Hospital Medicine

Most patients admitted with pulmonary embolism (PE) do not need transthoracic echocardiography (TTE); it should be performed in hemodynamically unstable patients, as well as in hemodynamically stable patients with specific elevated cardiac biomarkers and imaging features.

The decision to perform TTE should be based on clinical presentation, PE burden, and imaging findings (eg, computed tomographic angiography). TTE helps to stratify risk, guide management, monitor response to therapy, and give prognostic information for a subset of patients at increased risk for PE-related adverse events.

RISK STRATIFICATION IN PULMONARY EMBOLISM

PE has a spectrum of presentations ranging from no symptoms to shock. Based on the clinical presentation, PE can be categorized as high, intermediate, or low risk.

High-risk PE, often referred to as “massive” PE, is defined in current American Heart Association guidelines as acute PE with sustained hypotension (systolic blood pressure < 90 mm Hg for at least 15 minutes or requiring inotropic support), persistent profound bradycardia (heart rate < 40 beats per minute with signs or symptoms of shock), syncope, or cardiac arrest.¹

Intermediate-risk or “submassive” PE is more challenging to identify because patients are more hemodynamically stable, yet have evidence on electrocardiography, TTE, computed tomography, or cardiac biomarker testing—ie, N-terminal pro-B-type natriuretic peptide (NT-proBNP) or troponin—that indicates myocardial injury or volume overload.¹

Low-risk PE is acute PE in the absence of clinical markers of adverse prognosis that define massive or submassive PE.¹

ECHOCARDIOGRAPHIC FEATURES OF HIGH-RISK PULMONARY EMBOLISM

Certain TTE findings suggest increased risk of a poor outcome and may warrant therapy that is more invasive and aggressive. High-risk features include the following:

• Impaired right ventricular function
• Interventricular septum bulging into the left ventricle (“D-shaped” septum)
• Dilated proximal pulmonary arteries
• Increased severity of tricuspid regurgitation
• Elevated right atrial pressure
• Elevated pulmonary artery pressure
• Free-floating right ventricular thrombi, which are associated with a mortality rate of up to 45% and can be detected in 7% to 18% of patients⁶
• Tricuspid annular plane systolic excursion, an echocardiographic measure of right ventricular function¹; a value less than 17 mm suggests impaired right ventricular systolic function⁷
• The McConnell sign, a feature of acute massive PE: akinesia of the mid-free wall of the right ventricle and hypercontractility of the apex.

These TTE findings often lead to treatment with thrombolysis, transfer to the intensive care unit, and activation of the interventional team for catheter-based therapies.^1,8 Free-floating right heart thrombi or thrombus straddling the interatrial septum (“thrombus in transit”) through a patent foramen ovale may require surgical embolectomy.⁸

PATIENT SELECTION AND INDICATIONS FOR ECHOCARDIOGRAPHY

Most patients admitted with pulmonary embolism (PE) do not need transthoracic echocardiography (TTE); it should be performed in hemodynamically unstable patients, as well as in hemodynamically stable patients with specific elevated cardiac biomarkers and imaging features.

The decision to perform TTE should be based on clinical presentation, PE burden, and imaging findings (eg, computed tomographic angiography). TTE helps to stratify risk, guide management, monitor response to therapy, and give prognostic information for a subset of patients at increased risk for PE-related adverse events.

RISK STRATIFICATION IN PULMONARY EMBOLISM

PE has a spectrum of presentations ranging from no symptoms to shock. Based on the clinical presentation, PE can be categorized as high, intermediate, or low risk.

High-risk PE, often referred to as “massive” PE, is defined in current American Heart Association guidelines as acute PE with sustained hypotension (systolic blood pressure < 90 mm Hg for at least 15 minutes or requiring inotropic support), persistent profound bradycardia (heart rate < 40 beats per minute with signs or symptoms of shock), syncope, or cardiac arrest.¹

Intermediate-risk or “submassive” PE is more challenging to identify because patients are more hemodynamically stable, yet have evidence on electrocardiography, TTE, computed tomography, or cardiac biomarker testing—ie, N-terminal pro-B-type natriuretic peptide (NT-proBNP) or troponin—that indicates myocardial injury or volume overload.¹

Low-risk PE is acute PE in the absence of clinical markers of adverse prognosis that define massive or submassive PE.¹

ECHOCARDIOGRAPHIC FEATURES OF HIGH-RISK PULMONARY EMBOLISM

Certain TTE findings suggest increased risk of a poor outcome and may warrant therapy that is more invasive and aggressive. High-risk features include the following:

• Impaired right ventricular function
• Interventricular septum bulging into the left ventricle (“D-shaped” septum)
• Dilated proximal pulmonary arteries
• Increased severity of tricuspid regurgitation
• Elevated right atrial pressure
• Elevated pulmonary artery pressure
• Free-floating right ventricular thrombi, which are associated with a mortality rate of up to 45% and can be detected in 7% to 18% of patients⁶
• Tricuspid annular plane systolic excursion, an echocardiographic measure of right ventricular function¹; a value less than 17 mm suggests impaired right ventricular systolic function⁷
• The McConnell sign, a feature of acute massive PE: akinesia of the mid-free wall of the right ventricle and hypercontractility of the apex.

These TTE findings often lead to treatment with thrombolysis, transfer to the intensive care unit, and activation of the interventional team for catheter-based therapies.^1,8 Free-floating right heart thrombi or thrombus straddling the interatrial septum (“thrombus in transit”) through a patent foramen ovale may require surgical embolectomy.⁸

PATIENT SELECTION AND INDICATIONS FOR ECHOCARDIOGRAPHY

Most patients admitted with pulmonary embolism (PE) do not need transthoracic echocardiography (TTE); it should be performed in hemodynamically unstable patients, as well as in hemodynamically stable patients with specific elevated cardiac biomarkers and imaging features.

The decision to perform TTE should be based on clinical presentation, PE burden, and imaging findings (eg, computed tomographic angiography). TTE helps to stratify risk, guide management, monitor response to therapy, and give prognostic information for a subset of patients at increased risk for PE-related adverse events.

RISK STRATIFICATION IN PULMONARY EMBOLISM

PE has a spectrum of presentations ranging from no symptoms to shock. Based on the clinical presentation, PE can be categorized as high, intermediate, or low risk.

High-risk PE, often referred to as “massive” PE, is defined in current American Heart Association guidelines as acute PE with sustained hypotension (systolic blood pressure < 90 mm Hg for at least 15 minutes or requiring inotropic support), persistent profound bradycardia (heart rate < 40 beats per minute with signs or symptoms of shock), syncope, or cardiac arrest.¹

Intermediate-risk or “submassive” PE is more challenging to identify because patients are more hemodynamically stable, yet have evidence on electrocardiography, TTE, computed tomography, or cardiac biomarker testing—ie, N-terminal pro-B-type natriuretic peptide (NT-proBNP) or troponin—that indicates myocardial injury or volume overload.¹

Low-risk PE is acute PE in the absence of clinical markers of adverse prognosis that define massive or submassive PE.¹

ECHOCARDIOGRAPHIC FEATURES OF HIGH-RISK PULMONARY EMBOLISM

Certain TTE findings suggest increased risk of a poor outcome and may warrant therapy that is more invasive and aggressive. High-risk features include the following:

• Impaired right ventricular function
• Interventricular septum bulging into the left ventricle (“D-shaped” septum)
• Dilated proximal pulmonary arteries
• Increased severity of tricuspid regurgitation
• Elevated right atrial pressure
• Elevated pulmonary artery pressure
• Free-floating right ventricular thrombi, which are associated with a mortality rate of up to 45% and can be detected in 7% to 18% of patients⁶
• Tricuspid annular plane systolic excursion, an echocardiographic measure of right ventricular function¹; a value less than 17 mm suggests impaired right ventricular systolic function⁷
• The McConnell sign, a feature of acute massive PE: akinesia of the mid-free wall of the right ventricle and hypercontractility of the apex.

These TTE findings often lead to treatment with thrombolysis, transfer to the intensive care unit, and activation of the interventional team for catheter-based therapies.^1,8 Free-floating right heart thrombi or thrombus straddling the interatrial septum (“thrombus in transit”) through a patent foramen ovale may require surgical embolectomy.⁸

PATIENT SELECTION AND INDICATIONS FOR ECHOCARDIOGRAPHY

A 76-year-old man whose history included abdominal aortic aneurysm repair, bilateral femoral artery bypass for popliteal artery aneurysm, hypertension, and peptic ulcer disease was admitted to a community hospital with pleuritic chest pain and shortness of breath. Two days earlier, he had undergone repair of a ventral hernia.

At the time of that admission, he reported no fever, chills, night sweats, cough, or history of heart or lung disease. His vital signs were normal, and physical examination had revealed no apparent respiratory distress, no jugular venous distention, normal heart sounds, and no pedal edema; however, decreased air entry was noted in the right lung base. Initial serum levels of troponin and N-terminal pro-B-type natriuretic peptide were normal.

At that time, computed tomographic angiography of the chest showed segmental pulmonary emboli in the left upper and right lower lobes of the lungs and right pleural effusion. Transthoracic echocardiography showed normal atrial and ventricular sizes with no right or left ventricular systolic dysfunction and a left ventricular ejection fraction of 59%.

Treatment with intravenous heparin was started, and the patient was transferred to our hospital.

PLEURAL EFFUSION AND PULMONARY EMBOLISM

1. Which of the following is true about pleural effusion?

• It is rarely, if ever, associated with pulmonary embolism
• Most patients with pleural effusion due to pulmonary embolism do not have pleuritic chest pain
• Pulmonary embolism should be excluded in all cases of pleural effusion without a clear cause

Pulmonary embolism should be excluded in all cases of pleural effusion that do not have a clear cause. As for the other answer choices:

• Pulmonary embolism is the fourth leading cause of pleural effusion in the United States, after heart failure, pneumonia, and malignancy.¹
• About 75% of patients who develop pleural effusion in the setting of pulmonary embolism complain of pleuritic chest pain on the side of the effusion.² Most effusions are unilateral, small, and usually exudative.³

EVALUATION BEGINS: RESULTS OF THORACENTESIS

Our patient continued to receive intravenous heparin.

He underwent thoracentesis on hospital day 3, and 1,000 mL of turbid sanguineous pleural fluid was removed. Analysis of the fluid showed pH 7.27, white blood cell count 3.797 × 10⁹/L with 80% neutrophils, and lactate dehydrogenase (LDH) concentration 736 U/L (a ratio of pleural fluid LDH to a concurrent serum LDH > 0.6 is suggestive of an exudate); the fluid was also sent for culture and cytology. Thoracentesis was terminated early due to cough, and follow-up chest radiography showed a moderate-sized pneumothorax.

Computed tomography (CT) of the chest at this time showed a small wedge-shaped area of lung consolidation in the right lower lobe (also seen on CT done 1 day before admission to our hospital), with an intrinsic air-fluid level suggesting a focal infarct or lung abscess, now obscured by adjacent consolidation and atelectasis. In the interval since the previous CT, the multiloculated right pleural effusion had increased in size (Figure 1).

THE NEXT STEP

2. What is the most appropriate next step for this patient?

• Consult an interventional radiologist for chest tube placement
• Start empiric antibiotic therapy and ask an interventional radiologist to place a chest tube
• Start empiric antibiotic therapy, withhold anticoagulation, and consult a thoracic surgeon
• Start empiric antibiotic therapy and consult a thoracic surgeon while continuing anticoagulation

The most appropriate next step is to start empiric antibiotic therapy and consult a thoracic surgeon while continuing anticoagulation.

In this patient, it is appropriate to initiate antibiotics empirically on the basis of his significant pleural loculations, a wedge-shaped consolidation, and 80% neutrophils in the pleural fluid, all of which suggest infection. The unmasking of a wedge-shaped consolidation after thoracentesis, with a previously noted air-fluid level and an interval increase in multiloculated pleural fluid, raises suspicion of a necrotic infection that may have ruptured into the pleural space, a possible lung infarct, or a malignancy. Hence, simply placing a chest tube may not be enough.

Blood in the pleural fluid does not necessitate withholding anticoagulation unless the bleeding is heavy. A pleural fluid hematocrit greater than 50% of the peripheral blood hematocrit suggests hemothorax and is an indication to withhold anticoagulation.¹ Our patient’s pleural fluid was qualitatively sanguineous but not frankly bloody, and therefore we judged that it was not necessary to stop his heparin.

HOW DOES PULMONARY INFARCTION PRESENT CLINICALLY?

3. Which of the following statements about pulmonary infarction is incorrect?

• Cavitation and infarction are more common with larger emboli
• Cavitation occurs in fewer than 10% of pulmonary infarctions
• Lung abscess develops in more than 50% of pulmonary infarctions
• Pulmonary thromboembolism is the most common cause of pulmonary infarction

Lung abscess develops in far fewer than 50% of cases of pulmonary infarction. The rest of the statements are correct.

Cavitation complicates about 4% to 7% of infarctions and is more common when the infarction is 4 cm or greater in diameter.⁴ These cavities are usually single and predominantly on the right side in the apical or posterior segment of the upper lobe or the apical segment of the right lower lobe, as in our patient.^5–8 CT demonstrating scalloped inner margins and cross-cavity band shadows suggests a cavitary pulmonary infarction.^9,10

Infection and abscess in pulmonary infarction are poorly understood but have been linked to larger infarctions, coexistent congestion or atelectasis, and dental or oropharyngeal infection. In an early series of 550 cases of pulmonary infarction, 23 patients (4.2%) developed lung abscess and 6 (1.1%) developed empyema.¹¹ The mean time to cavitation for an infected pulmonary infarction has been reported to be 18 days.¹²

A reversed halo sign, generally described as a focal, rounded area of ground-glass opacity surrounded by a nearly complete ring of consolidation, has been reported to be more frequent with pulmonary infarction than with other diseases, especially when in the lower lobes.¹³

CASE CONTINUED: THORACOSCOPY

A cardiothoracic surgeon was consulted, intravenous heparin was discontinued, an inferior vena cava filter was placed, and the patient underwent video-assisted thoracoscopy.

Purulent fluid was noted on the lateral aspect of right lower lobe; this appeared to be the ruptured cavitary lesion functioning like an uncontrolled bronchopleural fistula. Two chest tubes, sizes 32F and 28F, were placed after decortication, resection of the lung abscess, and closure of the bronchopleural fistula. No significant air leak was noted after resection of this segment of lung.

Pathologic study showed acute organizing pneumonia with abscess formation; no malignant cells or granulomas were seen (Figure 2). Pleural fluid cultures grew Streptococcus intermedius, while the tissue culture was negative for any growth, including acid-fast bacilli and fungi.

On 3 different occasions, both chest tubes were shortened, backed out 2 cm, and resecured with sutures and pins, and Heimlich valves were applied before the patient was discharged.

Intravenous piperacillin-tazobactam was started on the fifth hospital day. On discharge, the patient was advised to continue this treatment for 3 weeks at home.

The patient was receiving enoxaparin subcutaneously in prophylactic doses; 72 hours after the thorascopic procedure this was increased to therapeutic doses, continuing after discharge. Bridging to warfarin was not advised in view of his chest tubes.

Our patient appeared to have developed a right lower lobe infarction that cavitated and ruptured into the pleural space, causing a bronchopleural fistula with empyema after a recent pulmonary embolism. Other reported causes of pulmonary infarction in pulmonary embolism are malignancy and heavy clot burden,⁶ but these have not been confirmed in subsequent studies.⁵ Malignancy was ruled out by biopsy of the resected portion of the lung, and our patient did not have a history of heart failure. A clear cavity was not noted (because it ruptured into the pleura), but an air-fluid level was described in a wedge-shaped consolidation, suggesting infarction.

How common is pulmonary infarction after pulmonary embolism?

Pulmonary infarction occurs in few patients with pulmonary embolism.¹³ Since the lungs receive oxygen from the airways and have a dual blood supply from the pulmonary and bronchial arteries, they are not particularly vulnerable to ischemia. However, the reported incidence of pulmonary infarction in patients with pulmonary embolism has ranged from 10% to higher than 30%.^5,14,15

The reasons behind pulmonary infarction with complications after pulmonary embolism have varied in different case series in different eras. CT, biopsy, or autopsy studies reveal pulmonary infarction after pulmonary embolism to be more common than suspected by clinical symptoms.

