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

A nticoagulation is the cornerstone of acute venous thromboembolism (VTE) management. Nonetheless, the use of inferior vena cava (IVC) filters in addition to anticoagulation is increasing, with wide variation in practice patterns and a growing recognition of filter-related complications. Rigorous randomized controlled data demonstrating that IVC filters, particularly the increasingly commonly placed retrievable filters, provide a mortality benefit are sparse. Given our review of IVC filter use and the lack of evidence demonstrating that IVC filters provide a mortality benefit, we recommend using anticoagulation alone for stable medical service patients admitted with acute VTE. In nuanced cases, hospitalists should engage in multidisciplinary care to develop individualized treatment options.

A 65-year-old woman with a history of diabetes mellitus, metastatic breast cancer, and peptic ulcer disease presents to the Emergency Department for the evaluation of right thigh swelling, chest pain, and dyspnea after a transcontinental flight. Physical examination is notable for a pulse of 114 beats per minute, blood pressure of 136/93 mm Hg, respiratory rate of 14 breaths per minute, oxygen saturation of 95% on room air, and swelling of the right thigh. Computerized tomography imaging demonstrates multiple bilateral pulmonary emboli. Emergency department physicians begin anticoagulation and inform you that they have ordered the placement of a retrievable inferior vena cava (IVC) filter.

Acute venous thromboembolism (VTE) accounts for more than 500,000 hospitalizations in the United States each year.¹ Although the management of VTE centers around anticoagulation, the concurrent use of IVC filters has increased over the past several decades.² Several observational studies have attempted to quantify IVC filter usage and have shown that overall filter placement has increased at an impressive rate. Within two decades, the number of patients undergoing IVC filter placement has increased nearly 25 times from 2,000 in 1979 to 49,000 in 1999.² Recent Medicare data show that claims for IVC filter placement procedures have increased from 30,756 in 1999 to 65,041 in 2008.³ IVC filter placement rates are higher in the US than in other developed countries; one review projected that in 2012, the IVC filter placement rate in a given population in the US is 25 times higher than that in a similar population in Europe.⁴

The guidelines for IVC filter usage are largely based on expert opinion, and solid data regarding this intervention are lacking. This combination is problematic, especially because the practice is becoming commonplace, and filter-related complications are increasingly recognized. Additionally, the appropriateness of filter use varies among providers, as evidenced by a retrospective study in which three VTE experts reviewed medical records to determine the appropriateness of filter placement. They unanimously agreed that filter use was appropriate in 51% of the cases, unanimously agreed that filter use was inappropriate in 26% of the cases, and lacked consensus on the appropriateness of filter use in 23% of the cases.⁵ The striking lack of consensus among experts underscores the wide range of opinion regarding the appropriateness of IVC filter placement on a case-by-case basis. Moreover, evidence suggests that physician adherence to guidelines for appropriate IVC filter use is suboptimal. One single-center study showed that only 43.5% of filters placed by interventional radiology practitioners met the guidelines established by the American College of Chest Physicians (ACCP), with a slightly increased percentage of filter placement meeting guidelines if the requesting provider is an IM-trained physician.⁶

In theory, the concept of IVC filters makes intuitive sense—filters block the ascent of any thrombus from the lower extremities to prevent the feared complication of a pulmonary embolism (PE). Unfortunately, rigorous data are limited, and consensus guidelines vary between different specialty organizations, further obfuscating the role of IVC filter placement in the management of VTE. For example, the ACCP recommends against the use of IVC filters in most patients with VTE receiving anticoagulation and does not list any prophylactic indications.^7,8 Meanwhile, the Society of Interventional Radiology lists prophylactic indications for IVC filter placement in certain patient populations, such patients with a risk of VTE and a high risk of bleeding, and notes numerous relative indications for IVC filter placement.⁸ Notably, these differences in expert opinion likely influence practice patterns, as evidenced by the increase in IVC filter placement for relative indications.^9,10

