statistically significant (Figure 3). However, patients receiving the addition of the PCSK9 inhibitor had a statistically significant change in PAV of –0.95% (P < .001); they had less plaque at the end of the 18-month trial than at the start.

Polynomial regression analysis was used to evaluate the relationship between the achieved LDL-C levels and the rate of atheroma progression. Starting at an LDL-C of 110 mg/dL to 20 mg/dL, there was a linear relationship between lower LDL-C and less atheroma progression (Figure 4). This striking relationship was a uniform benefit across the full population and held for virtually every subgroup including by age, sex, baseline non-high-density lipoprotein cholesterol, diabetes presence or absence, and intensity of statin therapy.

Total atheroma volume and percent of patients with atheroma regression

The secondary end point measuring the total atheroma volume in the coronaries showed no change in total volume of atherosclerotic plaque in the statin monotherapy group and a decrease in the statin plus evolocumab group.

Based on information from reference 7.

An additional secondary end point was the percent of patients with atheroma regression, defined as any decrease in total atheroma volume or PAV. The percent of patients with total atheroma volume regression was greater in the statin plus evolocumab group (61.5%) than in the monotherapy group (48.9%; P < .001). PAV regression was also greater in patients in the statin plus evolocumab group (64%) compared with patients in the statin monotherapy group (47%; P < .001) (Figure 5). It is important to note that atheroma regression cannot occur in all patients, as other factors drive atherosclerotic disease, but the high percentage of patients with manifest coronary disease experiencing regression in this study is encouraging.

Patients with LDL-C < 70 mg/dL

A subgroup of patients had a baseline LDL-C below 70 mg/dL, the lowest level recommended by guideline. Patients in this subgroup who received statin monotherapy remained at a mean LDL-C of 70 mg/dL whereas patients on statin plus evolocumab achieved a mean LDL-C of 24 mg/dL with a mean 2-week post-dosing trough level of 15 mg/dL, an unbelievably low level of LDL-C. In this subgroup, 81% of patients receiving statin plus evolocumab had atheroma regression, compared with 48% of patients in the statin monotherapy group. The percent of patients with atheroma regression in this subgroup of patients with low LDL-C at baseline was twice that seen in the larger study population (33% vs 17%), revealing profound levels of regression in patients treated with dual therapy.

 

 

Safety

Many people have expressed concerns about adverse effects of very low cholesterol levels. While this study was too small to evaluate morbidity and mortality, the rates of death, nonfatal myocardial infarction, nonfatal stroke, hospitalization for unstable angina, and coronary vascularization trended in a favorable direction (Table 2). Essentially, no safety findings of any significance were reported in patients treated to these extremely low LDL-C levels.

Limitations

Like all trials, this one has limitations. The population is very select: these are patients with clinically indicated angiogram, not a primary prevention population. Some study participants dropped out, which is always a limitation. And of course, this is a surrogate measure; it is a measure of disease activity, not a measure of morbidity and mortality. Morbidity and mortality data for this new class of drugs should be available in about a year, though this study suggests that those data will be favorable.

CONCLUSION

High LDL-C is universally accepted as a factor in the formation of arterial plaque and atherosclerosis. Statin therapy reduces LDL-C levels to slow or induce regression of coronary atherosclerosis in proportion to the magnitude of LDL-C reduction as measured by IVUS. However, the question of how far to reduce lipid levels has evolved over the last 4 decades. In the 1970s, a normal total cholesterol was < 300 mg/dL. More recent data that suggest optimal LDL-C levels for patients with coronary artery disease may be much lower than commonly achieved.

In this study, in patients with symptomatic coronary artery disease, treatment with statins and the addition of the PCSK9 inhibitor evolocumab achieved mean LDL-C levels of 36.6 mg/dL, produced atheroma regression with a mean change in PAV of about 1% (P < .001), induced regression in a greater percentage of patients, and showed incremental benefit for treatment of LDL-C down to as low as 20 mg/dL. The GLAGOV trial provides intriguing evidence that clinical benefits may extend to LDL-C levels as low as 20 mg/dL; however, the sample size of the trial was modest, providing limited power for safety assessments.

Since this presentation, the Further Cardiovascular Outcomes Research with PCSK9 Inhibition in Subjects with Elevated Risk (FOURIER) trial achieved a median LDL-C of 30 mg/dL and reduced risk of cardiovascular events in patients with atherosclerotic cardiovascular disease treated with evolocumab added to statin therapy.⁸ Additional large outcomes trials of PCSK9 inhibitors and their role in reducing LDL-C and regression of coronary atheroma and atherosclerosis are eagerly awaited.

Many clinical improvements in treating patients with acute ST-elevation myocardial infarction (STEMI) have been realized in the past 20 years, including angiotensin-converting enzyme inhibitors, antiplatelet agents, and reduced time to cardiac cauterization procedures for acute myocardial infaction.¹ Presumably, primary and secondary prevention measures have also resulted in changes in coronary artery disease (CAD) risk factors over the past 20 years. We sought to quantify mortality outcomes for patients treated in our catherization laboratory and to investigate trends in cardiovascular risk factors in patients during the same period.²

STEMI OUTCOMES

Data from our catherization laboratory database of 3,913 patients treated for STEMI at our tertiary care center from 1995 through 2014 were analyzed. To evaluate outcomes over time, patients were grouped based on years treated in 5-year increments resulting in 4 groups spanning 20 years.²

Analysis showed reduced mortality rates for patients with STEMI over the past 20 years: the 30-day mortality rate in patients treated from 2010 to 2014 was 7.8%, nearly half the rate of 14% in patients treated from 1995 to 1999. The trend in reduced mortality rates for patients with STEMI was also noted at 1 year and 3 years (Figure 1).³

CARDIOVASCULAR RISK FACTORS

A reduction in mortality rates in patients treated for STEMI is to be expected over time, given the improvements in clinical practices and procedures and novel medications developed since 1996. But it is also possible that patients presenting with STEMI are healthier than in the past as a result of primary prevention efforts to minimize CAD risk factors and changes in CAD risk factors over time.

To determine whether CAD risk factors have changed over time, we analyzed the risk factors in the 3,913 patients treated for STEMI in our database. Risk factors included in the analysis were:

• Age
• Sex
• Diabetes mellitus
• Hypertension
• Smoking
• Hyperlipidemia
• Chronic renal impairment (serum creatinine greater than 1.5 mg/dL)
• Obesity (body mass index greater than 30 kg/m²).²

The prevalence of risk factors was determined in the entire cohort as well as in the 34% (n = 1,325) of patients previously diagnosed with CAD. The trend in risk factors in patients previously diagnosed with CAD could indicate the effectiveness of secondary prevention efforts compared with primary prevention in the broader patient population.

Based on data from reference 2.