In a Mayo Clinic series of 43 cases of pulmonary infarction diagnosed over a 6-year period by surgical lung biopsy, 18 (42%) of the patients had underlying pulmonary thromboembolism, which was the most common cause.¹⁶

4. Which statement about risk factors for pulmonary infarction in pulmonary embolism is incorrect?

• Heart failure may be a risk factor for pulmonary infarction
• Pulmonary hemorrhage is a risk factor for pulmonary infarction
• Pulmonary infarction is more common with more proximal sites of pulmonary embolism
• Collateral circulation may protect against pulmonary infarction

Infarction is more common with emboli that are distal rather than proximal.

Dalen et al¹⁵ suggested that after pulmonary embolism, pulmonary hemorrhage is an important contributor to the development of pulmonary infarction independent of the presence or absence of associated cardiac or pulmonary disease, but that the effect depends on the site of obstruction.

This idea was first proposed in 1913, when Karsner and Ghoreyeb¹⁷ showed that when pulmonary arteries are completely obstructed, the bronchial arteries take over, except when the embolism is present in a small branch of the pulmonary artery. This is because the physiologic anastomosis between the pulmonary artery and the bronchial arteries is located at the precapillary level of the pulmonary artery, and the bronchial circulation does not take over until the pulmonary arterial pressure in the area of the embolism drops to zero.

Using CT data, Kirchner et al⁵ confirmed that the risk of pulmonary infarction is higher if the obstruction is peripheral, ie, distal.

Using autopsy data, Tsao et al¹⁸ reported a higher risk of pulmonary infarction in embolic occlusion of pulmonary vessels less than 3 mm in diameter.

Collateral circulation has been shown to protect against pulmonary infarction. For example, Miniati et al¹⁴ showed that healthy young patients with pulmonary embolism were more prone to develop pulmonary infarction, probably because they had less efficient collateral systems in the peripheral lung fields. In lung transplant recipients, it has been shown that the risk of infarction decreased with development of collateral circulation.¹⁹

Dalen et al,¹⁵ however, attributed delayed resolution of pulmonary hemorrhage (as measured by resolution of infiltrate on chest radiography) to higher underlying pulmonary venous pressure in patients with heart failure and consequent pulmonary infarction. In comparison, healthy patients without cardiac or pulmonary disease have faster resolution of pulmonary hemorrhage when present, and less likelihood of pulmonary infarction (and death in submassive pulmonary embolism).

Data on the management of infected pulmonary infarction are limited. Mortality rates have been as high as 41% with noninfected and 73% with infected cavitary infarctions.⁴ Some authors have advocated early surgical resection in view of high rates of failure of medical treatment due to lack of blood supply within the cavity and continued risk of infection.

KEY POINTS

In patients with a recently diagnosed pulmonary embolism and concurrent symptoms of bacterial pneumonia, a diagnosis of cavitary pulmonary infarction should be considered.

Consolidations that are pleural-based with sharp, rounded margins and with focal areas of central hyperlucencies representing hemorrhage on the mediastinal windows on CT are more likely to represent a pulmonary infarct.²⁰

A 76-year-old man whose history included abdominal aortic aneurysm repair, bilateral femoral artery bypass for popliteal artery aneurysm, hypertension, and peptic ulcer disease was admitted to a community hospital with pleuritic chest pain and shortness of breath. Two days earlier, he had undergone repair of a ventral hernia.

At the time of that admission, he reported no fever, chills, night sweats, cough, or history of heart or lung disease. His vital signs were normal, and physical examination had revealed no apparent respiratory distress, no jugular venous distention, normal heart sounds, and no pedal edema; however, decreased air entry was noted in the right lung base. Initial serum levels of troponin and N-terminal pro-B-type natriuretic peptide were normal.

At that time, computed tomographic angiography of the chest showed segmental pulmonary emboli in the left upper and right lower lobes of the lungs and right pleural effusion. Transthoracic echocardiography showed normal atrial and ventricular sizes with no right or left ventricular systolic dysfunction and a left ventricular ejection fraction of 59%.

Treatment with intravenous heparin was started, and the patient was transferred to our hospital.

PLEURAL EFFUSION AND PULMONARY EMBOLISM

1. Which of the following is true about pleural effusion?

• It is rarely, if ever, associated with pulmonary embolism
• Most patients with pleural effusion due to pulmonary embolism do not have pleuritic chest pain
• Pulmonary embolism should be excluded in all cases of pleural effusion without a clear cause

Pulmonary embolism should be excluded in all cases of pleural effusion that do not have a clear cause. As for the other answer choices:

• Pulmonary embolism is the fourth leading cause of pleural effusion in the United States, after heart failure, pneumonia, and malignancy.¹
• About 75% of patients who develop pleural effusion in the setting of pulmonary embolism complain of pleuritic chest pain on the side of the effusion.² Most effusions are unilateral, small, and usually exudative.³

EVALUATION BEGINS: RESULTS OF THORACENTESIS

Our patient continued to receive intravenous heparin.

He underwent thoracentesis on hospital day 3, and 1,000 mL of turbid sanguineous pleural fluid was removed. Analysis of the fluid showed pH 7.27, white blood cell count 3.797 × 10⁹/L with 80% neutrophils, and lactate dehydrogenase (LDH) concentration 736 U/L (a ratio of pleural fluid LDH to a concurrent serum LDH > 0.6 is suggestive of an exudate); the fluid was also sent for culture and cytology. Thoracentesis was terminated early due to cough, and follow-up chest radiography showed a moderate-sized pneumothorax.

Computed tomography (CT) of the chest at this time showed a small wedge-shaped area of lung consolidation in the right lower lobe (also seen on CT done 1 day before admission to our hospital), with an intrinsic air-fluid level suggesting a focal infarct or lung abscess, now obscured by adjacent consolidation and atelectasis. In the interval since the previous CT, the multiloculated right pleural effusion had increased in size (Figure 1).

THE NEXT STEP

2. What is the most appropriate next step for this patient?

• Consult an interventional radiologist for chest tube placement
• Start empiric antibiotic therapy and ask an interventional radiologist to place a chest tube
• Start empiric antibiotic therapy, withhold anticoagulation, and consult a thoracic surgeon
• Start empiric antibiotic therapy and consult a thoracic surgeon while continuing anticoagulation

The most appropriate next step is to start empiric antibiotic therapy and consult a thoracic surgeon while continuing anticoagulation.

In this patient, it is appropriate to initiate antibiotics empirically on the basis of his significant pleural loculations, a wedge-shaped consolidation, and 80% neutrophils in the pleural fluid, all of which suggest infection. The unmasking of a wedge-shaped consolidation after thoracentesis, with a previously noted air-fluid level and an interval increase in multiloculated pleural fluid, raises suspicion of a necrotic infection that may have ruptured into the pleural space, a possible lung infarct, or a malignancy. Hence, simply placing a chest tube may not be enough.

Blood in the pleural fluid does not necessitate withholding anticoagulation unless the bleeding is heavy. A pleural fluid hematocrit greater than 50% of the peripheral blood hematocrit suggests hemothorax and is an indication to withhold anticoagulation.¹ Our patient’s pleural fluid was qualitatively sanguineous but not frankly bloody, and therefore we judged that it was not necessary to stop his heparin.

HOW DOES PULMONARY INFARCTION PRESENT CLINICALLY?

3. Which of the following statements about pulmonary infarction is incorrect?

• Cavitation and infarction are more common with larger emboli
• Cavitation occurs in fewer than 10% of pulmonary infarctions
• Lung abscess develops in more than 50% of pulmonary infarctions
• Pulmonary thromboembolism is the most common cause of pulmonary infarction

Lung abscess develops in far fewer than 50% of cases of pulmonary infarction. The rest of the statements are correct.

Cavitation complicates about 4% to 7% of infarctions and is more common when the infarction is 4 cm or greater in diameter.⁴ These cavities are usually single and predominantly on the right side in the apical or posterior segment of the upper lobe or the apical segment of the right lower lobe, as in our patient.^5–8 CT demonstrating scalloped inner margins and cross-cavity band shadows suggests a cavitary pulmonary infarction.^9,10

Infection and abscess in pulmonary infarction are poorly understood but have been linked to larger infarctions, coexistent congestion or atelectasis, and dental or oropharyngeal infection. In an early series of 550 cases of pulmonary infarction, 23 patients (4.2%) developed lung abscess and 6 (1.1%) developed empyema.¹¹ The mean time to cavitation for an infected pulmonary infarction has been reported to be 18 days.¹²

A reversed halo sign, generally described as a focal, rounded area of ground-glass opacity surrounded by a nearly complete ring of consolidation, has been reported to be more frequent with pulmonary infarction than with other diseases, especially when in the lower lobes.¹³

CASE CONTINUED: THORACOSCOPY

A cardiothoracic surgeon was consulted, intravenous heparin was discontinued, an inferior vena cava filter was placed, and the patient underwent video-assisted thoracoscopy.

Purulent fluid was noted on the lateral aspect of right lower lobe; this appeared to be the ruptured cavitary lesion functioning like an uncontrolled bronchopleural fistula. Two chest tubes, sizes 32F and 28F, were placed after decortication, resection of the lung abscess, and closure of the bronchopleural fistula. No significant air leak was noted after resection of this segment of lung.

Pathologic study showed acute organizing pneumonia with abscess formation; no malignant cells or granulomas were seen (Figure 2). Pleural fluid cultures grew Streptococcus intermedius, while the tissue culture was negative for any growth, including acid-fast bacilli and fungi.

On 3 different occasions, both chest tubes were shortened, backed out 2 cm, and resecured with sutures and pins, and Heimlich valves were applied before the patient was discharged.

Intravenous piperacillin-tazobactam was started on the fifth hospital day. On discharge, the patient was advised to continue this treatment for 3 weeks at home.

The patient was receiving enoxaparin subcutaneously in prophylactic doses; 72 hours after the thorascopic procedure this was increased to therapeutic doses, continuing after discharge. Bridging to warfarin was not advised in view of his chest tubes.

Our patient appeared to have developed a right lower lobe infarction that cavitated and ruptured into the pleural space, causing a bronchopleural fistula with empyema after a recent pulmonary embolism. Other reported causes of pulmonary infarction in pulmonary embolism are malignancy and heavy clot burden,⁶ but these have not been confirmed in subsequent studies.⁵ Malignancy was ruled out by biopsy of the resected portion of the lung, and our patient did not have a history of heart failure. A clear cavity was not noted (because it ruptured into the pleura), but an air-fluid level was described in a wedge-shaped consolidation, suggesting infarction.

How common is pulmonary infarction after pulmonary embolism?

Pulmonary infarction occurs in few patients with pulmonary embolism.¹³ Since the lungs receive oxygen from the airways and have a dual blood supply from the pulmonary and bronchial arteries, they are not particularly vulnerable to ischemia. However, the reported incidence of pulmonary infarction in patients with pulmonary embolism has ranged from 10% to higher than 30%.^5,14,15

The reasons behind pulmonary infarction with complications after pulmonary embolism have varied in different case series in different eras. CT, biopsy, or autopsy studies reveal pulmonary infarction after pulmonary embolism to be more common than suspected by clinical symptoms.

In a Mayo Clinic series of 43 cases of pulmonary infarction diagnosed over a 6-year period by surgical lung biopsy, 18 (42%) of the patients had underlying pulmonary thromboembolism, which was the most common cause.¹⁶

4. Which statement about risk factors for pulmonary infarction in pulmonary embolism is incorrect?

• Heart failure may be a risk factor for pulmonary infarction
• Pulmonary hemorrhage is a risk factor for pulmonary infarction
• Pulmonary infarction is more common with more proximal sites of pulmonary embolism
• Collateral circulation may protect against pulmonary infarction

Infarction is more common with emboli that are distal rather than proximal.

Dalen et al¹⁵ suggested that after pulmonary embolism, pulmonary hemorrhage is an important contributor to the development of pulmonary infarction independent of the presence or absence of associated cardiac or pulmonary disease, but that the effect depends on the site of obstruction.

This idea was first proposed in 1913, when Karsner and Ghoreyeb¹⁷ showed that when pulmonary arteries are completely obstructed, the bronchial arteries take over, except when the embolism is present in a small branch of the pulmonary artery. This is because the physiologic anastomosis between the pulmonary artery and the bronchial arteries is located at the precapillary level of the pulmonary artery, and the bronchial circulation does not take over until the pulmonary arterial pressure in the area of the embolism drops to zero.

Using CT data, Kirchner et al⁵ confirmed that the risk of pulmonary infarction is higher if the obstruction is peripheral, ie, distal.

Using autopsy data, Tsao et al¹⁸ reported a higher risk of pulmonary infarction in embolic occlusion of pulmonary vessels less than 3 mm in diameter.

Collateral circulation has been shown to protect against pulmonary infarction. For example, Miniati et al¹⁴ showed that healthy young patients with pulmonary embolism were more prone to develop pulmonary infarction, probably because they had less efficient collateral systems in the peripheral lung fields. In lung transplant recipients, it has been shown that the risk of infarction decreased with development of collateral circulation.¹⁹

Dalen et al,¹⁵ however, attributed delayed resolution of pulmonary hemorrhage (as measured by resolution of infiltrate on chest radiography) to higher underlying pulmonary venous pressure in patients with heart failure and consequent pulmonary infarction. In comparison, healthy patients without cardiac or pulmonary disease have faster resolution of pulmonary hemorrhage when present, and less likelihood of pulmonary infarction (and death in submassive pulmonary embolism).

Data on the management of infected pulmonary infarction are limited. Mortality rates have been as high as 41% with noninfected and 73% with infected cavitary infarctions.⁴ Some authors have advocated early surgical resection in view of high rates of failure of medical treatment due to lack of blood supply within the cavity and continued risk of infection.

KEY POINTS

In patients with a recently diagnosed pulmonary embolism and concurrent symptoms of bacterial pneumonia, a diagnosis of cavitary pulmonary infarction should be considered.

Consolidations that are pleural-based with sharp, rounded margins and with focal areas of central hyperlucencies representing hemorrhage on the mediastinal windows on CT are more likely to represent a pulmonary infarct.²⁰

A 76-year-old man whose history included abdominal aortic aneurysm repair, bilateral femoral artery bypass for popliteal artery aneurysm, hypertension, and peptic ulcer disease was admitted to a community hospital with pleuritic chest pain and shortness of breath. Two days earlier, he had undergone repair of a ventral hernia.

At the time of that admission, he reported no fever, chills, night sweats, cough, or history of heart or lung disease. His vital signs were normal, and physical examination had revealed no apparent respiratory distress, no jugular venous distention, normal heart sounds, and no pedal edema; however, decreased air entry was noted in the right lung base. Initial serum levels of troponin and N-terminal pro-B-type natriuretic peptide were normal.

At that time, computed tomographic angiography of the chest showed segmental pulmonary emboli in the left upper and right lower lobes of the lungs and right pleural effusion. Transthoracic echocardiography showed normal atrial and ventricular sizes with no right or left ventricular systolic dysfunction and a left ventricular ejection fraction of 59%.

Treatment with intravenous heparin was started, and the patient was transferred to our hospital.

PLEURAL EFFUSION AND PULMONARY EMBOLISM

1. Which of the following is true about pleural effusion?

• It is rarely, if ever, associated with pulmonary embolism
• Most patients with pleural effusion due to pulmonary embolism do not have pleuritic chest pain
• Pulmonary embolism should be excluded in all cases of pleural effusion without a clear cause

Pulmonary embolism should be excluded in all cases of pleural effusion that do not have a clear cause. As for the other answer choices:

• Pulmonary embolism is the fourth leading cause of pleural effusion in the United States, after heart failure, pneumonia, and malignancy.¹
• About 75% of patients who develop pleural effusion in the setting of pulmonary embolism complain of pleuritic chest pain on the side of the effusion.² Most effusions are unilateral, small, and usually exudative.³

EVALUATION BEGINS: RESULTS OF THORACENTESIS

Our patient continued to receive intravenous heparin.

He underwent thoracentesis on hospital day 3, and 1,000 mL of turbid sanguineous pleural fluid was removed. Analysis of the fluid showed pH 7.27, white blood cell count 3.797 × 10⁹/L with 80% neutrophils, and lactate dehydrogenase (LDH) concentration 736 U/L (a ratio of pleural fluid LDH to a concurrent serum LDH > 0.6 is suggestive of an exudate); the fluid was also sent for culture and cytology. Thoracentesis was terminated early due to cough, and follow-up chest radiography showed a moderate-sized pneumothorax.

Computed tomography (CT) of the chest at this time showed a small wedge-shaped area of lung consolidation in the right lower lobe (also seen on CT done 1 day before admission to our hospital), with an intrinsic air-fluid level suggesting a focal infarct or lung abscess, now obscured by adjacent consolidation and atelectasis. In the interval since the previous CT, the multiloculated right pleural effusion had increased in size (Figure 1).