The Prevention du Risque d’Embolie Pulmonaire par Interruption Cave (PRECIP) trial is the most robust study supporting the 2016 ACCP recommendation against IVC filter use in patients that can receive anticoagulation.^7,11 This study randomized 400 patients with deep vein thrombosis (DVT) at high risk for PE to anticoagulation with or without permanent filter placement to address VTE and mortality rates associated with IVC filter placement. The trial showed that the VTE burden shifts in the presence of IVC filters. At 2-year follow-up, the group with IVC filters had nonsignificantly fewer PEs than the control group and an increased incidence of DVT. Mortality rates did not differ between groups.¹¹ At eight-year follow-up this shift in VTE burden is again seen given that the number of PEs in patients who received IVC filters decreased and the incidence of DVTs increased. Again, mortality did not differ between groups.¹² A subsequent study randomized 399 patients with DVT and acute symptomatic PE with at least one additional marker of severity to anticoagulation with or without retrievable IVC filter placement and showed no difference in recurrent PE or mortality at 3 or 6 months.¹³ These results argue against placing retrievable filters in patients receiving anticoagulation.

The identification of associated adverse events further favor the judicious use of IVC filters. A retrospective review of the long-term complications of IVC filters based on imaging data showed a 14% fracture rate, 13% IVC thrombosis rate, and a 48% perforation rate.¹⁴ Multiple studies have shown that the associated complication rates of retrievable filters are higher than those of permanent filters; such an association is concerning given that retrievable filter usage exceeds permanent filter usage.^14,15 The increase in retrievable filter usage is likely attributable to their attractive risk-benefit calculation. In theory, retrievable IVC filters should be perfect for patients who have conditions that increase VTE risk but create temporary contraindications, such as trauma or major surgery, to anticoagulation. However, anticoagulation is preferred over IVC filters in the long term because the complication rates of IVC filters increase with dwell time.¹⁶ Given the reports of adverse events and concern that IVC filters are not appropriately removed, the Food and Drug Administration recommends removing retrievable IVC filters once the risk of filters outweighs the benefits, which appears to be 29-54 days after implantation.¹⁷ However, successful retrieval rates are low, both because of the low rates of removal attempts and because of the interference of complications, such as embedded or thrombosed filters, with removal.^10,18 As an example, in a retrospective review of all patients who received an IVC filter at an academic medical center over the period of 2003-2011, nearly 25% of patients were discharged on anticoagulation after IVC filter placement.¹⁰ This suggests that their contraindication to anticoagulation and need for IVC placement have passed by the time of discharge. Nevertheless, clinicians attempted filter retrieval in only 9.6% of these patients, representing a significant missed opportunity of treatment with anticoagulation rather than IVC filters.¹⁰

Factors such as filter plan documentation, hematology involvement, patient age ≤70 years, and establishment of dedicated IVC filter clinics are correlated with improved rates of filter removal; these correlations emphasize the importance of a clear follow-up plan in the timely removal of these devices.^18,19

IVC filter placement is inappropriate in the vast majority of patients with VTE who can be anticoagulated. However the ACCP does acknowledge that a small subset of patients – specifically, those with severe or massive PE – may fall outside this guideline.⁷ Clinicians fear that these patients have low cardiopulmonary reserve and may experience hemodynamic collapse and death with another “hit” from a recurrent PE. This recommendation is consistent with the evidence that in unstable patients with PE, IVC filter placement is associated with decreased in-hospital mortality.²⁰ Data remain limited for this situation, and the decision to place an IVC filter in anticoagulated but unstable patients is an individualized one.

In stable patients admitted to the medical service with VTE and who can be anticoagulated, there is little evidence that placement of an IVC filter will improve short- or long-term mortality. Hospitalists should anticoagulate these patients with a vitamin-K antagonist, heparin product, or novel oral anticoagulants.

