“I cannot say whether things will get better if we change; what I can say is they must change if they are to get better.”

—Georg C. Lichtenburg

Leading change is never easy. Many a physician has joined a committee, hired a promising project manager, assumed responsibility for an operational or clinical task—only to have it painfully falter or agonizingly fail. Unfortunately, some of us become disillusioned with the process, donning our white coats to return to the safe ensconce of clinical work rather than take on another perilous change or leadership task. But ask those that have tried and failed and those that have succeeded and they will tell you this: the lessons learned in the journey were invaluable.

Academic medical centers and healthcare organizations are increasingly turning to hospitalists to assume a myriad of leadership roles. With very little formal training, many of us jump in to improve organizational culture, financial accountability, and patient safety, literally building the bridge as we walk on it. The practical knowledge and know-how gleaned in efforts during these endeavors are perhaps just as important as evidence-based medicine. And yet, few venues to share and disseminate these insights currently exist.

This void represents the motivation behind the new Journal series entitled, “Leadership & Professional Development” or “LPD.” In these brief excerpts, lessons on leadership/followership, mentorship/menteeship, leading change and professional development will be shared using a conversational and pragmatic tone. Like a clinical case, pearls to help you navigate development and organizational challenges will be shared. The goal is simple: read an LPD and walk away with an “a-ha,” a new tool, or a strategy that you can use ASAP. For example, in the debut LPD—Hire Hard¹—we emphasize a cardinal rule for hiring: wait for the right person. Waiting is not easy, but it is well worth it in the long run—the right person will make your job that much better. Remember the aphorism: A’s hire A’s while B’s hire C’s.

Many other nuggets of wisdom can fit an LPD model. For example, when it comes to stress, a technique that brings mindfulness to your day—one you can practice with every patient encounter—might be the ticket.² Interested in mentoring? You’ll need to know the Six Golden Rules.³ And don’t forget about emotional intelligence, tight-loose-tight management or the tree-climbing monkey! Don’t know what these are? Time to read an LPD or two to find out!

As you might have guessed—some of these pieces are already written. They come from a book that my colleague, Sanjay Saint and I have been busy writing for over a year. The book distills much of what we have learned as clinicians, researchers and administrators into a collection we call, “Thirty Leadership Rules for Healthcare Providers.” But LPD is not an advert for the book; rather, our contributions will only account for some of the series. We hope this venue will become a platform in where readers like you can offer “pearls” to the broader community. The rules are simple: coin a rule/pearl, open with an illustrative quote, frame it in 650 words with no more than five references, and write it so that a reader can apply it to their work tomorrow. And don’t worry—we on the editorial team will help you craft them if the message makes sense. Interested? Send us an email at [email protected] with an idea and watch your Inbox—an invitation for an LPD might be in your future.

Disclosures

Dr. Chopra has nothing to disclose.

“I cannot say whether things will get better if we change; what I can say is they must change if they are to get better.”

—Georg C. Lichtenburg

Leading change is never easy. Many a physician has joined a committee, hired a promising project manager, assumed responsibility for an operational or clinical task—only to have it painfully falter or agonizingly fail. Unfortunately, some of us become disillusioned with the process, donning our white coats to return to the safe ensconce of clinical work rather than take on another perilous change or leadership task. But ask those that have tried and failed and those that have succeeded and they will tell you this: the lessons learned in the journey were invaluable.

Academic medical centers and healthcare organizations are increasingly turning to hospitalists to assume a myriad of leadership roles. With very little formal training, many of us jump in to improve organizational culture, financial accountability, and patient safety, literally building the bridge as we walk on it. The practical knowledge and know-how gleaned in efforts during these endeavors are perhaps just as important as evidence-based medicine. And yet, few venues to share and disseminate these insights currently exist.

This void represents the motivation behind the new Journal series entitled, “Leadership & Professional Development” or “LPD.” In these brief excerpts, lessons on leadership/followership, mentorship/menteeship, leading change and professional development will be shared using a conversational and pragmatic tone. Like a clinical case, pearls to help you navigate development and organizational challenges will be shared. The goal is simple: read an LPD and walk away with an “a-ha,” a new tool, or a strategy that you can use ASAP. For example, in the debut LPD—Hire Hard¹—we emphasize a cardinal rule for hiring: wait for the right person. Waiting is not easy, but it is well worth it in the long run—the right person will make your job that much better. Remember the aphorism: A’s hire A’s while B’s hire C’s.

Many other nuggets of wisdom can fit an LPD model. For example, when it comes to stress, a technique that brings mindfulness to your day—one you can practice with every patient encounter—might be the ticket.² Interested in mentoring? You’ll need to know the Six Golden Rules.³ And don’t forget about emotional intelligence, tight-loose-tight management or the tree-climbing monkey! Don’t know what these are? Time to read an LPD or two to find out!

As you might have guessed—some of these pieces are already written. They come from a book that my colleague, Sanjay Saint and I have been busy writing for over a year. The book distills much of what we have learned as clinicians, researchers and administrators into a collection we call, “Thirty Leadership Rules for Healthcare Providers.” But LPD is not an advert for the book; rather, our contributions will only account for some of the series. We hope this venue will become a platform in where readers like you can offer “pearls” to the broader community. The rules are simple: coin a rule/pearl, open with an illustrative quote, frame it in 650 words with no more than five references, and write it so that a reader can apply it to their work tomorrow. And don’t worry—we on the editorial team will help you craft them if the message makes sense. Interested? Send us an email at [email protected] with an idea and watch your Inbox—an invitation for an LPD might be in your future.

Disclosures

Dr. Chopra has nothing to disclose.

“I cannot say whether things will get better if we change; what I can say is they must change if they are to get better.”

—Georg C. Lichtenburg

Leading change is never easy. Many a physician has joined a committee, hired a promising project manager, assumed responsibility for an operational or clinical task—only to have it painfully falter or agonizingly fail. Unfortunately, some of us become disillusioned with the process, donning our white coats to return to the safe ensconce of clinical work rather than take on another perilous change or leadership task. But ask those that have tried and failed and those that have succeeded and they will tell you this: the lessons learned in the journey were invaluable.

Academic medical centers and healthcare organizations are increasingly turning to hospitalists to assume a myriad of leadership roles. With very little formal training, many of us jump in to improve organizational culture, financial accountability, and patient safety, literally building the bridge as we walk on it. The practical knowledge and know-how gleaned in efforts during these endeavors are perhaps just as important as evidence-based medicine. And yet, few venues to share and disseminate these insights currently exist.

This void represents the motivation behind the new Journal series entitled, “Leadership & Professional Development” or “LPD.” In these brief excerpts, lessons on leadership/followership, mentorship/menteeship, leading change and professional development will be shared using a conversational and pragmatic tone. Like a clinical case, pearls to help you navigate development and organizational challenges will be shared. The goal is simple: read an LPD and walk away with an “a-ha,” a new tool, or a strategy that you can use ASAP. For example, in the debut LPD—Hire Hard¹—we emphasize a cardinal rule for hiring: wait for the right person. Waiting is not easy, but it is well worth it in the long run—the right person will make your job that much better. Remember the aphorism: A’s hire A’s while B’s hire C’s.

Many other nuggets of wisdom can fit an LPD model. For example, when it comes to stress, a technique that brings mindfulness to your day—one you can practice with every patient encounter—might be the ticket.² Interested in mentoring? You’ll need to know the Six Golden Rules.³ And don’t forget about emotional intelligence, tight-loose-tight management or the tree-climbing monkey! Don’t know what these are? Time to read an LPD or two to find out!

As you might have guessed—some of these pieces are already written. They come from a book that my colleague, Sanjay Saint and I have been busy writing for over a year. The book distills much of what we have learned as clinicians, researchers and administrators into a collection we call, “Thirty Leadership Rules for Healthcare Providers.” But LPD is not an advert for the book; rather, our contributions will only account for some of the series. We hope this venue will become a platform in where readers like you can offer “pearls” to the broader community. The rules are simple: coin a rule/pearl, open with an illustrative quote, frame it in 650 words with no more than five references, and write it so that a reader can apply it to their work tomorrow. And don’t worry—we on the editorial team will help you craft them if the message makes sense. Interested? Send us an email at [email protected] with an idea and watch your Inbox—an invitation for an LPD might be in your future.

Disclosures

Dr. Chopra has nothing to disclose.

A 39-year-old man presented to a neurologist with three weeks of progressive leg weakness associated with numbness in his feet and fingertips. His medical history included hypertriglyceridemia, hypogonadism, and gout. He was taking fenofibrate and colchicine as needed. There was no family history of neurologic issues. He did not smoke or drink alcohol.

The patient appeared well with a heart rate of 76 beats per minute, blood pressure 133/72 mm Hg, temperature 36.6°C, respiratory rate 16 breaths per minute, and oxygen saturation 100% on room air. His cardiopulmonary and abdominal examinations were normal. His skin was warm and dry without rashes. On neurologic examination, upper extremity strength and sensation was normal. Bilateral hip flexion, knee flexion, and knee extension strength was 4/5; bilateral ankle dorsiflexion and plantar flexion strength was 3/5. Reflexes were trace in the arms and absent at the patellae and ankles. He had symmetric, length-dependent reduction in vibration, pinprick, and light touch sensation in his legs.

Peripheral neuropathy presenting with ascending symmetric motor and sensory deficits progressing over three weeks raises the suspicion of an acquired inflammatory demyelinating polyneuropathy (AIDP), a variant of Guillain-Barre Syndrome. Alternative causes of acute polyneuropathy include thiamine (B1) deficiency, vasculitis, sarcoidosis, or malignancy, particularly lymphoma and multiple myeloma. Further evaluation should include electromyography, nerve conduction studies, lumbar puncture with cerebrospinal fluid (CSF) protein, glucose, and cell count differential. Follow-up laboratory testing based on results of the above may include serum protein electrophoresis (SPEP), serum free light chains (sFLC), vitamin B12, human immunodeficiency virus (HIV), hepatitis B and C testing, antinuclear antibody, and erythrocyte sedimentation rate.

Electromyography and nerve conduction studies revealed a sensorimotor mixed axonal/demyelinating polyneuropathy in all extremities. CSF analysis found one white cell per mm³, glucose of 93 mg/dL, and protein of 313 mg/dL. Magnetic resonance imaging (MRI) of the spine without contrast showed normal cord parenchyma. The vitamin B12 level was 441 pg/mL (normal >200 pg/mL). Antibodies to HIV-1, HIV-2, hepatitis C virus, and Borrelia burgdorferi were negative. Serum protein electrophoresis (SPEP) and immunofixation were normal.

The patient received two courses of intravenous immunoglobulin (IVIG) for suspected AIDP. His weakness progressed over the next several weeks to the point that he required a wheelchair.

Progression of symptoms beyond three weeks and lack of response to IVIG are atypical for AIDP. Alternate diagnoses for a sensorimotor polyneuropathy should be considered. Causes of subacute or chronic demyelinating polyneuropathy include inflammatory conditions (chronic inflammatory demyelinating polyneuropathy [CIDP], connective-tissue disorders), paraprotein disorders (myeloma, amyloidosis, lymphoplasmacytic lymphoma), paraneoplastic syndromes, infectious diseases (HIV, Lyme disease), infiltrative disorders (sarcoidosis), medications or toxins, and hereditary disorders. Of these etiologies, the first three seem the most likely given the history and clinical course, the negative HIV and Lyme testing, and the absence of exposures and family history. Normal SPEP and immunofixation make paraprotein disorders less likely, but sFLC testing should be sent to evaluate for a light chain-only paraprotein. A paraneoplastic antibody panel and a CT of the chest, abdomen, and pelvis should be ordered to evaluate for sarcoidosis, lymphoma, or other malignancies. Although a peripheral nerve biopsy would further classify the polyneuropathy, it is of low diagnostic yield in patients with subacute and chronic distal symmetric polyneuropathies and is associated with significant morbidity. In the absence of history or physical exam findings to narrow the differential diagnosis for polyneuropathy, testing for paraneoplastic antibodies and imaging is appropriate.

The patient tested negative for antiganglioside GM1 and antimyelin-associated glycoprotein antibodies. Urine arsenic, lead, and mercury levels were normal. Tests for serum antinuclear antibody, rapid plasmin reagin, and a paraneoplastic neuropathy panel including amphiphysin antibody, CV2 antibody, and Hu auto-antibody were negative. Repeat electrodiagnostic testing was consistent with CIDP. The patient received prednisone 60 mg daily for six weeks and was then tapered to 30 mg daily over six weeks. Concurrently, he underwent twelve cycles of plasma exchange. His strength improved, and he could walk with a cane; however, weakness recurred when steroids were further tapered.

He was maintained on prednisone 50 mg daily. Over the next year, the patient’s lower extremities became flaccid and severely atrophied. He developed hyperpigmented patches on his trunk, severe gastroesophageal reflux disease (GERD), dysphonia, and gynecomastia. He had lost 60 pounds since symptom onset. He was prescribed levothyroxine for subclinical hypothyroidism (thyroid stimulating hormone 12.63 µIU/mL [normal 0.10-5.50 µIU/mL], free thyroxine 0.8 ng/dL [0.8-1.7 ng/dL]).

At this point, the diagnosis of CIDP should be questioned, and additional investigation is warranted. Although improvement was initially observed with plasma exchange and steroids, subsequent progression of symptoms despite prednisone suggests a nonimmune-mediated etiology, such as a neoplastic or infiltrative process. Conversely, negative serologic testing for paraneoplastic antibodies may be due to an antibody that has not been well characterized.

While prednisone could explain GERD and gynecomastia, the weight loss, dysphonia, and subclinical hypothyroidism may offer clues to the diagnosis underlying the neurological symptoms. Weight loss raises suspicion of a hypercatabolic process such as cancer, cachexia, systemic inflammation, heart failure, or chronic obstructive pulmonary disease. Causes of dysphonia relevant to this presentation include neurologic dysfunction related to malignant invasion of the vagus nerve or demyelinating disease. Subclinical hypothyroidism due to chronic autoimmune thyroiditis seems most likely in the absence of a medication effect or thyroid injury, yet infiltrative disorders of the thyroid (eg, amyloidosis, sarcoidosis, lymphoma) should also be considered. A diagnosis that unifies the neurologic and nonneurologic findings would be desirable; lymphoma with paraneoplastic peripheral neuropathy manifesting as CIDP seems most likely. As of yet, CT of the chest, abdomen, and pelvis or an 18-Fluoro-deoxyglucose positron emission tomography (FDG-PET) scan have not been obtained and would be helpful to evaluate for underlying malignancy. Further evaluation for a paraprotein disorder that includes sFLC is also still indicated to rule out a paraneoplastic disorder that may be associated with polyneuropathy.

Repeat SPEP and serum immunofixation were normal. sFLC assay showed elevated levels of both kappa and lambda light chains with a ratio of 0.61 (reference range: 0.26-1.25). Urine protein electrophoresis (UPEP) from a 24-hour specimen showed a homogenous band in the gamma region, but urine immunofixation demonstrated polyclonal light chains. The plasma vascular endothelial growth factor (VEGF) level was 612 pg/mL (reference range, 31-86 pg/mL).

CT imaging of the chest, abdomen, and pelvis with contrast demonstrated an enlarged liver and spleen and possible splenic infarcts. A skeletal survey and whole-body FGD-PET scan were normal. The patient declined bone marrow biopsy.

Polyneuropathy secondary to a monoclonal protein was previously considered, and an SPEP was normal. Full evaluation for a monoclonal protein additionally requires sFLC testing. If clinical suspicion remains high after a negative result, 24-hour UPEP and urine immunofixation should be obtained. Normal results in this case argue against the presence of a monoclonal protein.

The presence of a monoclonal protein and polyneuropathy are mandatory diagnostic criteria for POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal protein, and skin changes), a plasma cell proliferative disorder. Major diagnostic criteria include osteosclerotic bone lesions, Castleman’s disease, and markedly elevated VEGF levels. Castleman’s disease is a lymphoproliferative disorder characterized by angiofollicular lymphoid hyperplasia that results in lymphadenopathy in one or multiple lymph node regions. Imaging studies reveal organomegaly, one of many minor criteria, but not bone lesions or lymphadenopathy. A diagnosis of POEMS syndrome requires the presence of both mandatory, one major, and one minor criteria. Since only one of two of the mandatory criteria are met at this point, a diagnosis of POEMS syndrome cannot be made.

Eighteen months after symptom onset, the patient presented to the emergency department with dyspnea, orthopnea, and lower extremity edema. B-type natriuretic peptide was 1564 pg/mL. Transthoracic echocardiography showed a severely dilated and hypertrophied left ventricle. Left ventricular ejection fraction was 20%. A furosemide infusion was initiated. Angiography of the coronary vessels was not performed. Congo red stain of an abdominal adipose biopsy was negative for amyloid.

On hospital day five, he developed gangrenous changes in his right first toe. CT angiography of the abdomen and lower extremities demonstrated patent three vessel runoff to the foot with an infrarenal aortic thrombus. Heparin infusion was started. On hospital day 10, the patient developed expressive aphasia and somnolence, prompting intubation for airway protection. MRI and MR angiography (MRA) of the brain and cerebral vessels revealed multiple bilateral acute ischemic strokes (Figure 1) without flow limiting stenosis in cerebral vessels.

These clinical developments lead to an important opportunity to rethink this patient’s working diagnosis. The new diagnosis of heart failure in this young patient with polyneuropathy raises suspicion for an infiltrative cardiomyopathy such as amyloidosis, sarcoidosis, or Fabry disease. Of these, Fabry disease is the least likely because it is typically characterized by a painful burning sensation in response to specific triggers. Although polyneuropathy and heart failure may be concurrently observed with both sarcoidosis and amyloidosis, the absence of an apparent arrhythmia make amyloidosis the more likely of these two diagnoses. The development of an arterial thrombus and multiple strokes may represent emboli from a cardiac thrombus.

Cardiac imaging and tissue biopsy of the heart or other affected organs would distinguish between these diagnostic possibilities. An abdominal adipose biopsy negative for amyloid does not rule out amyloidosis, as the test is approximately 80% sensitive when cardiac amyloidosis is present and varies depending on the etiology of the amyloid protein (ie, light chain vs transthyretin). Evaluation of cardiac amyloid in the setting of peripheral neuropathy should include echocardiography (as was performed here) and repeat testing for a monoclonal protein.

If clinical suspicion of a paraprotein-associated disorder remains high and both SPEP and sFLC are normal, it is important to obtain a 24-hour UPEP and immunofixation. A monoclonal protein can be overlooked by SPEP and serum immunofixation if the monoclonal protein is composed only of a light chain or if the monoclonal protein is IgD or IgE. In these rare circumstances, sFLC analysis or 24-hour UPEP and immunofixation should mitigate the potential for a falsely negative SPEP/IFE. These studies are normal in this case, which argues against the presence of a monoclonal protein.

Transesophageal echocardiography showed grade IV atheromatous plaque within the descending thoracic aorta with mobile elements suggesting a superimposed thrombus; there was no intracardiac shunt or thrombus. MRA of the neck and great vessels was normal.

Testing for heparin-induced thrombocytopenia (HIT) was sent due to thrombocytopenia and the presence of thrombosis. An immunoassay for antiheparin-platelet factor 4 (anti-PF4) antibodies was substantially positive (optical density 2.178); however, functional testing with a washed platelet heparin-induced platelet activation assay was negative. Anticoagulation was changed to argatroban due to concern for HIT. Dry gangrenous changes developed in all distal toes on the right foot and three toes on the left foot. A right radial artery thrombus formed at the site of a prior arterial line.

Thrombocytopenia that develops between the fifth and tenth day following heparin exposure in a patient with new thromboses is consistent with HIT. However, the patient’s infrarenal aortic thrombus preceded the initiation of heparin, and negative functional testing undermines the diagnosis of HIT in this case. Therefore, the arterial thromboses may be related to an underlying unifying diagnosis.