THE NEXT STEP

2. What is the most appropriate next step for this patient?

• Consult an interventional radiologist for chest tube placement
• Start empiric antibiotic therapy and ask an interventional radiologist to place a chest tube
• Start empiric antibiotic therapy, withhold anticoagulation, and consult a thoracic surgeon
• Start empiric antibiotic therapy and consult a thoracic surgeon while continuing anticoagulation

The most appropriate next step is to start empiric antibiotic therapy and consult a thoracic surgeon while continuing anticoagulation.

In this patient, it is appropriate to initiate antibiotics empirically on the basis of his significant pleural loculations, a wedge-shaped consolidation, and 80% neutrophils in the pleural fluid, all of which suggest infection. The unmasking of a wedge-shaped consolidation after thoracentesis, with a previously noted air-fluid level and an interval increase in multiloculated pleural fluid, raises suspicion of a necrotic infection that may have ruptured into the pleural space, a possible lung infarct, or a malignancy. Hence, simply placing a chest tube may not be enough.

Blood in the pleural fluid does not necessitate withholding anticoagulation unless the bleeding is heavy. A pleural fluid hematocrit greater than 50% of the peripheral blood hematocrit suggests hemothorax and is an indication to withhold anticoagulation.¹ Our patient’s pleural fluid was qualitatively sanguineous but not frankly bloody, and therefore we judged that it was not necessary to stop his heparin.

HOW DOES PULMONARY INFARCTION PRESENT CLINICALLY?

3. Which of the following statements about pulmonary infarction is incorrect?

• Cavitation and infarction are more common with larger emboli
• Cavitation occurs in fewer than 10% of pulmonary infarctions
• Lung abscess develops in more than 50% of pulmonary infarctions
• Pulmonary thromboembolism is the most common cause of pulmonary infarction

Lung abscess develops in far fewer than 50% of cases of pulmonary infarction. The rest of the statements are correct.

Cavitation complicates about 4% to 7% of infarctions and is more common when the infarction is 4 cm or greater in diameter.⁴ These cavities are usually single and predominantly on the right side in the apical or posterior segment of the upper lobe or the apical segment of the right lower lobe, as in our patient.^5–8 CT demonstrating scalloped inner margins and cross-cavity band shadows suggests a cavitary pulmonary infarction.^9,10

Infection and abscess in pulmonary infarction are poorly understood but have been linked to larger infarctions, coexistent congestion or atelectasis, and dental or oropharyngeal infection. In an early series of 550 cases of pulmonary infarction, 23 patients (4.2%) developed lung abscess and 6 (1.1%) developed empyema.¹¹ The mean time to cavitation for an infected pulmonary infarction has been reported to be 18 days.¹²

A reversed halo sign, generally described as a focal, rounded area of ground-glass opacity surrounded by a nearly complete ring of consolidation, has been reported to be more frequent with pulmonary infarction than with other diseases, especially when in the lower lobes.¹³

CASE CONTINUED: THORACOSCOPY

A cardiothoracic surgeon was consulted, intravenous heparin was discontinued, an inferior vena cava filter was placed, and the patient underwent video-assisted thoracoscopy.

Purulent fluid was noted on the lateral aspect of right lower lobe; this appeared to be the ruptured cavitary lesion functioning like an uncontrolled bronchopleural fistula. Two chest tubes, sizes 32F and 28F, were placed after decortication, resection of the lung abscess, and closure of the bronchopleural fistula. No significant air leak was noted after resection of this segment of lung.

Pathologic study showed acute organizing pneumonia with abscess formation; no malignant cells or granulomas were seen (Figure 2). Pleural fluid cultures grew Streptococcus intermedius, while the tissue culture was negative for any growth, including acid-fast bacilli and fungi.

On 3 different occasions, both chest tubes were shortened, backed out 2 cm, and resecured with sutures and pins, and Heimlich valves were applied before the patient was discharged.

Intravenous piperacillin-tazobactam was started on the fifth hospital day. On discharge, the patient was advised to continue this treatment for 3 weeks at home.

The patient was receiving enoxaparin subcutaneously in prophylactic doses; 72 hours after the thorascopic procedure this was increased to therapeutic doses, continuing after discharge. Bridging to warfarin was not advised in view of his chest tubes.

Our patient appeared to have developed a right lower lobe infarction that cavitated and ruptured into the pleural space, causing a bronchopleural fistula with empyema after a recent pulmonary embolism. Other reported causes of pulmonary infarction in pulmonary embolism are malignancy and heavy clot burden,⁶ but these have not been confirmed in subsequent studies.⁵ Malignancy was ruled out by biopsy of the resected portion of the lung, and our patient did not have a history of heart failure. A clear cavity was not noted (because it ruptured into the pleura), but an air-fluid level was described in a wedge-shaped consolidation, suggesting infarction.

How common is pulmonary infarction after pulmonary embolism?

Pulmonary infarction occurs in few patients with pulmonary embolism.¹³ Since the lungs receive oxygen from the airways and have a dual blood supply from the pulmonary and bronchial arteries, they are not particularly vulnerable to ischemia. However, the reported incidence of pulmonary infarction in patients with pulmonary embolism has ranged from 10% to higher than 30%.^5,14,15

The reasons behind pulmonary infarction with complications after pulmonary embolism have varied in different case series in different eras. CT, biopsy, or autopsy studies reveal pulmonary infarction after pulmonary embolism to be more common than suspected by clinical symptoms.

In a Mayo Clinic series of 43 cases of pulmonary infarction diagnosed over a 6-year period by surgical lung biopsy, 18 (42%) of the patients had underlying pulmonary thromboembolism, which was the most common cause.¹⁶

4. Which statement about risk factors for pulmonary infarction in pulmonary embolism is incorrect?

• Heart failure may be a risk factor for pulmonary infarction
• Pulmonary hemorrhage is a risk factor for pulmonary infarction
• Pulmonary infarction is more common with more proximal sites of pulmonary embolism
• Collateral circulation may protect against pulmonary infarction

Infarction is more common with emboli that are distal rather than proximal.

Dalen et al¹⁵ suggested that after pulmonary embolism, pulmonary hemorrhage is an important contributor to the development of pulmonary infarction independent of the presence or absence of associated cardiac or pulmonary disease, but that the effect depends on the site of obstruction.

This idea was first proposed in 1913, when Karsner and Ghoreyeb¹⁷ showed that when pulmonary arteries are completely obstructed, the bronchial arteries take over, except when the embolism is present in a small branch of the pulmonary artery. This is because the physiologic anastomosis between the pulmonary artery and the bronchial arteries is located at the precapillary level of the pulmonary artery, and the bronchial circulation does not take over until the pulmonary arterial pressure in the area of the embolism drops to zero.

Using CT data, Kirchner et al⁵ confirmed that the risk of pulmonary infarction is higher if the obstruction is peripheral, ie, distal.

Using autopsy data, Tsao et al¹⁸ reported a higher risk of pulmonary infarction in embolic occlusion of pulmonary vessels less than 3 mm in diameter.

Collateral circulation has been shown to protect against pulmonary infarction. For example, Miniati et al¹⁴ showed that healthy young patients with pulmonary embolism were more prone to develop pulmonary infarction, probably because they had less efficient collateral systems in the peripheral lung fields. In lung transplant recipients, it has been shown that the risk of infarction decreased with development of collateral circulation.¹⁹

Dalen et al,¹⁵ however, attributed delayed resolution of pulmonary hemorrhage (as measured by resolution of infiltrate on chest radiography) to higher underlying pulmonary venous pressure in patients with heart failure and consequent pulmonary infarction. In comparison, healthy patients without cardiac or pulmonary disease have faster resolution of pulmonary hemorrhage when present, and less likelihood of pulmonary infarction (and death in submassive pulmonary embolism).

Data on the management of infected pulmonary infarction are limited. Mortality rates have been as high as 41% with noninfected and 73% with infected cavitary infarctions.⁴ Some authors have advocated early surgical resection in view of high rates of failure of medical treatment due to lack of blood supply within the cavity and continued risk of infection.

KEY POINTS

In patients with a recently diagnosed pulmonary embolism and concurrent symptoms of bacterial pneumonia, a diagnosis of cavitary pulmonary infarction should be considered.

Consolidations that are pleural-based with sharp, rounded margins and with focal areas of central hyperlucencies representing hemorrhage on the mediastinal windows on CT are more likely to represent a pulmonary infarct.²⁰

Mary, age 38, was hospitalized for acute cholecystitis requiring laparoscopic surgery. Her hospital course was uneventful. At the time of discharge, I, her inpatient doctor, prescribed 15 hydrocodone tablets for postoperative pain. I never saw her again. Did she struggle to stop taking the hydrocodone I prescribed?

Heather is a 50-year-old patient in my addiction medicine clinic who developed opioid use disorder while being treated for chronic pain. After much hardship and to her credit, she is now in long-term remission. Did her opioid use disorder start with an opioid prescription for an accepted indication?

The issues Mary and Heather face seem unrelated, but these 2 patients may be at different time points in the progression of the same disease. As a hospitalist, I want to optimize the chances that patients taking opioids for acute pain will be able to stop taking them.

CHRONIC USE VS OPIOID USE DISORDER

There is a distinction between chronic use of opioids and opioid use disorder. The latter is also known as addiction.

Patients who take opioids daily do not necessarily have opioid use disorder, even if they have physiologic dependence on them. Physiologic opioid dependence is commonly confused with opioid use disorder, but it is the expected result of regularly taking these drugs.

Opioid use disorder is a chronic disease of the brain characterized by loss of control over opioid use, resulting in harm. The Diagnostic and Statistical Manual, fifth edition, excludes physiologic dependence on opioids (tolerance and withdrawal) from its criteria for opioid use disorder if the patient is taking opioids solely under medical supervision.¹ To be diagnosed with opioid use disorder, patients need to do only 2 of the following within 12 months:

• Take more of the drug than intended
• Want or try to cut down without success
• Spend a lot of time in getting, using, or recovering from the drug
• Crave the drug
• Fail to meet commitments due to the drug
• Continue to use the drug, even though it causes social or relationship problems
• Give up or reduce other activities because of the drug
• Use the drug even when it isn’t safe
• Continue to use even when it causes physical or psychological problems
• Develop tolerance (but, as noted, not if taking the drug as directed under a doctor’s supervision)
• Experience withdrawal (again, but not if taking the drug under medical supervision).

WHY DO SOME PATIENTS STRUGGLE TO STOP TAKING OPIOIDS?

Studying opioid use disorder as an outcome in large groups of patients is complicated by imperfect medical documentation. However, using pharmacy claims data, researchers can accurately describe opioid prescription patterns in large groups of patients over time. This means we can count how many patients keep taking prescribed opioids but not how many become addicted.

In a country where nearly 40% of adults are prescribed an opioid annually, the question is not why people start taking opioids, but why some have to struggle to stop.² Several recent studies used pharmacy claims data to identify factors that may predict chronic opioid use in patients prescribed opioids for acute pain. The findings suggest that we can better treat acute pain to prevent chronic opioid use.

We don’t yet know how to protect patients like Mary from opioid use disorder, but the following 3 studies have already changed my practice.

HIGHER TOTAL DOSE MEANS HIGHER RISK

[Shah A, Hayes CJ, Martin BC. Characteristics of initial prescription episodes and likelihood of long-term opioid use—United States, 2006–2015. MMWR Morb Mortal Wkly Rep 2017; 66(10):265–269.]

Shah et al³ reported a study of nearly 1.3 million opioid-naive patients who received opioid prescriptions. Of those prescribed at least 1 day of opioids, 6% were still taking them 1 year later, and 2.9% were still taking them 3 years later.

Opioid exposure in acute pain was measured in total “morphine milligram equivalents” (MME), ie, the cumulative amount of opioids prescribed in the treatment episode, standardized across different types of opioids. We usually think of exposure in terms of how many milligrams a patient takes per day, which correlates with mortality in chronic opioid use.⁴ But this study showed a linear relationship between total MME prescribed for acute pain and ongoing opioid use in opioid-naive patients. By itself, the difference between daily and total MME made the article revelatory.

But the study went further, asking how much is too much: ie, What is the cutoff MME above which the patient is at risk of chronic opioid use? The relationship between acute opioid dose and chronic use is linear and starts early. Shah et al suggested that a total threshold of 700 MME predicts chronic opioid use—140 hydrocodone tablets, or 1 month of regular use.³

Many doctors worry that specific opioids such as oxycodone, hydromorphone, and fentanyl may be more habit-forming. Surprisingly, this study showed that these drugs were associated with rates of chronic use similar to those of other opioids when they controlled for potency.

Bottom line. Total opioid use in acute pain was the best predictor of chronic opioid use, and it showed that chronicity begins earlier than thought.

[Barnett ML, Olenski AR, Jena AB. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N Engl J Med 2017; 376(7):663–673.]

Barnett et al⁵ analyzed opioid prescribing for acute pain in the emergency department, using Medicare pharmacy data from 377,629 previously opioid-naive patients. They categorized the emergency providers into quartiles based on the frequency of opioid prescribing.

The relative risk of ongoing opioid use 1 year after being treated by a “high-intensity” prescriber (ie, one in the top quartile) was 30% greater than in similar patients seen by a low-intensity prescriber (ie, one in the bottom quartile). In addition, those who were treated by high-intensity prescribers were more likely to have a serious fall.

In designing the study, the authors assumed that patients visiting an emergency department had their doctor assigned randomly. They controlled for many patient variables that might have confounded the results, such as age, sex, race, depression, medical comorbidities, and geographic region. Were the higher rates of ongoing opioid use in the high-intensity-prescriber group due to the higher prescribing rates of their emergency providers, or did the providers counsel patients differently? This is not known.

Bottom line. Different doctors manage similar patients differently when it comes to pain, and those who prescribe more opioids for acute pain put their patients at risk of chronic opioid use and falls. I don’t want to be a high-intensity opioid prescriber.

SURGERY AND CHRONIC OPIOID USE

[Brummett CM, Waljee JF, Goesling J, et al. New persistent opioid use after minor and major surgical procedures in US adults. JAMA Surg 2017; 152(6):e170504.]

Brummett et al⁶ examined ongoing opioid use after surgery in 36,177 opioid-naive patients and in a nonsurgical control group. After 3 months, 6% of the patients who underwent surgery remained on opioids, compared with only 0.4% of the nonsurgical controls. Whether the surgery was major or minor did not affect the rate of postoperative opioid use.

Risk factors for ongoing opioid use were preexisting addiction to anything (including tobacco), mood disorders, and preoperative pain disorders. These risk factors have previously been reported in nonsurgical patients.⁷

Brummett et al speculated that patients are counseled about postoperative opioids in a way that leads them to overestimate the safety and efficacy of these drugs for treating other common pain conditions.⁶ 

Bottom line. Patients with mental health comorbidities have a hard time stopping opioids. The remarkable finding in this study was the similarity between major and minor surgery in terms of chronic opioid use. If postoperative opioids treat only the pain caused by the surgery, major surgery should be associated with greater opioid use. The similarity suggests that a mechanism other than postoperative pain confers risk of chronic opioid use.

THINKING ABOUT OPIOIDS

Collectively, these articles describe elements of acute pain treatment that correlate with chronic ongoing opioid use: a higher cumulative dose,³ being seen by a physician who prescribes a lot of opioids,⁵ undergoing surgery,⁶ and psychiatric comorbidity.⁶ They made me wonder if opioid use for acute pain acts as an inoculation, analogous to inoculating a Petri dish with bacteria.  The likelihood of chronic opioid use arises from the inoculum dose, the host response, and the context of inoculation. 

These articles do not show how patients taking opioids chronically for pain become addicted. Stumbo et al⁸ interviewed 283 opioid-dependent patients and identified 5 pathways to opioid use disorder, 3 of which were related to pain control: inadequately controlled chronic pain, exposure to opioids during acute pain episodes, and chronic pain in patients who already had substance use disorders. Brat et al⁹ recently estimated the risk of opioid use disorder after receiving opioids postoperatively to be less than 1%, but it increased dramatically with duration of opioid treatment.

A patient who fills an opioid prescription does not necessarily have chronic pain. Nor do all patients with chronic pain require an opioid prescription. These studies did not establish whether the patients had a pain syndrome. In practice, we call our patients who chronically take opioids our “chronic pain patients.” But 40% of Americans have chronic pain, while only 5% take opioids daily for pain.^11,12

We assume that those taking opioids have the most severe pain. But Brummett et al suggested that continued opioid use is predicted less by pain and more by psychiatric comorbidity.⁶ More than half of the opioid prescriptions in the United States are written for patients with serious mental illness, who represent one-sixth of that population.¹¹ Maybe chronic opioid use for pain has more to do with vulnerability to opioids and less to do with a pain syndrome.