• Anticoagulate hemodynamically stable patients who are admitted to the medical service with VTE and who do not have a contraindication to anticoagulation. Do not place a permanent or retrievable IVC filter.
• IVC filter placement may benefit unstable patients who may experience hemodynamic collapse with an increased PE burden. IVC filter placement should be discussed with a multidisciplinary team.
• When discharging a patient with an IVC filter, hospitalists should improve retrieval rates by scheduling subsequent removal. The discharge summary should contain information about the IVC filter, as well as clear instructions regarding the plan for removal. The instructions should include radiology follow-up information and the designation of responsible physicians in case of questions.

Although IVC filter use is increasing, the evidence does not support their use in hemodynamically stable patients who can be anticoagulated. The patient described in the initial case has no contraindication to systemic anticoagulation. Therefore, she should be started on anticoagulation, and an IVC filter should not be placed.

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

Disclosures

The authors do not have any conflicts of interest to disclose

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

A nticoagulation is the cornerstone of acute venous thromboembolism (VTE) management. Nonetheless, the use of inferior vena cava (IVC) filters in addition to anticoagulation is increasing, with wide variation in practice patterns and a growing recognition of filter-related complications. Rigorous randomized controlled data demonstrating that IVC filters, particularly the increasingly commonly placed retrievable filters, provide a mortality benefit are sparse. Given our review of IVC filter use and the lack of evidence demonstrating that IVC filters provide a mortality benefit, we recommend using anticoagulation alone for stable medical service patients admitted with acute VTE. In nuanced cases, hospitalists should engage in multidisciplinary care to develop individualized treatment options.

A 65-year-old woman with a history of diabetes mellitus, metastatic breast cancer, and peptic ulcer disease presents to the Emergency Department for the evaluation of right thigh swelling, chest pain, and dyspnea after a transcontinental flight. Physical examination is notable for a pulse of 114 beats per minute, blood pressure of 136/93 mm Hg, respiratory rate of 14 breaths per minute, oxygen saturation of 95% on room air, and swelling of the right thigh. Computerized tomography imaging demonstrates multiple bilateral pulmonary emboli. Emergency department physicians begin anticoagulation and inform you that they have ordered the placement of a retrievable inferior vena cava (IVC) filter.

Acute venous thromboembolism (VTE) accounts for more than 500,000 hospitalizations in the United States each year.¹ Although the management of VTE centers around anticoagulation, the concurrent use of IVC filters has increased over the past several decades.² Several observational studies have attempted to quantify IVC filter usage and have shown that overall filter placement has increased at an impressive rate. Within two decades, the number of patients undergoing IVC filter placement has increased nearly 25 times from 2,000 in 1979 to 49,000 in 1999.² Recent Medicare data show that claims for IVC filter placement procedures have increased from 30,756 in 1999 to 65,041 in 2008.³ IVC filter placement rates are higher in the US than in other developed countries; one review projected that in 2012, the IVC filter placement rate in a given population in the US is 25 times higher than that in a similar population in Europe.⁴

The guidelines for IVC filter usage are largely based on expert opinion, and solid data regarding this intervention are lacking. This combination is problematic, especially because the practice is becoming commonplace, and filter-related complications are increasingly recognized. Additionally, the appropriateness of filter use varies among providers, as evidenced by a retrospective study in which three VTE experts reviewed medical records to determine the appropriateness of filter placement. They unanimously agreed that filter use was appropriate in 51% of the cases, unanimously agreed that filter use was inappropriate in 26% of the cases, and lacked consensus on the appropriateness of filter use in 23% of the cases.⁵ The striking lack of consensus among experts underscores the wide range of opinion regarding the appropriateness of IVC filter placement on a case-by-case basis. Moreover, evidence suggests that physician adherence to guidelines for appropriate IVC filter use is suboptimal. One single-center study showed that only 43.5% of filters placed by interventional radiology practitioners met the guidelines established by the American College of Chest Physicians (ACCP), with a slightly increased percentage of filter placement meeting guidelines if the requesting provider is an IM-trained physician.⁶