A third SPEP showed a 0.1 g/dL M-spike in the gamma region, but standard immunofixation did not reveal a monoclonal protein (Figure 2). However, a specific request for immunofixation testing using IgD antisera detected an IgD heavy chain. A lambda chain comprising 3% of urine protein was detected on 24-hour urine immunofixation but was not detectable by serum immunofixation. Bone marrow biopsy demonstrated plasma cells comprising 5% of bone marrow cellularity (Figure 3); flow cytometry of the aspirate demonstrated an abnormal lambda-restricted plasma cell population.

An autopsy revealed acute bronchopneumonia and multiple acute and subacute cerebral infarctions. There was extensive peripheral mixed axonal/demyelinating neuropathy, hepatosplenomegaly, atrophy of the thyroid and adrenal glands, hyperpigmented patches and thickened integument, and severe aortic and coronary atherosclerotic disease with a healed myocardial infarction.

POEMS syndrome¹ is a rare constellation of clinical and laboratory findings resulting from an underlying plasma cell proliferative disorder. This paraneoplastic syndrome is characterized by the chronic overproduction of proinflammatory and proangiogenic cytokines, including VEGF, which are postulated to drive its manifestations,² though the exact pathogenesis is not understood. Some of the disease’s most common features are summarized by its name: polyneuropathy, organomegaly, endocrinopathy, monoclonal plasma cell disorder, and skin changes.³

The International Myeloma Working Group (IMWG) diagnostic criteria¹ (Table) require the presence of both mandatory criteria (polyneuropathy and monoclonal plasma cell proliferation), plus at least one major and one minor criterion. Delayed diagnosis or misdiagnosis of this protean disorder is often driven by its rarity and clinical overlap with other paraprotein-associated polyneuropathies. These include amyloidosis, cryoglobulinemia, and monoclonal gammopathy of undetermined significance (MGUS), which can all produce antibodies directed against neural antigens. In addition, polyneuropathy is often the first and most striking manifestation of POEMS syndrome, fostering confusion with CIDP as both disorders are subacute, symmetric, motor-dominant, mixed axonal/demyelinating polyneuropathies.⁴

• If a monoclonal protein is suggested by SPEP but cannot be identified by standard immunofixation, request immunofixation for IgD or IgE. Screen patients for IgD and IgE paraproteins before making a diagnosis of light chain multiple myeloma.
• Polyclonal gammopathy or reduced renal clearance with accumulation of free light chains in the serum may mask the presence of low levels of monoclonal FLC and result in a normal sFLC ratio.
• Thrombosis is a less-recognized but documented feature of POEMS syndrome which may be mediated by the overproduction of proinflammatory and proangiogenic cytokines, though the precise pathogenesis is unknown.

Acknowledgment

The authors thank Dr. Theodore Kurtz and Dr. Anne Deucher from the department of laboratory medicine at the University of California, San Francisco for providing their respective expertise in clinical chemistry and hematopathology.

Disclosures

The authors have no conflicts of interests to disclose.

1. Rajkumar SV, Dimopoulos MA, Palumbo A, et al. International Myeloma Working Group. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538-ee548. doi: 10.1016/S1470-2045(14)70442-5.

A 39-year-old man presented to a neurologist with three weeks of progressive leg weakness associated with numbness in his feet and fingertips. His medical history included hypertriglyceridemia, hypogonadism, and gout. He was taking fenofibrate and colchicine as needed. There was no family history of neurologic issues. He did not smoke or drink alcohol.

The patient appeared well with a heart rate of 76 beats per minute, blood pressure 133/72 mm Hg, temperature 36.6°C, respiratory rate 16 breaths per minute, and oxygen saturation 100% on room air. His cardiopulmonary and abdominal examinations were normal. His skin was warm and dry without rashes. On neurologic examination, upper extremity strength and sensation was normal. Bilateral hip flexion, knee flexion, and knee extension strength was 4/5; bilateral ankle dorsiflexion and plantar flexion strength was 3/5. Reflexes were trace in the arms and absent at the patellae and ankles. He had symmetric, length-dependent reduction in vibration, pinprick, and light touch sensation in his legs.

Peripheral neuropathy presenting with ascending symmetric motor and sensory deficits progressing over three weeks raises the suspicion of an acquired inflammatory demyelinating polyneuropathy (AIDP), a variant of Guillain-Barre Syndrome. Alternative causes of acute polyneuropathy include thiamine (B1) deficiency, vasculitis, sarcoidosis, or malignancy, particularly lymphoma and multiple myeloma. Further evaluation should include electromyography, nerve conduction studies, lumbar puncture with cerebrospinal fluid (CSF) protein, glucose, and cell count differential. Follow-up laboratory testing based on results of the above may include serum protein electrophoresis (SPEP), serum free light chains (sFLC), vitamin B12, human immunodeficiency virus (HIV), hepatitis B and C testing, antinuclear antibody, and erythrocyte sedimentation rate.

Electromyography and nerve conduction studies revealed a sensorimotor mixed axonal/demyelinating polyneuropathy in all extremities. CSF analysis found one white cell per mm³, glucose of 93 mg/dL, and protein of 313 mg/dL. Magnetic resonance imaging (MRI) of the spine without contrast showed normal cord parenchyma. The vitamin B12 level was 441 pg/mL (normal >200 pg/mL). Antibodies to HIV-1, HIV-2, hepatitis C virus, and Borrelia burgdorferi were negative. Serum protein electrophoresis (SPEP) and immunofixation were normal.

The patient received two courses of intravenous immunoglobulin (IVIG) for suspected AIDP. His weakness progressed over the next several weeks to the point that he required a wheelchair.

Progression of symptoms beyond three weeks and lack of response to IVIG are atypical for AIDP. Alternate diagnoses for a sensorimotor polyneuropathy should be considered. Causes of subacute or chronic demyelinating polyneuropathy include inflammatory conditions (chronic inflammatory demyelinating polyneuropathy [CIDP], connective-tissue disorders), paraprotein disorders (myeloma, amyloidosis, lymphoplasmacytic lymphoma), paraneoplastic syndromes, infectious diseases (HIV, Lyme disease), infiltrative disorders (sarcoidosis), medications or toxins, and hereditary disorders. Of these etiologies, the first three seem the most likely given the history and clinical course, the negative HIV and Lyme testing, and the absence of exposures and family history. Normal SPEP and immunofixation make paraprotein disorders less likely, but sFLC testing should be sent to evaluate for a light chain-only paraprotein. A paraneoplastic antibody panel and a CT of the chest, abdomen, and pelvis should be ordered to evaluate for sarcoidosis, lymphoma, or other malignancies. Although a peripheral nerve biopsy would further classify the polyneuropathy, it is of low diagnostic yield in patients with subacute and chronic distal symmetric polyneuropathies and is associated with significant morbidity. In the absence of history or physical exam findings to narrow the differential diagnosis for polyneuropathy, testing for paraneoplastic antibodies and imaging is appropriate.

The patient tested negative for antiganglioside GM1 and antimyelin-associated glycoprotein antibodies. Urine arsenic, lead, and mercury levels were normal. Tests for serum antinuclear antibody, rapid plasmin reagin, and a paraneoplastic neuropathy panel including amphiphysin antibody, CV2 antibody, and Hu auto-antibody were negative. Repeat electrodiagnostic testing was consistent with CIDP. The patient received prednisone 60 mg daily for six weeks and was then tapered to 30 mg daily over six weeks. Concurrently, he underwent twelve cycles of plasma exchange. His strength improved, and he could walk with a cane; however, weakness recurred when steroids were further tapered.

He was maintained on prednisone 50 mg daily. Over the next year, the patient’s lower extremities became flaccid and severely atrophied. He developed hyperpigmented patches on his trunk, severe gastroesophageal reflux disease (GERD), dysphonia, and gynecomastia. He had lost 60 pounds since symptom onset. He was prescribed levothyroxine for subclinical hypothyroidism (thyroid stimulating hormone 12.63 µIU/mL [normal 0.10-5.50 µIU/mL], free thyroxine 0.8 ng/dL [0.8-1.7 ng/dL]).

At this point, the diagnosis of CIDP should be questioned, and additional investigation is warranted. Although improvement was initially observed with plasma exchange and steroids, subsequent progression of symptoms despite prednisone suggests a nonimmune-mediated etiology, such as a neoplastic or infiltrative process. Conversely, negative serologic testing for paraneoplastic antibodies may be due to an antibody that has not been well characterized.

While prednisone could explain GERD and gynecomastia, the weight loss, dysphonia, and subclinical hypothyroidism may offer clues to the diagnosis underlying the neurological symptoms. Weight loss raises suspicion of a hypercatabolic process such as cancer, cachexia, systemic inflammation, heart failure, or chronic obstructive pulmonary disease. Causes of dysphonia relevant to this presentation include neurologic dysfunction related to malignant invasion of the vagus nerve or demyelinating disease. Subclinical hypothyroidism due to chronic autoimmune thyroiditis seems most likely in the absence of a medication effect or thyroid injury, yet infiltrative disorders of the thyroid (eg, amyloidosis, sarcoidosis, lymphoma) should also be considered. A diagnosis that unifies the neurologic and nonneurologic findings would be desirable; lymphoma with paraneoplastic peripheral neuropathy manifesting as CIDP seems most likely. As of yet, CT of the chest, abdomen, and pelvis or an 18-Fluoro-deoxyglucose positron emission tomography (FDG-PET) scan have not been obtained and would be helpful to evaluate for underlying malignancy. Further evaluation for a paraprotein disorder that includes sFLC is also still indicated to rule out a paraneoplastic disorder that may be associated with polyneuropathy.

Repeat SPEP and serum immunofixation were normal. sFLC assay showed elevated levels of both kappa and lambda light chains with a ratio of 0.61 (reference range: 0.26-1.25). Urine protein electrophoresis (UPEP) from a 24-hour specimen showed a homogenous band in the gamma region, but urine immunofixation demonstrated polyclonal light chains. The plasma vascular endothelial growth factor (VEGF) level was 612 pg/mL (reference range, 31-86 pg/mL).

CT imaging of the chest, abdomen, and pelvis with contrast demonstrated an enlarged liver and spleen and possible splenic infarcts. A skeletal survey and whole-body FGD-PET scan were normal. The patient declined bone marrow biopsy.

Polyneuropathy secondary to a monoclonal protein was previously considered, and an SPEP was normal. Full evaluation for a monoclonal protein additionally requires sFLC testing. If clinical suspicion remains high after a negative result, 24-hour UPEP and urine immunofixation should be obtained. Normal results in this case argue against the presence of a monoclonal protein.

The presence of a monoclonal protein and polyneuropathy are mandatory diagnostic criteria for POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal protein, and skin changes), a plasma cell proliferative disorder. Major diagnostic criteria include osteosclerotic bone lesions, Castleman’s disease, and markedly elevated VEGF levels. Castleman’s disease is a lymphoproliferative disorder characterized by angiofollicular lymphoid hyperplasia that results in lymphadenopathy in one or multiple lymph node regions. Imaging studies reveal organomegaly, one of many minor criteria, but not bone lesions or lymphadenopathy. A diagnosis of POEMS syndrome requires the presence of both mandatory, one major, and one minor criteria. Since only one of two of the mandatory criteria are met at this point, a diagnosis of POEMS syndrome cannot be made.

Eighteen months after symptom onset, the patient presented to the emergency department with dyspnea, orthopnea, and lower extremity edema. B-type natriuretic peptide was 1564 pg/mL. Transthoracic echocardiography showed a severely dilated and hypertrophied left ventricle. Left ventricular ejection fraction was 20%. A furosemide infusion was initiated. Angiography of the coronary vessels was not performed. Congo red stain of an abdominal adipose biopsy was negative for amyloid.

On hospital day five, he developed gangrenous changes in his right first toe. CT angiography of the abdomen and lower extremities demonstrated patent three vessel runoff to the foot with an infrarenal aortic thrombus. Heparin infusion was started. On hospital day 10, the patient developed expressive aphasia and somnolence, prompting intubation for airway protection. MRI and MR angiography (MRA) of the brain and cerebral vessels revealed multiple bilateral acute ischemic strokes (Figure 1) without flow limiting stenosis in cerebral vessels.

These clinical developments lead to an important opportunity to rethink this patient’s working diagnosis. The new diagnosis of heart failure in this young patient with polyneuropathy raises suspicion for an infiltrative cardiomyopathy such as amyloidosis, sarcoidosis, or Fabry disease. Of these, Fabry disease is the least likely because it is typically characterized by a painful burning sensation in response to specific triggers. Although polyneuropathy and heart failure may be concurrently observed with both sarcoidosis and amyloidosis, the absence of an apparent arrhythmia make amyloidosis the more likely of these two diagnoses. The development of an arterial thrombus and multiple strokes may represent emboli from a cardiac thrombus.

Cardiac imaging and tissue biopsy of the heart or other affected organs would distinguish between these diagnostic possibilities. An abdominal adipose biopsy negative for amyloid does not rule out amyloidosis, as the test is approximately 80% sensitive when cardiac amyloidosis is present and varies depending on the etiology of the amyloid protein (ie, light chain vs transthyretin). Evaluation of cardiac amyloid in the setting of peripheral neuropathy should include echocardiography (as was performed here) and repeat testing for a monoclonal protein.

If clinical suspicion of a paraprotein-associated disorder remains high and both SPEP and sFLC are normal, it is important to obtain a 24-hour UPEP and immunofixation. A monoclonal protein can be overlooked by SPEP and serum immunofixation if the monoclonal protein is composed only of a light chain or if the monoclonal protein is IgD or IgE. In these rare circumstances, sFLC analysis or 24-hour UPEP and immunofixation should mitigate the potential for a falsely negative SPEP/IFE. These studies are normal in this case, which argues against the presence of a monoclonal protein.

Transesophageal echocardiography showed grade IV atheromatous plaque within the descending thoracic aorta with mobile elements suggesting a superimposed thrombus; there was no intracardiac shunt or thrombus. MRA of the neck and great vessels was normal.

Testing for heparin-induced thrombocytopenia (HIT) was sent due to thrombocytopenia and the presence of thrombosis. An immunoassay for antiheparin-platelet factor 4 (anti-PF4) antibodies was substantially positive (optical density 2.178); however, functional testing with a washed platelet heparin-induced platelet activation assay was negative. Anticoagulation was changed to argatroban due to concern for HIT. Dry gangrenous changes developed in all distal toes on the right foot and three toes on the left foot. A right radial artery thrombus formed at the site of a prior arterial line.

Thrombocytopenia that develops between the fifth and tenth day following heparin exposure in a patient with new thromboses is consistent with HIT. However, the patient’s infrarenal aortic thrombus preceded the initiation of heparin, and negative functional testing undermines the diagnosis of HIT in this case. Therefore, the arterial thromboses may be related to an underlying unifying diagnosis.

A third SPEP showed a 0.1 g/dL M-spike in the gamma region, but standard immunofixation did not reveal a monoclonal protein (Figure 2). However, a specific request for immunofixation testing using IgD antisera detected an IgD heavy chain. A lambda chain comprising 3% of urine protein was detected on 24-hour urine immunofixation but was not detectable by serum immunofixation. Bone marrow biopsy demonstrated plasma cells comprising 5% of bone marrow cellularity (Figure 3); flow cytometry of the aspirate demonstrated an abnormal lambda-restricted plasma cell population.

An autopsy revealed acute bronchopneumonia and multiple acute and subacute cerebral infarctions. There was extensive peripheral mixed axonal/demyelinating neuropathy, hepatosplenomegaly, atrophy of the thyroid and adrenal glands, hyperpigmented patches and thickened integument, and severe aortic and coronary atherosclerotic disease with a healed myocardial infarction.

POEMS syndrome¹ is a rare constellation of clinical and laboratory findings resulting from an underlying plasma cell proliferative disorder. This paraneoplastic syndrome is characterized by the chronic overproduction of proinflammatory and proangiogenic cytokines, including VEGF, which are postulated to drive its manifestations,² though the exact pathogenesis is not understood. Some of the disease’s most common features are summarized by its name: polyneuropathy, organomegaly, endocrinopathy, monoclonal plasma cell disorder, and skin changes.³

The International Myeloma Working Group (IMWG) diagnostic criteria¹ (Table) require the presence of both mandatory criteria (polyneuropathy and monoclonal plasma cell proliferation), plus at least one major and one minor criterion. Delayed diagnosis or misdiagnosis of this protean disorder is often driven by its rarity and clinical overlap with other paraprotein-associated polyneuropathies. These include amyloidosis, cryoglobulinemia, and monoclonal gammopathy of undetermined significance (MGUS), which can all produce antibodies directed against neural antigens. In addition, polyneuropathy is often the first and most striking manifestation of POEMS syndrome, fostering confusion with CIDP as both disorders are subacute, symmetric, motor-dominant, mixed axonal/demyelinating polyneuropathies.⁴

• If a monoclonal protein is suggested by SPEP but cannot be identified by standard immunofixation, request immunofixation for IgD or IgE. Screen patients for IgD and IgE paraproteins before making a diagnosis of light chain multiple myeloma.
• Polyclonal gammopathy or reduced renal clearance with accumulation of free light chains in the serum may mask the presence of low levels of monoclonal FLC and result in a normal sFLC ratio.
• Thrombosis is a less-recognized but documented feature of POEMS syndrome which may be mediated by the overproduction of proinflammatory and proangiogenic cytokines, though the precise pathogenesis is unknown.

Acknowledgment

The authors thank Dr. Theodore Kurtz and Dr. Anne Deucher from the department of laboratory medicine at the University of California, San Francisco for providing their respective expertise in clinical chemistry and hematopathology.

Disclosures

The authors have no conflicts of interests to disclose.

1. Rajkumar SV, Dimopoulos MA, Palumbo A, et al. International Myeloma Working Group. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538-ee548. doi: 10.1016/S1470-2045(14)70442-5.

A 39-year-old man presented to a neurologist with three weeks of progressive leg weakness associated with numbness in his feet and fingertips. His medical history included hypertriglyceridemia, hypogonadism, and gout. He was taking fenofibrate and colchicine as needed. There was no family history of neurologic issues. He did not smoke or drink alcohol.

The patient appeared well with a heart rate of 76 beats per minute, blood pressure 133/72 mm Hg, temperature 36.6°C, respiratory rate 16 breaths per minute, and oxygen saturation 100% on room air. His cardiopulmonary and abdominal examinations were normal. His skin was warm and dry without rashes. On neurologic examination, upper extremity strength and sensation was normal. Bilateral hip flexion, knee flexion, and knee extension strength was 4/5; bilateral ankle dorsiflexion and plantar flexion strength was 3/5. Reflexes were trace in the arms and absent at the patellae and ankles. He had symmetric, length-dependent reduction in vibration, pinprick, and light touch sensation in his legs.

Peripheral neuropathy presenting with ascending symmetric motor and sensory deficits progressing over three weeks raises the suspicion of an acquired inflammatory demyelinating polyneuropathy (AIDP), a variant of Guillain-Barre Syndrome. Alternative causes of acute polyneuropathy include thiamine (B1) deficiency, vasculitis, sarcoidosis, or malignancy, particularly lymphoma and multiple myeloma. Further evaluation should include electromyography, nerve conduction studies, lumbar puncture with cerebrospinal fluid (CSF) protein, glucose, and cell count differential. Follow-up laboratory testing based on results of the above may include serum protein electrophoresis (SPEP), serum free light chains (sFLC), vitamin B12, human immunodeficiency virus (HIV), hepatitis B and C testing, antinuclear antibody, and erythrocyte sedimentation rate.

Electromyography and nerve conduction studies revealed a sensorimotor mixed axonal/demyelinating polyneuropathy in all extremities. CSF analysis found one white cell per mm³, glucose of 93 mg/dL, and protein of 313 mg/dL. Magnetic resonance imaging (MRI) of the spine without contrast showed normal cord parenchyma. The vitamin B12 level was 441 pg/mL (normal >200 pg/mL). Antibodies to HIV-1, HIV-2, hepatitis C virus, and Borrelia burgdorferi were negative. Serum protein electrophoresis (SPEP) and immunofixation were normal.

The patient received two courses of intravenous immunoglobulin (IVIG) for suspected AIDP. His weakness progressed over the next several weeks to the point that he required a wheelchair.