Results show that the average age of patients presenting with STEMI has decreased from 64 to 60 over the past 20 years, and the trend is consistent regardless of a history of CAD (Figure 2).²

Based on data from reference 2.

The prevalence of the cardiovascular risk factors of tobacco use, obesity, hypertension, and diabetes in patients with STEMI increased from 1995 to 2014, as well as patients with a history of CAD (Figure 3).²

These data suggest that despite a better understanding of cardiovascular risk factors, the cardiovascular risk profiles of patients with acute STEMI have deteriorated over the past 20 years: patients are younger at presentation and more likely to be obese, to smoke, and to have hypertension and diabetes. These trends hold true in patients with and without a history of CAD, suggesting primary and secondary prevention efforts are ineffective.

 

 

TRENDS IN THE UNITED STATES

To evaluate whether geographic or patient population characteristics could have biased our results, we analyzed mortality and risk factor data from the National (Nationwide) Inpatient Sample (NIS) for patients presenting with STEMI (N = 445,319), non-STEMI (N = 915,341), and stroke (N = 937,425) from 2003 to 2013.^4,5

Mortality rates

Consistent with the trend in our data, the 10-year NIS data showed a lower mortality rate in 2003 compared with 2013 in patients admitted with extreme-severity STEMI (22% vs 18%), non-STEMI (13% vs 8%), and stroke (15% vs 10%), as well as in patients with moderate-severity disease.⁴

Risk factors

NIS data also revealed a reduction in the percentage of patients age 75 and older admitted for STEMI, non-STEMI, and stroke consistent with younger age at presentation and an increased prevalence of CAD risk factors from 2003 to 2013 (Table 1).⁴ The percentage of female patients admitted is also decreasing, indicating the increasing prevalence of these conditions in males.

Unfortunately, the prevalence of these relatively preventable CAD risk factors is moving in the wrong direction. The prevalence of smoking in patients presenting with non-STEMI, STEMI, or acute stroke is higher than in the past, contrary to the nationwide trend of decreasing rates of smoking.⁶ The increased rate of obesity evident in our data and the NSI data is consistent with rising obesity rates in the United States, which went from 30% to 37% in adults and from 14% to 17% in youth from 2000 to 2014.⁷ The percentage of adults with diabetes has increased tremendously in the United States, from 4.4% of adults in 1994 to 9.1% of adults in 2015.⁸ The rise in diabetes has led to increased rates of CAD, heart disease, and stroke in patients with diabetes.⁹

OPPORTUNITIES AHEAD

Despite improved STEMI outcomes, trends in cardiovascular risk profiles are deteriorating, emphasizing the critical need to educate people about primary and secondary prevention. Folsom et al¹⁰ conducted an analysis of a community-based sample to determine the prevalence of ideal cardiovascular health based on 4 ideal health behaviors (nonsmoking, low body mass index, adequate physical activity, healthy diet) and 3 ideal risk health factors (total cholesterol, blood pressure, and moderate glucose control).¹⁰ Each of the 7 behavior and risk factors was defined by ideal, intermediate, and poor characteristics. Very few study participants (0.1%) had ideal levels for all 7 healthy cardiovascular behaviors and risk factors, and over 82% had poor levels for all 7 behaviors and characteristics. The need to educate and improve cardiovascular health exists for both adults and youth. Measures of cardiovascular health in the United States indicate that 18% of adults age 50 or older and 46% of youth (ages 12 to 19) have 5 or more of the 7 health cardiovascular behaviors and risk factors at ideal levels.¹¹

Improvement in primary and secondary prevention measures may also present opportunities to contain or reduce the cost of care. Thus far, according to NIS registry data from 2003 to 2013, the mean adjusted cost of hospitalization for patients with STEMI increased about 14%, remained about the same for patients with non-STEMI, and increased about 3% for patients with stroke.⁴

CONCLUSION

Advances in clinical care have improved outcomes for patients with CAD during the past 2 decades. These gains have come despite a higher prevalence of CAD risk factors in patients. More emphasis on primary and secondary prevention to reduce CAD risk factors may further improve outcomes and possibly lower the cost of care. Aggressive encouragement of risk factor modification is necessary and should go beyond cardiologists to include primary care physicians, preventive clinics, secondary cardiovascular prevention, and population-based efforts.

Many clinical improvements in treating patients with acute ST-elevation myocardial infarction (STEMI) have been realized in the past 20 years, including angiotensin-converting enzyme inhibitors, antiplatelet agents, and reduced time to cardiac cauterization procedures for acute myocardial infaction.¹ Presumably, primary and secondary prevention measures have also resulted in changes in coronary artery disease (CAD) risk factors over the past 20 years. We sought to quantify mortality outcomes for patients treated in our catherization laboratory and to investigate trends in cardiovascular risk factors in patients during the same period.²

STEMI OUTCOMES

Data from our catherization laboratory database of 3,913 patients treated for STEMI at our tertiary care center from 1995 through 2014 were analyzed. To evaluate outcomes over time, patients were grouped based on years treated in 5-year increments resulting in 4 groups spanning 20 years.²

Analysis showed reduced mortality rates for patients with STEMI over the past 20 years: the 30-day mortality rate in patients treated from 2010 to 2014 was 7.8%, nearly half the rate of 14% in patients treated from 1995 to 1999. The trend in reduced mortality rates for patients with STEMI was also noted at 1 year and 3 years (Figure 1).³

CARDIOVASCULAR RISK FACTORS

A reduction in mortality rates in patients treated for STEMI is to be expected over time, given the improvements in clinical practices and procedures and novel medications developed since 1996. But it is also possible that patients presenting with STEMI are healthier than in the past as a result of primary prevention efforts to minimize CAD risk factors and changes in CAD risk factors over time.

To determine whether CAD risk factors have changed over time, we analyzed the risk factors in the 3,913 patients treated for STEMI in our database. Risk factors included in the analysis were:

• Age
• Sex
• Diabetes mellitus
• Hypertension
• Smoking
• Hyperlipidemia
• Chronic renal impairment (serum creatinine greater than 1.5 mg/dL)
• Obesity (body mass index greater than 30 kg/m²).²

The prevalence of risk factors was determined in the entire cohort as well as in the 34% (n = 1,325) of patients previously diagnosed with CAD. The trend in risk factors in patients previously diagnosed with CAD could indicate the effectiveness of secondary prevention efforts compared with primary prevention in the broader patient population.

Based on data from reference 2.

Results show that the average age of patients presenting with STEMI has decreased from 64 to 60 over the past 20 years, and the trend is consistent regardless of a history of CAD (Figure 2).²

Based on data from reference 2.

The prevalence of the cardiovascular risk factors of tobacco use, obesity, hypertension, and diabetes in patients with STEMI increased from 1995 to 2014, as well as patients with a history of CAD (Figure 3).²

These data suggest that despite a better understanding of cardiovascular risk factors, the cardiovascular risk profiles of patients with acute STEMI have deteriorated over the past 20 years: patients are younger at presentation and more likely to be obese, to smoke, and to have hypertension and diabetes. These trends hold true in patients with and without a history of CAD, suggesting primary and secondary prevention efforts are ineffective.