In theory, the concept of IVC filters makes intuitive sense—filters block the ascent of any thrombus from the lower extremities to prevent the feared complication of a pulmonary embolism (PE). Unfortunately, rigorous data are limited, and consensus guidelines vary between different specialty organizations, further obfuscating the role of IVC filter placement in the management of VTE. For example, the ACCP recommends against the use of IVC filters in most patients with VTE receiving anticoagulation and does not list any prophylactic indications.^7,8 Meanwhile, the Society of Interventional Radiology lists prophylactic indications for IVC filter placement in certain patient populations, such patients with a risk of VTE and a high risk of bleeding, and notes numerous relative indications for IVC filter placement.⁸ Notably, these differences in expert opinion likely influence practice patterns, as evidenced by the increase in IVC filter placement for relative indications.^9,10

The Prevention du Risque d’Embolie Pulmonaire par Interruption Cave (PRECIP) trial is the most robust study supporting the 2016 ACCP recommendation against IVC filter use in patients that can receive anticoagulation.^7,11 This study randomized 400 patients with deep vein thrombosis (DVT) at high risk for PE to anticoagulation with or without permanent filter placement to address VTE and mortality rates associated with IVC filter placement. The trial showed that the VTE burden shifts in the presence of IVC filters. At 2-year follow-up, the group with IVC filters had nonsignificantly fewer PEs than the control group and an increased incidence of DVT. Mortality rates did not differ between groups.¹¹ At eight-year follow-up this shift in VTE burden is again seen given that the number of PEs in patients who received IVC filters decreased and the incidence of DVTs increased. Again, mortality did not differ between groups.¹² A subsequent study randomized 399 patients with DVT and acute symptomatic PE with at least one additional marker of severity to anticoagulation with or without retrievable IVC filter placement and showed no difference in recurrent PE or mortality at 3 or 6 months.¹³ These results argue against placing retrievable filters in patients receiving anticoagulation.

The identification of associated adverse events further favor the judicious use of IVC filters. A retrospective review of the long-term complications of IVC filters based on imaging data showed a 14% fracture rate, 13% IVC thrombosis rate, and a 48% perforation rate.¹⁴ Multiple studies have shown that the associated complication rates of retrievable filters are higher than those of permanent filters; such an association is concerning given that retrievable filter usage exceeds permanent filter usage.^14,15 The increase in retrievable filter usage is likely attributable to their attractive risk-benefit calculation. In theory, retrievable IVC filters should be perfect for patients who have conditions that increase VTE risk but create temporary contraindications, such as trauma or major surgery, to anticoagulation. However, anticoagulation is preferred over IVC filters in the long term because the complication rates of IVC filters increase with dwell time.¹⁶ Given the reports of adverse events and concern that IVC filters are not appropriately removed, the Food and Drug Administration recommends removing retrievable IVC filters once the risk of filters outweighs the benefits, which appears to be 29-54 days after implantation.¹⁷ However, successful retrieval rates are low, both because of the low rates of removal attempts and because of the interference of complications, such as embedded or thrombosed filters, with removal.^10,18 As an example, in a retrospective review of all patients who received an IVC filter at an academic medical center over the period of 2003-2011, nearly 25% of patients were discharged on anticoagulation after IVC filter placement.¹⁰ This suggests that their contraindication to anticoagulation and need for IVC placement have passed by the time of discharge. Nevertheless, clinicians attempted filter retrieval in only 9.6% of these patients, representing a significant missed opportunity of treatment with anticoagulation rather than IVC filters.¹⁰

Factors such as filter plan documentation, hematology involvement, patient age ≤70 years, and establishment of dedicated IVC filter clinics are correlated with improved rates of filter removal; these correlations emphasize the importance of a clear follow-up plan in the timely removal of these devices.^18,19

IVC filter placement is inappropriate in the vast majority of patients with VTE who can be anticoagulated. However the ACCP does acknowledge that a small subset of patients – specifically, those with severe or massive PE – may fall outside this guideline.⁷ Clinicians fear that these patients have low cardiopulmonary reserve and may experience hemodynamic collapse and death with another “hit” from a recurrent PE. This recommendation is consistent with the evidence that in unstable patients with PE, IVC filter placement is associated with decreased in-hospital mortality.²⁰ Data remain limited for this situation, and the decision to place an IVC filter in anticoagulated but unstable patients is an individualized one.