Progression of symptoms beyond three weeks and lack of response to IVIG are atypical for AIDP. Alternate diagnoses for a sensorimotor polyneuropathy should be considered. Causes of subacute or chronic demyelinating polyneuropathy include inflammatory conditions (chronic inflammatory demyelinating polyneuropathy [CIDP], connective-tissue disorders), paraprotein disorders (myeloma, amyloidosis, lymphoplasmacytic lymphoma), paraneoplastic syndromes, infectious diseases (HIV, Lyme disease), infiltrative disorders (sarcoidosis), medications or toxins, and hereditary disorders. Of these etiologies, the first three seem the most likely given the history and clinical course, the negative HIV and Lyme testing, and the absence of exposures and family history. Normal SPEP and immunofixation make paraprotein disorders less likely, but sFLC testing should be sent to evaluate for a light chain-only paraprotein. A paraneoplastic antibody panel and a CT of the chest, abdomen, and pelvis should be ordered to evaluate for sarcoidosis, lymphoma, or other malignancies. Although a peripheral nerve biopsy would further classify the polyneuropathy, it is of low diagnostic yield in patients with subacute and chronic distal symmetric polyneuropathies and is associated with significant morbidity. In the absence of history or physical exam findings to narrow the differential diagnosis for polyneuropathy, testing for paraneoplastic antibodies and imaging is appropriate.

The patient tested negative for antiganglioside GM1 and antimyelin-associated glycoprotein antibodies. Urine arsenic, lead, and mercury levels were normal. Tests for serum antinuclear antibody, rapid plasmin reagin, and a paraneoplastic neuropathy panel including amphiphysin antibody, CV2 antibody, and Hu auto-antibody were negative. Repeat electrodiagnostic testing was consistent with CIDP. The patient received prednisone 60 mg daily for six weeks and was then tapered to 30 mg daily over six weeks. Concurrently, he underwent twelve cycles of plasma exchange. His strength improved, and he could walk with a cane; however, weakness recurred when steroids were further tapered.

He was maintained on prednisone 50 mg daily. Over the next year, the patient’s lower extremities became flaccid and severely atrophied. He developed hyperpigmented patches on his trunk, severe gastroesophageal reflux disease (GERD), dysphonia, and gynecomastia. He had lost 60 pounds since symptom onset. He was prescribed levothyroxine for subclinical hypothyroidism (thyroid stimulating hormone 12.63 µIU/mL [normal 0.10-5.50 µIU/mL], free thyroxine 0.8 ng/dL [0.8-1.7 ng/dL]).

At this point, the diagnosis of CIDP should be questioned, and additional investigation is warranted. Although improvement was initially observed with plasma exchange and steroids, subsequent progression of symptoms despite prednisone suggests a nonimmune-mediated etiology, such as a neoplastic or infiltrative process. Conversely, negative serologic testing for paraneoplastic antibodies may be due to an antibody that has not been well characterized.

While prednisone could explain GERD and gynecomastia, the weight loss, dysphonia, and subclinical hypothyroidism may offer clues to the diagnosis underlying the neurological symptoms. Weight loss raises suspicion of a hypercatabolic process such as cancer, cachexia, systemic inflammation, heart failure, or chronic obstructive pulmonary disease. Causes of dysphonia relevant to this presentation include neurologic dysfunction related to malignant invasion of the vagus nerve or demyelinating disease. Subclinical hypothyroidism due to chronic autoimmune thyroiditis seems most likely in the absence of a medication effect or thyroid injury, yet infiltrative disorders of the thyroid (eg, amyloidosis, sarcoidosis, lymphoma) should also be considered. A diagnosis that unifies the neurologic and nonneurologic findings would be desirable; lymphoma with paraneoplastic peripheral neuropathy manifesting as CIDP seems most likely. As of yet, CT of the chest, abdomen, and pelvis or an 18-Fluoro-deoxyglucose positron emission tomography (FDG-PET) scan have not been obtained and would be helpful to evaluate for underlying malignancy. Further evaluation for a paraprotein disorder that includes sFLC is also still indicated to rule out a paraneoplastic disorder that may be associated with polyneuropathy.

Repeat SPEP and serum immunofixation were normal. sFLC assay showed elevated levels of both kappa and lambda light chains with a ratio of 0.61 (reference range: 0.26-1.25). Urine protein electrophoresis (UPEP) from a 24-hour specimen showed a homogenous band in the gamma region, but urine immunofixation demonstrated polyclonal light chains. The plasma vascular endothelial growth factor (VEGF) level was 612 pg/mL (reference range, 31-86 pg/mL).

CT imaging of the chest, abdomen, and pelvis with contrast demonstrated an enlarged liver and spleen and possible splenic infarcts. A skeletal survey and whole-body FGD-PET scan were normal. The patient declined bone marrow biopsy.

Polyneuropathy secondary to a monoclonal protein was previously considered, and an SPEP was normal. Full evaluation for a monoclonal protein additionally requires sFLC testing. If clinical suspicion remains high after a negative result, 24-hour UPEP and urine immunofixation should be obtained. Normal results in this case argue against the presence of a monoclonal protein.

The presence of a monoclonal protein and polyneuropathy are mandatory diagnostic criteria for POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, monoclonal protein, and skin changes), a plasma cell proliferative disorder. Major diagnostic criteria include osteosclerotic bone lesions, Castleman’s disease, and markedly elevated VEGF levels. Castleman’s disease is a lymphoproliferative disorder characterized by angiofollicular lymphoid hyperplasia that results in lymphadenopathy in one or multiple lymph node regions. Imaging studies reveal organomegaly, one of many minor criteria, but not bone lesions or lymphadenopathy. A diagnosis of POEMS syndrome requires the presence of both mandatory, one major, and one minor criteria. Since only one of two of the mandatory criteria are met at this point, a diagnosis of POEMS syndrome cannot be made.

Eighteen months after symptom onset, the patient presented to the emergency department with dyspnea, orthopnea, and lower extremity edema. B-type natriuretic peptide was 1564 pg/mL. Transthoracic echocardiography showed a severely dilated and hypertrophied left ventricle. Left ventricular ejection fraction was 20%. A furosemide infusion was initiated. Angiography of the coronary vessels was not performed. Congo red stain of an abdominal adipose biopsy was negative for amyloid.

On hospital day five, he developed gangrenous changes in his right first toe. CT angiography of the abdomen and lower extremities demonstrated patent three vessel runoff to the foot with an infrarenal aortic thrombus. Heparin infusion was started. On hospital day 10, the patient developed expressive aphasia and somnolence, prompting intubation for airway protection. MRI and MR angiography (MRA) of the brain and cerebral vessels revealed multiple bilateral acute ischemic strokes (Figure 1) without flow limiting stenosis in cerebral vessels.

These clinical developments lead to an important opportunity to rethink this patient’s working diagnosis. The new diagnosis of heart failure in this young patient with polyneuropathy raises suspicion for an infiltrative cardiomyopathy such as amyloidosis, sarcoidosis, or Fabry disease. Of these, Fabry disease is the least likely because it is typically characterized by a painful burning sensation in response to specific triggers. Although polyneuropathy and heart failure may be concurrently observed with both sarcoidosis and amyloidosis, the absence of an apparent arrhythmia make amyloidosis the more likely of these two diagnoses. The development of an arterial thrombus and multiple strokes may represent emboli from a cardiac thrombus.

Cardiac imaging and tissue biopsy of the heart or other affected organs would distinguish between these diagnostic possibilities. An abdominal adipose biopsy negative for amyloid does not rule out amyloidosis, as the test is approximately 80% sensitive when cardiac amyloidosis is present and varies depending on the etiology of the amyloid protein (ie, light chain vs transthyretin). Evaluation of cardiac amyloid in the setting of peripheral neuropathy should include echocardiography (as was performed here) and repeat testing for a monoclonal protein.

If clinical suspicion of a paraprotein-associated disorder remains high and both SPEP and sFLC are normal, it is important to obtain a 24-hour UPEP and immunofixation. A monoclonal protein can be overlooked by SPEP and serum immunofixation if the monoclonal protein is composed only of a light chain or if the monoclonal protein is IgD or IgE. In these rare circumstances, sFLC analysis or 24-hour UPEP and immunofixation should mitigate the potential for a falsely negative SPEP/IFE. These studies are normal in this case, which argues against the presence of a monoclonal protein.

Transesophageal echocardiography showed grade IV atheromatous plaque within the descending thoracic aorta with mobile elements suggesting a superimposed thrombus; there was no intracardiac shunt or thrombus. MRA of the neck and great vessels was normal.

Testing for heparin-induced thrombocytopenia (HIT) was sent due to thrombocytopenia and the presence of thrombosis. An immunoassay for antiheparin-platelet factor 4 (anti-PF4) antibodies was substantially positive (optical density 2.178); however, functional testing with a washed platelet heparin-induced platelet activation assay was negative. Anticoagulation was changed to argatroban due to concern for HIT. Dry gangrenous changes developed in all distal toes on the right foot and three toes on the left foot. A right radial artery thrombus formed at the site of a prior arterial line.

Thrombocytopenia that develops between the fifth and tenth day following heparin exposure in a patient with new thromboses is consistent with HIT. However, the patient’s infrarenal aortic thrombus preceded the initiation of heparin, and negative functional testing undermines the diagnosis of HIT in this case. Therefore, the arterial thromboses may be related to an underlying unifying diagnosis.

A third SPEP showed a 0.1 g/dL M-spike in the gamma region, but standard immunofixation did not reveal a monoclonal protein (Figure 2). However, a specific request for immunofixation testing using IgD antisera detected an IgD heavy chain. A lambda chain comprising 3% of urine protein was detected on 24-hour urine immunofixation but was not detectable by serum immunofixation. Bone marrow biopsy demonstrated plasma cells comprising 5% of bone marrow cellularity (Figure 3); flow cytometry of the aspirate demonstrated an abnormal lambda-restricted plasma cell population.

An autopsy revealed acute bronchopneumonia and multiple acute and subacute cerebral infarctions. There was extensive peripheral mixed axonal/demyelinating neuropathy, hepatosplenomegaly, atrophy of the thyroid and adrenal glands, hyperpigmented patches and thickened integument, and severe aortic and coronary atherosclerotic disease with a healed myocardial infarction.

POEMS syndrome¹ is a rare constellation of clinical and laboratory findings resulting from an underlying plasma cell proliferative disorder. This paraneoplastic syndrome is characterized by the chronic overproduction of proinflammatory and proangiogenic cytokines, including VEGF, which are postulated to drive its manifestations,² though the exact pathogenesis is not understood. Some of the disease’s most common features are summarized by its name: polyneuropathy, organomegaly, endocrinopathy, monoclonal plasma cell disorder, and skin changes.³

The International Myeloma Working Group (IMWG) diagnostic criteria¹ (Table) require the presence of both mandatory criteria (polyneuropathy and monoclonal plasma cell proliferation), plus at least one major and one minor criterion. Delayed diagnosis or misdiagnosis of this protean disorder is often driven by its rarity and clinical overlap with other paraprotein-associated polyneuropathies. These include amyloidosis, cryoglobulinemia, and monoclonal gammopathy of undetermined significance (MGUS), which can all produce antibodies directed against neural antigens. In addition, polyneuropathy is often the first and most striking manifestation of POEMS syndrome, fostering confusion with CIDP as both disorders are subacute, symmetric, motor-dominant, mixed axonal/demyelinating polyneuropathies.⁴

• If a monoclonal protein is suggested by SPEP but cannot be identified by standard immunofixation, request immunofixation for IgD or IgE. Screen patients for IgD and IgE paraproteins before making a diagnosis of light chain multiple myeloma.
• Polyclonal gammopathy or reduced renal clearance with accumulation of free light chains in the serum may mask the presence of low levels of monoclonal FLC and result in a normal sFLC ratio.
• Thrombosis is a less-recognized but documented feature of POEMS syndrome which may be mediated by the overproduction of proinflammatory and proangiogenic cytokines, though the precise pathogenesis is unknown.

Acknowledgment

The authors thank Dr. Theodore Kurtz and Dr. Anne Deucher from the department of laboratory medicine at the University of California, San Francisco for providing their respective expertise in clinical chemistry and hematopathology.

Disclosures

The authors have no conflicts of interests to disclose.

1. Rajkumar SV, Dimopoulos MA, Palumbo A, et al. International Myeloma Working Group. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538-ee548. doi: 10.1016/S1470-2045(14)70442-5.

“Lord have mercy on me, was the kneeling drunkard’s plea.”

—Johnny Cash

The Diagnostic and Statistical Manual of the American Psychiatric Association defines opioid-use disorder (OUD) as a problematic pattern of prescription and/or illicit opioid medication use leading to clinically significant impairment or distress.¹ Compared with their non-OUD counterparts, patients with OUD have poorer overall health and worse health service outcomes, including higher rates of morbidity, mortality, HIV and HCV transmission, and 30-day readmissions.² With the rate of fatal overdoses from opioids at crisis levels, leading scientific and professional organizations have declared OUD to be a public health emergency in the United States.³

The opioid epidemic affects hospitalists through the rising incidence of hospitalization, not only as a result of OUD’s indirect complications, but also its direct effects of intoxication and withdrawal.⁴ In caring for patients with OUD, hospitalists are often presented with many ethical dilemmas. Whether the dilemma involves timing and circumstances of discharge or the permission to leave the hospital floor, they often involve elements of mutual mistrust. In qualitative ethnographic studies, patients with OUD report not trusting that the medical staff will take their concerns of inadequately treated pain and other needs seriously. Providers may mistrust the patient’s report of pain and withhold treatment for OUD for nonclinical reasons.⁵ Here, we examine two ethical dilemmas specific to OUD in hospitalized patients. Our aim in describing these dilemmas is to help hospitalists recognize that targeting issues of mistrust may assist them to deliver better care to hospitalized patients with OUD.

In the inpatient setting, ethical dilemmas surrounding discharge are common among people who inject drugs (PWID). These patients have disproportionately high rates of soft tissue and systemic infections, such as endocarditis and osteomyelitis, and subsequently often require long-term, outpatient parenteral antibiotic therapy (OPAT).⁶ From both the clinical and ethical perspectives, discharging PWID requiring OPAT to an unsupervised setting or continuing inpatient hospitalization to prevent a potential adverse event are equally imperfect solutions.

These patients may be clinically stable, suitable for discharge, and prefer to be discharged, but the practitioner’s concerns regarding untoward complications frequently override the patient’s wishes. Valid reasons for this exercise of what could be considered soft-paternalism are considered when physicians unilaterally decide what is best for patients, including refusal of community agencies to provide OPAT to PWID, inadequate social support and/or health literacy to administer the therapy, or varying degrees of homelessness that can affect timely follow-up. However, surveys of both hospitalists and infectious disease specialists also indicate that they may avoid discharge because of concerns the PWID will tamper with the intravenous (IV) catheter to inject drugs.⁷ This reluctance to discharge otherwise socially and medically suitable patients increases length of stay,⁷ decreases patient satisfaction, and could lead to misuse of limited hospital resources.

Both patient mistrust and stigmatization may contribute to this dilemma. Healthcare professionals have been shown to share and reflect a long-standing bias in their attitudes toward patients with substance-use disorders and OUD, in particular.⁸ Studies of providers’ attitudes are limited but suggest that legal concerns over liability and professional sanctions,⁹ reluctance to contribute to the development or relapse of addiction,¹⁰ and a strong psychological investment in not being deceived by the patient¹¹ may influence physicians’ decisions about care.

Closely supervising IV antibiotic therapy for all PWID may not reflect current medical knowledge and may imply a moral assessment of patients’ culpability and lack of will power to resist using drugs.¹² No evidence is available to suggest that inpatient parenteral antibiotic treatment offers superior adherence, and emerging evidence showing that carefully selected patients with an injection drug-use history can be safely and effectively treated as outpatients has been obtained.^13,14 Ho et al. found high rates of treatment success in patients with adequate housing, a reliable guardian, and willingness to comply with appropriate IV catheter use.¹³ Although the study by Buehrle et al. found higher rates of OPAT failure among PWIDs, 25% of these failures were due to adverse drug reactions and only 2% were due to documented line manipulations.¹⁴ This research suggests that disposition to alternative settings for OPAT in PWID may be feasible, reasonable, and deserving of further study. Rather than treating PWIDs as a homogenous group of increased risk, contextualizing care based on individual risk stratification promotes more patient-centered care that is medically appropriate and potentially more cost efficient. A thorough risk assessment includes medical evaluation of remote versus recent drug use, other psychiatric comorbidities, and a current willingness to avoid drug use and initiate treatment for it.

Patient-centered approaches that respond to the individual needs of patients have altered the care delivery model in order to improve health services outcomes. In developing an alternative care model to inpatient treatment in PWID who required OPAT, Jafari et al.¹⁵ evaluated a community model of care that provided a home-like residence as an alternative to hospitalization where patients could receive OPAT in a medically and socially supportive environment. This environment, which included RN and mental health staff for substance-use counseling, wound care, medication management, and IV therapy, demonstrated lower rates of against medical advice (AMA) discharge and higher patient satisfaction compared with hospitalization.¹⁵

Ethical dilemmas may also arise when patients with OUD desire greater mobility in the hospital. Although some inpatients may be permitted to leave the floor, some treatment teams may believe that patients with OUD leave the floor to use drugs and that the patient’s IV will facilitate such behavior. Nursing and medical staff may also believe that, if they agree to a request to leave the floor, they are complicit in any potential drug use or harmful consequences resulting from this use. For their part, patients may have a desire for more mobility because of the sometimes unpleasant constraints of hospitalization, which are not unique to these patients¹⁶ or to distract them from their cravings. Patients, unable to tolerate the restriction emotionally or believing they are being treated unfairly, even punitively, may leave AMA rather than complete needed medical care. Once more, distrust of the patient and fear of liability may lead hospital staff to respond in counterproductive ways.

Addressing this dilemma depends, in part on creating an environment where PWID and patients with OUD are treated fairly and appropriately for their underlying illness. Such treatment includes ensuring withdrawal symptoms and pain are adequately treated, building trust by empathically addressing patients’ needs and preferences,¹⁷ and having a systematic (ie, policy-based) approach for requests to leave the floor. The latter intervention assures a transparent, referable standard that providers can apply and refer to as needed.

Efforts to adequately treat withdrawal symptoms in the hospital setting have shown promise in maintaining patient engagement, reducing the rate of AMA discharges, and improving follow up with outpatient medical and substance-use treatment.¹⁸ Because physicians consistently cite the lack of advanced training in addiction medicine as a treatment limitation,¹² training may go a long way in closing this knowledge and skill gap. Furthermore, systematic efforts to better educate and train hospitalists in the care of patients with addiction can improve both knowledge and attitudes about caring for this vulnerable population,¹⁹ thereby enhancing therapeutic relationships and patient centeredness. Finally, institutional policies promoting fair, systematic, and transparent guidance are needed for front-line practitioners to manage the legal, clinical, and ethical ambiguities involved when PWID wish to leave the hospital floor.

In addressing the mistrust some staff may have toward the patients described in the preceding ethical dilemmas, the use of universal precautions is an ethical and efficacious approach that balances reliance on patients’ veracity with due diligence in objective clinical assessments.²⁰ These universal precautions, which are grounded in mutual respect and responsibility between physician and patient, include a set of strategies originally established in infectious disease practice and adapted to the management of chronic pain particularly when opioids are used.²¹ They are based on the recognition that identifying which patients prescribed opioids will develop an OUD or misuse opioids is difficult. Hence, the safest and least-stigmatizing approach is to treat all patients as individuals who could potentially be at risk. This is an ethically strong approach that seeks to balance the competing values of patent safety and patient centeredness, and involves taking a substance-use history from all patients admitted to the hospital and routinely checking state prescription-drug monitoring programs among other steps. Although self-reporting, at least of prescription-drug misuse, is fairly reliable,²² establishing expectations for mutual respect when working with patients with OUD and other addictive disorders is more likely to garner valid reports and a positive alliance. Once this relationship is established, the practitioner can respond to problematic behaviors with clear, compassionate limit setting.