I now think about daily opioid use in much the same way as I think about daily prednisone use. Patients on daily prednisone have a characteristic set of medical risks from the prednisone itself, regardless of its indication. Yet we do not consider these patients addicted to prednisone. Opioid use may be similar.

Like most doctors, I am troubled by the continued rise in the opioid overdose rate.¹³ Yet addiction and death from overdose are not the only risks that patients on chronic opioids face; they also have higher rates of falls, cardiovascular death, pneumonia, death from chronic obstructive pulmonary disease, and motor vehicle crashes.^14–17 Patients on chronic opioids for pain have greater mental health comorbidity and worse function.¹⁸

Most concerning, chronic opioid treatment for pain lacks proof of benefit. In fact, a recent study disproved the benefit of opioids for chronic pain compared with nonopioid options.¹⁹ When I meet with patients who are taking chronic opioids for pain, I often can’t identify why the drugs were started or ought to be continued, and I anticipate a bad outcome. Yet the patient is afraid to stop the drug. For these reasons, chronic opioid use for pain strikes me as worth considering separately from opioid use disorder.

The studies described above have had a powerful effect on my clinical care as a hospitalist.

I now talk to all patients starting opioids about how hard it can be to stop. Some patients are defensive at first, believing this does not apply to them. But I politely continue.

People with depression and anxiety can have a harder time stopping opioids. Addiction is both a risk with ongoing opioid use and a possible outcome of acute opioid use.⁸ But one can struggle to stop opioids without being addicted or depressed. Even the healthiest person may wish to continue opioids past the point of benefit.

I am careful not to invalidate the patient’s experience of pain. It is challenging for patients to find the balance between current discomfort and a possible future adverse effect. In these conversations, I imagine how I would want a loved one counseled on their pain control. This centers me as I choose my words and my tone.

I now monitor the total amount of opioid I prescribe for acute pain in addition to the daily dose. I give my patients as few opioids as reasonable, and advise them to take the minimum dose required for tolerable comfort. I offer nonopioid options as the preferred choice, presenting them as effective and safe. I do this irrespective of the indication for opioids.

I limit opioids in all patients, not just those with comorbidities. I include in my shared decision-making process the risk of chronic opioid use when I prescribe opioids for acute pain, carefully distinguishing it from opioid use disorder. Instead of excess opioids, I give patients my office phone number to call in case they struggle. I rarely get calls. But I find patients would rather have access to a doctor than extra pills. And offering them my contact information lets me limit opioids while letting them know that I am committed to their comfort and health.

As an addiction medicine doctor, I consult on patients not taking their opioids as prescribed. Caring for these patients is intellectually and emotionally draining; they suffer daily, and the opioids they take provide a modicum of relief at a high cost. The publications I have discussed here provide insight into how a troubled relationship with opioids begins. I remind myself that these patients have an iatrogenic condition. Their behaviors that we label “aberrant” may reflect an adverse reaction to medications prescribed to them for acute pain.

Mary, my patient with postoperative pain after cholecystectomy, may over time develop opioid use disorder as Heather did. That progression may have begun with the hydrocodone I prescribed and the counseling I gave her, and it may proceed to chronic opioid use and then opioid use disorder.

I am looking closely at the care I give for acute pain in light of these innovative studies. But even more so, they have increased the compassion with which I care for patients like Heather, those harmed by prescribed opioids.

Mary, age 38, was hospitalized for acute cholecystitis requiring laparoscopic surgery. Her hospital course was uneventful. At the time of discharge, I, her inpatient doctor, prescribed 15 hydrocodone tablets for postoperative pain. I never saw her again. Did she struggle to stop taking the hydrocodone I prescribed?

Heather is a 50-year-old patient in my addiction medicine clinic who developed opioid use disorder while being treated for chronic pain. After much hardship and to her credit, she is now in long-term remission. Did her opioid use disorder start with an opioid prescription for an accepted indication?

The issues Mary and Heather face seem unrelated, but these 2 patients may be at different time points in the progression of the same disease. As a hospitalist, I want to optimize the chances that patients taking opioids for acute pain will be able to stop taking them.

CHRONIC USE VS OPIOID USE DISORDER

There is a distinction between chronic use of opioids and opioid use disorder. The latter is also known as addiction.

Patients who take opioids daily do not necessarily have opioid use disorder, even if they have physiologic dependence on them. Physiologic opioid dependence is commonly confused with opioid use disorder, but it is the expected result of regularly taking these drugs.

Opioid use disorder is a chronic disease of the brain characterized by loss of control over opioid use, resulting in harm. The Diagnostic and Statistical Manual, fifth edition, excludes physiologic dependence on opioids (tolerance and withdrawal) from its criteria for opioid use disorder if the patient is taking opioids solely under medical supervision.¹ To be diagnosed with opioid use disorder, patients need to do only 2 of the following within 12 months:

• Take more of the drug than intended
• Want or try to cut down without success
• Spend a lot of time in getting, using, or recovering from the drug
• Crave the drug
• Fail to meet commitments due to the drug
• Continue to use the drug, even though it causes social or relationship problems
• Give up or reduce other activities because of the drug
• Use the drug even when it isn’t safe
• Continue to use even when it causes physical or psychological problems
• Develop tolerance (but, as noted, not if taking the drug as directed under a doctor’s supervision)
• Experience withdrawal (again, but not if taking the drug under medical supervision).

WHY DO SOME PATIENTS STRUGGLE TO STOP TAKING OPIOIDS?

Studying opioid use disorder as an outcome in large groups of patients is complicated by imperfect medical documentation. However, using pharmacy claims data, researchers can accurately describe opioid prescription patterns in large groups of patients over time. This means we can count how many patients keep taking prescribed opioids but not how many become addicted.

In a country where nearly 40% of adults are prescribed an opioid annually, the question is not why people start taking opioids, but why some have to struggle to stop.² Several recent studies used pharmacy claims data to identify factors that may predict chronic opioid use in patients prescribed opioids for acute pain. The findings suggest that we can better treat acute pain to prevent chronic opioid use.

We don’t yet know how to protect patients like Mary from opioid use disorder, but the following 3 studies have already changed my practice.

HIGHER TOTAL DOSE MEANS HIGHER RISK

[Shah A, Hayes CJ, Martin BC. Characteristics of initial prescription episodes and likelihood of long-term opioid use—United States, 2006–2015. MMWR Morb Mortal Wkly Rep 2017; 66(10):265–269.]

Shah et al³ reported a study of nearly 1.3 million opioid-naive patients who received opioid prescriptions. Of those prescribed at least 1 day of opioids, 6% were still taking them 1 year later, and 2.9% were still taking them 3 years later.

Opioid exposure in acute pain was measured in total “morphine milligram equivalents” (MME), ie, the cumulative amount of opioids prescribed in the treatment episode, standardized across different types of opioids. We usually think of exposure in terms of how many milligrams a patient takes per day, which correlates with mortality in chronic opioid use.⁴ But this study showed a linear relationship between total MME prescribed for acute pain and ongoing opioid use in opioid-naive patients. By itself, the difference between daily and total MME made the article revelatory.

But the study went further, asking how much is too much: ie, What is the cutoff MME above which the patient is at risk of chronic opioid use? The relationship between acute opioid dose and chronic use is linear and starts early. Shah et al suggested that a total threshold of 700 MME predicts chronic opioid use—140 hydrocodone tablets, or 1 month of regular use.³

Many doctors worry that specific opioids such as oxycodone, hydromorphone, and fentanyl may be more habit-forming. Surprisingly, this study showed that these drugs were associated with rates of chronic use similar to those of other opioids when they controlled for potency.

Bottom line. Total opioid use in acute pain was the best predictor of chronic opioid use, and it showed that chronicity begins earlier than thought.

[Barnett ML, Olenski AR, Jena AB. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N Engl J Med 2017; 376(7):663–673.]

Barnett et al⁵ analyzed opioid prescribing for acute pain in the emergency department, using Medicare pharmacy data from 377,629 previously opioid-naive patients. They categorized the emergency providers into quartiles based on the frequency of opioid prescribing.

The relative risk of ongoing opioid use 1 year after being treated by a “high-intensity” prescriber (ie, one in the top quartile) was 30% greater than in similar patients seen by a low-intensity prescriber (ie, one in the bottom quartile). In addition, those who were treated by high-intensity prescribers were more likely to have a serious fall.

In designing the study, the authors assumed that patients visiting an emergency department had their doctor assigned randomly. They controlled for many patient variables that might have confounded the results, such as age, sex, race, depression, medical comorbidities, and geographic region. Were the higher rates of ongoing opioid use in the high-intensity-prescriber group due to the higher prescribing rates of their emergency providers, or did the providers counsel patients differently? This is not known.

Bottom line. Different doctors manage similar patients differently when it comes to pain, and those who prescribe more opioids for acute pain put their patients at risk of chronic opioid use and falls. I don’t want to be a high-intensity opioid prescriber.

SURGERY AND CHRONIC OPIOID USE

[Brummett CM, Waljee JF, Goesling J, et al. New persistent opioid use after minor and major surgical procedures in US adults. JAMA Surg 2017; 152(6):e170504.]

Brummett et al⁶ examined ongoing opioid use after surgery in 36,177 opioid-naive patients and in a nonsurgical control group. After 3 months, 6% of the patients who underwent surgery remained on opioids, compared with only 0.4% of the nonsurgical controls. Whether the surgery was major or minor did not affect the rate of postoperative opioid use.

Risk factors for ongoing opioid use were preexisting addiction to anything (including tobacco), mood disorders, and preoperative pain disorders. These risk factors have previously been reported in nonsurgical patients.⁷

Brummett et al speculated that patients are counseled about postoperative opioids in a way that leads them to overestimate the safety and efficacy of these drugs for treating other common pain conditions.⁶ 

Bottom line. Patients with mental health comorbidities have a hard time stopping opioids. The remarkable finding in this study was the similarity between major and minor surgery in terms of chronic opioid use. If postoperative opioids treat only the pain caused by the surgery, major surgery should be associated with greater opioid use. The similarity suggests that a mechanism other than postoperative pain confers risk of chronic opioid use.

THINKING ABOUT OPIOIDS

Collectively, these articles describe elements of acute pain treatment that correlate with chronic ongoing opioid use: a higher cumulative dose,³ being seen by a physician who prescribes a lot of opioids,⁵ undergoing surgery,⁶ and psychiatric comorbidity.⁶ They made me wonder if opioid use for acute pain acts as an inoculation, analogous to inoculating a Petri dish with bacteria.  The likelihood of chronic opioid use arises from the inoculum dose, the host response, and the context of inoculation. 

These articles do not show how patients taking opioids chronically for pain become addicted. Stumbo et al⁸ interviewed 283 opioid-dependent patients and identified 5 pathways to opioid use disorder, 3 of which were related to pain control: inadequately controlled chronic pain, exposure to opioids during acute pain episodes, and chronic pain in patients who already had substance use disorders. Brat et al⁹ recently estimated the risk of opioid use disorder after receiving opioids postoperatively to be less than 1%, but it increased dramatically with duration of opioid treatment.

A patient who fills an opioid prescription does not necessarily have chronic pain. Nor do all patients with chronic pain require an opioid prescription. These studies did not establish whether the patients had a pain syndrome. In practice, we call our patients who chronically take opioids our “chronic pain patients.” But 40% of Americans have chronic pain, while only 5% take opioids daily for pain.^11,12

We assume that those taking opioids have the most severe pain. But Brummett et al suggested that continued opioid use is predicted less by pain and more by psychiatric comorbidity.⁶ More than half of the opioid prescriptions in the United States are written for patients with serious mental illness, who represent one-sixth of that population.¹¹ Maybe chronic opioid use for pain has more to do with vulnerability to opioids and less to do with a pain syndrome.

I now think about daily opioid use in much the same way as I think about daily prednisone use. Patients on daily prednisone have a characteristic set of medical risks from the prednisone itself, regardless of its indication. Yet we do not consider these patients addicted to prednisone. Opioid use may be similar.

Like most doctors, I am troubled by the continued rise in the opioid overdose rate.¹³ Yet addiction and death from overdose are not the only risks that patients on chronic opioids face; they also have higher rates of falls, cardiovascular death, pneumonia, death from chronic obstructive pulmonary disease, and motor vehicle crashes.^14–17 Patients on chronic opioids for pain have greater mental health comorbidity and worse function.¹⁸

Most concerning, chronic opioid treatment for pain lacks proof of benefit. In fact, a recent study disproved the benefit of opioids for chronic pain compared with nonopioid options.¹⁹ When I meet with patients who are taking chronic opioids for pain, I often can’t identify why the drugs were started or ought to be continued, and I anticipate a bad outcome. Yet the patient is afraid to stop the drug. For these reasons, chronic opioid use for pain strikes me as worth considering separately from opioid use disorder.

The studies described above have had a powerful effect on my clinical care as a hospitalist.

I now talk to all patients starting opioids about how hard it can be to stop. Some patients are defensive at first, believing this does not apply to them. But I politely continue.

People with depression and anxiety can have a harder time stopping opioids. Addiction is both a risk with ongoing opioid use and a possible outcome of acute opioid use.⁸ But one can struggle to stop opioids without being addicted or depressed. Even the healthiest person may wish to continue opioids past the point of benefit.

I am careful not to invalidate the patient’s experience of pain. It is challenging for patients to find the balance between current discomfort and a possible future adverse effect. In these conversations, I imagine how I would want a loved one counseled on their pain control. This centers me as I choose my words and my tone.

I now monitor the total amount of opioid I prescribe for acute pain in addition to the daily dose. I give my patients as few opioids as reasonable, and advise them to take the minimum dose required for tolerable comfort. I offer nonopioid options as the preferred choice, presenting them as effective and safe. I do this irrespective of the indication for opioids.

I limit opioids in all patients, not just those with comorbidities. I include in my shared decision-making process the risk of chronic opioid use when I prescribe opioids for acute pain, carefully distinguishing it from opioid use disorder. Instead of excess opioids, I give patients my office phone number to call in case they struggle. I rarely get calls. But I find patients would rather have access to a doctor than extra pills. And offering them my contact information lets me limit opioids while letting them know that I am committed to their comfort and health.

As an addiction medicine doctor, I consult on patients not taking their opioids as prescribed. Caring for these patients is intellectually and emotionally draining; they suffer daily, and the opioids they take provide a modicum of relief at a high cost. The publications I have discussed here provide insight into how a troubled relationship with opioids begins. I remind myself that these patients have an iatrogenic condition. Their behaviors that we label “aberrant” may reflect an adverse reaction to medications prescribed to them for acute pain.

Mary, my patient with postoperative pain after cholecystectomy, may over time develop opioid use disorder as Heather did. That progression may have begun with the hydrocodone I prescribed and the counseling I gave her, and it may proceed to chronic opioid use and then opioid use disorder.

I am looking closely at the care I give for acute pain in light of these innovative studies. But even more so, they have increased the compassion with which I care for patients like Heather, those harmed by prescribed opioids.

Mary, age 38, was hospitalized for acute cholecystitis requiring laparoscopic surgery. Her hospital course was uneventful. At the time of discharge, I, her inpatient doctor, prescribed 15 hydrocodone tablets for postoperative pain. I never saw her again. Did she struggle to stop taking the hydrocodone I prescribed?

Heather is a 50-year-old patient in my addiction medicine clinic who developed opioid use disorder while being treated for chronic pain. After much hardship and to her credit, she is now in long-term remission. Did her opioid use disorder start with an opioid prescription for an accepted indication?

The issues Mary and Heather face seem unrelated, but these 2 patients may be at different time points in the progression of the same disease. As a hospitalist, I want to optimize the chances that patients taking opioids for acute pain will be able to stop taking them.

CHRONIC USE VS OPIOID USE DISORDER

There is a distinction between chronic use of opioids and opioid use disorder. The latter is also known as addiction.

Patients who take opioids daily do not necessarily have opioid use disorder, even if they have physiologic dependence on them. Physiologic opioid dependence is commonly confused with opioid use disorder, but it is the expected result of regularly taking these drugs.