In stable patients admitted to the medical service with VTE and who can be anticoagulated, there is little evidence that placement of an IVC filter will improve short- or long-term mortality. Hospitalists should anticoagulate these patients with a vitamin-K antagonist, heparin product, or novel oral anticoagulants.

• Anticoagulate hemodynamically stable patients who are admitted to the medical service with VTE and who do not have a contraindication to anticoagulation. Do not place a permanent or retrievable IVC filter.
• IVC filter placement may benefit unstable patients who may experience hemodynamic collapse with an increased PE burden. IVC filter placement should be discussed with a multidisciplinary team.
• When discharging a patient with an IVC filter, hospitalists should improve retrieval rates by scheduling subsequent removal. The discharge summary should contain information about the IVC filter, as well as clear instructions regarding the plan for removal. The instructions should include radiology follow-up information and the designation of responsible physicians in case of questions.

Although IVC filter use is increasing, the evidence does not support their use in hemodynamically stable patients who can be anticoagulated. The patient described in the initial case has no contraindication to systemic anticoagulation. Therefore, she should be started on anticoagulation, and an IVC filter should not be placed.

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

Disclosures

The authors do not have any conflicts of interest to disclose

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

A nticoagulation is the cornerstone of acute venous thromboembolism (VTE) management. Nonetheless, the use of inferior vena cava (IVC) filters in addition to anticoagulation is increasing, with wide variation in practice patterns and a growing recognition of filter-related complications. Rigorous randomized controlled data demonstrating that IVC filters, particularly the increasingly commonly placed retrievable filters, provide a mortality benefit are sparse. Given our review of IVC filter use and the lack of evidence demonstrating that IVC filters provide a mortality benefit, we recommend using anticoagulation alone for stable medical service patients admitted with acute VTE. In nuanced cases, hospitalists should engage in multidisciplinary care to develop individualized treatment options.

A 65-year-old woman with a history of diabetes mellitus, metastatic breast cancer, and peptic ulcer disease presents to the Emergency Department for the evaluation of right thigh swelling, chest pain, and dyspnea after a transcontinental flight. Physical examination is notable for a pulse of 114 beats per minute, blood pressure of 136/93 mm Hg, respiratory rate of 14 breaths per minute, oxygen saturation of 95% on room air, and swelling of the right thigh. Computerized tomography imaging demonstrates multiple bilateral pulmonary emboli. Emergency department physicians begin anticoagulation and inform you that they have ordered the placement of a retrievable inferior vena cava (IVC) filter.

Acute venous thromboembolism (VTE) accounts for more than 500,000 hospitalizations in the United States each year.¹ Although the management of VTE centers around anticoagulation, the concurrent use of IVC filters has increased over the past several decades.² Several observational studies have attempted to quantify IVC filter usage and have shown that overall filter placement has increased at an impressive rate. Within two decades, the number of patients undergoing IVC filter placement has increased nearly 25 times from 2,000 in 1979 to 49,000 in 1999.² Recent Medicare data show that claims for IVC filter placement procedures have increased from 30,756 in 1999 to 65,041 in 2008.³ IVC filter placement rates are higher in the US than in other developed countries; one review projected that in 2012, the IVC filter placement rate in a given population in the US is 25 times higher than that in a similar population in Europe.⁴