From a broader perspective, a hospital system and culture that is unable to promote trust and adequately treat pain and withdrawal can create a “risk environment” for PWID.²³ When providers are inadequately trained in the management of pain and addiction, or there is a shortage of addiction specialists, or inadequate policy guidance for managing the care of these patients, this can result in AMA discharges and reduced willingness to seek future care. Viewing this problem more expansively may persuade healthcare professionals that patients alone are not entirely responsible for the outcomes related to their illness but that modifying practices and structure at the hospital level has the potential to mitigate harm to this vulnerable population.

As inpatient team leaders, hospitalists have the unique opportunity to address the opioid crisis by enhancing the quality of care provided to hospitalized patients with OUD. This enhancement can be accomplished by destigmatizing substance-use disorders, establishing relationships of trust, and promoting remedies to structural deficiencies in the healthcare system that contribute to the problem. These approaches have the potential to enhance not only the care of patients with OUD but also the satisfaction of the treatment team caring for these patients.²⁴ Such changes will ideally allow physicians to better treat the illness, address ethical and clinical concerns when they arise, and promote enhanced participation in treatment planning.

Disclosures

The authors have no conflicts of interest to disclose, financial or otherwise. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the U.S. Department of Veterans Affairs, the U.S. Government, or the VA National Center for Ethics in Health Care.

“Lord have mercy on me, was the kneeling drunkard’s plea.”

—Johnny Cash

The Diagnostic and Statistical Manual of the American Psychiatric Association defines opioid-use disorder (OUD) as a problematic pattern of prescription and/or illicit opioid medication use leading to clinically significant impairment or distress.¹ Compared with their non-OUD counterparts, patients with OUD have poorer overall health and worse health service outcomes, including higher rates of morbidity, mortality, HIV and HCV transmission, and 30-day readmissions.² With the rate of fatal overdoses from opioids at crisis levels, leading scientific and professional organizations have declared OUD to be a public health emergency in the United States.³

The opioid epidemic affects hospitalists through the rising incidence of hospitalization, not only as a result of OUD’s indirect complications, but also its direct effects of intoxication and withdrawal.⁴ In caring for patients with OUD, hospitalists are often presented with many ethical dilemmas. Whether the dilemma involves timing and circumstances of discharge or the permission to leave the hospital floor, they often involve elements of mutual mistrust. In qualitative ethnographic studies, patients with OUD report not trusting that the medical staff will take their concerns of inadequately treated pain and other needs seriously. Providers may mistrust the patient’s report of pain and withhold treatment for OUD for nonclinical reasons.⁵ Here, we examine two ethical dilemmas specific to OUD in hospitalized patients. Our aim in describing these dilemmas is to help hospitalists recognize that targeting issues of mistrust may assist them to deliver better care to hospitalized patients with OUD.

In the inpatient setting, ethical dilemmas surrounding discharge are common among people who inject drugs (PWID). These patients have disproportionately high rates of soft tissue and systemic infections, such as endocarditis and osteomyelitis, and subsequently often require long-term, outpatient parenteral antibiotic therapy (OPAT).⁶ From both the clinical and ethical perspectives, discharging PWID requiring OPAT to an unsupervised setting or continuing inpatient hospitalization to prevent a potential adverse event are equally imperfect solutions.

These patients may be clinically stable, suitable for discharge, and prefer to be discharged, but the practitioner’s concerns regarding untoward complications frequently override the patient’s wishes. Valid reasons for this exercise of what could be considered soft-paternalism are considered when physicians unilaterally decide what is best for patients, including refusal of community agencies to provide OPAT to PWID, inadequate social support and/or health literacy to administer the therapy, or varying degrees of homelessness that can affect timely follow-up. However, surveys of both hospitalists and infectious disease specialists also indicate that they may avoid discharge because of concerns the PWID will tamper with the intravenous (IV) catheter to inject drugs.⁷ This reluctance to discharge otherwise socially and medically suitable patients increases length of stay,⁷ decreases patient satisfaction, and could lead to misuse of limited hospital resources.

Both patient mistrust and stigmatization may contribute to this dilemma. Healthcare professionals have been shown to share and reflect a long-standing bias in their attitudes toward patients with substance-use disorders and OUD, in particular.⁸ Studies of providers’ attitudes are limited but suggest that legal concerns over liability and professional sanctions,⁹ reluctance to contribute to the development or relapse of addiction,¹⁰ and a strong psychological investment in not being deceived by the patient¹¹ may influence physicians’ decisions about care.

Closely supervising IV antibiotic therapy for all PWID may not reflect current medical knowledge and may imply a moral assessment of patients’ culpability and lack of will power to resist using drugs.¹² No evidence is available to suggest that inpatient parenteral antibiotic treatment offers superior adherence, and emerging evidence showing that carefully selected patients with an injection drug-use history can be safely and effectively treated as outpatients has been obtained.^13,14 Ho et al. found high rates of treatment success in patients with adequate housing, a reliable guardian, and willingness to comply with appropriate IV catheter use.¹³ Although the study by Buehrle et al. found higher rates of OPAT failure among PWIDs, 25% of these failures were due to adverse drug reactions and only 2% were due to documented line manipulations.¹⁴ This research suggests that disposition to alternative settings for OPAT in PWID may be feasible, reasonable, and deserving of further study. Rather than treating PWIDs as a homogenous group of increased risk, contextualizing care based on individual risk stratification promotes more patient-centered care that is medically appropriate and potentially more cost efficient. A thorough risk assessment includes medical evaluation of remote versus recent drug use, other psychiatric comorbidities, and a current willingness to avoid drug use and initiate treatment for it.

Patient-centered approaches that respond to the individual needs of patients have altered the care delivery model in order to improve health services outcomes. In developing an alternative care model to inpatient treatment in PWID who required OPAT, Jafari et al.¹⁵ evaluated a community model of care that provided a home-like residence as an alternative to hospitalization where patients could receive OPAT in a medically and socially supportive environment. This environment, which included RN and mental health staff for substance-use counseling, wound care, medication management, and IV therapy, demonstrated lower rates of against medical advice (AMA) discharge and higher patient satisfaction compared with hospitalization.¹⁵

Ethical dilemmas may also arise when patients with OUD desire greater mobility in the hospital. Although some inpatients may be permitted to leave the floor, some treatment teams may believe that patients with OUD leave the floor to use drugs and that the patient’s IV will facilitate such behavior. Nursing and medical staff may also believe that, if they agree to a request to leave the floor, they are complicit in any potential drug use or harmful consequences resulting from this use. For their part, patients may have a desire for more mobility because of the sometimes unpleasant constraints of hospitalization, which are not unique to these patients¹⁶ or to distract them from their cravings. Patients, unable to tolerate the restriction emotionally or believing they are being treated unfairly, even punitively, may leave AMA rather than complete needed medical care. Once more, distrust of the patient and fear of liability may lead hospital staff to respond in counterproductive ways.

Addressing this dilemma depends, in part on creating an environment where PWID and patients with OUD are treated fairly and appropriately for their underlying illness. Such treatment includes ensuring withdrawal symptoms and pain are adequately treated, building trust by empathically addressing patients’ needs and preferences,¹⁷ and having a systematic (ie, policy-based) approach for requests to leave the floor. The latter intervention assures a transparent, referable standard that providers can apply and refer to as needed.

Efforts to adequately treat withdrawal symptoms in the hospital setting have shown promise in maintaining patient engagement, reducing the rate of AMA discharges, and improving follow up with outpatient medical and substance-use treatment.¹⁸ Because physicians consistently cite the lack of advanced training in addiction medicine as a treatment limitation,¹² training may go a long way in closing this knowledge and skill gap. Furthermore, systematic efforts to better educate and train hospitalists in the care of patients with addiction can improve both knowledge and attitudes about caring for this vulnerable population,¹⁹ thereby enhancing therapeutic relationships and patient centeredness. Finally, institutional policies promoting fair, systematic, and transparent guidance are needed for front-line practitioners to manage the legal, clinical, and ethical ambiguities involved when PWID wish to leave the hospital floor.

In addressing the mistrust some staff may have toward the patients described in the preceding ethical dilemmas, the use of universal precautions is an ethical and efficacious approach that balances reliance on patients’ veracity with due diligence in objective clinical assessments.²⁰ These universal precautions, which are grounded in mutual respect and responsibility between physician and patient, include a set of strategies originally established in infectious disease practice and adapted to the management of chronic pain particularly when opioids are used.²¹ They are based on the recognition that identifying which patients prescribed opioids will develop an OUD or misuse opioids is difficult. Hence, the safest and least-stigmatizing approach is to treat all patients as individuals who could potentially be at risk. This is an ethically strong approach that seeks to balance the competing values of patent safety and patient centeredness, and involves taking a substance-use history from all patients admitted to the hospital and routinely checking state prescription-drug monitoring programs among other steps. Although self-reporting, at least of prescription-drug misuse, is fairly reliable,²² establishing expectations for mutual respect when working with patients with OUD and other addictive disorders is more likely to garner valid reports and a positive alliance. Once this relationship is established, the practitioner can respond to problematic behaviors with clear, compassionate limit setting.

From a broader perspective, a hospital system and culture that is unable to promote trust and adequately treat pain and withdrawal can create a “risk environment” for PWID.²³ When providers are inadequately trained in the management of pain and addiction, or there is a shortage of addiction specialists, or inadequate policy guidance for managing the care of these patients, this can result in AMA discharges and reduced willingness to seek future care. Viewing this problem more expansively may persuade healthcare professionals that patients alone are not entirely responsible for the outcomes related to their illness but that modifying practices and structure at the hospital level has the potential to mitigate harm to this vulnerable population.

As inpatient team leaders, hospitalists have the unique opportunity to address the opioid crisis by enhancing the quality of care provided to hospitalized patients with OUD. This enhancement can be accomplished by destigmatizing substance-use disorders, establishing relationships of trust, and promoting remedies to structural deficiencies in the healthcare system that contribute to the problem. These approaches have the potential to enhance not only the care of patients with OUD but also the satisfaction of the treatment team caring for these patients.²⁴ Such changes will ideally allow physicians to better treat the illness, address ethical and clinical concerns when they arise, and promote enhanced participation in treatment planning.

Disclosures

The authors have no conflicts of interest to disclose, financial or otherwise. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the U.S. Department of Veterans Affairs, the U.S. Government, or the VA National Center for Ethics in Health Care.

“Lord have mercy on me, was the kneeling drunkard’s plea.”

—Johnny Cash

The Diagnostic and Statistical Manual of the American Psychiatric Association defines opioid-use disorder (OUD) as a problematic pattern of prescription and/or illicit opioid medication use leading to clinically significant impairment or distress.¹ Compared with their non-OUD counterparts, patients with OUD have poorer overall health and worse health service outcomes, including higher rates of morbidity, mortality, HIV and HCV transmission, and 30-day readmissions.² With the rate of fatal overdoses from opioids at crisis levels, leading scientific and professional organizations have declared OUD to be a public health emergency in the United States.³

The opioid epidemic affects hospitalists through the rising incidence of hospitalization, not only as a result of OUD’s indirect complications, but also its direct effects of intoxication and withdrawal.⁴ In caring for patients with OUD, hospitalists are often presented with many ethical dilemmas. Whether the dilemma involves timing and circumstances of discharge or the permission to leave the hospital floor, they often involve elements of mutual mistrust. In qualitative ethnographic studies, patients with OUD report not trusting that the medical staff will take their concerns of inadequately treated pain and other needs seriously. Providers may mistrust the patient’s report of pain and withhold treatment for OUD for nonclinical reasons.⁵ Here, we examine two ethical dilemmas specific to OUD in hospitalized patients. Our aim in describing these dilemmas is to help hospitalists recognize that targeting issues of mistrust may assist them to deliver better care to hospitalized patients with OUD.

In the inpatient setting, ethical dilemmas surrounding discharge are common among people who inject drugs (PWID). These patients have disproportionately high rates of soft tissue and systemic infections, such as endocarditis and osteomyelitis, and subsequently often require long-term, outpatient parenteral antibiotic therapy (OPAT).⁶ From both the clinical and ethical perspectives, discharging PWID requiring OPAT to an unsupervised setting or continuing inpatient hospitalization to prevent a potential adverse event are equally imperfect solutions.

These patients may be clinically stable, suitable for discharge, and prefer to be discharged, but the practitioner’s concerns regarding untoward complications frequently override the patient’s wishes. Valid reasons for this exercise of what could be considered soft-paternalism are considered when physicians unilaterally decide what is best for patients, including refusal of community agencies to provide OPAT to PWID, inadequate social support and/or health literacy to administer the therapy, or varying degrees of homelessness that can affect timely follow-up. However, surveys of both hospitalists and infectious disease specialists also indicate that they may avoid discharge because of concerns the PWID will tamper with the intravenous (IV) catheter to inject drugs.⁷ This reluctance to discharge otherwise socially and medically suitable patients increases length of stay,⁷ decreases patient satisfaction, and could lead to misuse of limited hospital resources.

Both patient mistrust and stigmatization may contribute to this dilemma. Healthcare professionals have been shown to share and reflect a long-standing bias in their attitudes toward patients with substance-use disorders and OUD, in particular.⁸ Studies of providers’ attitudes are limited but suggest that legal concerns over liability and professional sanctions,⁹ reluctance to contribute to the development or relapse of addiction,¹⁰ and a strong psychological investment in not being deceived by the patient¹¹ may influence physicians’ decisions about care.

Closely supervising IV antibiotic therapy for all PWID may not reflect current medical knowledge and may imply a moral assessment of patients’ culpability and lack of will power to resist using drugs.¹² No evidence is available to suggest that inpatient parenteral antibiotic treatment offers superior adherence, and emerging evidence showing that carefully selected patients with an injection drug-use history can be safely and effectively treated as outpatients has been obtained.^13,14 Ho et al. found high rates of treatment success in patients with adequate housing, a reliable guardian, and willingness to comply with appropriate IV catheter use.¹³ Although the study by Buehrle et al. found higher rates of OPAT failure among PWIDs, 25% of these failures were due to adverse drug reactions and only 2% were due to documented line manipulations.¹⁴ This research suggests that disposition to alternative settings for OPAT in PWID may be feasible, reasonable, and deserving of further study. Rather than treating PWIDs as a homogenous group of increased risk, contextualizing care based on individual risk stratification promotes more patient-centered care that is medically appropriate and potentially more cost efficient. A thorough risk assessment includes medical evaluation of remote versus recent drug use, other psychiatric comorbidities, and a current willingness to avoid drug use and initiate treatment for it.

Patient-centered approaches that respond to the individual needs of patients have altered the care delivery model in order to improve health services outcomes. In developing an alternative care model to inpatient treatment in PWID who required OPAT, Jafari et al.¹⁵ evaluated a community model of care that provided a home-like residence as an alternative to hospitalization where patients could receive OPAT in a medically and socially supportive environment. This environment, which included RN and mental health staff for substance-use counseling, wound care, medication management, and IV therapy, demonstrated lower rates of against medical advice (AMA) discharge and higher patient satisfaction compared with hospitalization.¹⁵

Ethical dilemmas may also arise when patients with OUD desire greater mobility in the hospital. Although some inpatients may be permitted to leave the floor, some treatment teams may believe that patients with OUD leave the floor to use drugs and that the patient’s IV will facilitate such behavior. Nursing and medical staff may also believe that, if they agree to a request to leave the floor, they are complicit in any potential drug use or harmful consequences resulting from this use. For their part, patients may have a desire for more mobility because of the sometimes unpleasant constraints of hospitalization, which are not unique to these patients¹⁶ or to distract them from their cravings. Patients, unable to tolerate the restriction emotionally or believing they are being treated unfairly, even punitively, may leave AMA rather than complete needed medical care. Once more, distrust of the patient and fear of liability may lead hospital staff to respond in counterproductive ways.

Addressing this dilemma depends, in part on creating an environment where PWID and patients with OUD are treated fairly and appropriately for their underlying illness. Such treatment includes ensuring withdrawal symptoms and pain are adequately treated, building trust by empathically addressing patients’ needs and preferences,¹⁷ and having a systematic (ie, policy-based) approach for requests to leave the floor. The latter intervention assures a transparent, referable standard that providers can apply and refer to as needed.

Efforts to adequately treat withdrawal symptoms in the hospital setting have shown promise in maintaining patient engagement, reducing the rate of AMA discharges, and improving follow up with outpatient medical and substance-use treatment.¹⁸ Because physicians consistently cite the lack of advanced training in addiction medicine as a treatment limitation,¹² training may go a long way in closing this knowledge and skill gap. Furthermore, systematic efforts to better educate and train hospitalists in the care of patients with addiction can improve both knowledge and attitudes about caring for this vulnerable population,¹⁹ thereby enhancing therapeutic relationships and patient centeredness. Finally, institutional policies promoting fair, systematic, and transparent guidance are needed for front-line practitioners to manage the legal, clinical, and ethical ambiguities involved when PWID wish to leave the hospital floor.

In addressing the mistrust some staff may have toward the patients described in the preceding ethical dilemmas, the use of universal precautions is an ethical and efficacious approach that balances reliance on patients’ veracity with due diligence in objective clinical assessments.²⁰ These universal precautions, which are grounded in mutual respect and responsibility between physician and patient, include a set of strategies originally established in infectious disease practice and adapted to the management of chronic pain particularly when opioids are used.²¹ They are based on the recognition that identifying which patients prescribed opioids will develop an OUD or misuse opioids is difficult. Hence, the safest and least-stigmatizing approach is to treat all patients as individuals who could potentially be at risk. This is an ethically strong approach that seeks to balance the competing values of patent safety and patient centeredness, and involves taking a substance-use history from all patients admitted to the hospital and routinely checking state prescription-drug monitoring programs among other steps. Although self-reporting, at least of prescription-drug misuse, is fairly reliable,²² establishing expectations for mutual respect when working with patients with OUD and other addictive disorders is more likely to garner valid reports and a positive alliance. Once this relationship is established, the practitioner can respond to problematic behaviors with clear, compassionate limit setting.

From a broader perspective, a hospital system and culture that is unable to promote trust and adequately treat pain and withdrawal can create a “risk environment” for PWID.²³ When providers are inadequately trained in the management of pain and addiction, or there is a shortage of addiction specialists, or inadequate policy guidance for managing the care of these patients, this can result in AMA discharges and reduced willingness to seek future care. Viewing this problem more expansively may persuade healthcare professionals that patients alone are not entirely responsible for the outcomes related to their illness but that modifying practices and structure at the hospital level has the potential to mitigate harm to this vulnerable population.

As inpatient team leaders, hospitalists have the unique opportunity to address the opioid crisis by enhancing the quality of care provided to hospitalized patients with OUD. This enhancement can be accomplished by destigmatizing substance-use disorders, establishing relationships of trust, and promoting remedies to structural deficiencies in the healthcare system that contribute to the problem. These approaches have the potential to enhance not only the care of patients with OUD but also the satisfaction of the treatment team caring for these patients.²⁴ Such changes will ideally allow physicians to better treat the illness, address ethical and clinical concerns when they arise, and promote enhanced participation in treatment planning.

Disclosures

The authors have no conflicts of interest to disclose, financial or otherwise. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the U.S. Department of Veterans Affairs, the U.S. Government, or the VA National Center for Ethics in Health Care.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Medicare observation stays are increasingly common. From 2006 to 2012, Medicare observation stays increased by 88%,¹ whereas inpatient discharges decreased by 13.9%.² In 2012, 1.7 million Medicare observation stays were recorded, and an additional 700,000 inpatient stays were preceded by observation services; the latter represented a 96% increase in status change since 2006.¹ Yet no standard research methodology for identifying observation stays exists, including methods to identify and properly characterize “status change” events, which are hospital stays where initial and final inpatient or outpatient (observation) statuses differ.

With the increasing number of hospitalized patients classified as observation, a standard methodology for Medicare claims research is needed so that observation stays can be consistently identified and potentially included in future quality measures and outcomes. Existing research studies and government reports use widely varying criteria to identify observation stays, often lack detailed methods on observation stay case finding, and contain limited information on how status changes between inpatient and outpatient (observation) statuses are incorporated. This variability in approach may lead to omission and/or miscategorization of events and raises concern about the comparability of prior work.