Opioid use disorder is a chronic disease of the brain characterized by loss of control over opioid use, resulting in harm. The Diagnostic and Statistical Manual, fifth edition, excludes physiologic dependence on opioids (tolerance and withdrawal) from its criteria for opioid use disorder if the patient is taking opioids solely under medical supervision.¹ To be diagnosed with opioid use disorder, patients need to do only 2 of the following within 12 months:

• Take more of the drug than intended
• Want or try to cut down without success
• Spend a lot of time in getting, using, or recovering from the drug
• Crave the drug
• Fail to meet commitments due to the drug
• Continue to use the drug, even though it causes social or relationship problems
• Give up or reduce other activities because of the drug
• Use the drug even when it isn’t safe
• Continue to use even when it causes physical or psychological problems
• Develop tolerance (but, as noted, not if taking the drug as directed under a doctor’s supervision)
• Experience withdrawal (again, but not if taking the drug under medical supervision).

WHY DO SOME PATIENTS STRUGGLE TO STOP TAKING OPIOIDS?

Studying opioid use disorder as an outcome in large groups of patients is complicated by imperfect medical documentation. However, using pharmacy claims data, researchers can accurately describe opioid prescription patterns in large groups of patients over time. This means we can count how many patients keep taking prescribed opioids but not how many become addicted.

In a country where nearly 40% of adults are prescribed an opioid annually, the question is not why people start taking opioids, but why some have to struggle to stop.² Several recent studies used pharmacy claims data to identify factors that may predict chronic opioid use in patients prescribed opioids for acute pain. The findings suggest that we can better treat acute pain to prevent chronic opioid use.

We don’t yet know how to protect patients like Mary from opioid use disorder, but the following 3 studies have already changed my practice.

HIGHER TOTAL DOSE MEANS HIGHER RISK

[Shah A, Hayes CJ, Martin BC. Characteristics of initial prescription episodes and likelihood of long-term opioid use—United States, 2006–2015. MMWR Morb Mortal Wkly Rep 2017; 66(10):265–269.]

Shah et al³ reported a study of nearly 1.3 million opioid-naive patients who received opioid prescriptions. Of those prescribed at least 1 day of opioids, 6% were still taking them 1 year later, and 2.9% were still taking them 3 years later.

Opioid exposure in acute pain was measured in total “morphine milligram equivalents” (MME), ie, the cumulative amount of opioids prescribed in the treatment episode, standardized across different types of opioids. We usually think of exposure in terms of how many milligrams a patient takes per day, which correlates with mortality in chronic opioid use.⁴ But this study showed a linear relationship between total MME prescribed for acute pain and ongoing opioid use in opioid-naive patients. By itself, the difference between daily and total MME made the article revelatory.

But the study went further, asking how much is too much: ie, What is the cutoff MME above which the patient is at risk of chronic opioid use? The relationship between acute opioid dose and chronic use is linear and starts early. Shah et al suggested that a total threshold of 700 MME predicts chronic opioid use—140 hydrocodone tablets, or 1 month of regular use.³

Many doctors worry that specific opioids such as oxycodone, hydromorphone, and fentanyl may be more habit-forming. Surprisingly, this study showed that these drugs were associated with rates of chronic use similar to those of other opioids when they controlled for potency.

Bottom line. Total opioid use in acute pain was the best predictor of chronic opioid use, and it showed that chronicity begins earlier than thought.

[Barnett ML, Olenski AR, Jena AB. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N Engl J Med 2017; 376(7):663–673.]

Barnett et al⁵ analyzed opioid prescribing for acute pain in the emergency department, using Medicare pharmacy data from 377,629 previously opioid-naive patients. They categorized the emergency providers into quartiles based on the frequency of opioid prescribing.

The relative risk of ongoing opioid use 1 year after being treated by a “high-intensity” prescriber (ie, one in the top quartile) was 30% greater than in similar patients seen by a low-intensity prescriber (ie, one in the bottom quartile). In addition, those who were treated by high-intensity prescribers were more likely to have a serious fall.

In designing the study, the authors assumed that patients visiting an emergency department had their doctor assigned randomly. They controlled for many patient variables that might have confounded the results, such as age, sex, race, depression, medical comorbidities, and geographic region. Were the higher rates of ongoing opioid use in the high-intensity-prescriber group due to the higher prescribing rates of their emergency providers, or did the providers counsel patients differently? This is not known.

Bottom line. Different doctors manage similar patients differently when it comes to pain, and those who prescribe more opioids for acute pain put their patients at risk of chronic opioid use and falls. I don’t want to be a high-intensity opioid prescriber.

SURGERY AND CHRONIC OPIOID USE

[Brummett CM, Waljee JF, Goesling J, et al. New persistent opioid use after minor and major surgical procedures in US adults. JAMA Surg 2017; 152(6):e170504.]

Brummett et al⁶ examined ongoing opioid use after surgery in 36,177 opioid-naive patients and in a nonsurgical control group. After 3 months, 6% of the patients who underwent surgery remained on opioids, compared with only 0.4% of the nonsurgical controls. Whether the surgery was major or minor did not affect the rate of postoperative opioid use.

Risk factors for ongoing opioid use were preexisting addiction to anything (including tobacco), mood disorders, and preoperative pain disorders. These risk factors have previously been reported in nonsurgical patients.⁷

Brummett et al speculated that patients are counseled about postoperative opioids in a way that leads them to overestimate the safety and efficacy of these drugs for treating other common pain conditions.⁶ 

Bottom line. Patients with mental health comorbidities have a hard time stopping opioids. The remarkable finding in this study was the similarity between major and minor surgery in terms of chronic opioid use. If postoperative opioids treat only the pain caused by the surgery, major surgery should be associated with greater opioid use. The similarity suggests that a mechanism other than postoperative pain confers risk of chronic opioid use.

THINKING ABOUT OPIOIDS

Collectively, these articles describe elements of acute pain treatment that correlate with chronic ongoing opioid use: a higher cumulative dose,³ being seen by a physician who prescribes a lot of opioids,⁵ undergoing surgery,⁶ and psychiatric comorbidity.⁶ They made me wonder if opioid use for acute pain acts as an inoculation, analogous to inoculating a Petri dish with bacteria.  The likelihood of chronic opioid use arises from the inoculum dose, the host response, and the context of inoculation. 

These articles do not show how patients taking opioids chronically for pain become addicted. Stumbo et al⁸ interviewed 283 opioid-dependent patients and identified 5 pathways to opioid use disorder, 3 of which were related to pain control: inadequately controlled chronic pain, exposure to opioids during acute pain episodes, and chronic pain in patients who already had substance use disorders. Brat et al⁹ recently estimated the risk of opioid use disorder after receiving opioids postoperatively to be less than 1%, but it increased dramatically with duration of opioid treatment.

A patient who fills an opioid prescription does not necessarily have chronic pain. Nor do all patients with chronic pain require an opioid prescription. These studies did not establish whether the patients had a pain syndrome. In practice, we call our patients who chronically take opioids our “chronic pain patients.” But 40% of Americans have chronic pain, while only 5% take opioids daily for pain.^11,12

We assume that those taking opioids have the most severe pain. But Brummett et al suggested that continued opioid use is predicted less by pain and more by psychiatric comorbidity.⁶ More than half of the opioid prescriptions in the United States are written for patients with serious mental illness, who represent one-sixth of that population.¹¹ Maybe chronic opioid use for pain has more to do with vulnerability to opioids and less to do with a pain syndrome.

I now think about daily opioid use in much the same way as I think about daily prednisone use. Patients on daily prednisone have a characteristic set of medical risks from the prednisone itself, regardless of its indication. Yet we do not consider these patients addicted to prednisone. Opioid use may be similar.

Like most doctors, I am troubled by the continued rise in the opioid overdose rate.¹³ Yet addiction and death from overdose are not the only risks that patients on chronic opioids face; they also have higher rates of falls, cardiovascular death, pneumonia, death from chronic obstructive pulmonary disease, and motor vehicle crashes.^14–17 Patients on chronic opioids for pain have greater mental health comorbidity and worse function.¹⁸

Most concerning, chronic opioid treatment for pain lacks proof of benefit. In fact, a recent study disproved the benefit of opioids for chronic pain compared with nonopioid options.¹⁹ When I meet with patients who are taking chronic opioids for pain, I often can’t identify why the drugs were started or ought to be continued, and I anticipate a bad outcome. Yet the patient is afraid to stop the drug. For these reasons, chronic opioid use for pain strikes me as worth considering separately from opioid use disorder.

The studies described above have had a powerful effect on my clinical care as a hospitalist.

I now talk to all patients starting opioids about how hard it can be to stop. Some patients are defensive at first, believing this does not apply to them. But I politely continue.

People with depression and anxiety can have a harder time stopping opioids. Addiction is both a risk with ongoing opioid use and a possible outcome of acute opioid use.⁸ But one can struggle to stop opioids without being addicted or depressed. Even the healthiest person may wish to continue opioids past the point of benefit.

I am careful not to invalidate the patient’s experience of pain. It is challenging for patients to find the balance between current discomfort and a possible future adverse effect. In these conversations, I imagine how I would want a loved one counseled on their pain control. This centers me as I choose my words and my tone.

I now monitor the total amount of opioid I prescribe for acute pain in addition to the daily dose. I give my patients as few opioids as reasonable, and advise them to take the minimum dose required for tolerable comfort. I offer nonopioid options as the preferred choice, presenting them as effective and safe. I do this irrespective of the indication for opioids.

I limit opioids in all patients, not just those with comorbidities. I include in my shared decision-making process the risk of chronic opioid use when I prescribe opioids for acute pain, carefully distinguishing it from opioid use disorder. Instead of excess opioids, I give patients my office phone number to call in case they struggle. I rarely get calls. But I find patients would rather have access to a doctor than extra pills. And offering them my contact information lets me limit opioids while letting them know that I am committed to their comfort and health.

As an addiction medicine doctor, I consult on patients not taking their opioids as prescribed. Caring for these patients is intellectually and emotionally draining; they suffer daily, and the opioids they take provide a modicum of relief at a high cost. The publications I have discussed here provide insight into how a troubled relationship with opioids begins. I remind myself that these patients have an iatrogenic condition. Their behaviors that we label “aberrant” may reflect an adverse reaction to medications prescribed to them for acute pain.

Mary, my patient with postoperative pain after cholecystectomy, may over time develop opioid use disorder as Heather did. That progression may have begun with the hydrocodone I prescribed and the counseling I gave her, and it may proceed to chronic opioid use and then opioid use disorder.

I am looking closely at the care I give for acute pain in light of these innovative studies. But even more so, they have increased the compassion with which I care for patients like Heather, those harmed by prescribed opioids.

The finding by Kachman et al. that consultations have decreased at their institution is an interesting and important observation.¹ In contrast, our study found that more than a third of hospitalists reported an increase in consultation requests.² There may be several explanations for this discrepancy. First, as Kachman et al. suggest, there may be differences between hospitalist perception and actual consultation use. Second, a significant variability in consultation may exist between hospitals. Although our study examined four institutions, we were unable to examine the variability between them, which requires further study. Third, there may be considerable variability between individual hospitalist practices, which is consistent with the findings reported by Kachman et al. Finally, the fact that our study examined only nonteaching services may be another explanation as Kachman et al. found that hospitalists on nonteaching services ordered more consultations than those on teaching services. These findings are consistent with a recent study conducted by Perez et al., who found that hospitalists on teaching services utilized fewer consultations and had lower direct care costs and shorter lengths of stay compared with those on nonteaching services.³ This finding raises the question of whether consultations impact care costs and lengths of stay, a topic that should be explored in future studies.

Disclosures

The authors report no conflicts of interest.

The finding by Kachman et al. that consultations have decreased at their institution is an interesting and important observation.¹ In contrast, our study found that more than a third of hospitalists reported an increase in consultation requests.² There may be several explanations for this discrepancy. First, as Kachman et al. suggest, there may be differences between hospitalist perception and actual consultation use. Second, a significant variability in consultation may exist between hospitals. Although our study examined four institutions, we were unable to examine the variability between them, which requires further study. Third, there may be considerable variability between individual hospitalist practices, which is consistent with the findings reported by Kachman et al. Finally, the fact that our study examined only nonteaching services may be another explanation as Kachman et al. found that hospitalists on nonteaching services ordered more consultations than those on teaching services. These findings are consistent with a recent study conducted by Perez et al., who found that hospitalists on teaching services utilized fewer consultations and had lower direct care costs and shorter lengths of stay compared with those on nonteaching services.³ This finding raises the question of whether consultations impact care costs and lengths of stay, a topic that should be explored in future studies.

Disclosures

The authors report no conflicts of interest.

The finding by Kachman et al. that consultations have decreased at their institution is an interesting and important observation.¹ In contrast, our study found that more than a third of hospitalists reported an increase in consultation requests.² There may be several explanations for this discrepancy. First, as Kachman et al. suggest, there may be differences between hospitalist perception and actual consultation use. Second, a significant variability in consultation may exist between hospitals. Although our study examined four institutions, we were unable to examine the variability between them, which requires further study. Third, there may be considerable variability between individual hospitalist practices, which is consistent with the findings reported by Kachman et al. Finally, the fact that our study examined only nonteaching services may be another explanation as Kachman et al. found that hospitalists on nonteaching services ordered more consultations than those on teaching services. These findings are consistent with a recent study conducted by Perez et al., who found that hospitalists on teaching services utilized fewer consultations and had lower direct care costs and shorter lengths of stay compared with those on nonteaching services.³ This finding raises the question of whether consultations impact care costs and lengths of stay, a topic that should be explored in future studies.

Disclosures

The authors report no conflicts of interest.

We thank Dr. Santhosh and colleagues for their letter concerning our article.¹ We agree that the diagnostic journey includes interactions both between and across teams, not just those within the patient’s team. In an article currently in press in Diagnosis, we examine how systems and cognitive factors interact during the process of diagnosis. Specifically, we reported on how communication between consultants can be both a barrier and facilitator to the diagnostic process.² We found that the frequency, quality, and pace of communication between and across inpatient teams and specialists are essential to timely diagnoses. As diagnostic errors remain a costly and morbid issue in the hospital setting, efforts to improve communication are clearly needed.³

Santhosh et al. raise an interesting point regarding cognitive load in evaluating diagnosis. Cognitive load is a multidimensional construct that represents the load that performing a specific task poses on a learner’s cognitive system.⁴ Components often used for measuring load include (a) task characteristics such as format, complexity, and time pressure; (b) subject characteristics such as expertise level, age, and spatial abilities; and (c) mental load and effort that originate from the interaction between task and subject characteristics.⁵ While there is little doubt that measuring these constructs has face value in diagnosis, we know of no instruments that are nimble, straightforward, or suitable for such measurement in the clinical setting. Furthermore, unlike handoffs (which lend themselves to structured frameworks), diagnostic evolution occurs across multiple individuals (from attendings to house staff and students), specialties (from emergency physicians to medical and surgical specialists), and over time. A unifying framework and tool to measure cognitive load across these elements would not only be novel, but a welcomed and much-needed component to facilitate diagnostic efforts. We hope that our ethnographic work will spur the development of these types of instruments and highlight opportunities for implementation. A future that both measures cognitive load and targets interventions to reduce or balance these across members of the diagnostic team would be welcomed.

Disclosures

The authors have nothing to disclose.

Funding

This project was supported by grant number P30HS024385 from the Agency for Healthcare Research and Quality. The funding source played no role in study design, data acquisition, analysis or decision to report these data.

We thank Dr. Santhosh and colleagues for their letter concerning our article.¹ We agree that the diagnostic journey includes interactions both between and across teams, not just those within the patient’s team. In an article currently in press in Diagnosis, we examine how systems and cognitive factors interact during the process of diagnosis. Specifically, we reported on how communication between consultants can be both a barrier and facilitator to the diagnostic process.² We found that the frequency, quality, and pace of communication between and across inpatient teams and specialists are essential to timely diagnoses. As diagnostic errors remain a costly and morbid issue in the hospital setting, efforts to improve communication are clearly needed.³

Santhosh et al. raise an interesting point regarding cognitive load in evaluating diagnosis. Cognitive load is a multidimensional construct that represents the load that performing a specific task poses on a learner’s cognitive system.⁴ Components often used for measuring load include (a) task characteristics such as format, complexity, and time pressure; (b) subject characteristics such as expertise level, age, and spatial abilities; and (c) mental load and effort that originate from the interaction between task and subject characteristics.⁵ While there is little doubt that measuring these constructs has face value in diagnosis, we know of no instruments that are nimble, straightforward, or suitable for such measurement in the clinical setting. Furthermore, unlike handoffs (which lend themselves to structured frameworks), diagnostic evolution occurs across multiple individuals (from attendings to house staff and students), specialties (from emergency physicians to medical and surgical specialists), and over time. A unifying framework and tool to measure cognitive load across these elements would not only be novel, but a welcomed and much-needed component to facilitate diagnostic efforts. We hope that our ethnographic work will spur the development of these types of instruments and highlight opportunities for implementation. A future that both measures cognitive load and targets interventions to reduce or balance these across members of the diagnostic team would be welcomed.