The guidelines for IVC filter usage are largely based on expert opinion, and solid data regarding this intervention are lacking. This combination is problematic, especially because the practice is becoming commonplace, and filter-related complications are increasingly recognized. Additionally, the appropriateness of filter use varies among providers, as evidenced by a retrospective study in which three VTE experts reviewed medical records to determine the appropriateness of filter placement. They unanimously agreed that filter use was appropriate in 51% of the cases, unanimously agreed that filter use was inappropriate in 26% of the cases, and lacked consensus on the appropriateness of filter use in 23% of the cases.⁵ The striking lack of consensus among experts underscores the wide range of opinion regarding the appropriateness of IVC filter placement on a case-by-case basis. Moreover, evidence suggests that physician adherence to guidelines for appropriate IVC filter use is suboptimal. One single-center study showed that only 43.5% of filters placed by interventional radiology practitioners met the guidelines established by the American College of Chest Physicians (ACCP), with a slightly increased percentage of filter placement meeting guidelines if the requesting provider is an IM-trained physician.⁶

In theory, the concept of IVC filters makes intuitive sense—filters block the ascent of any thrombus from the lower extremities to prevent the feared complication of a pulmonary embolism (PE). Unfortunately, rigorous data are limited, and consensus guidelines vary between different specialty organizations, further obfuscating the role of IVC filter placement in the management of VTE. For example, the ACCP recommends against the use of IVC filters in most patients with VTE receiving anticoagulation and does not list any prophylactic indications.^7,8 Meanwhile, the Society of Interventional Radiology lists prophylactic indications for IVC filter placement in certain patient populations, such patients with a risk of VTE and a high risk of bleeding, and notes numerous relative indications for IVC filter placement.⁸ Notably, these differences in expert opinion likely influence practice patterns, as evidenced by the increase in IVC filter placement for relative indications.^9,10

The Prevention du Risque d’Embolie Pulmonaire par Interruption Cave (PRECIP) trial is the most robust study supporting the 2016 ACCP recommendation against IVC filter use in patients that can receive anticoagulation.^7,11 This study randomized 400 patients with deep vein thrombosis (DVT) at high risk for PE to anticoagulation with or without permanent filter placement to address VTE and mortality rates associated with IVC filter placement. The trial showed that the VTE burden shifts in the presence of IVC filters. At 2-year follow-up, the group with IVC filters had nonsignificantly fewer PEs than the control group and an increased incidence of DVT. Mortality rates did not differ between groups.¹¹ At eight-year follow-up this shift in VTE burden is again seen given that the number of PEs in patients who received IVC filters decreased and the incidence of DVTs increased. Again, mortality did not differ between groups.¹² A subsequent study randomized 399 patients with DVT and acute symptomatic PE with at least one additional marker of severity to anticoagulation with or without retrievable IVC filter placement and showed no difference in recurrent PE or mortality at 3 or 6 months.¹³ These results argue against placing retrievable filters in patients receiving anticoagulation.

The identification of associated adverse events further favor the judicious use of IVC filters. A retrospective review of the long-term complications of IVC filters based on imaging data showed a 14% fracture rate, 13% IVC thrombosis rate, and a 48% perforation rate.¹⁴ Multiple studies have shown that the associated complication rates of retrievable filters are higher than those of permanent filters; such an association is concerning given that retrievable filter usage exceeds permanent filter usage.^14,15 The increase in retrievable filter usage is likely attributable to their attractive risk-benefit calculation. In theory, retrievable IVC filters should be perfect for patients who have conditions that increase VTE risk but create temporary contraindications, such as trauma or major surgery, to anticoagulation. However, anticoagulation is preferred over IVC filters in the long term because the complication rates of IVC filters increase with dwell time.¹⁶ Given the reports of adverse events and concern that IVC filters are not appropriately removed, the Food and Drug Administration recommends removing retrievable IVC filters once the risk of filters outweighs the benefits, which appears to be 29-54 days after implantation.¹⁷ However, successful retrieval rates are low, both because of the low rates of removal attempts and because of the interference of complications, such as embedded or thrombosed filters, with removal.^10,18 As an example, in a retrospective review of all patients who received an IVC filter at an academic medical center over the period of 2003-2011, nearly 25% of patients were discharged on anticoagulation after IVC filter placement.¹⁰ This suggests that their contraindication to anticoagulation and need for IVC placement have passed by the time of discharge. Nevertheless, clinicians attempted filter retrieval in only 9.6% of these patients, representing a significant missed opportunity of treatment with anticoagulation rather than IVC filters.¹⁰