 

 

TRENDS IN THE UNITED STATES

To evaluate whether geographic or patient population characteristics could have biased our results, we analyzed mortality and risk factor data from the National (Nationwide) Inpatient Sample (NIS) for patients presenting with STEMI (N = 445,319), non-STEMI (N = 915,341), and stroke (N = 937,425) from 2003 to 2013.^4,5

Mortality rates

Consistent with the trend in our data, the 10-year NIS data showed a lower mortality rate in 2003 compared with 2013 in patients admitted with extreme-severity STEMI (22% vs 18%), non-STEMI (13% vs 8%), and stroke (15% vs 10%), as well as in patients with moderate-severity disease.⁴

Risk factors

NIS data also revealed a reduction in the percentage of patients age 75 and older admitted for STEMI, non-STEMI, and stroke consistent with younger age at presentation and an increased prevalence of CAD risk factors from 2003 to 2013 (Table 1).⁴ The percentage of female patients admitted is also decreasing, indicating the increasing prevalence of these conditions in males.

Unfortunately, the prevalence of these relatively preventable CAD risk factors is moving in the wrong direction. The prevalence of smoking in patients presenting with non-STEMI, STEMI, or acute stroke is higher than in the past, contrary to the nationwide trend of decreasing rates of smoking.⁶ The increased rate of obesity evident in our data and the NSI data is consistent with rising obesity rates in the United States, which went from 30% to 37% in adults and from 14% to 17% in youth from 2000 to 2014.⁷ The percentage of adults with diabetes has increased tremendously in the United States, from 4.4% of adults in 1994 to 9.1% of adults in 2015.⁸ The rise in diabetes has led to increased rates of CAD, heart disease, and stroke in patients with diabetes.⁹

OPPORTUNITIES AHEAD

Despite improved STEMI outcomes, trends in cardiovascular risk profiles are deteriorating, emphasizing the critical need to educate people about primary and secondary prevention. Folsom et al¹⁰ conducted an analysis of a community-based sample to determine the prevalence of ideal cardiovascular health based on 4 ideal health behaviors (nonsmoking, low body mass index, adequate physical activity, healthy diet) and 3 ideal risk health factors (total cholesterol, blood pressure, and moderate glucose control).¹⁰ Each of the 7 behavior and risk factors was defined by ideal, intermediate, and poor characteristics. Very few study participants (0.1%) had ideal levels for all 7 healthy cardiovascular behaviors and risk factors, and over 82% had poor levels for all 7 behaviors and characteristics. The need to educate and improve cardiovascular health exists for both adults and youth. Measures of cardiovascular health in the United States indicate that 18% of adults age 50 or older and 46% of youth (ages 12 to 19) have 5 or more of the 7 health cardiovascular behaviors and risk factors at ideal levels.¹¹

Improvement in primary and secondary prevention measures may also present opportunities to contain or reduce the cost of care. Thus far, according to NIS registry data from 2003 to 2013, the mean adjusted cost of hospitalization for patients with STEMI increased about 14%, remained about the same for patients with non-STEMI, and increased about 3% for patients with stroke.⁴

CONCLUSION

Advances in clinical care have improved outcomes for patients with CAD during the past 2 decades. These gains have come despite a higher prevalence of CAD risk factors in patients. More emphasis on primary and secondary prevention to reduce CAD risk factors may further improve outcomes and possibly lower the cost of care. Aggressive encouragement of risk factor modification is necessary and should go beyond cardiologists to include primary care physicians, preventive clinics, secondary cardiovascular prevention, and population-based efforts.

Many clinical improvements in treating patients with acute ST-elevation myocardial infarction (STEMI) have been realized in the past 20 years, including angiotensin-converting enzyme inhibitors, antiplatelet agents, and reduced time to cardiac cauterization procedures for acute myocardial infaction.¹ Presumably, primary and secondary prevention measures have also resulted in changes in coronary artery disease (CAD) risk factors over the past 20 years. We sought to quantify mortality outcomes for patients treated in our catherization laboratory and to investigate trends in cardiovascular risk factors in patients during the same period.²

STEMI OUTCOMES

Data from our catherization laboratory database of 3,913 patients treated for STEMI at our tertiary care center from 1995 through 2014 were analyzed. To evaluate outcomes over time, patients were grouped based on years treated in 5-year increments resulting in 4 groups spanning 20 years.²

Analysis showed reduced mortality rates for patients with STEMI over the past 20 years: the 30-day mortality rate in patients treated from 2010 to 2014 was 7.8%, nearly half the rate of 14% in patients treated from 1995 to 1999. The trend in reduced mortality rates for patients with STEMI was also noted at 1 year and 3 years (Figure 1).³

CARDIOVASCULAR RISK FACTORS

A reduction in mortality rates in patients treated for STEMI is to be expected over time, given the improvements in clinical practices and procedures and novel medications developed since 1996. But it is also possible that patients presenting with STEMI are healthier than in the past as a result of primary prevention efforts to minimize CAD risk factors and changes in CAD risk factors over time.

To determine whether CAD risk factors have changed over time, we analyzed the risk factors in the 3,913 patients treated for STEMI in our database. Risk factors included in the analysis were:

• Age
• Sex
• Diabetes mellitus
• Hypertension
• Smoking
• Hyperlipidemia
• Chronic renal impairment (serum creatinine greater than 1.5 mg/dL)
• Obesity (body mass index greater than 30 kg/m²).²

The prevalence of risk factors was determined in the entire cohort as well as in the 34% (n = 1,325) of patients previously diagnosed with CAD. The trend in risk factors in patients previously diagnosed with CAD could indicate the effectiveness of secondary prevention efforts compared with primary prevention in the broader patient population.

Based on data from reference 2.

Results show that the average age of patients presenting with STEMI has decreased from 64 to 60 over the past 20 years, and the trend is consistent regardless of a history of CAD (Figure 2).²

Based on data from reference 2.

The prevalence of the cardiovascular risk factors of tobacco use, obesity, hypertension, and diabetes in patients with STEMI increased from 1995 to 2014, as well as patients with a history of CAD (Figure 3).²

These data suggest that despite a better understanding of cardiovascular risk factors, the cardiovascular risk profiles of patients with acute STEMI have deteriorated over the past 20 years: patients are younger at presentation and more likely to be obese, to smoke, and to have hypertension and diabetes. These trends hold true in patients with and without a history of CAD, suggesting primary and secondary prevention efforts are ineffective.