This study aimed to determine the claims patterns of Medicare observation stays, define comprehensive claims-based methodology for future Medicare observation research and data reporting, and identify policy implications of such definition. We are poised to do this work because of our access to the nationally generalizable Centers for Medicare & Medicaid Services (CMS) linked Part A inpatient and outpatient data sets for 2013 and 2014, as well as our prior expertise in hospital observation research and Medicare claims studies.

General Methods and Data Source

A 2014 national 20% random sample Part A and B Medicare data set was used. In accordance with the Centers for Medicare & Medicaid (CMS) data use agreement, all included beneficiaries had at least 1 acute care inpatient hospitalization. Included beneficiaries were enrolled for 12 months prior to their first 2014 inpatient stay. Those with Medicare Advantage or railroad benefits were excluded because of incomplete data per prior methods.³ The University of Wisconsin Institutional Review Board approved this study.

Comparison of Methods

The PubMED query “Medicare AND (observation OR observation unit),” limited to English and publication between January 1, 2010 and October 1, 2017, was conducted to determine the universe of prior observation stay definitions used in research for comparison (Appendix).^4-20 The Office of Inspector General report,²¹ the Research Data Assistance Center (ResDAC),²² and Medicare Claims Processing Manual (MCPM)²³ were also included. Methods stated in each publication were used to extrapolate observation stay case finding to the study data set.

Observation Stay Case Finding

Inpatient and outpatient revenue centers were queried for observation revenue center (ORC) codes identified by ResDAC,²² including 0760 (Treatment or observation room - general classification), 0761 (Treatment or observation room - treatment room), 0762 (Treatment or observation room – observation room), and 0769 (Treatment or observation room – other) occurring within 30 days of an inpatient stay. Healthcare Common Procedure Coding System (HCPCS) codes G0378 (Hospital observation service, per hour) and G0379 (Direct referral of patient for hospital observation care) were included per MCPM.²³ A combination of these ORC and HCPCS codes was also used to identify observation stays in every Medicare claims observation study since 2010. When more than one ORC code per event was found, each ORC was recorded as part of that event. Presence of HCPCS G0378 and/or G0379 was determined for each event in association with event ORC(s), as was association of ORC codes with inpatient claims. In this manuscript, “observation stay” refers to an observation hospital stay, and “event” refers to a hospitalization that may include inpatient and/or outpatient (observation) services and ORC codes.

Status Change

All ORC codes found in the inpatient revenue center were assumed to represent status changes from outpatient (observation) to inpatient, as ORC codes may remain in claims data when the status changes to inpatient.²⁴ Therefore, all events with ORC codes in the inpatient revenue center were considered inpatient hospitalizations.

For each ORC code found in the outpatient revenue center and also associated with an inpatient claim, timing of the ORC code in the inpatient claim was grouped into four categories to determine events with the final status of outpatient (observation stay). ResDAC defines the “From” date as “…the first day on the billing statement covering services rendered to the beneficiary.”²⁴ For most hospitals, this is a three-day period prior to an inpatient admission where outpatient services are included in the Part A claim.²⁵ We defined Category 1 as ORC codes occurring prior to claim “From” date; Category 2 as ORC codes on the inpatient “From” date, between the inpatient “From” date and admission date, or on the admission date; Category 3 as ORC codes between admission and discharge dates; and Category 4 as ORC codes occurring on or after the discharge date. Given that Category 4 represents the final hospitalization status, we considered Category 4 ORC codes in the outpatient revenue center associated with inpatient claims to be observation stays that had undergone a status change from inpatient to outpatient (observation).

University of Wisconsin Method

After excluding ORC codes in the inpatient revenue center as true inpatient hospitalizations, we defined an observation stay as 0760 and/or 0761 and/or 0762 and/or 0769 in the outpatient revenue center and having no association with an inpatient claim. To address a status change from inpatient to outpatient (observation), for those ORC codes in the outpatient revenue center also associated with an inpatient claim, claims with ORC codes in Category 4 were also considered observation stays.

Of 1,667,660 hospital events, 125,920 (7.6%) had an ORC code within 30 days of an inpatient hospitalization, of which 50,418 (3.0%) were found in the inpatient revenue center and 75,502 (4.5%) were from the outpatient revenue center. A total of 59,529 (47.3%) ORC codes occurred with an inpatient claim (50,418 in the inpatient revenue center and 9,111 in the outpatient revenue center), 5,628 (4.5%) had more than one ORC code on a single hospitalization, and more than 90% of codes were 0761 or 0762. These results illustrated variability in claims submissions as measured by the claims themselves and demonstrated a high rate of status changes (Table).

Observation stay definitions varied in the literature, with different methods capturing variable numbers of observation stays (Figure, Appendix). No methods clearly identified how to categorize events with status changes, directly addressed ORC codes in the inpatient revenue center, or discussed events with more than one ORC code. As such, some assumptions were made to extrapolate observation stay case findings as detailed in the Figure (see also Appendix). Notably, reference 4 methods were obtained via personal communication with the manuscript’s first author. The University of Wisconsin definition offers a comprehensive definition that classifies status change events, yielding 72,858 of 75,502 (96.5%) potential observation events as observation stays (Figure). These observation stays include 66,391 stays with ORC codes in the outpatient revenue center without status change or relation to inpatient claim, and 6467 (71.0%) of 9111 events with ORC codes in the outpatient revenue center were associated with an inpatient claim where ORC code(s) is located in Category 4.

This study confirmed the importance of a standard observation stay case finding methodology. Variability in prior methodology resulted in studies that may have included less than half of potential observation stays. In addition, most studies did not include, or were unclear, on how to address the increasing number of status changes. Others may have erroneously included hospitalizations that were ultimately billed as inpatient, and some publications lacked sufficient detailed methodology to extrapolate results with absolute certainty, a limitation of our comparative results. Although excluding some ORC codes in the outpatient revenue center associated with inpatient claims may possibly miss some observation stays, or including certain ORC codes, such as 0761 (treatment or observation room - treatment room), may erroneously include a different type of observation stay, the proposed University of Wisconsin method could be used as a comprehensive and reproducible method for observation stay case finding, including encounters with status change.

This study has other important policy implications. More than 90% of ORC codes were either 0761 or 0762, and in almost one in 20 claims, two or more distinct codes were identified. Given the lack of clinical relevance of terms “treatment” or “observation” room, and the frequency of more than 1 ORC code per claim, CMS may consider simplification to a single ORC code. Studies of observation encounter length of stay by hour may require G0378 in addition to an ORC code to define an observation stay, but doing so eliminates nearly half of observation claims. Additionally, G0379 adds minimal value to G0378 in case finding.

Finally, this study illustrates overall confusion with outpatient (observation) and inpatient status designations, with almost half (47.3%) of all hospitalizations with ORC codes also associated with an inpatient claim, demonstrating a high status change rate. More than 40% of all nurse case manager job postings are now for status determination work, shifting duties from patient care and quality improvement.²⁶ We previously demonstrated a need for 5.1 FTE combined physician, attorney, and other personnel to manage the status, audit, and appeals process per institution.²⁷ The frequency of status changes and personnel needed to maintain a two-tiered billing system argues for a single hospital status.

In summary, our study highlights the need for federal observation policy reform. We propose a standardized and reproducible approach for Medicare observation claims research, including status changes that can be used for further studies of observation stays.

Acknowledgments

The authors thank Jinn-ing Liou for analyst support, Jen Birstler for figure creation, and Carol Hermann for technical support. This work was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD010243 (Dr. Kind).

Disclosures

The authors have no relevant conflicts of interest to disclose.Funding: This work was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD010243 (

Medicare observation stays are increasingly common. From 2006 to 2012, Medicare observation stays increased by 88%,¹ whereas inpatient discharges decreased by 13.9%.² In 2012, 1.7 million Medicare observation stays were recorded, and an additional 700,000 inpatient stays were preceded by observation services; the latter represented a 96% increase in status change since 2006.¹ Yet no standard research methodology for identifying observation stays exists, including methods to identify and properly characterize “status change” events, which are hospital stays where initial and final inpatient or outpatient (observation) statuses differ.

With the increasing number of hospitalized patients classified as observation, a standard methodology for Medicare claims research is needed so that observation stays can be consistently identified and potentially included in future quality measures and outcomes. Existing research studies and government reports use widely varying criteria to identify observation stays, often lack detailed methods on observation stay case finding, and contain limited information on how status changes between inpatient and outpatient (observation) statuses are incorporated. This variability in approach may lead to omission and/or miscategorization of events and raises concern about the comparability of prior work.

This study aimed to determine the claims patterns of Medicare observation stays, define comprehensive claims-based methodology for future Medicare observation research and data reporting, and identify policy implications of such definition. We are poised to do this work because of our access to the nationally generalizable Centers for Medicare & Medicaid Services (CMS) linked Part A inpatient and outpatient data sets for 2013 and 2014, as well as our prior expertise in hospital observation research and Medicare claims studies.

General Methods and Data Source

A 2014 national 20% random sample Part A and B Medicare data set was used. In accordance with the Centers for Medicare & Medicaid (CMS) data use agreement, all included beneficiaries had at least 1 acute care inpatient hospitalization. Included beneficiaries were enrolled for 12 months prior to their first 2014 inpatient stay. Those with Medicare Advantage or railroad benefits were excluded because of incomplete data per prior methods.³ The University of Wisconsin Institutional Review Board approved this study.

Comparison of Methods

The PubMED query “Medicare AND (observation OR observation unit),” limited to English and publication between January 1, 2010 and October 1, 2017, was conducted to determine the universe of prior observation stay definitions used in research for comparison (Appendix).^4-20 The Office of Inspector General report,²¹ the Research Data Assistance Center (ResDAC),²² and Medicare Claims Processing Manual (MCPM)²³ were also included. Methods stated in each publication were used to extrapolate observation stay case finding to the study data set.

Observation Stay Case Finding

Inpatient and outpatient revenue centers were queried for observation revenue center (ORC) codes identified by ResDAC,²² including 0760 (Treatment or observation room - general classification), 0761 (Treatment or observation room - treatment room), 0762 (Treatment or observation room – observation room), and 0769 (Treatment or observation room – other) occurring within 30 days of an inpatient stay. Healthcare Common Procedure Coding System (HCPCS) codes G0378 (Hospital observation service, per hour) and G0379 (Direct referral of patient for hospital observation care) were included per MCPM.²³ A combination of these ORC and HCPCS codes was also used to identify observation stays in every Medicare claims observation study since 2010. When more than one ORC code per event was found, each ORC was recorded as part of that event. Presence of HCPCS G0378 and/or G0379 was determined for each event in association with event ORC(s), as was association of ORC codes with inpatient claims. In this manuscript, “observation stay” refers to an observation hospital stay, and “event” refers to a hospitalization that may include inpatient and/or outpatient (observation) services and ORC codes.

Status Change

All ORC codes found in the inpatient revenue center were assumed to represent status changes from outpatient (observation) to inpatient, as ORC codes may remain in claims data when the status changes to inpatient.²⁴ Therefore, all events with ORC codes in the inpatient revenue center were considered inpatient hospitalizations.

For each ORC code found in the outpatient revenue center and also associated with an inpatient claim, timing of the ORC code in the inpatient claim was grouped into four categories to determine events with the final status of outpatient (observation stay). ResDAC defines the “From” date as “…the first day on the billing statement covering services rendered to the beneficiary.”²⁴ For most hospitals, this is a three-day period prior to an inpatient admission where outpatient services are included in the Part A claim.²⁵ We defined Category 1 as ORC codes occurring prior to claim “From” date; Category 2 as ORC codes on the inpatient “From” date, between the inpatient “From” date and admission date, or on the admission date; Category 3 as ORC codes between admission and discharge dates; and Category 4 as ORC codes occurring on or after the discharge date. Given that Category 4 represents the final hospitalization status, we considered Category 4 ORC codes in the outpatient revenue center associated with inpatient claims to be observation stays that had undergone a status change from inpatient to outpatient (observation).

University of Wisconsin Method

After excluding ORC codes in the inpatient revenue center as true inpatient hospitalizations, we defined an observation stay as 0760 and/or 0761 and/or 0762 and/or 0769 in the outpatient revenue center and having no association with an inpatient claim. To address a status change from inpatient to outpatient (observation), for those ORC codes in the outpatient revenue center also associated with an inpatient claim, claims with ORC codes in Category 4 were also considered observation stays.

Of 1,667,660 hospital events, 125,920 (7.6%) had an ORC code within 30 days of an inpatient hospitalization, of which 50,418 (3.0%) were found in the inpatient revenue center and 75,502 (4.5%) were from the outpatient revenue center. A total of 59,529 (47.3%) ORC codes occurred with an inpatient claim (50,418 in the inpatient revenue center and 9,111 in the outpatient revenue center), 5,628 (4.5%) had more than one ORC code on a single hospitalization, and more than 90% of codes were 0761 or 0762. These results illustrated variability in claims submissions as measured by the claims themselves and demonstrated a high rate of status changes (Table).

Observation stay definitions varied in the literature, with different methods capturing variable numbers of observation stays (Figure, Appendix). No methods clearly identified how to categorize events with status changes, directly addressed ORC codes in the inpatient revenue center, or discussed events with more than one ORC code. As such, some assumptions were made to extrapolate observation stay case findings as detailed in the Figure (see also Appendix). Notably, reference 4 methods were obtained via personal communication with the manuscript’s first author. The University of Wisconsin definition offers a comprehensive definition that classifies status change events, yielding 72,858 of 75,502 (96.5%) potential observation events as observation stays (Figure). These observation stays include 66,391 stays with ORC codes in the outpatient revenue center without status change or relation to inpatient claim, and 6467 (71.0%) of 9111 events with ORC codes in the outpatient revenue center were associated with an inpatient claim where ORC code(s) is located in Category 4.

This study confirmed the importance of a standard observation stay case finding methodology. Variability in prior methodology resulted in studies that may have included less than half of potential observation stays. In addition, most studies did not include, or were unclear, on how to address the increasing number of status changes. Others may have erroneously included hospitalizations that were ultimately billed as inpatient, and some publications lacked sufficient detailed methodology to extrapolate results with absolute certainty, a limitation of our comparative results. Although excluding some ORC codes in the outpatient revenue center associated with inpatient claims may possibly miss some observation stays, or including certain ORC codes, such as 0761 (treatment or observation room - treatment room), may erroneously include a different type of observation stay, the proposed University of Wisconsin method could be used as a comprehensive and reproducible method for observation stay case finding, including encounters with status change.

This study has other important policy implications. More than 90% of ORC codes were either 0761 or 0762, and in almost one in 20 claims, two or more distinct codes were identified. Given the lack of clinical relevance of terms “treatment” or “observation” room, and the frequency of more than 1 ORC code per claim, CMS may consider simplification to a single ORC code. Studies of observation encounter length of stay by hour may require G0378 in addition to an ORC code to define an observation stay, but doing so eliminates nearly half of observation claims. Additionally, G0379 adds minimal value to G0378 in case finding.

Finally, this study illustrates overall confusion with outpatient (observation) and inpatient status designations, with almost half (47.3%) of all hospitalizations with ORC codes also associated with an inpatient claim, demonstrating a high status change rate. More than 40% of all nurse case manager job postings are now for status determination work, shifting duties from patient care and quality improvement.²⁶ We previously demonstrated a need for 5.1 FTE combined physician, attorney, and other personnel to manage the status, audit, and appeals process per institution.²⁷ The frequency of status changes and personnel needed to maintain a two-tiered billing system argues for a single hospital status.

In summary, our study highlights the need for federal observation policy reform. We propose a standardized and reproducible approach for Medicare observation claims research, including status changes that can be used for further studies of observation stays.

Acknowledgments

The authors thank Jinn-ing Liou for analyst support, Jen Birstler for figure creation, and Carol Hermann for technical support. This work was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD010243 (Dr. Kind).

Disclosures

The authors have no relevant conflicts of interest to disclose.Funding: This work was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD010243 (

Medicare observation stays are increasingly common. From 2006 to 2012, Medicare observation stays increased by 88%,¹ whereas inpatient discharges decreased by 13.9%.² In 2012, 1.7 million Medicare observation stays were recorded, and an additional 700,000 inpatient stays were preceded by observation services; the latter represented a 96% increase in status change since 2006.¹ Yet no standard research methodology for identifying observation stays exists, including methods to identify and properly characterize “status change” events, which are hospital stays where initial and final inpatient or outpatient (observation) statuses differ.

With the increasing number of hospitalized patients classified as observation, a standard methodology for Medicare claims research is needed so that observation stays can be consistently identified and potentially included in future quality measures and outcomes. Existing research studies and government reports use widely varying criteria to identify observation stays, often lack detailed methods on observation stay case finding, and contain limited information on how status changes between inpatient and outpatient (observation) statuses are incorporated. This variability in approach may lead to omission and/or miscategorization of events and raises concern about the comparability of prior work.

This study aimed to determine the claims patterns of Medicare observation stays, define comprehensive claims-based methodology for future Medicare observation research and data reporting, and identify policy implications of such definition. We are poised to do this work because of our access to the nationally generalizable Centers for Medicare & Medicaid Services (CMS) linked Part A inpatient and outpatient data sets for 2013 and 2014, as well as our prior expertise in hospital observation research and Medicare claims studies.

General Methods and Data Source

A 2014 national 20% random sample Part A and B Medicare data set was used. In accordance with the Centers for Medicare & Medicaid (CMS) data use agreement, all included beneficiaries had at least 1 acute care inpatient hospitalization. Included beneficiaries were enrolled for 12 months prior to their first 2014 inpatient stay. Those with Medicare Advantage or railroad benefits were excluded because of incomplete data per prior methods.³ The University of Wisconsin Institutional Review Board approved this study.

Comparison of Methods

The PubMED query “Medicare AND (observation OR observation unit),” limited to English and publication between January 1, 2010 and October 1, 2017, was conducted to determine the universe of prior observation stay definitions used in research for comparison (Appendix).^4-20 The Office of Inspector General report,²¹ the Research Data Assistance Center (ResDAC),²² and Medicare Claims Processing Manual (MCPM)²³ were also included. Methods stated in each publication were used to extrapolate observation stay case finding to the study data set.

Observation Stay Case Finding

Inpatient and outpatient revenue centers were queried for observation revenue center (ORC) codes identified by ResDAC,²² including 0760 (Treatment or observation room - general classification), 0761 (Treatment or observation room - treatment room), 0762 (Treatment or observation room – observation room), and 0769 (Treatment or observation room – other) occurring within 30 days of an inpatient stay. Healthcare Common Procedure Coding System (HCPCS) codes G0378 (Hospital observation service, per hour) and G0379 (Direct referral of patient for hospital observation care) were included per MCPM.²³ A combination of these ORC and HCPCS codes was also used to identify observation stays in every Medicare claims observation study since 2010. When more than one ORC code per event was found, each ORC was recorded as part of that event. Presence of HCPCS G0378 and/or G0379 was determined for each event in association with event ORC(s), as was association of ORC codes with inpatient claims. In this manuscript, “observation stay” refers to an observation hospital stay, and “event” refers to a hospitalization that may include inpatient and/or outpatient (observation) services and ORC codes.

Status Change

All ORC codes found in the inpatient revenue center were assumed to represent status changes from outpatient (observation) to inpatient, as ORC codes may remain in claims data when the status changes to inpatient.²⁴ Therefore, all events with ORC codes in the inpatient revenue center were considered inpatient hospitalizations.

For each ORC code found in the outpatient revenue center and also associated with an inpatient claim, timing of the ORC code in the inpatient claim was grouped into four categories to determine events with the final status of outpatient (observation stay). ResDAC defines the “From” date as “…the first day on the billing statement covering services rendered to the beneficiary.”²⁴ For most hospitals, this is a three-day period prior to an inpatient admission where outpatient services are included in the Part A claim.²⁵ We defined Category 1 as ORC codes occurring prior to claim “From” date; Category 2 as ORC codes on the inpatient “From” date, between the inpatient “From” date and admission date, or on the admission date; Category 3 as ORC codes between admission and discharge dates; and Category 4 as ORC codes occurring on or after the discharge date. Given that Category 4 represents the final hospitalization status, we considered Category 4 ORC codes in the outpatient revenue center associated with inpatient claims to be observation stays that had undergone a status change from inpatient to outpatient (observation).