Factors such as filter plan documentation, hematology involvement, patient age ≤70 years, and establishment of dedicated IVC filter clinics are correlated with improved rates of filter removal; these correlations emphasize the importance of a clear follow-up plan in the timely removal of these devices.^18,19

IVC filter placement is inappropriate in the vast majority of patients with VTE who can be anticoagulated. However the ACCP does acknowledge that a small subset of patients – specifically, those with severe or massive PE – may fall outside this guideline.⁷ Clinicians fear that these patients have low cardiopulmonary reserve and may experience hemodynamic collapse and death with another “hit” from a recurrent PE. This recommendation is consistent with the evidence that in unstable patients with PE, IVC filter placement is associated with decreased in-hospital mortality.²⁰ Data remain limited for this situation, and the decision to place an IVC filter in anticoagulated but unstable patients is an individualized one.

In stable patients admitted to the medical service with VTE and who can be anticoagulated, there is little evidence that placement of an IVC filter will improve short- or long-term mortality. Hospitalists should anticoagulate these patients with a vitamin-K antagonist, heparin product, or novel oral anticoagulants.

• Anticoagulate hemodynamically stable patients who are admitted to the medical service with VTE and who do not have a contraindication to anticoagulation. Do not place a permanent or retrievable IVC filter.
• IVC filter placement may benefit unstable patients who may experience hemodynamic collapse with an increased PE burden. IVC filter placement should be discussed with a multidisciplinary team.
• When discharging a patient with an IVC filter, hospitalists should improve retrieval rates by scheduling subsequent removal. The discharge summary should contain information about the IVC filter, as well as clear instructions regarding the plan for removal. The instructions should include radiology follow-up information and the designation of responsible physicians in case of questions.

Although IVC filter use is increasing, the evidence does not support their use in hemodynamically stable patients who can be anticoagulated. The patient described in the initial case has no contraindication to systemic anticoagulation. Therefore, she should be started on anticoagulation, and an IVC filter should not be placed.

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

Disclosures

The authors do not have any conflicts of interest to disclose

Recent events, domestically and globally, have highlighted the numerous complex challenges that disasters and mass casualty incidents (MCIs) impose on hospitals. Mass casualty events result from natural phenomena (eg, hurricanes, tornadoes, and wildfires), accidents (eg, plane crashes, building collapses, and toxic waste spills), or man-made crises (eg, terrorism).^1-4 These events feature the potential to cause an acute surge of patients, which can overwhelm available hospital resources and personnel. Mass effect incidents are sustained crises, which often occur due to loss of infrastructure, epidemic infectious diseases, or need for hospital evacuations, and can completely overtax local and regional resources, thus requiring federal and state coordination.⁵

Hospital disaster response plans have traditionally centered on responses by the emergency department (ED) and associated surgical services to mass trauma-type events, without commensurate involvement of other hospital departments in either incident management operations or the planning process for mass effect incidents.^6,7 In particular, the role of hospitalists in the leadership structure of various hospital disaster command structures remains undefined.⁸ However, recent disasters suggest that hospitalist involvement will highly benefit hospital emergency preparedness.⁹ Hospitalists possess specialized expertise in patient triage and disposition; medical comanagement with surgical services; coordination of complex medical care (usually with continuous 24/7 in-house coverage); integration with the full spectrum of affiliated services, such as case management or patient rehabilitation; and quality improvement research.^10-12 At our institution, hospitalists are involved in the direct care of over 60% of the patients admitted across all medical and surgical services. Thus, we believe that hospitalists are uniquely qualified to offer leadership in disaster preparation, response, and recovery if integrated into hospitals’ incident command architectures. For example, although numerous acute patient surges are due to trauma MCIs, hospitalists may nevertheless act as the primary care providers in disasters that are medical in nature or that require rapid hospital evacuation and patient transfer (Table 1).