 

 

TRENDS IN THE UNITED STATES

To evaluate whether geographic or patient population characteristics could have biased our results, we analyzed mortality and risk factor data from the National (Nationwide) Inpatient Sample (NIS) for patients presenting with STEMI (N = 445,319), non-STEMI (N = 915,341), and stroke (N = 937,425) from 2003 to 2013.^4,5

Mortality rates

Consistent with the trend in our data, the 10-year NIS data showed a lower mortality rate in 2003 compared with 2013 in patients admitted with extreme-severity STEMI (22% vs 18%), non-STEMI (13% vs 8%), and stroke (15% vs 10%), as well as in patients with moderate-severity disease.⁴

Risk factors

NIS data also revealed a reduction in the percentage of patients age 75 and older admitted for STEMI, non-STEMI, and stroke consistent with younger age at presentation and an increased prevalence of CAD risk factors from 2003 to 2013 (Table 1).⁴ The percentage of female patients admitted is also decreasing, indicating the increasing prevalence of these conditions in males.

Unfortunately, the prevalence of these relatively preventable CAD risk factors is moving in the wrong direction. The prevalence of smoking in patients presenting with non-STEMI, STEMI, or acute stroke is higher than in the past, contrary to the nationwide trend of decreasing rates of smoking.⁶ The increased rate of obesity evident in our data and the NSI data is consistent with rising obesity rates in the United States, which went from 30% to 37% in adults and from 14% to 17% in youth from 2000 to 2014.⁷ The percentage of adults with diabetes has increased tremendously in the United States, from 4.4% of adults in 1994 to 9.1% of adults in 2015.⁸ The rise in diabetes has led to increased rates of CAD, heart disease, and stroke in patients with diabetes.⁹

OPPORTUNITIES AHEAD

Despite improved STEMI outcomes, trends in cardiovascular risk profiles are deteriorating, emphasizing the critical need to educate people about primary and secondary prevention. Folsom et al¹⁰ conducted an analysis of a community-based sample to determine the prevalence of ideal cardiovascular health based on 4 ideal health behaviors (nonsmoking, low body mass index, adequate physical activity, healthy diet) and 3 ideal risk health factors (total cholesterol, blood pressure, and moderate glucose control).¹⁰ Each of the 7 behavior and risk factors was defined by ideal, intermediate, and poor characteristics. Very few study participants (0.1%) had ideal levels for all 7 healthy cardiovascular behaviors and risk factors, and over 82% had poor levels for all 7 behaviors and characteristics. The need to educate and improve cardiovascular health exists for both adults and youth. Measures of cardiovascular health in the United States indicate that 18% of adults age 50 or older and 46% of youth (ages 12 to 19) have 5 or more of the 7 health cardiovascular behaviors and risk factors at ideal levels.¹¹

Improvement in primary and secondary prevention measures may also present opportunities to contain or reduce the cost of care. Thus far, according to NIS registry data from 2003 to 2013, the mean adjusted cost of hospitalization for patients with STEMI increased about 14%, remained about the same for patients with non-STEMI, and increased about 3% for patients with stroke.⁴

CONCLUSION

Advances in clinical care have improved outcomes for patients with CAD during the past 2 decades. These gains have come despite a higher prevalence of CAD risk factors in patients. More emphasis on primary and secondary prevention to reduce CAD risk factors may further improve outcomes and possibly lower the cost of care. Aggressive encouragement of risk factor modification is necessary and should go beyond cardiologists to include primary care physicians, preventive clinics, secondary cardiovascular prevention, and population-based efforts.

Surgical aortic valve replacement (SAVR) started in the 1960s with a porcine aortic valve sutured to a stainless steel frame. The first human transcatheter aortic valve replacement (TAVR) procedure in the United States was in 2002. In the past 15 years, technological advances in heart valve design have made TAVR the preferred alternative in patients at high risk for surgical complications. This article outlines studies comparing balloon-expandable TAVR vs SAVR for patients at extreme, high, and intermediate surgical risk, and presents evidence that supports the expanded use of TAVR in patients at lower surgical risk.

TAVR: THE PREFERRED ALTERNATIVE TO SURGERY

For patients needing aortic valve replacement, the initial step was to show that TAVR recipients have better outcomes than those who receive no treatment. In the Placement of Aortic Transcatheter Valves (PARTNER) trial, investigators evaluated all-cause mortality in patients who needed valve replacement but were not candidates for surgery because of an extreme risk for complications (cohort B) (Table 1). In those who were not treated with TAVR, the mortality rate was 50% at 1 year. At 5 years, the mortality rate was 94%. In short, virtually all patients died under conservative medical management. For those undergoing TAVR, mortality rates were significantly lower: 31% at 1 year and 72% at 5 years (P < .0001).¹

Investigators next established TAVR outcomes as being noninferior to SAVR in high surgical risk patients (PARTNER trial cohort A) at 1 year.² A midterm follow-up of this study published in 2015 reported comparable rates of all-cause mortality at 5 years in high-risk patients undergoing TAVR vs SAVR, thus confirming the noninferiority of TAVR vs a surgical approach in high-risk patients for the longest duration of follow-up currently available.³

For patients, if the results of 2 different procedures are similar, they are typically going to choose the less invasive option. As a result, use of TAVR has increased: nearly 300,000 procedures have been performed worldwide, and approximately 75,000 were completed in 2016 alone. These numbers are projected to increase fourfold in the next 10 years. In the United States, almost one-third of Medicare-reported aortic valve replacements in 2015 were performed using TAVR.⁴

These data show that TAVR has become the preferred alternative to SAVR in inoperable and high-risk patients.

TAVR IN INTERMEDIATE-RISK PATIENTS

The US Food and Drug Administration (FDA) initially approved TAVR for patients judged to be ineligible for open-chest valve replacement cardiac surgery or at high risk for SAVR. This represents a small percentage of the total patient population needing aortic valve replacement. The Society of Thoracic Surgeons database of aortic valve disease cases during 2002 to 2010 (N = 141,905) shows that just 6.2% were ranked as high risk (ie, population eligible for TAVR in 2016). Most patients (79.9%) were low risk, and 13.9% were intermediate risk.⁵

The PARTNER 2A and PARTNER S3i trials evaluated TAVR in intermediate-risk patients. In PARTNER 2A, 2,032 intermediate-risk patients were randomized to either TAVR or SAVR. Results after 2 years showed no difference between TAVR and SAVR in the primary end point of all-cause mortality or disabling stroke at 24 months (rates 19.3% vs 21.1% for SAVR) (Figure 1).¹

A subanalysis of the transfemoral-access cohort provided additional support for TAVR. It showed that the rate of death and stroke in this cohort began to trend more favorably for TAVR. At 24 months, the difference in the primary end point was statistically significant in favor of TAVR (16.3% vs 20.0% for surgery; P = .04).¹