Disclosures

The authors have nothing to disclose.

Funding

This project was supported by grant number P30HS024385 from the Agency for Healthcare Research and Quality. The funding source played no role in study design, data acquisition, analysis or decision to report these data.

We thank Dr. Santhosh and colleagues for their letter concerning our article.¹ We agree that the diagnostic journey includes interactions both between and across teams, not just those within the patient’s team. In an article currently in press in Diagnosis, we examine how systems and cognitive factors interact during the process of diagnosis. Specifically, we reported on how communication between consultants can be both a barrier and facilitator to the diagnostic process.² We found that the frequency, quality, and pace of communication between and across inpatient teams and specialists are essential to timely diagnoses. As diagnostic errors remain a costly and morbid issue in the hospital setting, efforts to improve communication are clearly needed.³

Santhosh et al. raise an interesting point regarding cognitive load in evaluating diagnosis. Cognitive load is a multidimensional construct that represents the load that performing a specific task poses on a learner’s cognitive system.⁴ Components often used for measuring load include (a) task characteristics such as format, complexity, and time pressure; (b) subject characteristics such as expertise level, age, and spatial abilities; and (c) mental load and effort that originate from the interaction between task and subject characteristics.⁵ While there is little doubt that measuring these constructs has face value in diagnosis, we know of no instruments that are nimble, straightforward, or suitable for such measurement in the clinical setting. Furthermore, unlike handoffs (which lend themselves to structured frameworks), diagnostic evolution occurs across multiple individuals (from attendings to house staff and students), specialties (from emergency physicians to medical and surgical specialists), and over time. A unifying framework and tool to measure cognitive load across these elements would not only be novel, but a welcomed and much-needed component to facilitate diagnostic efforts. We hope that our ethnographic work will spur the development of these types of instruments and highlight opportunities for implementation. A future that both measures cognitive load and targets interventions to reduce or balance these across members of the diagnostic team would be welcomed.

Disclosures

The authors have nothing to disclose.

Funding

This project was supported by grant number P30HS024385 from the Agency for Healthcare Research and Quality. The funding source played no role in study design, data acquisition, analysis or decision to report these data.

We read the article by Chopra et al. “Focused Ethnography of Diagnosis in Academic Medical Centers” with great interest.¹ This ethnographic study provided valuable insights into possible interventions to encourage diagnostic thinking.

Duty hour regulations and the resulting increase in handoffs have shifted the social experience of diagnosis from one that occurs within teams to one that often occurs between teams during handoffs between providers.² While the article highlighted barriers to diagnosis, including distractions and time pressure, it did not explicitly discuss cognitive load theory. Cognitive load theory is an educational framework that has been described by Young et al.³ to improve instructions in the handoff process. These investigators showed how progressively experienced learners retain more information when using a structured scaffold or framework for information, such as the IPASS mnemonic,⁴ for example.

To mitigate the effects of distraction on the transfer of information, especially in cases with high diagnostic uncertainty, cognitive load must be explicitly considered. A structured framework for communication about diagnostic uncertainty informed by cognitive load theory would be a novel innovation that would help not only graduate medical education but could also improve diagnostic accuracy.

Disclosures

The authors have no conflicts of interest to disclose

We read the article by Chopra et al. “Focused Ethnography of Diagnosis in Academic Medical Centers” with great interest.¹ This ethnographic study provided valuable insights into possible interventions to encourage diagnostic thinking.

Duty hour regulations and the resulting increase in handoffs have shifted the social experience of diagnosis from one that occurs within teams to one that often occurs between teams during handoffs between providers.² While the article highlighted barriers to diagnosis, including distractions and time pressure, it did not explicitly discuss cognitive load theory. Cognitive load theory is an educational framework that has been described by Young et al.³ to improve instructions in the handoff process. These investigators showed how progressively experienced learners retain more information when using a structured scaffold or framework for information, such as the IPASS mnemonic,⁴ for example.

To mitigate the effects of distraction on the transfer of information, especially in cases with high diagnostic uncertainty, cognitive load must be explicitly considered. A structured framework for communication about diagnostic uncertainty informed by cognitive load theory would be a novel innovation that would help not only graduate medical education but could also improve diagnostic accuracy.

Disclosures

The authors have no conflicts of interest to disclose

We read the article by Chopra et al. “Focused Ethnography of Diagnosis in Academic Medical Centers” with great interest.¹ This ethnographic study provided valuable insights into possible interventions to encourage diagnostic thinking.

Duty hour regulations and the resulting increase in handoffs have shifted the social experience of diagnosis from one that occurs within teams to one that often occurs between teams during handoffs between providers.² While the article highlighted barriers to diagnosis, including distractions and time pressure, it did not explicitly discuss cognitive load theory. Cognitive load theory is an educational framework that has been described by Young et al.³ to improve instructions in the handoff process. These investigators showed how progressively experienced learners retain more information when using a structured scaffold or framework for information, such as the IPASS mnemonic,⁴ for example.

To mitigate the effects of distraction on the transfer of information, especially in cases with high diagnostic uncertainty, cognitive load must be explicitly considered. A structured framework for communication about diagnostic uncertainty informed by cognitive load theory would be a novel innovation that would help not only graduate medical education but could also improve diagnostic accuracy.

Disclosures

The authors have no conflicts of interest to disclose

We read with interest the article, “Hospitalist Perspective of Interactions with Medicine Subspecialty Consult Services.”¹ We applaud the authors for their work, but were surprised by the authors’ findings of hospitalist perceptions of consultation utilization. The authors reported that more hospitalists felt that their personal use of consultation was increasing (38.5%) versus those who reported that use was decreasing (30.3%).¹ The lack of true consensus on this issue may hint at significant variability in hospitalist use of inpatient consultation. We examined consultation use in 4,023 general medicine admissions to the University of Chicago from 2011 to 2015. Consultation use varied widely, with a 3.5-fold difference between the lowest and the highest quartiles of use (P < .01).² Contrary to the survey findings, we found that the number of consultations per admission actually decreased with each year in our sample.² In addition, a particularly interesting effect was observed in hospitalists; in multivariate regression, hospitalists on nonteaching services ordered more consultations than those on teaching services.² These findings suggest a gap between hospitalist self-reported perceptions of consultation use and actual use, which is important to understand, and highlight the need for further characterization of factors driving the use of this valuable resource.

Disclosures

The authors have no conflicts of interest to disclose.

We read with interest the article, “Hospitalist Perspective of Interactions with Medicine Subspecialty Consult Services.”¹ We applaud the authors for their work, but were surprised by the authors’ findings of hospitalist perceptions of consultation utilization. The authors reported that more hospitalists felt that their personal use of consultation was increasing (38.5%) versus those who reported that use was decreasing (30.3%).¹ The lack of true consensus on this issue may hint at significant variability in hospitalist use of inpatient consultation. We examined consultation use in 4,023 general medicine admissions to the University of Chicago from 2011 to 2015. Consultation use varied widely, with a 3.5-fold difference between the lowest and the highest quartiles of use (P < .01).² Contrary to the survey findings, we found that the number of consultations per admission actually decreased with each year in our sample.² In addition, a particularly interesting effect was observed in hospitalists; in multivariate regression, hospitalists on nonteaching services ordered more consultations than those on teaching services.² These findings suggest a gap between hospitalist self-reported perceptions of consultation use and actual use, which is important to understand, and highlight the need for further characterization of factors driving the use of this valuable resource.

Disclosures

The authors have no conflicts of interest to disclose.

We read with interest the article, “Hospitalist Perspective of Interactions with Medicine Subspecialty Consult Services.”¹ We applaud the authors for their work, but were surprised by the authors’ findings of hospitalist perceptions of consultation utilization. The authors reported that more hospitalists felt that their personal use of consultation was increasing (38.5%) versus those who reported that use was decreasing (30.3%).¹ The lack of true consensus on this issue may hint at significant variability in hospitalist use of inpatient consultation. We examined consultation use in 4,023 general medicine admissions to the University of Chicago from 2011 to 2015. Consultation use varied widely, with a 3.5-fold difference between the lowest and the highest quartiles of use (P < .01).² Contrary to the survey findings, we found that the number of consultations per admission actually decreased with each year in our sample.² In addition, a particularly interesting effect was observed in hospitalists; in multivariate regression, hospitalists on nonteaching services ordered more consultations than those on teaching services.² These findings suggest a gap between hospitalist self-reported perceptions of consultation use and actual use, which is important to understand, and highlight the need for further characterization of factors driving the use of this valuable resource.

Disclosures

The authors have no conflicts of interest to disclose.

Suicide is the second most common cause of death among children, adolescents, and young adults in the United States. In 2016, over 6,000 children and youth 5 to 24 years of age succumbed to suicide, thus reflecting a mortality rate nearly three times higher than deaths from malignancies and 28 times higher than deaths from sepsis in this age group.¹ Suicidal ideation and suicide attempts are even more common, with 17% of high school students reporting seriously considering suicide and 8% reporting suicide attempts in the previous 12 months.² These tragic statistics are reflected in our health system use, emergency department (ED) utilization for suicide attempts and suicidal ideation is growing at a tremendous rate, and over 50% of the children seen in EDs are subsequently admitted to the hospital for ongoing care.^3,4

In this issue of Journal of Hospital Medicine, Doupnik and colleagues present an analysis of pediatric hospitalizations for suicide attempts and suicidal ideation at acute care hospitals contained within the 2013 and 2014 National Readmissions Dataset.⁵ This dataset reflects a nationally representative sample of pediatric hospitalizations, weighted to allow for national estimates. Although their focus was on hospital readmission, their analysis yielded additional valuable data about suicide attempts and suicidal ideation in American youth. The investigators identified 181,575 pediatric acute care hospitalizations for suicide attempts and suicidal ideation over the two-year study period, accounting for 9.5% of all acute care hospitalizations among children and adolescents 6 to 17 years of age nationally. This number exceeds the biennial number of pediatric hospitalizations for cellulitis, dehydration, and urinary tract infections, all of which are generally considered the “bread and butter” of pediatric hospital medicine.⁶

Doupnik and colleagues rightly pointed out that hospital readmission is not a nationally endorsed measure to evaluate the quality of pediatric mental health hospitalizations. At the same time, their work highlights that acute care hospitals need strategies to measure the quality of pediatric hospitalizations for suicide attempts and suicidal ideation. Beyond readmissions, how should the quality of these hospital stays be evaluated? A recent review of 15 national quality measure sets identified 257 unique measures to evaluate pediatric quality of care.⁷ Of these, only one focused on mental health hospitalization. This measure, which was endorsed by the National Quality Forum, determines the percentage of discharges for patients six years of age and older who were hospitalized for mental health diagnoses and who had a follow-up visit with a mental health practitioner within 7 and 30 days of hospital discharge.⁸ Given Doupnik et al.’s finding that one-third of all 30-day hospital readmissions occurred within seven days of hospital discharge, early follow-up visits with mental health practitioners is arguably essential.

Although evidence-based quality measures to evaluate hospital-based mental healthcare are limited, quality measure development is ongoing, facilitated by recent federal health policy and associated research efforts. Four newly developed measures focus on the quality of inpatient care for suicidality, including two evaluated using data from health records and two derived from caregiver surveys. The first medical records-based measure identifies whether caregivers of patients admitted to hospital for dangerous self-harm or suicidality have documentation that they were counseled on how to restrict their child’s or adolescent’s access to potentially lethal means of suicide before discharge. The second record-based measure evaluates documentation in the medical record of discussion between the hospital provider and the patient’s outpatient provider regarding the plan for follow-up.⁹ The two survey-based measures ask caregivers whether they were counseled on how to restrict access to potentially lethal means of suicide, and, for children and adolescents started on a new antidepressant medication or dose, whether they were counseled regarding the potential benefits and risks of the medication.¹⁰ All measures were field-tested at children’s hospitals to ensure feasibility in data collection. However, as shown by Doupnik et al., only 7.4% of acute care hospitalizations for suicide attempts and suicidal ideation occurred at freestanding children’s hospitals; most occurred at urban nonteaching centers. Evaluation of these new quality measures across structurally diverse hospitals is an important next step.

Beyond the healthcare constructs evaluated by these quality measures, many foundational questions about what constitutes high quality inpatient healthcare for suicide attempts and suicidal ideation remain. An American Academy of Child and Adolescent Psychiatry (AACAP) practice parameter, which was published in 2001, established minimal standards for the assessment and treatment of children and adolescents with suicidal behavior.¹¹ This guideline recommends inpatient treatment until the mental state or level of suicidality has stabilized, with discharge considered only when the clinician is satisfied that adequate supervision and support will be available and when a responsible adult has agreed to secure or dispose of potentially lethal medications and firearms. It further recommends that the clinician treating the child or adolescent during the days following a suicide attempt be available to the patient and family – for example, to receive and make telephone calls outside of regular clinic hours. Recognizing the growing prevalence of suicidality in American children and youth, coupled with critical shortages in pediatric psychiatrists and fragmentation of inpatient and outpatient care, these minimal standards may be difficult to implement across the many settings where children receive their mental healthcare.^4,12,13

The large number of children and adolescents being hospitalized for suicide attempts and suicidal ideation at acute care hospitals demands that we take stock of how we manage this vulnerable population. Although Doupnik and colleagues suggest that exclusion of specialty psychiatric hospitals from their dataset is a limitation, their presentation of suicide attempts and suicidal ideation epidemiology at acute care hospitals provides valuable data for pediatric hospitalists. Given the presence of pediatric hospitalists at many acute care hospitals, comanagement by hospital medicine and psychiatry services may prove both efficient and effective while breaking down the silos that traditionally separate these specialties. Alternatively, extending the role of collaborative care teams, which are increasingly embedded in pediatric primary care, into inpatient settings may enable continuity of care and improve healthcare quality.¹⁴ Finally, nearly 20 years have passed since the AACAP published its practice parameter for the assessment and treatment of children and adolescents with suicidal behavior. An update to reflect contemporary suicide attempts and suicidal ideation statistics and evidence-based practices is needed, and collaboration between professional pediatric and psychiatric organizations in the creation of this update would recognize the growing role of pediatricians, including hospitalists, in the provision of mental healthcare for children.

Updated guidelines must take into account the transitions of care experienced by children and adolescents throughout their hospital stay: at admission, at discharge, and during their hospitalization if they move from medical to psychiatric care. Research is needed to determine what proportion of children and adolescents receive evidence-based mental health therapies while in hospital and how many are connected with wraparound mental health services before hospital discharge.¹⁵ Doupnik et al. excluded children and adolescents who were transferred to other hospitals, which included over 18,000 youth. How long did these patients spend “boarding,” and did they receive any mental health assessment or treatment during this period? Although the Joint Commission recommends that holding times for patients awaiting bed placement should not exceed four4 hours, hospitals have described average pediatric inpatient boarding times of 2-3 days while awaiting inpatient psychiatric care.^16,17 In one study of children and adolescents awaiting transfer for inpatient psychiatric care, mental health counseling was received by only 6%, which reflects lost time that could have been spent treating this highly vulnerable population.¹⁶ Multidisciplinary collaboration is needed to address these issues and inform best practices.

Although mortality is a rare outcome for most conditions we treat in pediatric hospital medicine, mortality following suicide attempts is all too common. The data presented by Doupnik and colleagues provide a powerful call to improve healthcare quality across the diverse settings where children with suicidality receive their care.

Disclosures

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

Funding

Dr. Leyenaar was supported by grant number K08HS024133 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ.