University of Wisconsin Method

After excluding ORC codes in the inpatient revenue center as true inpatient hospitalizations, we defined an observation stay as 0760 and/or 0761 and/or 0762 and/or 0769 in the outpatient revenue center and having no association with an inpatient claim. To address a status change from inpatient to outpatient (observation), for those ORC codes in the outpatient revenue center also associated with an inpatient claim, claims with ORC codes in Category 4 were also considered observation stays.

Of 1,667,660 hospital events, 125,920 (7.6%) had an ORC code within 30 days of an inpatient hospitalization, of which 50,418 (3.0%) were found in the inpatient revenue center and 75,502 (4.5%) were from the outpatient revenue center. A total of 59,529 (47.3%) ORC codes occurred with an inpatient claim (50,418 in the inpatient revenue center and 9,111 in the outpatient revenue center), 5,628 (4.5%) had more than one ORC code on a single hospitalization, and more than 90% of codes were 0761 or 0762. These results illustrated variability in claims submissions as measured by the claims themselves and demonstrated a high rate of status changes (Table).

Observation stay definitions varied in the literature, with different methods capturing variable numbers of observation stays (Figure, Appendix). No methods clearly identified how to categorize events with status changes, directly addressed ORC codes in the inpatient revenue center, or discussed events with more than one ORC code. As such, some assumptions were made to extrapolate observation stay case findings as detailed in the Figure (see also Appendix). Notably, reference 4 methods were obtained via personal communication with the manuscript’s first author. The University of Wisconsin definition offers a comprehensive definition that classifies status change events, yielding 72,858 of 75,502 (96.5%) potential observation events as observation stays (Figure). These observation stays include 66,391 stays with ORC codes in the outpatient revenue center without status change or relation to inpatient claim, and 6467 (71.0%) of 9111 events with ORC codes in the outpatient revenue center were associated with an inpatient claim where ORC code(s) is located in Category 4.

This study confirmed the importance of a standard observation stay case finding methodology. Variability in prior methodology resulted in studies that may have included less than half of potential observation stays. In addition, most studies did not include, or were unclear, on how to address the increasing number of status changes. Others may have erroneously included hospitalizations that were ultimately billed as inpatient, and some publications lacked sufficient detailed methodology to extrapolate results with absolute certainty, a limitation of our comparative results. Although excluding some ORC codes in the outpatient revenue center associated with inpatient claims may possibly miss some observation stays, or including certain ORC codes, such as 0761 (treatment or observation room - treatment room), may erroneously include a different type of observation stay, the proposed University of Wisconsin method could be used as a comprehensive and reproducible method for observation stay case finding, including encounters with status change.

This study has other important policy implications. More than 90% of ORC codes were either 0761 or 0762, and in almost one in 20 claims, two or more distinct codes were identified. Given the lack of clinical relevance of terms “treatment” or “observation” room, and the frequency of more than 1 ORC code per claim, CMS may consider simplification to a single ORC code. Studies of observation encounter length of stay by hour may require G0378 in addition to an ORC code to define an observation stay, but doing so eliminates nearly half of observation claims. Additionally, G0379 adds minimal value to G0378 in case finding.

Finally, this study illustrates overall confusion with outpatient (observation) and inpatient status designations, with almost half (47.3%) of all hospitalizations with ORC codes also associated with an inpatient claim, demonstrating a high status change rate. More than 40% of all nurse case manager job postings are now for status determination work, shifting duties from patient care and quality improvement.²⁶ We previously demonstrated a need for 5.1 FTE combined physician, attorney, and other personnel to manage the status, audit, and appeals process per institution.²⁷ The frequency of status changes and personnel needed to maintain a two-tiered billing system argues for a single hospital status.

In summary, our study highlights the need for federal observation policy reform. We propose a standardized and reproducible approach for Medicare observation claims research, including status changes that can be used for further studies of observation stays.

Acknowledgments

The authors thank Jinn-ing Liou for analyst support, Jen Birstler for figure creation, and Carol Hermann for technical support. This work was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD010243 (Dr. Kind).

Disclosures

The authors have no relevant conflicts of interest to disclose.Funding: This work was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD010243 (

A new iteration of the SPRINT MIND hypertension trial will seek to conclusively prove that intensiveblood pressure control decreases the risk of developing mild cognitive impairment. Also today, medical ethics and economics, PTSD after traumatic brain injury may be predicted by race and ethnicity, and aspirin for primary cardiovascular prevention.

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Apple Podcasts

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A new iteration of the SPRINT MIND hypertension trial will seek to conclusively prove that intensiveblood pressure control decreases the risk of developing mild cognitive impairment. Also today, medical ethics and economics, PTSD after traumatic brain injury may be predicted by race and ethnicity, and aspirin for primary cardiovascular prevention.

Amazon Alexa

Apple Podcasts

Google Podcasts

Spotify
 

A new iteration of the SPRINT MIND hypertension trial will seek to conclusively prove that intensiveblood pressure control decreases the risk of developing mild cognitive impairment. Also today, medical ethics and economics, PTSD after traumatic brain injury may be predicted by race and ethnicity, and aspirin for primary cardiovascular prevention.

Amazon Alexa

Apple Podcasts

Google Podcasts

Spotify
 

Click here to access February 2019 Prostate Cancer

Table of Contents

• Prostate Cancer Surveillance After Radiation Therapy in a National Delivery System
• Skeletal-Related Events in Patients With Multiple Myeloma and Prostate Cancer Who Receive Standard vs Extended-Interval Bisphosphonate Dosing
• Primary Urethral Carcinoma With Nodal Metastasis
• Presentation of a Rare Malignancy: Leiomyosarcoma of the Prostate
• Research News: Prostate Cancer
• Management of Patients With Treatment-Resistant Metastatic Prostate Cancer

Click here to access February 2019 Prostate Cancer

Table of Contents

• Prostate Cancer Surveillance After Radiation Therapy in a National Delivery System
• Skeletal-Related Events in Patients With Multiple Myeloma and Prostate Cancer Who Receive Standard vs Extended-Interval Bisphosphonate Dosing
• Primary Urethral Carcinoma With Nodal Metastasis
• Presentation of a Rare Malignancy: Leiomyosarcoma of the Prostate
• Research News: Prostate Cancer
• Management of Patients With Treatment-Resistant Metastatic Prostate Cancer

Click here to access February 2019 Prostate Cancer

Table of Contents

• Prostate Cancer Surveillance After Radiation Therapy in a National Delivery System
• Skeletal-Related Events in Patients With Multiple Myeloma and Prostate Cancer Who Receive Standard vs Extended-Interval Bisphosphonate Dosing
• Primary Urethral Carcinoma With Nodal Metastasis
• Presentation of a Rare Malignancy: Leiomyosarcoma of the Prostate
• Research News: Prostate Cancer
• Management of Patients With Treatment-Resistant Metastatic Prostate Cancer

Click here to access the 2019 Directory of VA and DoD Facilities Digital Edition

Table of Contents

• Department of Defense Health Care Facilities
• Ad Index

 

Click here to access the 2019 Directory of VA and DoD Facilities Digital Edition

Table of Contents

• Department of Defense Health Care Facilities
• Ad Index

 

Click here to access the 2019 Directory of VA and DoD Facilities Digital Edition

Table of Contents

• Department of Defense Health Care Facilities
• Ad Index

 

When patients with inflammatory bowel disease report persistent gastrointestinal symptoms, clinicians should perform a thorough clinical assessment and then take a stepwise approach to rule out ongoing inflammation, according to a clinical practice update from the American Gastroenterological Association.

A fecal calprotectin test can be useful because values under 50 mcg/mL may suggest endoscopic remission, which may, in turn, point to another etiology of gastrointestinal symptoms, wrote Jean-Frederic Colombel, MD, of the Icahn School of Medicine at Mount Sinai, New York, together with his associates in Clinical Gastroenterology and Hepatology.

However, a result between 50 and 250 mcg/mL is harder to interpret because the upper limit of normal varies and mild increases can occur secondary to nonspecific low-grade inflammation, according to the experts. For mild gastrointestinal symptoms, they suggested testing fecal calprotectin every 3-6 months to identify flares as early as possible. If a flare is suspected, they advised considering cross-sectional imaging or endoscopy with biopsy.

Imaging also is indicated for patients with obstructive symptoms such as abdominal pain, obstipation, or constipation, the practice update states. Such symptoms can indicate fecal stasis proximal to distal colitis in patients with ulcerative colitis, or intestinal stenosis in patients with Crohn’s disease.

Other pathophysiologies of gastrointestinal symptoms also should be considered based on constellations of symptoms. For example, steatorrhea with chronic abdominal pain may indicate pancreatic exocrine insufficiency, which fecal elastase testing can help confirm. Symptoms of diarrhea-predominant irritable bowel syndrome can result from bile acid diarrhea, for which several screening tests are available. Diarrhea, abdominal pain, and bloating may indicate carbohydrate malabsorption or small-intestinal bacterial overgrowth, which can be evaluated with breath testing.

If patients with inflammatory bowel disease have persistent gastrointestinal symptoms but lack objective evidence of ongoing inflammation or another etiology, then clinicians should increase their suspicion of an overlapping functional gastrointestinal disorder. These conditions actually “share many common pathophysiologic disturbances that, in some inflammatory bowel disease patients, may be a consequence of prior structural and functional bowel damage,” the experts wrote.

For patients with chronic constipation who do not have an underlying obstruction, they suggest osmotic or stimulant laxatives. For chronic diarrhea, they recommend hypomobility agents or bile-acid sequestrants. Patients with fecal symptoms of irritable bowel syndrome also should be evaluated for pelvic floor disorders, which may improve with biofeedback therapy, the experts state.

A low-FODMAP diet (a diet low in lactose, fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) also can improve symptoms of irritable bowel syndrome, including patients with inflammatory bowel disease. However, a dietitian always should deliver this restrictive diet because patients with inflammatory bowel disease already are at increased risk for undernutrition.

Patients with functional gastrointestinal pain may benefit from antispasmodics, neuropathic-directed agents, and antidepressants, but they should not receive opiates, the experts emphasized. Anxiety and depression are common in both inflammatory bowel disease and irritable bowel syndrome, and patients may benefit from psychotherapy (cognitive-behavioral therapy, hypnotherapy, and mindfulness therapy), antidepressants, anxiolytics, or combinations of these treatments. The practice update also recommends physical exercise, which has been shown to decrease the risk of recurrent active disease in the setting of inflammatory bowel disease.

Finally, persistent gut symptoms can indicate intestinal barrier dysfunction, even if endoscopy shows mucosal healing. Barrier dysfunction is a potential therapeutic target that needs further study in this setting, the experts noted. They also called for studies of the potential benefits and risks of probiotics and other alternative approaches, such as herbal treatments and supplements, yoga, acupuncture, and moxibustion. Until further evidence, however, they have recommended against complementary or alternative medicine or fecal microbiota transplantation.

Dr. Colombel has served as consultant, advisory board member, or speaker for AbbVie, Amgen, Boehringer-Ingelheim, Celgene Corporation, and many other pharmaceutical companies. He has received research grants from AbbVie, Takeda, and Janssen and Janssen.

SOURCE: Colombel J-F et al. Clin Gastroenterol Hepatol. 2018 Aug 9. doi: 10.1016/j.cgh.2018.08.001.

When patients with inflammatory bowel disease report persistent gastrointestinal symptoms, clinicians should perform a thorough clinical assessment and then take a stepwise approach to rule out ongoing inflammation, according to a clinical practice update from the American Gastroenterological Association.

A fecal calprotectin test can be useful because values under 50 mcg/mL may suggest endoscopic remission, which may, in turn, point to another etiology of gastrointestinal symptoms, wrote Jean-Frederic Colombel, MD, of the Icahn School of Medicine at Mount Sinai, New York, together with his associates in Clinical Gastroenterology and Hepatology.

However, a result between 50 and 250 mcg/mL is harder to interpret because the upper limit of normal varies and mild increases can occur secondary to nonspecific low-grade inflammation, according to the experts. For mild gastrointestinal symptoms, they suggested testing fecal calprotectin every 3-6 months to identify flares as early as possible. If a flare is suspected, they advised considering cross-sectional imaging or endoscopy with biopsy.

Imaging also is indicated for patients with obstructive symptoms such as abdominal pain, obstipation, or constipation, the practice update states. Such symptoms can indicate fecal stasis proximal to distal colitis in patients with ulcerative colitis, or intestinal stenosis in patients with Crohn’s disease.

Other pathophysiologies of gastrointestinal symptoms also should be considered based on constellations of symptoms. For example, steatorrhea with chronic abdominal pain may indicate pancreatic exocrine insufficiency, which fecal elastase testing can help confirm. Symptoms of diarrhea-predominant irritable bowel syndrome can result from bile acid diarrhea, for which several screening tests are available. Diarrhea, abdominal pain, and bloating may indicate carbohydrate malabsorption or small-intestinal bacterial overgrowth, which can be evaluated with breath testing.

If patients with inflammatory bowel disease have persistent gastrointestinal symptoms but lack objective evidence of ongoing inflammation or another etiology, then clinicians should increase their suspicion of an overlapping functional gastrointestinal disorder. These conditions actually “share many common pathophysiologic disturbances that, in some inflammatory bowel disease patients, may be a consequence of prior structural and functional bowel damage,” the experts wrote.

For patients with chronic constipation who do not have an underlying obstruction, they suggest osmotic or stimulant laxatives. For chronic diarrhea, they recommend hypomobility agents or bile-acid sequestrants. Patients with fecal symptoms of irritable bowel syndrome also should be evaluated for pelvic floor disorders, which may improve with biofeedback therapy, the experts state.

A low-FODMAP diet (a diet low in lactose, fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) also can improve symptoms of irritable bowel syndrome, including patients with inflammatory bowel disease. However, a dietitian always should deliver this restrictive diet because patients with inflammatory bowel disease already are at increased risk for undernutrition.

Patients with functional gastrointestinal pain may benefit from antispasmodics, neuropathic-directed agents, and antidepressants, but they should not receive opiates, the experts emphasized. Anxiety and depression are common in both inflammatory bowel disease and irritable bowel syndrome, and patients may benefit from psychotherapy (cognitive-behavioral therapy, hypnotherapy, and mindfulness therapy), antidepressants, anxiolytics, or combinations of these treatments. The practice update also recommends physical exercise, which has been shown to decrease the risk of recurrent active disease in the setting of inflammatory bowel disease.

Finally, persistent gut symptoms can indicate intestinal barrier dysfunction, even if endoscopy shows mucosal healing. Barrier dysfunction is a potential therapeutic target that needs further study in this setting, the experts noted. They also called for studies of the potential benefits and risks of probiotics and other alternative approaches, such as herbal treatments and supplements, yoga, acupuncture, and moxibustion. Until further evidence, however, they have recommended against complementary or alternative medicine or fecal microbiota transplantation.

Dr. Colombel has served as consultant, advisory board member, or speaker for AbbVie, Amgen, Boehringer-Ingelheim, Celgene Corporation, and many other pharmaceutical companies. He has received research grants from AbbVie, Takeda, and Janssen and Janssen.

SOURCE: Colombel J-F et al. Clin Gastroenterol Hepatol. 2018 Aug 9. doi: 10.1016/j.cgh.2018.08.001.

When patients with inflammatory bowel disease report persistent gastrointestinal symptoms, clinicians should perform a thorough clinical assessment and then take a stepwise approach to rule out ongoing inflammation, according to a clinical practice update from the American Gastroenterological Association.

A fecal calprotectin test can be useful because values under 50 mcg/mL may suggest endoscopic remission, which may, in turn, point to another etiology of gastrointestinal symptoms, wrote Jean-Frederic Colombel, MD, of the Icahn School of Medicine at Mount Sinai, New York, together with his associates in Clinical Gastroenterology and Hepatology.

However, a result between 50 and 250 mcg/mL is harder to interpret because the upper limit of normal varies and mild increases can occur secondary to nonspecific low-grade inflammation, according to the experts. For mild gastrointestinal symptoms, they suggested testing fecal calprotectin every 3-6 months to identify flares as early as possible. If a flare is suspected, they advised considering cross-sectional imaging or endoscopy with biopsy.

Imaging also is indicated for patients with obstructive symptoms such as abdominal pain, obstipation, or constipation, the practice update states. Such symptoms can indicate fecal stasis proximal to distal colitis in patients with ulcerative colitis, or intestinal stenosis in patients with Crohn’s disease.

Other pathophysiologies of gastrointestinal symptoms also should be considered based on constellations of symptoms. For example, steatorrhea with chronic abdominal pain may indicate pancreatic exocrine insufficiency, which fecal elastase testing can help confirm. Symptoms of diarrhea-predominant irritable bowel syndrome can result from bile acid diarrhea, for which several screening tests are available. Diarrhea, abdominal pain, and bloating may indicate carbohydrate malabsorption or small-intestinal bacterial overgrowth, which can be evaluated with breath testing.

If patients with inflammatory bowel disease have persistent gastrointestinal symptoms but lack objective evidence of ongoing inflammation or another etiology, then clinicians should increase their suspicion of an overlapping functional gastrointestinal disorder. These conditions actually “share many common pathophysiologic disturbances that, in some inflammatory bowel disease patients, may be a consequence of prior structural and functional bowel damage,” the experts wrote.

For patients with chronic constipation who do not have an underlying obstruction, they suggest osmotic or stimulant laxatives. For chronic diarrhea, they recommend hypomobility agents or bile-acid sequestrants. Patients with fecal symptoms of irritable bowel syndrome also should be evaluated for pelvic floor disorders, which may improve with biofeedback therapy, the experts state.

A low-FODMAP diet (a diet low in lactose, fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) also can improve symptoms of irritable bowel syndrome, including patients with inflammatory bowel disease. However, a dietitian always should deliver this restrictive diet because patients with inflammatory bowel disease already are at increased risk for undernutrition.

Patients with functional gastrointestinal pain may benefit from antispasmodics, neuropathic-directed agents, and antidepressants, but they should not receive opiates, the experts emphasized. Anxiety and depression are common in both inflammatory bowel disease and irritable bowel syndrome, and patients may benefit from psychotherapy (cognitive-behavioral therapy, hypnotherapy, and mindfulness therapy), antidepressants, anxiolytics, or combinations of these treatments. The practice update also recommends physical exercise, which has been shown to decrease the risk of recurrent active disease in the setting of inflammatory bowel disease.

Finally, persistent gut symptoms can indicate intestinal barrier dysfunction, even if endoscopy shows mucosal healing. Barrier dysfunction is a potential therapeutic target that needs further study in this setting, the experts noted. They also called for studies of the potential benefits and risks of probiotics and other alternative approaches, such as herbal treatments and supplements, yoga, acupuncture, and moxibustion. Until further evidence, however, they have recommended against complementary or alternative medicine or fecal microbiota transplantation.

Dr. Colombel has served as consultant, advisory board member, or speaker for AbbVie, Amgen, Boehringer-Ingelheim, Celgene Corporation, and many other pharmaceutical companies. He has received research grants from AbbVie, Takeda, and Janssen and Janssen.

SOURCE: Colombel J-F et al. Clin Gastroenterol Hepatol. 2018 Aug 9. doi: 10.1016/j.cgh.2018.08.001.

From the Division of Rheumatology & Immunology, University of Nebraska Medical Center, and Veterans Affairs Nebraska-Western Iowa Health Care System, Omaha, NE.