One potential reason to explain the data in favor of TAVR was the introduction of the Sapien 3 valve midway through the PARTNER 2 trial. The FDA allowed the device to be evaluated in a propensity-score analysis comparing TAVR with the Sapien 3 valve vs results for the surgical arm in the PARTNER 2A trial in intermediate-risk patients.⁶ Results showed a 75% lower rate of all-cause mortality at 30 days with TAVR (1.1% vs 4.0% for surgery), which extended out to 12 months (7.4% vs 13.0%). Rates of disabling stroke were similar: 30-day rates were 1.0% for TAVR vs 4.4% for surgery; 12-month rates were 2.3% vs 5.9%. Data for combined mortality and stroke reflected the differences: 3.7% for TAVR vs 9.7% for SAVR at 30 days, and 10.8% vs 18.8% at 12 months (Figure 2). Both the noninferiority data and superiority data on the primary end point of mortality and stroke were statistically significant for TAVR vs SAVR (P < .001).^6,7

Based on these data, in August 2016, the FDA approved the Sapien valves for use in patients with aortic valve stenosis who are at intermediate risk of death or complications associated with open-heart surgery. If the differences in outcomes reported during the PARTNER S3i trial are extrapolated to the total number of valve replacement surgeries performed worldwide, the potential number of patients who may benefit from TAVR is substantial.

 

 

DOWNSIDE OF TAVR

Although results with TAVR appear promising, there are important issues to address before it can be adopted in a wider patient population (ie, low-risk patients). These primarily focus on the following:

• Stroke
• Paravalvular leak
• Need for pacemaker replacement
• Valve durability
• Leaflet immobility or valve thrombosis.

Stroke

The incidence of stroke associated with TAVR is a concern, but it has decreased with the introduction of the Sapien 3 valve. In the PARTNER 2 trial, the 30-day stroke rate in intermediate-risk patients who received the Sapien 3 valve was 2.6%.¹ This compares with a 5.6% overall rate in the PARTNER 1A trials using the first Sapien valve.² The rate of stroke events is expected to decrease further as TAVR is expanded into healthier populations with better vasculature.

Paravalvular leak

Rates of moderate or severe paravalvular leak at 30 days have also decreased with the Sapien 3 valve and were 4.2% overall in the PARTNER S3i trial.⁶ These rates have ranged from 11.5% overall in the PARTNER 1A trial² to 4.2% in the PARTNER 2B trial¹ that used the Sapien XT valve for transfemoral-access TAVR.

New pacemakers

The percentage of TAVR procedures that result in a new requirement for a pacemaker increased to about 11% in 2014, up from 6.8% in 2012 to 2013.⁸ The requirement for a new pacemaker within 30 days following TAVR appeared to decrease again in the PARTER 2 trial, to 8.5%.¹ 

Durability

Evidence is emerging showing the limited durability of bioprosthetic aortic valve. Multiple studies have reportedly shown this, and this is true for all tissue valves, including those surgically inserted. A study assessing data from 357 patients showed that structural valve degeneration begins at 7 years post­operatively. By 10 years, only about 86% of valves were free from degeneration. At 12 years, that dropped to 69%.⁹

A study comparing TAVR vs SAVR showed that under identical loading conditions and with identical leaflet tissue properties, leaflets of valves placed via TAVR sustained higher stresses, strains, and fatigue damage.¹⁰

Overall, these results provide the possibility that TAVR valves may have reduced valve life compared with SAVR valves. Unknown durability may be an issue to consider when evaluating TAVR for implantation in intermediate- and low-risk patients.

Leaflet immobility and valve thrombosis

In the past 2 years, the problem of potential subclinical valve leaflet thrombosis, on both surgically inserted and TAVR valves, has emerged.¹¹ The FDA is monitoring these complications because of their potential impact on the safety and efficacy of these valves.

This complication was first reported as an unexpected finding of reduced leaflet motion on 4-dimensional computed tomography, a sign suspicious for valve thrombosis, in a subgroup of patients evaluated 30 days after implantation.¹² A study from Denmark found a 7% incidence of valve thrombosis in TAVR valves. They reported that warfarin could prevent thrombosis.¹³

At the Heart Hospital Baylor Plano, our TAVR team has identified approximately 50 cases of thrombosis that caused partial valve occlusion. Administering warfarin for 3 months resolved the thrombosis in virtually all cases. In 1 case, a thrombosed valve was surgically explanted with good patient outcome. Pathological analysis confirmed that reduced leaflet motion seen on 4-dimensional CT was valve thrombosis, as suspected by imaging specialists.¹⁴

 

 

IS TAVR APPROPRIATE FOR INTERMEDIATE-RISK PATIENTS?

Although there are ample data supporting the use of TAVR in intermediate-risk patients, SAVR remains the most effective option in certain clinical situations: 

• Younger patients who will need valve replacement later in life
• Bicuspid valves with eccentric bulky calcification
• Aortopathy (aortic disease above the valve)
• Small calcified roots
• Severe calcification of left ventricular outflow tract
• Low-lying coronary arteries (typically, ≤ 6 mm from the aortic annulus)
• Severe septal bulging
• Severe mitral regurgitation and/or tricuspid regurgitation
• Conduction system disease that puts the patient at high risk for pacemaker implantation
• Valve replacement in valves with a diameter 20 mm or smaller.

Nevertheless, outcomes seem to support TAVR in intermediate-risk patients. At the Heart Hospital Baylor Plano, 30-day outcomes with the Sapien 3 valve have shown all-cause mortality of 1.1% and all-stroke mortality of 2.6% (1.0% for disabling stroke). Large registries of the Sapien 3 valve have reported similar outcomes at 30 days: mortality 1%, disabling stroke 2%, major vascular complications 2%, and moderate to severe paravalvular leak 2%.¹⁵

Overall, the rates of major vascular complications and of life-threatening bleeding are 2%, and the need for new pacemakers is 4%. Results from several trials support TAVR as an alternative to surgery in intermediate-risk patients. In patients who are candidates for transfemoral access, TAVR may provide additional clinical advantages. However, questions about long-term durability and new requirements for pacemakers are issues for TAVR use in intermediate- and low-risk patients. More data are needed to answer these questions. 

At the Heart Hospital Baylor Plano, the number of TAVR procedures from 2012 to 2015 increased from 49 cases to 215, while the number of SAVR procedures remained constant (166 in 2012 and 162 in 2015). During that time, outcomes improved dramatically: in-hospital mortality rates dropped from 2% to 0% and 30-day mortality dropped from 3% to 0%. There have been 227 consecutive SAVR patients with no in-hospital or 30-day mortality and 261 consecutive TAVR patients with no mortality.

These results support initiating clinical trials of TAVR in low-risk patients. In 2016, the FDA approved TAVR valves for 2 clinical trials in patients with aortic stenosis who are at low risk of surgical mortality. These large clinical trials, each with about 1,200 patients, are expected to provide data that will help determine whether TAVR is a safe and effective option for low-risk patients.