Suicide is the second most common cause of death among children, adolescents, and young adults in the United States. In 2016, over 6,000 children and youth 5 to 24 years of age succumbed to suicide, thus reflecting a mortality rate nearly three times higher than deaths from malignancies and 28 times higher than deaths from sepsis in this age group.¹ Suicidal ideation and suicide attempts are even more common, with 17% of high school students reporting seriously considering suicide and 8% reporting suicide attempts in the previous 12 months.² These tragic statistics are reflected in our health system use, emergency department (ED) utilization for suicide attempts and suicidal ideation is growing at a tremendous rate, and over 50% of the children seen in EDs are subsequently admitted to the hospital for ongoing care.^3,4

In this issue of Journal of Hospital Medicine, Doupnik and colleagues present an analysis of pediatric hospitalizations for suicide attempts and suicidal ideation at acute care hospitals contained within the 2013 and 2014 National Readmissions Dataset.⁵ This dataset reflects a nationally representative sample of pediatric hospitalizations, weighted to allow for national estimates. Although their focus was on hospital readmission, their analysis yielded additional valuable data about suicide attempts and suicidal ideation in American youth. The investigators identified 181,575 pediatric acute care hospitalizations for suicide attempts and suicidal ideation over the two-year study period, accounting for 9.5% of all acute care hospitalizations among children and adolescents 6 to 17 years of age nationally. This number exceeds the biennial number of pediatric hospitalizations for cellulitis, dehydration, and urinary tract infections, all of which are generally considered the “bread and butter” of pediatric hospital medicine.⁶

Doupnik and colleagues rightly pointed out that hospital readmission is not a nationally endorsed measure to evaluate the quality of pediatric mental health hospitalizations. At the same time, their work highlights that acute care hospitals need strategies to measure the quality of pediatric hospitalizations for suicide attempts and suicidal ideation. Beyond readmissions, how should the quality of these hospital stays be evaluated? A recent review of 15 national quality measure sets identified 257 unique measures to evaluate pediatric quality of care.⁷ Of these, only one focused on mental health hospitalization. This measure, which was endorsed by the National Quality Forum, determines the percentage of discharges for patients six years of age and older who were hospitalized for mental health diagnoses and who had a follow-up visit with a mental health practitioner within 7 and 30 days of hospital discharge.⁸ Given Doupnik et al.’s finding that one-third of all 30-day hospital readmissions occurred within seven days of hospital discharge, early follow-up visits with mental health practitioners is arguably essential.

Although evidence-based quality measures to evaluate hospital-based mental healthcare are limited, quality measure development is ongoing, facilitated by recent federal health policy and associated research efforts. Four newly developed measures focus on the quality of inpatient care for suicidality, including two evaluated using data from health records and two derived from caregiver surveys. The first medical records-based measure identifies whether caregivers of patients admitted to hospital for dangerous self-harm or suicidality have documentation that they were counseled on how to restrict their child’s or adolescent’s access to potentially lethal means of suicide before discharge. The second record-based measure evaluates documentation in the medical record of discussion between the hospital provider and the patient’s outpatient provider regarding the plan for follow-up.⁹ The two survey-based measures ask caregivers whether they were counseled on how to restrict access to potentially lethal means of suicide, and, for children and adolescents started on a new antidepressant medication or dose, whether they were counseled regarding the potential benefits and risks of the medication.¹⁰ All measures were field-tested at children’s hospitals to ensure feasibility in data collection. However, as shown by Doupnik et al., only 7.4% of acute care hospitalizations for suicide attempts and suicidal ideation occurred at freestanding children’s hospitals; most occurred at urban nonteaching centers. Evaluation of these new quality measures across structurally diverse hospitals is an important next step.

Beyond the healthcare constructs evaluated by these quality measures, many foundational questions about what constitutes high quality inpatient healthcare for suicide attempts and suicidal ideation remain. An American Academy of Child and Adolescent Psychiatry (AACAP) practice parameter, which was published in 2001, established minimal standards for the assessment and treatment of children and adolescents with suicidal behavior.¹¹ This guideline recommends inpatient treatment until the mental state or level of suicidality has stabilized, with discharge considered only when the clinician is satisfied that adequate supervision and support will be available and when a responsible adult has agreed to secure or dispose of potentially lethal medications and firearms. It further recommends that the clinician treating the child or adolescent during the days following a suicide attempt be available to the patient and family – for example, to receive and make telephone calls outside of regular clinic hours. Recognizing the growing prevalence of suicidality in American children and youth, coupled with critical shortages in pediatric psychiatrists and fragmentation of inpatient and outpatient care, these minimal standards may be difficult to implement across the many settings where children receive their mental healthcare.^4,12,13

The large number of children and adolescents being hospitalized for suicide attempts and suicidal ideation at acute care hospitals demands that we take stock of how we manage this vulnerable population. Although Doupnik and colleagues suggest that exclusion of specialty psychiatric hospitals from their dataset is a limitation, their presentation of suicide attempts and suicidal ideation epidemiology at acute care hospitals provides valuable data for pediatric hospitalists. Given the presence of pediatric hospitalists at many acute care hospitals, comanagement by hospital medicine and psychiatry services may prove both efficient and effective while breaking down the silos that traditionally separate these specialties. Alternatively, extending the role of collaborative care teams, which are increasingly embedded in pediatric primary care, into inpatient settings may enable continuity of care and improve healthcare quality.¹⁴ Finally, nearly 20 years have passed since the AACAP published its practice parameter for the assessment and treatment of children and adolescents with suicidal behavior. An update to reflect contemporary suicide attempts and suicidal ideation statistics and evidence-based practices is needed, and collaboration between professional pediatric and psychiatric organizations in the creation of this update would recognize the growing role of pediatricians, including hospitalists, in the provision of mental healthcare for children.

Updated guidelines must take into account the transitions of care experienced by children and adolescents throughout their hospital stay: at admission, at discharge, and during their hospitalization if they move from medical to psychiatric care. Research is needed to determine what proportion of children and adolescents receive evidence-based mental health therapies while in hospital and how many are connected with wraparound mental health services before hospital discharge.¹⁵ Doupnik et al. excluded children and adolescents who were transferred to other hospitals, which included over 18,000 youth. How long did these patients spend “boarding,” and did they receive any mental health assessment or treatment during this period? Although the Joint Commission recommends that holding times for patients awaiting bed placement should not exceed four4 hours, hospitals have described average pediatric inpatient boarding times of 2-3 days while awaiting inpatient psychiatric care.^16,17 In one study of children and adolescents awaiting transfer for inpatient psychiatric care, mental health counseling was received by only 6%, which reflects lost time that could have been spent treating this highly vulnerable population.¹⁶ Multidisciplinary collaboration is needed to address these issues and inform best practices.

Although mortality is a rare outcome for most conditions we treat in pediatric hospital medicine, mortality following suicide attempts is all too common. The data presented by Doupnik and colleagues provide a powerful call to improve healthcare quality across the diverse settings where children with suicidality receive their care.

Disclosures

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

Funding

Dr. Leyenaar was supported by grant number K08HS024133 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ.

Suicide is the second most common cause of death among children, adolescents, and young adults in the United States. In 2016, over 6,000 children and youth 5 to 24 years of age succumbed to suicide, thus reflecting a mortality rate nearly three times higher than deaths from malignancies and 28 times higher than deaths from sepsis in this age group.¹ Suicidal ideation and suicide attempts are even more common, with 17% of high school students reporting seriously considering suicide and 8% reporting suicide attempts in the previous 12 months.² These tragic statistics are reflected in our health system use, emergency department (ED) utilization for suicide attempts and suicidal ideation is growing at a tremendous rate, and over 50% of the children seen in EDs are subsequently admitted to the hospital for ongoing care.^3,4

In this issue of Journal of Hospital Medicine, Doupnik and colleagues present an analysis of pediatric hospitalizations for suicide attempts and suicidal ideation at acute care hospitals contained within the 2013 and 2014 National Readmissions Dataset.⁵ This dataset reflects a nationally representative sample of pediatric hospitalizations, weighted to allow for national estimates. Although their focus was on hospital readmission, their analysis yielded additional valuable data about suicide attempts and suicidal ideation in American youth. The investigators identified 181,575 pediatric acute care hospitalizations for suicide attempts and suicidal ideation over the two-year study period, accounting for 9.5% of all acute care hospitalizations among children and adolescents 6 to 17 years of age nationally. This number exceeds the biennial number of pediatric hospitalizations for cellulitis, dehydration, and urinary tract infections, all of which are generally considered the “bread and butter” of pediatric hospital medicine.⁶

Doupnik and colleagues rightly pointed out that hospital readmission is not a nationally endorsed measure to evaluate the quality of pediatric mental health hospitalizations. At the same time, their work highlights that acute care hospitals need strategies to measure the quality of pediatric hospitalizations for suicide attempts and suicidal ideation. Beyond readmissions, how should the quality of these hospital stays be evaluated? A recent review of 15 national quality measure sets identified 257 unique measures to evaluate pediatric quality of care.⁷ Of these, only one focused on mental health hospitalization. This measure, which was endorsed by the National Quality Forum, determines the percentage of discharges for patients six years of age and older who were hospitalized for mental health diagnoses and who had a follow-up visit with a mental health practitioner within 7 and 30 days of hospital discharge.⁸ Given Doupnik et al.’s finding that one-third of all 30-day hospital readmissions occurred within seven days of hospital discharge, early follow-up visits with mental health practitioners is arguably essential.

Although evidence-based quality measures to evaluate hospital-based mental healthcare are limited, quality measure development is ongoing, facilitated by recent federal health policy and associated research efforts. Four newly developed measures focus on the quality of inpatient care for suicidality, including two evaluated using data from health records and two derived from caregiver surveys. The first medical records-based measure identifies whether caregivers of patients admitted to hospital for dangerous self-harm or suicidality have documentation that they were counseled on how to restrict their child’s or adolescent’s access to potentially lethal means of suicide before discharge. The second record-based measure evaluates documentation in the medical record of discussion between the hospital provider and the patient’s outpatient provider regarding the plan for follow-up.⁹ The two survey-based measures ask caregivers whether they were counseled on how to restrict access to potentially lethal means of suicide, and, for children and adolescents started on a new antidepressant medication or dose, whether they were counseled regarding the potential benefits and risks of the medication.¹⁰ All measures were field-tested at children’s hospitals to ensure feasibility in data collection. However, as shown by Doupnik et al., only 7.4% of acute care hospitalizations for suicide attempts and suicidal ideation occurred at freestanding children’s hospitals; most occurred at urban nonteaching centers. Evaluation of these new quality measures across structurally diverse hospitals is an important next step.

Beyond the healthcare constructs evaluated by these quality measures, many foundational questions about what constitutes high quality inpatient healthcare for suicide attempts and suicidal ideation remain. An American Academy of Child and Adolescent Psychiatry (AACAP) practice parameter, which was published in 2001, established minimal standards for the assessment and treatment of children and adolescents with suicidal behavior.¹¹ This guideline recommends inpatient treatment until the mental state or level of suicidality has stabilized, with discharge considered only when the clinician is satisfied that adequate supervision and support will be available and when a responsible adult has agreed to secure or dispose of potentially lethal medications and firearms. It further recommends that the clinician treating the child or adolescent during the days following a suicide attempt be available to the patient and family – for example, to receive and make telephone calls outside of regular clinic hours. Recognizing the growing prevalence of suicidality in American children and youth, coupled with critical shortages in pediatric psychiatrists and fragmentation of inpatient and outpatient care, these minimal standards may be difficult to implement across the many settings where children receive their mental healthcare.^4,12,13

The large number of children and adolescents being hospitalized for suicide attempts and suicidal ideation at acute care hospitals demands that we take stock of how we manage this vulnerable population. Although Doupnik and colleagues suggest that exclusion of specialty psychiatric hospitals from their dataset is a limitation, their presentation of suicide attempts and suicidal ideation epidemiology at acute care hospitals provides valuable data for pediatric hospitalists. Given the presence of pediatric hospitalists at many acute care hospitals, comanagement by hospital medicine and psychiatry services may prove both efficient and effective while breaking down the silos that traditionally separate these specialties. Alternatively, extending the role of collaborative care teams, which are increasingly embedded in pediatric primary care, into inpatient settings may enable continuity of care and improve healthcare quality.¹⁴ Finally, nearly 20 years have passed since the AACAP published its practice parameter for the assessment and treatment of children and adolescents with suicidal behavior. An update to reflect contemporary suicide attempts and suicidal ideation statistics and evidence-based practices is needed, and collaboration between professional pediatric and psychiatric organizations in the creation of this update would recognize the growing role of pediatricians, including hospitalists, in the provision of mental healthcare for children.

Updated guidelines must take into account the transitions of care experienced by children and adolescents throughout their hospital stay: at admission, at discharge, and during their hospitalization if they move from medical to psychiatric care. Research is needed to determine what proportion of children and adolescents receive evidence-based mental health therapies while in hospital and how many are connected with wraparound mental health services before hospital discharge.¹⁵ Doupnik et al. excluded children and adolescents who were transferred to other hospitals, which included over 18,000 youth. How long did these patients spend “boarding,” and did they receive any mental health assessment or treatment during this period? Although the Joint Commission recommends that holding times for patients awaiting bed placement should not exceed four4 hours, hospitals have described average pediatric inpatient boarding times of 2-3 days while awaiting inpatient psychiatric care.^16,17 In one study of children and adolescents awaiting transfer for inpatient psychiatric care, mental health counseling was received by only 6%, which reflects lost time that could have been spent treating this highly vulnerable population.¹⁶ Multidisciplinary collaboration is needed to address these issues and inform best practices.

Although mortality is a rare outcome for most conditions we treat in pediatric hospital medicine, mortality following suicide attempts is all too common. The data presented by Doupnik and colleagues provide a powerful call to improve healthcare quality across the diverse settings where children with suicidality receive their care.

Disclosures

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

Funding

Dr. Leyenaar was supported by grant number K08HS024133 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ.

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Suicide is a leading cause of death among 10- to 34-year-olds in the United States.^1,2 During the past two decades, the youth suicide death rate has risen by 24%, and more than 5,000 young people die from suicide each year.³ Suicide ideation (SI) and suicide attempts (SAs) are well-established risk factors for suicide death and a source of morbidity for patients and families. One-third of youth with SI attempt suicide at some point in their lifetime.⁴ Approximately 11% of youth SAs result in suicide death, and 2% of youth who attempt suicide subsequently go on to die from suicide after recovering from a prior SA.⁵ More than 60,000 youth are hospitalized for SI or SA each year,⁶ and young people hospitalized for SA are at high short-term risk of repeat SA and suicide death.⁷ Hospitals need strategies for measuring the quality of SI and SA hospitalizations, monitoring postdischarge outcomes, and identifying the patients at the highest risk of poor outcomes. Readmissions are a useful hospital quality measure that can indicate re-emergence of SI, repeat SA, or inadequate community-based mental health treatment, and interventions designed for patients with readmissions can potentially avert morbidity or mortality.

The National Committee on Quality Assurance recommends measurement of quality metrics for 30-day mental health follow-up after psychiatric hospitalizations, 30-day readmissions after adult (but not pediatric) psychiatric hospitalizations, and 30-day readmissions in pediatric medical and surgical hospitalizations. Readmission measures are not consistently used to evaluate pediatric psychiatric hospitalizations, and psychiatric quality measures are not used to evaluate medical or surgical hospitalizations for SA. Recent research has investigated transfers to postacute care,⁸ readmission prevalence, variation in hospital readmission performance, and risk factors for readmissions after pediatric psychiatric hospitalizations.^9–11 However, no national study has investigated 30-day readmissions in youth hospitalized specifically for SI or SA.

To inform hospital quality measurement and improve hospital and postdischarge care for youth at risk of suicide, more information is needed about the characteristics of and the risk factors for readmissions after index SI/SA hospitalization. To address this knowledge gap, among SI/SA hospitalizations in 6- to 17-year-olds, we examined (1) unplanned 30-day readmissions and characteristics of hospitalizations by 30-day readmission status; (2) patient, hospital, and regional characteristics associated with 30-day readmissions; and (3) characteristics of 30-day readmissions.

Study Design and Data Source

We conducted a national, retrospective cohort study of hospitalizations for patients aged 6-17 years using the Agency for Healthcare Research and Quality (AHRQ) 2013 and 2014 Nationwide Readmissions Database (NRD). The combined 2013-2014 NRD includes administrative data from a nationally representative sample of 29 million hospitalizations in 22 states, accounting for 49.3% of all US hospitalizations, and is weighted for national projections. The NRD includes hospital information, patient demographic information, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (E-codes). The database includes one primary diagnosis, up to 24 additional diagnoses, and up to 4 E-codes for each hospitalization. The NRD includes information about hospitalizations in acute-care general hospitals (including their psychiatric units) but not from specialty psychiatric hospitals. The database also includes de-identified, verified patient linkage numbers so that patients can be tracked across multiple hospitalizations at the same institution or different institutions within the same state. This study was considered to be exempt from review by the Children’s Hospital of Philadelphia Institutional Review Board.