Abstract

• Objective: To review cardiovascular disease (CVD) risk assessment in patients with rheumatoid arthritis (RA).
• Methods: Literature review of the assessment of CVD risk in RA.
• Results: CVD is the leading cause of death among RA patients. Because of the increased risk of CVD events and CVD mortality in patients with RA, regular assessment of CVD risk and aggressive management of CVD risk in these patients is crucial. CVD risk estimation typically centers on the use of well-established CVD risk calculators. Most CVD risk scores from the general population do not contain RA-related factors predictive of CVD but have had more extensive performance testing, while novel RA-derived CVD risk scores that incorporate RA-related factors have had limited external validity testing. Neither set of risk scores incorporates novel imaging modalities or serum biomarkers, which are most likely to be helpful among individuals at intermediate risk.
• Conclusion: Primary care and rheumatology providers must be aware of the increased risk of CVD in RA, a risk that approaches that of diabetic patients. Routine assessment of CVD risk is an essential first step in minimizing CVD risk in this population. Until the performance of RA-specific CVD risk scores can be better established, we recommend the use of nationally endorsed CVD risk scores, with the frequency of reassessment based on CVD risk.

Keywords: rheumatoid arthritis; cardiovascular disease; cardiovascular risk assessment.

Editor’s note: This article is part 1 of a 2-part article. “Management of Cardiovascular Disease Risk in Rheumatoid Arthritis” was published in the March/April 2019 issue.

Rheumatoid arthritis (RA) is a chronic, autoimmune inflammatory arthritis affecting up to 1% of the US population that can lead to joint damage, functional disability, and reduced quality of life.¹ In addition to articular involvement, systemic inflammation accompanying RA may lead to extra-articular manifestations and increase the risk of premature death.² Cardiovascular disease (CVD), accounting for nearly half of all deaths among RA patients, is now recognized as a critical extra-articular manifestation of RA.^2,3 As such, assessment and management of CVD risk is essential to the comprehensive care of the RA patient. This article reviews the approach to assessing CVD risk in patients with RA; the management of both traditional and RA-specific risk factors is discussed in a separate article.

Scope of the Problem

In a large meta-analysis of observational studies that included more than 111,000 patients with RA, CVD-related mortality rates were 1.5 times higher among RA patients than among general population controls.⁴ The risk of overall CVD, including nonfatal events, is similar; a separate meta-analysis of observational studies that included more than 41,000 patients with RA calculated a pooled relative risk for incident CVD of 1.48.⁵ Individual analyses identified heightened risk of acute coronary syndrome (ACS), cerebrovascular accident, and congestive heart failure (CHF).⁵ Perhaps more illustrative of the magnitude of the problem, the risk of CVD in RA approaches that observed among individuals with diabetes mellitus.^6,7

Coronary artery disease (CAD) accounts for a significant portion of the CVD risk in RA, but its presentation may be atypical in RA patients. RA patients are at higher risk of suffering unrecognized myocardial infarction (MI) and sudden cardiac death.⁸ The reasons for silent ischemia in RA are not fully known, but have been hypothesized to include imbalances of inflammatory cytokines, alterations in pain sensitization, or the female predominance of RA (with women more often presenting with atypical symptoms of myocardial ischemia).⁹ Alarmingly, a retrospective chart review study reported that RA patients admitted for an acute MI were less likely to receive appropriate reperfusion therapy as well as secondary prevention with beta-blockers and lipid-lowering agents.¹⁰ Even with appropriate therapy, long-term outcomes such as mortality and recurrent ischemic events are more likely to occur in RA patients after acute MI.^11-13

Independent of ischemic heart disease, RA patients are at increased risk of CHF.^14-16 RA patients are at particular risk for CHF with preserved ejection fraction,¹⁷ which may be a result of systemic inflammation causing left ventricular stiffening.^18,19 Similar to CAD, patients with RA are less likely to present with typical CHF symptoms, are less likely to receive guideline-concordant care, and have higher mortality rates following presentation with CHF.¹⁷

Although accounting for a lower proportion of the excess CVD morbidity and mortality in RA, the risk of noncardiac vascular disease is also increased in RA patients. Large meta-analyses have identified positive associations between RA with both ischemic (odds ratio [OR], 1.64 [95% confidence interval {CI}, 1.32-2.05]) and hemorrhagic (OR, 1.68 [95% CI, 1.11-2.53]) stroke.²⁰ Similarly, RA patients appear to have an approximately twofold higher risk of venous thromboembolic events.²¹ Less frequently studied than other forms of CVD, peripheral arterial disease may be increased in RA patients independent of other CVD and CVD risk factors.^22,23

Assessing CVD Risk in RA

CVD Risk Scores

In order to identify patients who may benefit from primary prevention interventions, such as lipid-lowering therapy, CVD risk estimation typically centers on the use of well-established CVD risk calculators (Table). CVD risk scores such as the Framingham Risk Score (FRS), Systematic Coronary Risk Evaluation (SCORE), and American College of Cardiology/ American Heart Association (ACC/AHA) Pooled Cohort Equation incorporate traditional CVD risk factors, including age, sex, smoking status, blood pressure, lipid levels, and presence of diabetes mellitus.^24,25 However, CVD risk in RA patients appears to be inadequately explained by traditional CVD risk factors,²⁶ with disease activity and inflammation being associated with higher CVD risk. Recognizing that inflammation may contribute to CVD risk even among non-RA patients, the Reynolds Risk Score includes high-sensitivity C-reactive protein (hsCRP) in its calculation.²⁷ In contrast to more robust performance in the general population, these well-established CVD risk scores have had variable predictive potential of incident CVD in RA patients.^28-30

Several models, or adaptations to existing models, have been proposed to improve CVD risk assessment in RA populations (Table). In 2009, the European League Against Rheumatism (EULAR) task force suggested using a correction factor of 1.5 with traditional CVD risk models in RA patients with 2 of the following criteria: disease duration exceeding 10 years, rheumatoid factor or anti-cyclic citrullinated peptide (CCP) antibody positivity, or extra-articular manifestations of RA.³¹ An update to these recommendations in 2015 continued to propose the use of a 1.5 correction factor, but suggested applying this to all RA patients.³² QRISK2, a modification to QRISK1 which was developed to predict CVD in the UK general population, includes the diagnosis of RA as a risk factor, and in early validation efforts more accurately discriminated patients in the general population at increased risk of CVD compared to the FRS.³³ Additional disease-specific risk factors such as systemic lupus, steroid use, severe mental illness, and steroid and atypical antipsychotic use were incorporated in the QRISK3 algorithm, with model performance similar to the QRISK2.³⁴ The Expanded Cardiovascular Risk Prediction Score for RA (ERS-RA) was specifically developed to assess CVD risk in RA patients by including RA disease activity, level of physical disability, RA disease duration, and prednisone use.³⁵ Despite efforts to develop “RA-specific” risk scores, these have not consistently outperformed traditional CVD risk calculators.^36-38 In one study involving more than 1700 RA patients, the ERS-RA performed similarly to the FRS and Reynolds Risk Score, with a net reclassification index of just 2.3% versus the FRS.³⁶

Imaging Modalities

Imaging modalities may assist in characterizing the increased risk of CVD in RA and the subclinical CVD manifestations that occur. For example, RA patients were shown to have more prevalent and unstable coronary plaque, higher carotid intima media thickness, and impaired myocardial function with computed tomography (CT) angiography and carotid ultrasound.^39,40 However, studies harnessing noninvasive imaging to augment CVD risk assessment in RA patients are limited.

Carotid ultrasound has been the most extensively studied imaging modality for CVD risk assessment in RA. In a cohort of 599 RA patients with no history of ACS, rates of ACS were nearly 4 times higher in RA patients with bilateral carotid plaque on carotid ultrasound, and the association with ACS was independent of other traditional and RA-related risk factors.⁴¹ Presence of bilateral carotid plaques was similarly associated with an increased risk of overall CVD events (hazard ratio [HR], 3.34 [95% CI, 1.21-9.22]), ACS alone (HR, 6.31 [95% CI, 1.27-31.40]), and a lower mean CVD event-free survival (13.9 versus 15.2 years, P = 0.01) in a separate inception cohort of 105 RA patients with no prior history of CVD.⁴² The most useful application of carotid ultrasound may be in conjunction with clinical CVD risk models. Use of carotid ultrasound improved CVD risk stratification among RA patients who were considered at moderate risk by the EULAR-modified SCORE calculator.⁴³ Beyond carotid ultrasound, measurement of arterial stiffness through ultrasound could also aid in CVD risk stratification. Aortic pulse wave velocity and augmentation index, measures of arterial stiffness, are predictive of CVD in the general population as well as RA patients and improve with reduction in RA disease activity.^44,45 Peripheral arterial stiffness (brachial-ankle elasticity index) is impaired in RA patients and predictive of CVD morbidity and mortality in the general population.^46,47

CT coronary angiography and coronary artery calcium (CAC) scores are reliable measures of coronary artery atherosclerosis and have been validated for CVD risk assessment in the general population.^48-52 While the association between RA and CT-related findings of atherosclerosis is well established, assessment of CT-mediated evaluation as a prognostic tool for CVD in RA is limited. In one cohort study, CAC predicted higher rates of CVD events in Chinese patients with RA and systemic lupus erythematosus in a pooled analysis, although results were limited by low event rates and the absence of RA-only subanalyses.⁵³

While the aforementioned imaging modalities have focused on enhancing the identification of atherosclerosis, echocardiography or cardiac magnetic resonance imaging (MRI) may be useful for detecting subclinical structural and/or functional abnormalities that predispose to CHF. Structural abnormalities including increased left ventricular mass and hypertrophy are more prevalent in RA patients and predict incident CHF in the general population.^54-56 MRI measures of myocardial inflammation, including T1 mapping and extracellular volume, are associated with higher mortality rates and also appear to be elevated in RA patients.^57,58 Whether identification of these imaging findings influences the cost-effective clinical management of RA patients needs further study.

Biomarkers

Serum biomarkers, such as the anti-CCP antibody, have become crucial to the evaluation of patients suspected to have RA. With the growing understanding of the role pro-inflammatory mediators play in CVD pathogenesis and the relative ease with which they can be measured, serum biomarkers have potential to inform CVD risk assessment. In the general population, hsCRP concentrations are predictive of CVD and are included in the Reynolds Risk Score.²⁷ In RA, CRP concentrations are typically much higher than those observed among individuals in the general population solely at increased CVD risk, yet elevated levels remain predictive of CVD death independent of RA disease activity and traditional CVD risk factors.⁵⁹ Several additional cytokines, chemokines, and adhesion molecules have been associated with surrogate markers of CVD in RA patients, although further study is needed to elucidate thresholds that signify increased CVD risk in a population characterized by the presence of systemic inflammation.⁶⁰

Cardiac biomarkers used frequently in the general population may be useful to assess CVD risk in RA patients. N-terminal-pro brain natriuretic peptide (NT-pro BNP) is a biomarker typically used to evaluate CHF severity, but it may also predict long-term mortality in patients with coronary heart disease.^61,62 Circulating NT-pro BNP concentrations are elevated in RA independent of prevalent CHF and may serve as a useful tool to identify subclinical cardiac disease in RA patients.⁶³ High-sensitivity cardiac troponin I (HS-cTnI) assays are capable of detecting levels of cardiac troponin below the threshold typically used to diagnose ACS. HS-cTnI levels are increased in RA patients independent of additional CVD risk factors, and elevated levels (> 1.5 pg/mL) were associated with more severe CT angiography findings of coronary plaque as well as increased risk of CVD events.^64,65

A fully validated algorithm for CVD risk assessment in RA is lacking. Most CVD risk scores from the general population do not contain RA-related factors predictive of CVD but have had more extensive performance testing. In contrast, novel RA-derived CVD risk scores incorporate RA-related factors, but have had limited external validity testing. Additionally, RA-derived risk scores are less likely to be utilized and adopted by primary care providers and cardiologists involved in RA patients’ care. Neither set of risk scores incorporates novel imaging modalities or serum biomarkers, which are most likely to be helpful among individuals at intermediate risk. Therefore, until the performance of RA-specific CVD risk scores can be better established, we recommend the use of nationally endorsed CVD risk scores, with the frequency of reassessment based on CVD risk.

Conclusion

RA patients are at increased risk of CVD and CVD-related mortality relative to the general population. The disproportionate CVD burden seen in RA appears to be multifactorial, owing to the complex effects of systemic inflammation, endothelial dysfunction, and pro-atherogenic lipoprotein modifications. Additionally, many traditional CVD risk factors are more prevalent and suboptimally managed in RA patients. To mitigate the increased risk of CVD in RA, primary care and subspecialty providers alike must be aware of this heightened risk in RA, perform frequent assessment of CVD risk, and aggressively manage both traditional and nontraditional CVD risk factors. The management of CVD risk factors is discussed in detail in the second part of this article.

Corresponding author: Bryant R. England, MD; 986270 Nebraska Medical Center, Omaha, NE 68198-6270; [email protected].

Financial disclosures: Dr. England is supported by UNMC Internal Medicine Scientist Development Award, UNMC Physician-Scientist Training Program, the UNMC Mentored Scholars Program, and the Rheumatology Research Foundation Scientist Development Award. Dr. Mikuls is supported by a VA Merit Award (CX000896) and grants from the National Institutes of Health: National Institute of General Medical Sciences (U54GM115458), National Institute on Alcohol Abuse and Alcoholism (R25AA020818), and National Institute of Arthritis and Musculoskeletal and Skin Diseases (2P50AR60772).

From the Division of Rheumatology & Immunology, University of Nebraska Medical Center, and Veterans Affairs Nebraska-Western Iowa Health Care System, Omaha, NE.

Abstract

• Objective: To review cardiovascular disease (CVD) risk assessment in patients with rheumatoid arthritis (RA).
• Methods: Literature review of the assessment of CVD risk in RA.
• Results: CVD is the leading cause of death among RA patients. Because of the increased risk of CVD events and CVD mortality in patients with RA, regular assessment of CVD risk and aggressive management of CVD risk in these patients is crucial. CVD risk estimation typically centers on the use of well-established CVD risk calculators. Most CVD risk scores from the general population do not contain RA-related factors predictive of CVD but have had more extensive performance testing, while novel RA-derived CVD risk scores that incorporate RA-related factors have had limited external validity testing. Neither set of risk scores incorporates novel imaging modalities or serum biomarkers, which are most likely to be helpful among individuals at intermediate risk.
• Conclusion: Primary care and rheumatology providers must be aware of the increased risk of CVD in RA, a risk that approaches that of diabetic patients. Routine assessment of CVD risk is an essential first step in minimizing CVD risk in this population. Until the performance of RA-specific CVD risk scores can be better established, we recommend the use of nationally endorsed CVD risk scores, with the frequency of reassessment based on CVD risk.

Keywords: rheumatoid arthritis; cardiovascular disease; cardiovascular risk assessment.

Editor’s note: This article is part 1 of a 2-part article. “Management of Cardiovascular Disease Risk in Rheumatoid Arthritis” was published in the March/April 2019 issue.

Rheumatoid arthritis (RA) is a chronic, autoimmune inflammatory arthritis affecting up to 1% of the US population that can lead to joint damage, functional disability, and reduced quality of life.¹ In addition to articular involvement, systemic inflammation accompanying RA may lead to extra-articular manifestations and increase the risk of premature death.² Cardiovascular disease (CVD), accounting for nearly half of all deaths among RA patients, is now recognized as a critical extra-articular manifestation of RA.^2,3 As such, assessment and management of CVD risk is essential to the comprehensive care of the RA patient. This article reviews the approach to assessing CVD risk in patients with RA; the management of both traditional and RA-specific risk factors is discussed in a separate article.

Scope of the Problem

In a large meta-analysis of observational studies that included more than 111,000 patients with RA, CVD-related mortality rates were 1.5 times higher among RA patients than among general population controls.⁴ The risk of overall CVD, including nonfatal events, is similar; a separate meta-analysis of observational studies that included more than 41,000 patients with RA calculated a pooled relative risk for incident CVD of 1.48.⁵ Individual analyses identified heightened risk of acute coronary syndrome (ACS), cerebrovascular accident, and congestive heart failure (CHF).⁵ Perhaps more illustrative of the magnitude of the problem, the risk of CVD in RA approaches that observed among individuals with diabetes mellitus.^6,7

Coronary artery disease (CAD) accounts for a significant portion of the CVD risk in RA, but its presentation may be atypical in RA patients. RA patients are at higher risk of suffering unrecognized myocardial infarction (MI) and sudden cardiac death.⁸ The reasons for silent ischemia in RA are not fully known, but have been hypothesized to include imbalances of inflammatory cytokines, alterations in pain sensitization, or the female predominance of RA (with women more often presenting with atypical symptoms of myocardial ischemia).⁹ Alarmingly, a retrospective chart review study reported that RA patients admitted for an acute MI were less likely to receive appropriate reperfusion therapy as well as secondary prevention with beta-blockers and lipid-lowering agents.¹⁰ Even with appropriate therapy, long-term outcomes such as mortality and recurrent ischemic events are more likely to occur in RA patients after acute MI.^11-13

Independent of ischemic heart disease, RA patients are at increased risk of CHF.^14-16 RA patients are at particular risk for CHF with preserved ejection fraction,¹⁷ which may be a result of systemic inflammation causing left ventricular stiffening.^18,19 Similar to CAD, patients with RA are less likely to present with typical CHF symptoms, are less likely to receive guideline-concordant care, and have higher mortality rates following presentation with CHF.¹⁷

Although accounting for a lower proportion of the excess CVD morbidity and mortality in RA, the risk of noncardiac vascular disease is also increased in RA patients. Large meta-analyses have identified positive associations between RA with both ischemic (odds ratio [OR], 1.64 [95% confidence interval {CI}, 1.32-2.05]) and hemorrhagic (OR, 1.68 [95% CI, 1.11-2.53]) stroke.²⁰ Similarly, RA patients appear to have an approximately twofold higher risk of venous thromboembolic events.²¹ Less frequently studied than other forms of CVD, peripheral arterial disease may be increased in RA patients independent of other CVD and CVD risk factors.^22,23

Assessing CVD Risk in RA

CVD Risk Scores

In order to identify patients who may benefit from primary prevention interventions, such as lipid-lowering therapy, CVD risk estimation typically centers on the use of well-established CVD risk calculators (Table). CVD risk scores such as the Framingham Risk Score (FRS), Systematic Coronary Risk Evaluation (SCORE), and American College of Cardiology/ American Heart Association (ACC/AHA) Pooled Cohort Equation incorporate traditional CVD risk factors, including age, sex, smoking status, blood pressure, lipid levels, and presence of diabetes mellitus.^24,25 However, CVD risk in RA patients appears to be inadequately explained by traditional CVD risk factors,²⁶ with disease activity and inflammation being associated with higher CVD risk. Recognizing that inflammation may contribute to CVD risk even among non-RA patients, the Reynolds Risk Score includes high-sensitivity C-reactive protein (hsCRP) in its calculation.²⁷ In contrast to more robust performance in the general population, these well-established CVD risk scores have had variable predictive potential of incident CVD in RA patients.^28-30

Several models, or adaptations to existing models, have been proposed to improve CVD risk assessment in RA populations (Table). In 2009, the European League Against Rheumatism (EULAR) task force suggested using a correction factor of 1.5 with traditional CVD risk models in RA patients with 2 of the following criteria: disease duration exceeding 10 years, rheumatoid factor or anti-cyclic citrullinated peptide (CCP) antibody positivity, or extra-articular manifestations of RA.³¹ An update to these recommendations in 2015 continued to propose the use of a 1.5 correction factor, but suggested applying this to all RA patients.³² QRISK2, a modification to QRISK1 which was developed to predict CVD in the UK general population, includes the diagnosis of RA as a risk factor, and in early validation efforts more accurately discriminated patients in the general population at increased risk of CVD compared to the FRS.³³ Additional disease-specific risk factors such as systemic lupus, steroid use, severe mental illness, and steroid and atypical antipsychotic use were incorporated in the QRISK3 algorithm, with model performance similar to the QRISK2.³⁴ The Expanded Cardiovascular Risk Prediction Score for RA (ERS-RA) was specifically developed to assess CVD risk in RA patients by including RA disease activity, level of physical disability, RA disease duration, and prednisone use.³⁵ Despite efforts to develop “RA-specific” risk scores, these have not consistently outperformed traditional CVD risk calculators.^36-38 In one study involving more than 1700 RA patients, the ERS-RA performed similarly to the FRS and Reynolds Risk Score, with a net reclassification index of just 2.3% versus the FRS.³⁶

Imaging Modalities

Imaging modalities may assist in characterizing the increased risk of CVD in RA and the subclinical CVD manifestations that occur. For example, RA patients were shown to have more prevalent and unstable coronary plaque, higher carotid intima media thickness, and impaired myocardial function with computed tomography (CT) angiography and carotid ultrasound.^39,40 However, studies harnessing noninvasive imaging to augment CVD risk assessment in RA patients are limited.