Surgical aortic valve replacement (SAVR) started in the 1960s with a porcine aortic valve sutured to a stainless steel frame. The first human transcatheter aortic valve replacement (TAVR) procedure in the United States was in 2002. In the past 15 years, technological advances in heart valve design have made TAVR the preferred alternative in patients at high risk for surgical complications. This article outlines studies comparing balloon-expandable TAVR vs SAVR for patients at extreme, high, and intermediate surgical risk, and presents evidence that supports the expanded use of TAVR in patients at lower surgical risk.

TAVR: THE PREFERRED ALTERNATIVE TO SURGERY

For patients needing aortic valve replacement, the initial step was to show that TAVR recipients have better outcomes than those who receive no treatment. In the Placement of Aortic Transcatheter Valves (PARTNER) trial, investigators evaluated all-cause mortality in patients who needed valve replacement but were not candidates for surgery because of an extreme risk for complications (cohort B) (Table 1). In those who were not treated with TAVR, the mortality rate was 50% at 1 year. At 5 years, the mortality rate was 94%. In short, virtually all patients died under conservative medical management. For those undergoing TAVR, mortality rates were significantly lower: 31% at 1 year and 72% at 5 years (P < .0001).¹

Investigators next established TAVR outcomes as being noninferior to SAVR in high surgical risk patients (PARTNER trial cohort A) at 1 year.² A midterm follow-up of this study published in 2015 reported comparable rates of all-cause mortality at 5 years in high-risk patients undergoing TAVR vs SAVR, thus confirming the noninferiority of TAVR vs a surgical approach in high-risk patients for the longest duration of follow-up currently available.³

For patients, if the results of 2 different procedures are similar, they are typically going to choose the less invasive option. As a result, use of TAVR has increased: nearly 300,000 procedures have been performed worldwide, and approximately 75,000 were completed in 2016 alone. These numbers are projected to increase fourfold in the next 10 years. In the United States, almost one-third of Medicare-reported aortic valve replacements in 2015 were performed using TAVR.⁴

These data show that TAVR has become the preferred alternative to SAVR in inoperable and high-risk patients.

TAVR IN INTERMEDIATE-RISK PATIENTS

The US Food and Drug Administration (FDA) initially approved TAVR for patients judged to be ineligible for open-chest valve replacement cardiac surgery or at high risk for SAVR. This represents a small percentage of the total patient population needing aortic valve replacement. The Society of Thoracic Surgeons database of aortic valve disease cases during 2002 to 2010 (N = 141,905) shows that just 6.2% were ranked as high risk (ie, population eligible for TAVR in 2016). Most patients (79.9%) were low risk, and 13.9% were intermediate risk.⁵

The PARTNER 2A and PARTNER S3i trials evaluated TAVR in intermediate-risk patients. In PARTNER 2A, 2,032 intermediate-risk patients were randomized to either TAVR or SAVR. Results after 2 years showed no difference between TAVR and SAVR in the primary end point of all-cause mortality or disabling stroke at 24 months (rates 19.3% vs 21.1% for SAVR) (Figure 1).¹

A subanalysis of the transfemoral-access cohort provided additional support for TAVR. It showed that the rate of death and stroke in this cohort began to trend more favorably for TAVR. At 24 months, the difference in the primary end point was statistically significant in favor of TAVR (16.3% vs 20.0% for surgery; P = .04).¹

One potential reason to explain the data in favor of TAVR was the introduction of the Sapien 3 valve midway through the PARTNER 2 trial. The FDA allowed the device to be evaluated in a propensity-score analysis comparing TAVR with the Sapien 3 valve vs results for the surgical arm in the PARTNER 2A trial in intermediate-risk patients.⁶ Results showed a 75% lower rate of all-cause mortality at 30 days with TAVR (1.1% vs 4.0% for surgery), which extended out to 12 months (7.4% vs 13.0%). Rates of disabling stroke were similar: 30-day rates were 1.0% for TAVR vs 4.4% for surgery; 12-month rates were 2.3% vs 5.9%. Data for combined mortality and stroke reflected the differences: 3.7% for TAVR vs 9.7% for SAVR at 30 days, and 10.8% vs 18.8% at 12 months (Figure 2). Both the noninferiority data and superiority data on the primary end point of mortality and stroke were statistically significant for TAVR vs SAVR (P < .001).^6,7

Based on these data, in August 2016, the FDA approved the Sapien valves for use in patients with aortic valve stenosis who are at intermediate risk of death or complications associated with open-heart surgery. If the differences in outcomes reported during the PARTNER S3i trial are extrapolated to the total number of valve replacement surgeries performed worldwide, the potential number of patients who may benefit from TAVR is substantial.

 

 

DOWNSIDE OF TAVR

Although results with TAVR appear promising, there are important issues to address before it can be adopted in a wider patient population (ie, low-risk patients). These primarily focus on the following:

• Stroke
• Paravalvular leak
• Need for pacemaker replacement
• Valve durability
• Leaflet immobility or valve thrombosis.

Stroke

The incidence of stroke associated with TAVR is a concern, but it has decreased with the introduction of the Sapien 3 valve. In the PARTNER 2 trial, the 30-day stroke rate in intermediate-risk patients who received the Sapien 3 valve was 2.6%.¹ This compares with a 5.6% overall rate in the PARTNER 1A trials using the first Sapien valve.² The rate of stroke events is expected to decrease further as TAVR is expanded into healthier populations with better vasculature.

Paravalvular leak

Rates of moderate or severe paravalvular leak at 30 days have also decreased with the Sapien 3 valve and were 4.2% overall in the PARTNER S3i trial.⁶ These rates have ranged from 11.5% overall in the PARTNER 1A trial² to 4.2% in the PARTNER 2B trial¹ that used the Sapien XT valve for transfemoral-access TAVR.

New pacemakers

The percentage of TAVR procedures that result in a new requirement for a pacemaker increased to about 11% in 2014, up from 6.8% in 2012 to 2013.⁸ The requirement for a new pacemaker within 30 days following TAVR appeared to decrease again in the PARTER 2 trial, to 8.5%.¹ 

Durability

Evidence is emerging showing the limited durability of bioprosthetic aortic valve. Multiple studies have reportedly shown this, and this is true for all tissue valves, including those surgically inserted. A study assessing data from 357 patients showed that structural valve degeneration begins at 7 years post­operatively. By 10 years, only about 86% of valves were free from degeneration. At 12 years, that dropped to 69%.⁹

A study comparing TAVR vs SAVR showed that under identical loading conditions and with identical leaflet tissue properties, leaflets of valves placed via TAVR sustained higher stresses, strains, and fatigue damage.¹⁰

Overall, these results provide the possibility that TAVR valves may have reduced valve life compared with SAVR valves. Unknown durability may be an issue to consider when evaluating TAVR for implantation in intermediate- and low-risk patients.