Sample

We identified a sample of 181,575 hospitalizations for SI (n = 119,037) or SA (n = 62,538) among 6- to 17-year-olds between January 1, 2013, and December 31, 2014 (Figure). We included children as young as 6 years because validated methods exist to identify SI and SA in this age group,¹² and because suicide deaths have recently increased among younger children.³ We excluded patients aged 18 years and older from this study since delivery of mental health services differs for adults.¹³ To create the sample, we first identified all hospitalizations of patients aged 6-17 years. We then used a validated algorithm relying on ICD-9-CM diagnosis codes for poisonings and E-codes for self-injury (E950-959) to identify hospitalizations related to SA.¹² Because E-code completeness varies among states,¹⁴ we also used the combination of having both a diagnosis code for injury (800-999) and an ICD-9-CM code for SI (V62.84) as a proxy for SA. Among hospitalizations without SA, we identified hospitalizations with SI using the ICD-9-CM code for SI (V62.84) in any position.

We identified 133,516 index hospitalizations with complete data at risk for an unplanned readmission. Because NRD data cannot be linked between calendar years, we limited the study time period for each calendar year to 10 months. We excluded hospitalizations in January because the full 30-day time frame to determine whether a hospitalization had occurred in the preceding 30 days, a known risk factor for readmissions in other samples,¹⁵ was not available. We excluded index hospitalizations occurring in December, because the full 30-day time frame to ascertain readmissions was not available. We excluded hospitalizations resulting in death, since these are not at risk for readmission, and hospitalizations resulting in transfer, since the timing of discharge to the community was not known. Readmission hospitalizations were eligible to be included as index hospitalizations if they met sample inclusion criteria. The final sample for readmission analyses included 95,354 SI hospitalizations and 38,162 SA hospitalizations (Figure).

Primary Outcome

The primary outcome was any unplanned, all-cause readmission within 30 days of index hospitalization for SI or SA. Among 30-day readmissions, we examined readmission timing, whether the readmission was to the same hospital or a different hospital, length of stay, and indication for readmission (medical/surgical or psychiatric, and presence of SI or SA diagnoses). Planned readmissions were identified using measure specifications endorsed by the AHRQ and the National Quality Forum¹⁶ and excluded from measurement.

Among index hospitalizations for SI, we specifically examined 30-day readmissions for subsequent SA, since one objective of hospitalization for SI is to prevent progression to SA or death. We could not identify hospitalizations for repeat SA after index hospitalization for SA, because diagnosis codes did not differentiate between readmission for complications of index SA and readmission for repeat SA.

Independent Variables

We analyzed demographic, clinical, and hospital factors associated with readmissions in other samples.^17–20 Demographic characteristics included patient gender and age, urban or rural residence, payer, and median national income quartile for a patient’s ZIP code. Race and ethnicity data are not available in the NRD.

Clinical characteristics included hospitalization in the 30-days preceding the index hospitalization, index hospitalization length of stay, and admission via the emergency department (ED) versus direct admission. A patient’s chronic condition profile was determined using index hospitalization diagnosis codes. Complex chronic conditions (eg, cancer, cystic fibrosis) were identified using a classification system used in several prior studies of hospital administrative datasets,²¹ and other noncomplex chronic medical conditions (eg, asthma, obesity) were identified using the Healthcare Cost and Utilization Project (HCUP) chronic condition indicator system.²² Psychiatric conditions (eg, anxiety disorders, substance abuse, autism) were identified and categorized using a classification system used in studies of hospital administrative datasets.²³ The number of psychiatric conditions was determined by counting the number of psychiatric condition categories in which a patient had a diagnosis. SA was categorized as having lower risk of death (eg, medication overdose, injury from cutting or piercing) or higher risk of death (eg, hanging, suffocation, or firearm injury).²⁴

Because of known temporal trends in SI and SA,^25,26 the month and year of admission were included as covariates. Hospital characteristics included teaching hospital and children’s hospital designations.

Statistical Analysis

We compared descriptive, summary statistics for characteristics of index hospitalizations with and without a 30-day readmission using Rao-Scott chi-square tests. In multivariable analyses, we derived logistic regression models to measure the associations of patient, hospital, and temporal factors with 30-day hospital readmissions. Analyses were conducted in SAS PROC SURVEYLOGISTIC and were weighted to achieve national estimates, clustered by sample stratum and hospital to account for the complex survey design,²⁷ clustered by patients to account for multiple index visits per patient, and adjusted for clinical, demographic, and hospital characteristics. SAS version 9.4 (SAS Institute, Cary, North Carolina) was used for all analyses. All tests were two-sided, and a P value <.05 was considered as statistically significant.

Sample Characteristics

In the weighted analyses, we identified 133,516 hospitalizations in acute-care hospitals for SI or SA, and 8.5% (n = 11,375) of hospitalizations had at least one unplanned 30-day readmission to an acute-care hospital. Unweighted, the sample included 37,683 patients and 42,198 hospitalizations. Among all patients represented in the sample, 90.5% had only a single SI or SA hospitalization, 7.7% had two hospitalizations, and 1.8% had >2 hospitalizations in one year.

Table 1 summarizes the sample characteristics and displays the demographic, clinical, and hospital characteristics of index hospitalizations by the 30-day readmission status. Patients represented in the index hospitalizations were 64.9% female, 3.6% were aged 6-9 years, 40.1% were aged 10-14 years, and 56.3% were aged 15-17 years. Nearly half of the patients (49.1%) used public insurance. Nearly half (44.9%) lived in metropolitan areas with >1 million residents, 36.1% lived in metropolitan areas with 50,000 to 1 million residents, and 14.7% lived in rural areas.

Association of Patient and Hospital Characteristics with Readmissions

Table 2 displays the patient and hospital characteristics associated with readmissions. Among demographic characteristics, 10- to 14-year-old patients had higher odds of readmission (odds ratio [OR]: 1.18, 95% confidence interval [CI]: 1.07-1.29) than 15- to 17-year-old patients. Having public insurance was associated with higher odds of readmission (OR: 1.14, 95% CI: 1.04-1.25). We found no differences in readmission rates based on sex, urban or rural location, or patient’s ZIP code income quartile.

Among clinical characteristics, hospitalizations with an admission for SI or SA in the preceding 30 days, meaning that the index hospitalization itself was a readmission, had the strongest association with readmissions (OR: 3.14, 95% CI: 2.73-3.61). In addition, patients admitted via the ED for the index hospitalization had higher odds of readmission (OR: 1.25, 95% CI: 1.15-1.36). Chronic psychiatric conditions associated with higher odds of readmission included psychotic disorders (OR: 1.39, 95% CI: 1.16-1.67) and bipolar disorder (OR: 1.27, 95% CI: 1.13-1.44).

Characteristics of 30-day Readmissions

Table 3 displays the characteristics of readmissions after SI compared to that after SA. Among the combined sample of 11,375 30-day readmissions, 34.1% occurred within 7 days, and 65.9% in 8-30 days. Eleven percent of patients with any readmission had more than one readmission within 30 days. Among readmissions, 94.5% were for a psychiatric problem and 5.5% were for a medical or surgical problem. A total of 43.9% had a diagnosis of SI and 19.5% a diagnosis of SA. Readmissions were more likely to occur at a different hospital after SI than after SA (48.1% vs. 31.3%, P < .001). Medical and surgical indications for readmission were less common after SI than after SA (4.4% vs. 8.7%, P < .001). Only 1.2% of SI hospitalizations had a readmission for SA within 30 days. Of these cases, 55.6% were aged 15-17 years, 43.3% were aged 10-14 years, and 1.1% were aged 6-9 years; 73.1% of the patients were female, and 49.1% used public insurance.

SI and SA in children and adolescents are substantial public health problems associated with significant hospital resource utilization. In 2013 and 2014, there were 181,575 pediatric acute-care hospitalizations for SI or SA, accounting for 9.5% of all hospitalizations in 6- to 17-year-old patients nationally. Among acute-care SI and SA hospitalizations, 8.5% had a readmission to an acute-care hospital within 30 days. The study data source did not include psychiatric specialty hospitals, and the number of index hospitalizations is likely substantially higher when psychiatric specialty hospitalizations are included. Readmissions may also be higher if patients were readmitted to psychiatric specialty hospitals after discharge from acute-care hospitals. The strongest risk factor for unplanned 30-day readmissions was a previous hospitalization in the 30 days before the index admission, likely a marker for severity or complexity of psychiatric illness. Other characteristics associated with higher odds of readmission were bipolar disorder, psychotic disorders, and age 10-14 years. More than one-third of readmissions occurred within the first 7 days after hospital discharge. The prevalence of SI and SA hospitalizations and readmissions was similar to findings in previous analyses of mental health hospitalizations.^10,28

A patient’s psychiatric illness type and severity, as evidenced by the need for frequent repeat hospitalizations, was highly associated with the risk of 30-day readmission. Any hospitalization in the 30 days preceding the index hospitalization, whether for SI/SA or for another problem, was a strong risk factor for readmissions. We suspect that prior SI/SA hospitalizations reflect a patient’s chronic elevated risk for suicide. Prior hospitalizations not for SI or SA could be hospitalizations for mental illness exacerbations that increase the risk of SI or SA, eg, bipolar disorder with acute mania, or they could represent physical health problems. Chronic physical health problems are a known risk factor for SI and SA.²⁹

A knowledge of those characteristics that increase the readmission risk can inform future resource allocation, research, and policy in several ways. First, longer hospital stays could mitigate readmission risk in some patients with severe psychiatric illness. European studies in older adolescents and adults show that for severe psychiatric illness, a longer hospital stay is associated with a lower risk of hospital readmission.^15,30 Second, better access to intensive community-based mental health (MH) services, including evidence-based psychotherapy and medication management, improves symptoms in young people.³¹ Access to these services likely affects the risk of hospital readmission. We found that readmission risk was highest in 10- to 14-year-olds. Taken in the context of existing evidence that suicide rates are rising in younger patients,^1,3 our findings suggest that particular attention to community services for younger patients is needed. Third, care coordination could help patients access beneficial services to reduce readmissions and improve other outcomes. Enhanced discharge care coordination reduced suicide deaths in high-risk populations in Europe³² and Japan³³ and improved attendance at mental health follow-up after pediatric ED discharge in a small United States sample.³⁴ Given that one-third of readmissions occurred within seven days, care coordination designed to ensure access to ambulatory services in the immediate postdischarge period may be particularly beneficial.

We found that ZIP code income quartile was not associated with readmissions. We suspect that poverty is not as closely correlated with MH hospitalization outcomes as it is with physical health hospitalization outcomes for several reasons. Medicaid insurance historically has more robust coverage of mental health services than some private insurance plans, which might offset some of the risk of poor mental health outcomes associated with poverty. Low-income families are eligible to use social services, and families accessing social services might have more opportunities to become familiar with community mental health programs. Further, the expectation of high achievement found in some higher income families is associated with MH problems in children and adolescents.³⁵ Therefore, being in a higher income quartile might not be as protective against poor mental health outcomes as it is against poor physical health outcomes.

Although the NRD provides a rich source of readmission data across hospitals nationally, several limitations are inherent to this administrative dataset. First, data from specialty psychiatric hospitals were not included in the NRD. The study underestimates the total number of index hospitalizations and readmissions, since index SI/SA hospitalizations at psychiatric hospitals are not included, and readmissions are not included if they occurred at specialty psychiatric hospitals. Second, because data cannot be linked between calendar years, we excluded January and December hospitalizations, and findings might not generalize to hospitalizations in January and December. Seasonal trends in SI/SA hospitalizations are known to occur.³⁶ Third, race, ethnicity, primary language, gender identity, and sexual orientation are not available in the NRD, and we could not examine the association of these characteristics with the likelihood of readmissions. Fourth, we did not have information about pre- or posthospitalization insurance enrollment or outpatient services that could affect the risk of readmission. Nevertheless, this study offers information on the characteristics of readmissions after hospitalizations for SI and SA in a large nationally representative sample of youth, and the findings can inform resource planning to prevent suicides.

Hospital readmissions are common in patients with SI and SA, and patients with a recent previous hospitalization have the highest risk of readmission. More than one-third of readmissions after SI or SA occurred within the first seven days. Due to the dearth of mental health services in the community, hospitals offer an important safety net for youth experiencing acute suicidal crises. Strategies to improve the continuum of care for patients at risk of suicide that solely focus on reducing readmissions are not likely to benefit patients. However, readmissions can identify opportunities for improving hospital discharge processes and outpatient services. Future research and clinical innovation to investigate and improve hospital discharge planning and access to community mental health services is likely to benefit patients and could reduce 30-day hospital readmissions.

Acknowledgments

The authors thank John Lawlor for his assistance with the analysis.

Disclosures

The authors have no potential conflicts of interest to disclose.

Funding

Dr. Zima received funding from the Behavioral Health Centers of Excellence for California (SB852).

Compared with other industries, medicine has been slow to embrace the digital age. Electronic health records have only recently become ubiquitous, and that was only realized after governmental prodding through Meaningful Use legislation. Other digital tools, such as video or remote sensor technologies, have been available for decades but had not been introduced into routine medical care until recently for various reasons, ranging from costs to security to reimbursement rules. However, we are currently in the midst of a paradigm shift in medicine toward virtual care, as exemplified by the Kaiser Permanente CEO’s proclamation in 2017 that this capitated care system had moved over half of its 100 million annual patient encounters to the virtual environment.¹

Regulation – both at the state and federal levels – has been the largest barrier to the adoption of virtual care. State licensure regulations for practicing medicine hamper virtual visits, which can otherwise be easily achieved without regard to geography. Although the Centers for Medicare & Medicaid Services (CMS) has had provisions for telehealth billing, these have been largely limited to rural areas. However, regulations are constantly evolving as the Interstate Medical Licensure Compact list is not CMS. The Interstate Medical Licensure Compact (www.imlcc.org) is an agreement involving 24 states that permits licensed physicians to practice medicine across state lines. CMS has recently proposed to add payments for virtual check-in visits, which will not be subject to the prior limitations on Medicare telehealth services.² These and future changes in regulation will likely spur the rapid adoption and evolution of virtual services.

Disclosures

Dr. Ong and Dr. Brotman have nothing to disclose.

Compared with other industries, medicine has been slow to embrace the digital age. Electronic health records have only recently become ubiquitous, and that was only realized after governmental prodding through Meaningful Use legislation. Other digital tools, such as video or remote sensor technologies, have been available for decades but had not been introduced into routine medical care until recently for various reasons, ranging from costs to security to reimbursement rules. However, we are currently in the midst of a paradigm shift in medicine toward virtual care, as exemplified by the Kaiser Permanente CEO’s proclamation in 2017 that this capitated care system had moved over half of its 100 million annual patient encounters to the virtual environment.¹

Regulation – both at the state and federal levels – has been the largest barrier to the adoption of virtual care. State licensure regulations for practicing medicine hamper virtual visits, which can otherwise be easily achieved without regard to geography. Although the Centers for Medicare & Medicaid Services (CMS) has had provisions for telehealth billing, these have been largely limited to rural areas. However, regulations are constantly evolving as the Interstate Medical Licensure Compact list is not CMS. The Interstate Medical Licensure Compact (www.imlcc.org) is an agreement involving 24 states that permits licensed physicians to practice medicine across state lines. CMS has recently proposed to add payments for virtual check-in visits, which will not be subject to the prior limitations on Medicare telehealth services.² These and future changes in regulation will likely spur the rapid adoption and evolution of virtual services.

Disclosures

Dr. Ong and Dr. Brotman have nothing to disclose.

Compared with other industries, medicine has been slow to embrace the digital age. Electronic health records have only recently become ubiquitous, and that was only realized after governmental prodding through Meaningful Use legislation. Other digital tools, such as video or remote sensor technologies, have been available for decades but had not been introduced into routine medical care until recently for various reasons, ranging from costs to security to reimbursement rules. However, we are currently in the midst of a paradigm shift in medicine toward virtual care, as exemplified by the Kaiser Permanente CEO’s proclamation in 2017 that this capitated care system had moved over half of its 100 million annual patient encounters to the virtual environment.¹

Regulation – both at the state and federal levels – has been the largest barrier to the adoption of virtual care. State licensure regulations for practicing medicine hamper virtual visits, which can otherwise be easily achieved without regard to geography. Although the Centers for Medicare & Medicaid Services (CMS) has had provisions for telehealth billing, these have been largely limited to rural areas. However, regulations are constantly evolving as the Interstate Medical Licensure Compact list is not CMS. The Interstate Medical Licensure Compact (www.imlcc.org) is an agreement involving 24 states that permits licensed physicians to practice medicine across state lines. CMS has recently proposed to add payments for virtual check-in visits, which will not be subject to the prior limitations on Medicare telehealth services.² These and future changes in regulation will likely spur the rapid adoption and evolution of virtual services.

Disclosures

Dr. Ong and Dr. Brotman have nothing to disclose.