Carotid ultrasound has been the most extensively studied imaging modality for CVD risk assessment in RA. In a cohort of 599 RA patients with no history of ACS, rates of ACS were nearly 4 times higher in RA patients with bilateral carotid plaque on carotid ultrasound, and the association with ACS was independent of other traditional and RA-related risk factors.⁴¹ Presence of bilateral carotid plaques was similarly associated with an increased risk of overall CVD events (hazard ratio [HR], 3.34 [95% CI, 1.21-9.22]), ACS alone (HR, 6.31 [95% CI, 1.27-31.40]), and a lower mean CVD event-free survival (13.9 versus 15.2 years, P = 0.01) in a separate inception cohort of 105 RA patients with no prior history of CVD.⁴² The most useful application of carotid ultrasound may be in conjunction with clinical CVD risk models. Use of carotid ultrasound improved CVD risk stratification among RA patients who were considered at moderate risk by the EULAR-modified SCORE calculator.⁴³ Beyond carotid ultrasound, measurement of arterial stiffness through ultrasound could also aid in CVD risk stratification. Aortic pulse wave velocity and augmentation index, measures of arterial stiffness, are predictive of CVD in the general population as well as RA patients and improve with reduction in RA disease activity.^44,45 Peripheral arterial stiffness (brachial-ankle elasticity index) is impaired in RA patients and predictive of CVD morbidity and mortality in the general population.^46,47

CT coronary angiography and coronary artery calcium (CAC) scores are reliable measures of coronary artery atherosclerosis and have been validated for CVD risk assessment in the general population.^48-52 While the association between RA and CT-related findings of atherosclerosis is well established, assessment of CT-mediated evaluation as a prognostic tool for CVD in RA is limited. In one cohort study, CAC predicted higher rates of CVD events in Chinese patients with RA and systemic lupus erythematosus in a pooled analysis, although results were limited by low event rates and the absence of RA-only subanalyses.⁵³

While the aforementioned imaging modalities have focused on enhancing the identification of atherosclerosis, echocardiography or cardiac magnetic resonance imaging (MRI) may be useful for detecting subclinical structural and/or functional abnormalities that predispose to CHF. Structural abnormalities including increased left ventricular mass and hypertrophy are more prevalent in RA patients and predict incident CHF in the general population.^54-56 MRI measures of myocardial inflammation, including T1 mapping and extracellular volume, are associated with higher mortality rates and also appear to be elevated in RA patients.^57,58 Whether identification of these imaging findings influences the cost-effective clinical management of RA patients needs further study.

Biomarkers

Serum biomarkers, such as the anti-CCP antibody, have become crucial to the evaluation of patients suspected to have RA. With the growing understanding of the role pro-inflammatory mediators play in CVD pathogenesis and the relative ease with which they can be measured, serum biomarkers have potential to inform CVD risk assessment. In the general population, hsCRP concentrations are predictive of CVD and are included in the Reynolds Risk Score.²⁷ In RA, CRP concentrations are typically much higher than those observed among individuals in the general population solely at increased CVD risk, yet elevated levels remain predictive of CVD death independent of RA disease activity and traditional CVD risk factors.⁵⁹ Several additional cytokines, chemokines, and adhesion molecules have been associated with surrogate markers of CVD in RA patients, although further study is needed to elucidate thresholds that signify increased CVD risk in a population characterized by the presence of systemic inflammation.⁶⁰

Cardiac biomarkers used frequently in the general population may be useful to assess CVD risk in RA patients. N-terminal-pro brain natriuretic peptide (NT-pro BNP) is a biomarker typically used to evaluate CHF severity, but it may also predict long-term mortality in patients with coronary heart disease.^61,62 Circulating NT-pro BNP concentrations are elevated in RA independent of prevalent CHF and may serve as a useful tool to identify subclinical cardiac disease in RA patients.⁶³ High-sensitivity cardiac troponin I (HS-cTnI) assays are capable of detecting levels of cardiac troponin below the threshold typically used to diagnose ACS. HS-cTnI levels are increased in RA patients independent of additional CVD risk factors, and elevated levels (> 1.5 pg/mL) were associated with more severe CT angiography findings of coronary plaque as well as increased risk of CVD events.^64,65

A fully validated algorithm for CVD risk assessment in RA is lacking. Most CVD risk scores from the general population do not contain RA-related factors predictive of CVD but have had more extensive performance testing. In contrast, novel RA-derived CVD risk scores incorporate RA-related factors, but have had limited external validity testing. Additionally, RA-derived risk scores are less likely to be utilized and adopted by primary care providers and cardiologists involved in RA patients’ care. Neither set of risk scores incorporates novel imaging modalities or serum biomarkers, which are most likely to be helpful among individuals at intermediate risk. Therefore, until the performance of RA-specific CVD risk scores can be better established, we recommend the use of nationally endorsed CVD risk scores, with the frequency of reassessment based on CVD risk.

Conclusion

RA patients are at increased risk of CVD and CVD-related mortality relative to the general population. The disproportionate CVD burden seen in RA appears to be multifactorial, owing to the complex effects of systemic inflammation, endothelial dysfunction, and pro-atherogenic lipoprotein modifications. Additionally, many traditional CVD risk factors are more prevalent and suboptimally managed in RA patients. To mitigate the increased risk of CVD in RA, primary care and subspecialty providers alike must be aware of this heightened risk in RA, perform frequent assessment of CVD risk, and aggressively manage both traditional and nontraditional CVD risk factors. The management of CVD risk factors is discussed in detail in the second part of this article.

Corresponding author: Bryant R. England, MD; 986270 Nebraska Medical Center, Omaha, NE 68198-6270; [email protected].

Financial disclosures: Dr. England is supported by UNMC Internal Medicine Scientist Development Award, UNMC Physician-Scientist Training Program, the UNMC Mentored Scholars Program, and the Rheumatology Research Foundation Scientist Development Award. Dr. Mikuls is supported by a VA Merit Award (CX000896) and grants from the National Institutes of Health: National Institute of General Medical Sciences (U54GM115458), National Institute on Alcohol Abuse and Alcoholism (R25AA020818), and National Institute of Arthritis and Musculoskeletal and Skin Diseases (2P50AR60772).

From the Division of Rheumatology & Immunology, University of Nebraska Medical Center, and Veterans Affairs Nebraska-Western Iowa Health Care System, Omaha, NE.

Abstract

• Objective: To review cardiovascular disease (CVD) risk assessment in patients with rheumatoid arthritis (RA).
• Methods: Literature review of the assessment of CVD risk in RA.
• Results: CVD is the leading cause of death among RA patients. Because of the increased risk of CVD events and CVD mortality in patients with RA, regular assessment of CVD risk and aggressive management of CVD risk in these patients is crucial. CVD risk estimation typically centers on the use of well-established CVD risk calculators. Most CVD risk scores from the general population do not contain RA-related factors predictive of CVD but have had more extensive performance testing, while novel RA-derived CVD risk scores that incorporate RA-related factors have had limited external validity testing. Neither set of risk scores incorporates novel imaging modalities or serum biomarkers, which are most likely to be helpful among individuals at intermediate risk.
• Conclusion: Primary care and rheumatology providers must be aware of the increased risk of CVD in RA, a risk that approaches that of diabetic patients. Routine assessment of CVD risk is an essential first step in minimizing CVD risk in this population. Until the performance of RA-specific CVD risk scores can be better established, we recommend the use of nationally endorsed CVD risk scores, with the frequency of reassessment based on CVD risk.

Keywords: rheumatoid arthritis; cardiovascular disease; cardiovascular risk assessment.

Editor’s note: This article is part 1 of a 2-part article. “Management of Cardiovascular Disease Risk in Rheumatoid Arthritis” was published in the March/April 2019 issue.

Rheumatoid arthritis (RA) is a chronic, autoimmune inflammatory arthritis affecting up to 1% of the US population that can lead to joint damage, functional disability, and reduced quality of life.¹ In addition to articular involvement, systemic inflammation accompanying RA may lead to extra-articular manifestations and increase the risk of premature death.² Cardiovascular disease (CVD), accounting for nearly half of all deaths among RA patients, is now recognized as a critical extra-articular manifestation of RA.^2,3 As such, assessment and management of CVD risk is essential to the comprehensive care of the RA patient. This article reviews the approach to assessing CVD risk in patients with RA; the management of both traditional and RA-specific risk factors is discussed in a separate article.

Scope of the Problem

In a large meta-analysis of observational studies that included more than 111,000 patients with RA, CVD-related mortality rates were 1.5 times higher among RA patients than among general population controls.⁴ The risk of overall CVD, including nonfatal events, is similar; a separate meta-analysis of observational studies that included more than 41,000 patients with RA calculated a pooled relative risk for incident CVD of 1.48.⁵ Individual analyses identified heightened risk of acute coronary syndrome (ACS), cerebrovascular accident, and congestive heart failure (CHF).⁵ Perhaps more illustrative of the magnitude of the problem, the risk of CVD in RA approaches that observed among individuals with diabetes mellitus.^6,7

Coronary artery disease (CAD) accounts for a significant portion of the CVD risk in RA, but its presentation may be atypical in RA patients. RA patients are at higher risk of suffering unrecognized myocardial infarction (MI) and sudden cardiac death.⁸ The reasons for silent ischemia in RA are not fully known, but have been hypothesized to include imbalances of inflammatory cytokines, alterations in pain sensitization, or the female predominance of RA (with women more often presenting with atypical symptoms of myocardial ischemia).⁹ Alarmingly, a retrospective chart review study reported that RA patients admitted for an acute MI were less likely to receive appropriate reperfusion therapy as well as secondary prevention with beta-blockers and lipid-lowering agents.¹⁰ Even with appropriate therapy, long-term outcomes such as mortality and recurrent ischemic events are more likely to occur in RA patients after acute MI.^11-13

Independent of ischemic heart disease, RA patients are at increased risk of CHF.^14-16 RA patients are at particular risk for CHF with preserved ejection fraction,¹⁷ which may be a result of systemic inflammation causing left ventricular stiffening.^18,19 Similar to CAD, patients with RA are less likely to present with typical CHF symptoms, are less likely to receive guideline-concordant care, and have higher mortality rates following presentation with CHF.¹⁷

Although accounting for a lower proportion of the excess CVD morbidity and mortality in RA, the risk of noncardiac vascular disease is also increased in RA patients. Large meta-analyses have identified positive associations between RA with both ischemic (odds ratio [OR], 1.64 [95% confidence interval {CI}, 1.32-2.05]) and hemorrhagic (OR, 1.68 [95% CI, 1.11-2.53]) stroke.²⁰ Similarly, RA patients appear to have an approximately twofold higher risk of venous thromboembolic events.²¹ Less frequently studied than other forms of CVD, peripheral arterial disease may be increased in RA patients independent of other CVD and CVD risk factors.^22,23

Assessing CVD Risk in RA

CVD Risk Scores

In order to identify patients who may benefit from primary prevention interventions, such as lipid-lowering therapy, CVD risk estimation typically centers on the use of well-established CVD risk calculators (Table). CVD risk scores such as the Framingham Risk Score (FRS), Systematic Coronary Risk Evaluation (SCORE), and American College of Cardiology/ American Heart Association (ACC/AHA) Pooled Cohort Equation incorporate traditional CVD risk factors, including age, sex, smoking status, blood pressure, lipid levels, and presence of diabetes mellitus.^24,25 However, CVD risk in RA patients appears to be inadequately explained by traditional CVD risk factors,²⁶ with disease activity and inflammation being associated with higher CVD risk. Recognizing that inflammation may contribute to CVD risk even among non-RA patients, the Reynolds Risk Score includes high-sensitivity C-reactive protein (hsCRP) in its calculation.²⁷ In contrast to more robust performance in the general population, these well-established CVD risk scores have had variable predictive potential of incident CVD in RA patients.^28-30

Several models, or adaptations to existing models, have been proposed to improve CVD risk assessment in RA populations (Table). In 2009, the European League Against Rheumatism (EULAR) task force suggested using a correction factor of 1.5 with traditional CVD risk models in RA patients with 2 of the following criteria: disease duration exceeding 10 years, rheumatoid factor or anti-cyclic citrullinated peptide (CCP) antibody positivity, or extra-articular manifestations of RA.³¹ An update to these recommendations in 2015 continued to propose the use of a 1.5 correction factor, but suggested applying this to all RA patients.³² QRISK2, a modification to QRISK1 which was developed to predict CVD in the UK general population, includes the diagnosis of RA as a risk factor, and in early validation efforts more accurately discriminated patients in the general population at increased risk of CVD compared to the FRS.³³ Additional disease-specific risk factors such as systemic lupus, steroid use, severe mental illness, and steroid and atypical antipsychotic use were incorporated in the QRISK3 algorithm, with model performance similar to the QRISK2.³⁴ The Expanded Cardiovascular Risk Prediction Score for RA (ERS-RA) was specifically developed to assess CVD risk in RA patients by including RA disease activity, level of physical disability, RA disease duration, and prednisone use.³⁵ Despite efforts to develop “RA-specific” risk scores, these have not consistently outperformed traditional CVD risk calculators.^36-38 In one study involving more than 1700 RA patients, the ERS-RA performed similarly to the FRS and Reynolds Risk Score, with a net reclassification index of just 2.3% versus the FRS.³⁶

Imaging Modalities

Imaging modalities may assist in characterizing the increased risk of CVD in RA and the subclinical CVD manifestations that occur. For example, RA patients were shown to have more prevalent and unstable coronary plaque, higher carotid intima media thickness, and impaired myocardial function with computed tomography (CT) angiography and carotid ultrasound.^39,40 However, studies harnessing noninvasive imaging to augment CVD risk assessment in RA patients are limited.

Carotid ultrasound has been the most extensively studied imaging modality for CVD risk assessment in RA. In a cohort of 599 RA patients with no history of ACS, rates of ACS were nearly 4 times higher in RA patients with bilateral carotid plaque on carotid ultrasound, and the association with ACS was independent of other traditional and RA-related risk factors.⁴¹ Presence of bilateral carotid plaques was similarly associated with an increased risk of overall CVD events (hazard ratio [HR], 3.34 [95% CI, 1.21-9.22]), ACS alone (HR, 6.31 [95% CI, 1.27-31.40]), and a lower mean CVD event-free survival (13.9 versus 15.2 years, P = 0.01) in a separate inception cohort of 105 RA patients with no prior history of CVD.⁴² The most useful application of carotid ultrasound may be in conjunction with clinical CVD risk models. Use of carotid ultrasound improved CVD risk stratification among RA patients who were considered at moderate risk by the EULAR-modified SCORE calculator.⁴³ Beyond carotid ultrasound, measurement of arterial stiffness through ultrasound could also aid in CVD risk stratification. Aortic pulse wave velocity and augmentation index, measures of arterial stiffness, are predictive of CVD in the general population as well as RA patients and improve with reduction in RA disease activity.^44,45 Peripheral arterial stiffness (brachial-ankle elasticity index) is impaired in RA patients and predictive of CVD morbidity and mortality in the general population.^46,47

CT coronary angiography and coronary artery calcium (CAC) scores are reliable measures of coronary artery atherosclerosis and have been validated for CVD risk assessment in the general population.^48-52 While the association between RA and CT-related findings of atherosclerosis is well established, assessment of CT-mediated evaluation as a prognostic tool for CVD in RA is limited. In one cohort study, CAC predicted higher rates of CVD events in Chinese patients with RA and systemic lupus erythematosus in a pooled analysis, although results were limited by low event rates and the absence of RA-only subanalyses.⁵³

While the aforementioned imaging modalities have focused on enhancing the identification of atherosclerosis, echocardiography or cardiac magnetic resonance imaging (MRI) may be useful for detecting subclinical structural and/or functional abnormalities that predispose to CHF. Structural abnormalities including increased left ventricular mass and hypertrophy are more prevalent in RA patients and predict incident CHF in the general population.^54-56 MRI measures of myocardial inflammation, including T1 mapping and extracellular volume, are associated with higher mortality rates and also appear to be elevated in RA patients.^57,58 Whether identification of these imaging findings influences the cost-effective clinical management of RA patients needs further study.

Biomarkers

Serum biomarkers, such as the anti-CCP antibody, have become crucial to the evaluation of patients suspected to have RA. With the growing understanding of the role pro-inflammatory mediators play in CVD pathogenesis and the relative ease with which they can be measured, serum biomarkers have potential to inform CVD risk assessment. In the general population, hsCRP concentrations are predictive of CVD and are included in the Reynolds Risk Score.²⁷ In RA, CRP concentrations are typically much higher than those observed among individuals in the general population solely at increased CVD risk, yet elevated levels remain predictive of CVD death independent of RA disease activity and traditional CVD risk factors.⁵⁹ Several additional cytokines, chemokines, and adhesion molecules have been associated with surrogate markers of CVD in RA patients, although further study is needed to elucidate thresholds that signify increased CVD risk in a population characterized by the presence of systemic inflammation.⁶⁰

Cardiac biomarkers used frequently in the general population may be useful to assess CVD risk in RA patients. N-terminal-pro brain natriuretic peptide (NT-pro BNP) is a biomarker typically used to evaluate CHF severity, but it may also predict long-term mortality in patients with coronary heart disease.^61,62 Circulating NT-pro BNP concentrations are elevated in RA independent of prevalent CHF and may serve as a useful tool to identify subclinical cardiac disease in RA patients.⁶³ High-sensitivity cardiac troponin I (HS-cTnI) assays are capable of detecting levels of cardiac troponin below the threshold typically used to diagnose ACS. HS-cTnI levels are increased in RA patients independent of additional CVD risk factors, and elevated levels (> 1.5 pg/mL) were associated with more severe CT angiography findings of coronary plaque as well as increased risk of CVD events.^64,65

A fully validated algorithm for CVD risk assessment in RA is lacking. Most CVD risk scores from the general population do not contain RA-related factors predictive of CVD but have had more extensive performance testing. In contrast, novel RA-derived CVD risk scores incorporate RA-related factors, but have had limited external validity testing. Additionally, RA-derived risk scores are less likely to be utilized and adopted by primary care providers and cardiologists involved in RA patients’ care. Neither set of risk scores incorporates novel imaging modalities or serum biomarkers, which are most likely to be helpful among individuals at intermediate risk. Therefore, until the performance of RA-specific CVD risk scores can be better established, we recommend the use of nationally endorsed CVD risk scores, with the frequency of reassessment based on CVD risk.

Conclusion

RA patients are at increased risk of CVD and CVD-related mortality relative to the general population. The disproportionate CVD burden seen in RA appears to be multifactorial, owing to the complex effects of systemic inflammation, endothelial dysfunction, and pro-atherogenic lipoprotein modifications. Additionally, many traditional CVD risk factors are more prevalent and suboptimally managed in RA patients. To mitigate the increased risk of CVD in RA, primary care and subspecialty providers alike must be aware of this heightened risk in RA, perform frequent assessment of CVD risk, and aggressively manage both traditional and nontraditional CVD risk factors. The management of CVD risk factors is discussed in detail in the second part of this article.

Corresponding author: Bryant R. England, MD; 986270 Nebraska Medical Center, Omaha, NE 68198-6270; [email protected].

Financial disclosures: Dr. England is supported by UNMC Internal Medicine Scientist Development Award, UNMC Physician-Scientist Training Program, the UNMC Mentored Scholars Program, and the Rheumatology Research Foundation Scientist Development Award. Dr. Mikuls is supported by a VA Merit Award (CX000896) and grants from the National Institutes of Health: National Institute of General Medical Sciences (U54GM115458), National Institute on Alcohol Abuse and Alcoholism (R25AA020818), and National Institute of Arthritis and Musculoskeletal and Skin Diseases (2P50AR60772).