Leaflet immobility and valve thrombosis

In the past 2 years, the problem of potential subclinical valve leaflet thrombosis, on both surgically inserted and TAVR valves, has emerged.¹¹ The FDA is monitoring these complications because of their potential impact on the safety and efficacy of these valves.

This complication was first reported as an unexpected finding of reduced leaflet motion on 4-dimensional computed tomography, a sign suspicious for valve thrombosis, in a subgroup of patients evaluated 30 days after implantation.¹² A study from Denmark found a 7% incidence of valve thrombosis in TAVR valves. They reported that warfarin could prevent thrombosis.¹³

At the Heart Hospital Baylor Plano, our TAVR team has identified approximately 50 cases of thrombosis that caused partial valve occlusion. Administering warfarin for 3 months resolved the thrombosis in virtually all cases. In 1 case, a thrombosed valve was surgically explanted with good patient outcome. Pathological analysis confirmed that reduced leaflet motion seen on 4-dimensional CT was valve thrombosis, as suspected by imaging specialists.¹⁴

 

 

IS TAVR APPROPRIATE FOR INTERMEDIATE-RISK PATIENTS?

Although there are ample data supporting the use of TAVR in intermediate-risk patients, SAVR remains the most effective option in certain clinical situations: 

• Younger patients who will need valve replacement later in life
• Bicuspid valves with eccentric bulky calcification
• Aortopathy (aortic disease above the valve)
• Small calcified roots
• Severe calcification of left ventricular outflow tract
• Low-lying coronary arteries (typically, ≤ 6 mm from the aortic annulus)
• Severe septal bulging
• Severe mitral regurgitation and/or tricuspid regurgitation
• Conduction system disease that puts the patient at high risk for pacemaker implantation
• Valve replacement in valves with a diameter 20 mm or smaller.

Nevertheless, outcomes seem to support TAVR in intermediate-risk patients. At the Heart Hospital Baylor Plano, 30-day outcomes with the Sapien 3 valve have shown all-cause mortality of 1.1% and all-stroke mortality of 2.6% (1.0% for disabling stroke). Large registries of the Sapien 3 valve have reported similar outcomes at 30 days: mortality 1%, disabling stroke 2%, major vascular complications 2%, and moderate to severe paravalvular leak 2%.¹⁵

Overall, the rates of major vascular complications and of life-threatening bleeding are 2%, and the need for new pacemakers is 4%. Results from several trials support TAVR as an alternative to surgery in intermediate-risk patients. In patients who are candidates for transfemoral access, TAVR may provide additional clinical advantages. However, questions about long-term durability and new requirements for pacemakers are issues for TAVR use in intermediate- and low-risk patients. More data are needed to answer these questions. 

At the Heart Hospital Baylor Plano, the number of TAVR procedures from 2012 to 2015 increased from 49 cases to 215, while the number of SAVR procedures remained constant (166 in 2012 and 162 in 2015). During that time, outcomes improved dramatically: in-hospital mortality rates dropped from 2% to 0% and 30-day mortality dropped from 3% to 0%. There have been 227 consecutive SAVR patients with no in-hospital or 30-day mortality and 261 consecutive TAVR patients with no mortality.

These results support initiating clinical trials of TAVR in low-risk patients. In 2016, the FDA approved TAVR valves for 2 clinical trials in patients with aortic stenosis who are at low risk of surgical mortality. These large clinical trials, each with about 1,200 patients, are expected to provide data that will help determine whether TAVR is a safe and effective option for low-risk patients.

Surgical aortic valve replacement (SAVR) started in the 1960s with a porcine aortic valve sutured to a stainless steel frame. The first human transcatheter aortic valve replacement (TAVR) procedure in the United States was in 2002. In the past 15 years, technological advances in heart valve design have made TAVR the preferred alternative in patients at high risk for surgical complications. This article outlines studies comparing balloon-expandable TAVR vs SAVR for patients at extreme, high, and intermediate surgical risk, and presents evidence that supports the expanded use of TAVR in patients at lower surgical risk.

TAVR: THE PREFERRED ALTERNATIVE TO SURGERY

For patients needing aortic valve replacement, the initial step was to show that TAVR recipients have better outcomes than those who receive no treatment. In the Placement of Aortic Transcatheter Valves (PARTNER) trial, investigators evaluated all-cause mortality in patients who needed valve replacement but were not candidates for surgery because of an extreme risk for complications (cohort B) (Table 1). In those who were not treated with TAVR, the mortality rate was 50% at 1 year. At 5 years, the mortality rate was 94%. In short, virtually all patients died under conservative medical management. For those undergoing TAVR, mortality rates were significantly lower: 31% at 1 year and 72% at 5 years (P < .0001).¹

Investigators next established TAVR outcomes as being noninferior to SAVR in high surgical risk patients (PARTNER trial cohort A) at 1 year.² A midterm follow-up of this study published in 2015 reported comparable rates of all-cause mortality at 5 years in high-risk patients undergoing TAVR vs SAVR, thus confirming the noninferiority of TAVR vs a surgical approach in high-risk patients for the longest duration of follow-up currently available.³

For patients, if the results of 2 different procedures are similar, they are typically going to choose the less invasive option. As a result, use of TAVR has increased: nearly 300,000 procedures have been performed worldwide, and approximately 75,000 were completed in 2016 alone. These numbers are projected to increase fourfold in the next 10 years. In the United States, almost one-third of Medicare-reported aortic valve replacements in 2015 were performed using TAVR.⁴

These data show that TAVR has become the preferred alternative to SAVR in inoperable and high-risk patients.

TAVR IN INTERMEDIATE-RISK PATIENTS

The US Food and Drug Administration (FDA) initially approved TAVR for patients judged to be ineligible for open-chest valve replacement cardiac surgery or at high risk for SAVR. This represents a small percentage of the total patient population needing aortic valve replacement. The Society of Thoracic Surgeons database of aortic valve disease cases during 2002 to 2010 (N = 141,905) shows that just 6.2% were ranked as high risk (ie, population eligible for TAVR in 2016). Most patients (79.9%) were low risk, and 13.9% were intermediate risk.⁵

The PARTNER 2A and PARTNER S3i trials evaluated TAVR in intermediate-risk patients. In PARTNER 2A, 2,032 intermediate-risk patients were randomized to either TAVR or SAVR. Results after 2 years showed no difference between TAVR and SAVR in the primary end point of all-cause mortality or disabling stroke at 24 months (rates 19.3% vs 21.1% for SAVR) (Figure 1).¹

A subanalysis of the transfemoral-access cohort provided additional support for TAVR. It showed that the rate of death and stroke in this cohort began to trend more favorably for TAVR. At 24 months, the difference in the primary end point was statistically significant in favor of TAVR (16.3% vs 20.0% for surgery; P = .04).¹

One potential reason to explain the data in favor of TAVR was the introduction of the Sapien 3 valve midway through the PARTNER 2 trial. The FDA allowed the device to be evaluated in a propensity-score analysis comparing TAVR with the Sapien 3 valve vs results for the surgical arm in the PARTNER 2A trial in intermediate-risk patients.⁶ Results showed a 75% lower rate of all-cause mortality at 30 days with TAVR (1.1% vs 4.0% for surgery), which extended out to 12 months (7.4% vs 13.0%). Rates of disabling stroke were similar: 30-day rates were 1.0% for TAVR vs 4.4% for surgery; 12-month rates were 2.3% vs 5.9%. Data for combined mortality and stroke reflected the differences: 3.7% for TAVR vs 9.7% for SAVR at 30 days, and 10.8% vs 18.8% at 12 months (Figure 2). Both the noninferiority data and superiority data on the primary end point of mortality and stroke were statistically significant for TAVR vs SAVR (P < .001).^6,7

Based on these data, in August 2016, the FDA approved the Sapien valves for use in patients with aortic valve stenosis who are at intermediate risk of death or complications associated with open-heart surgery. If the differences in outcomes reported during the PARTNER S3i trial are extrapolated to the total number of valve replacement surgeries performed worldwide, the potential number of patients who may benefit from TAVR is substantial.

 

 

DOWNSIDE OF TAVR

Although results with TAVR appear promising, there are important issues to address before it can be adopted in a wider patient population (ie, low-risk patients). These primarily focus on the following:

• Stroke
• Paravalvular leak
• Need for pacemaker replacement
• Valve durability
• Leaflet immobility or valve thrombosis.

Stroke

The incidence of stroke associated with TAVR is a concern, but it has decreased with the introduction of the Sapien 3 valve. In the PARTNER 2 trial, the 30-day stroke rate in intermediate-risk patients who received the Sapien 3 valve was 2.6%.¹ This compares with a 5.6% overall rate in the PARTNER 1A trials using the first Sapien valve.² The rate of stroke events is expected to decrease further as TAVR is expanded into healthier populations with better vasculature.

Paravalvular leak

Rates of moderate or severe paravalvular leak at 30 days have also decreased with the Sapien 3 valve and were 4.2% overall in the PARTNER S3i trial.⁶ These rates have ranged from 11.5% overall in the PARTNER 1A trial² to 4.2% in the PARTNER 2B trial¹ that used the Sapien XT valve for transfemoral-access TAVR.

New pacemakers

The percentage of TAVR procedures that result in a new requirement for a pacemaker increased to about 11% in 2014, up from 6.8% in 2012 to 2013.⁸ The requirement for a new pacemaker within 30 days following TAVR appeared to decrease again in the PARTER 2 trial, to 8.5%.¹ 

Durability

Evidence is emerging showing the limited durability of bioprosthetic aortic valve. Multiple studies have reportedly shown this, and this is true for all tissue valves, including those surgically inserted. A study assessing data from 357 patients showed that structural valve degeneration begins at 7 years post­operatively. By 10 years, only about 86% of valves were free from degeneration. At 12 years, that dropped to 69%.⁹

A study comparing TAVR vs SAVR showed that under identical loading conditions and with identical leaflet tissue properties, leaflets of valves placed via TAVR sustained higher stresses, strains, and fatigue damage.¹⁰

Overall, these results provide the possibility that TAVR valves may have reduced valve life compared with SAVR valves. Unknown durability may be an issue to consider when evaluating TAVR for implantation in intermediate- and low-risk patients.

Leaflet immobility and valve thrombosis

In the past 2 years, the problem of potential subclinical valve leaflet thrombosis, on both surgically inserted and TAVR valves, has emerged.¹¹ The FDA is monitoring these complications because of their potential impact on the safety and efficacy of these valves.

This complication was first reported as an unexpected finding of reduced leaflet motion on 4-dimensional computed tomography, a sign suspicious for valve thrombosis, in a subgroup of patients evaluated 30 days after implantation.¹² A study from Denmark found a 7% incidence of valve thrombosis in TAVR valves. They reported that warfarin could prevent thrombosis.¹³

At the Heart Hospital Baylor Plano, our TAVR team has identified approximately 50 cases of thrombosis that caused partial valve occlusion. Administering warfarin for 3 months resolved the thrombosis in virtually all cases. In 1 case, a thrombosed valve was surgically explanted with good patient outcome. Pathological analysis confirmed that reduced leaflet motion seen on 4-dimensional CT was valve thrombosis, as suspected by imaging specialists.¹⁴

 

 

IS TAVR APPROPRIATE FOR INTERMEDIATE-RISK PATIENTS?

Although there are ample data supporting the use of TAVR in intermediate-risk patients, SAVR remains the most effective option in certain clinical situations: 

• Younger patients who will need valve replacement later in life
• Bicuspid valves with eccentric bulky calcification
• Aortopathy (aortic disease above the valve)
• Small calcified roots
• Severe calcification of left ventricular outflow tract
• Low-lying coronary arteries (typically, ≤ 6 mm from the aortic annulus)
• Severe septal bulging
• Severe mitral regurgitation and/or tricuspid regurgitation
• Conduction system disease that puts the patient at high risk for pacemaker implantation
• Valve replacement in valves with a diameter 20 mm or smaller.

Nevertheless, outcomes seem to support TAVR in intermediate-risk patients. At the Heart Hospital Baylor Plano, 30-day outcomes with the Sapien 3 valve have shown all-cause mortality of 1.1% and all-stroke mortality of 2.6% (1.0% for disabling stroke). Large registries of the Sapien 3 valve have reported similar outcomes at 30 days: mortality 1%, disabling stroke 2%, major vascular complications 2%, and moderate to severe paravalvular leak 2%.¹⁵

Overall, the rates of major vascular complications and of life-threatening bleeding are 2%, and the need for new pacemakers is 4%. Results from several trials support TAVR as an alternative to surgery in intermediate-risk patients. In patients who are candidates for transfemoral access, TAVR may provide additional clinical advantages. However, questions about long-term durability and new requirements for pacemakers are issues for TAVR use in intermediate- and low-risk patients. More data are needed to answer these questions. 

At the Heart Hospital Baylor Plano, the number of TAVR procedures from 2012 to 2015 increased from 49 cases to 215, while the number of SAVR procedures remained constant (166 in 2012 and 162 in 2015). During that time, outcomes improved dramatically: in-hospital mortality rates dropped from 2% to 0% and 30-day mortality dropped from 3% to 0%. There have been 227 consecutive SAVR patients with no in-hospital or 30-day mortality and 261 consecutive TAVR patients with no mortality.

These results support initiating clinical trials of TAVR in low-risk patients. In 2016, the FDA approved TAVR valves for 2 clinical trials in patients with aortic stenosis who are at low risk of surgical mortality. These large clinical trials, each with about 1,200 patients, are expected to provide data that will help determine whether TAVR is a safe and effective option for low-risk patients.