Effective participation in disaster response and planning requires a basic understanding of the organizational structures for incident management.^18,19 Overall disaster response within the United States is guided by the National Response Framework, a national-level strategy that directs coordination between military and civilian response efforts, the latter of which are structured by the National Incident Management System (NIMS).²⁰ NIMS organizes emergency management across all government levels (federal, state, and local) and the private sector under a common operational language and command structure. Both systems grew out of analyses of the September 11, 2001 attacks and Hurricane Katrina, indicating the need for a wider systemic organization to response efforts.¹ State-level efforts are designed to mobilize resources to assist in community-level operations. Incident management always falls to the local authorities. At this local level, discrete hospitals often take part in healthcare coalitions that act in conjunction with other health entities, local public health departments, and emergency medical services, forming a multiagency coordination system.⁵ This healthcare coalition (emergency support function #8 health and medical), in support of emergency managers of city and county governments, forms the core of the medical response. One commonality to all emergency management is the concept of an “all-hazards” approach, which aims to develop a broad and flexible strategy for efficient management of nearly any type of incident. This “all-hazards” approach allows effective management through each of the four phases of incident management: preparation, response, recovery, and ongoing mitigation.

Direct supervision over incident management is achieved through an Incident Command System (ICS), a hierarchical organization of positions involved in response. The top supervisory structure of ICS (Incident Command and General Staff) is the same regardless of the locale in which it operates, allowing coherent interoperability with other agencies. Incidents of any size are managed with a scalable approach; subordinate ICS positions, which are tailored according to specific needs, can be activated as needed. Healthcare implementation of the ICS structure led to the development of the Hospital Incident Command System (HICS), which now serves as the national standard for hospital-based incident management and facilitates the capacity of individual hospitals to coordinate with other resources regionally and is a part of NIMS for emergency response (Figure 1).²¹ The success of HICS-led regulatory agencies (namely the Centers for Medicare and Medicaid Services and the Joint Commission) to require ICS/HICS in-hospital incident response plans.²² The most recent HICS (Version V) excludes physician involvement in the overall management chart. However, we demonstrate how the inherent flexibility in ICS can adapt to involve hospitalists. Although HICS serves as a backbone that requires institutionally specific modifications, other institutions, such as ours, commonly have entire branches or positions renamed, reapportioned, or created to fill their specific needs. Specialized training in ICS, NIMS, and other aspects of hospital emergency response is beyond the scope of this article but is available for free through the Department of Homeland Security and FEMA.²³

Although incidents vary in terms of their severity, acuity of onset, duration, and composition of patients, a defining feature of MCIs is the rapid surge of patients with acute needs. Many MCIs are easily absorbed by local facilities. However, smaller hospitals or hospitals receiving patients from larger-scale incidents may become overwhelmed, in which larger incidents may result in an acute surge of over 20% of hospital capacity.¹³ Moreover, hospital surge capabilities have markedly diminished over the past decade due to overcrowding of emergency rooms, in part by admitted patients occupying the room space within the ED (“boarding”), further decreasing the hospitals’ capacities to accept new patients.²⁵

Our proposed framework for hospitalist involvement in MCI disaster response focuses on such a situation, with emphasis on augmentation of hospital surge capacity and facilitation of patient throughput and discharge. Notably, these goals are modified from the standard HICS architecture (Figures 1-2 and Table 2). In this framework, hospitalists can play a critical role in decompressing the emergency room through admitting medical patients as rapidly as possible (even if preliminary workup is still pending), facilitating rapid discharge of patients to allow newer admissions to reach the floor, and prioritizing patients that could be transferred to other facilities or services and thus opening additional beds for admission (eg, accepting patients from the ICU or surgical floors to increase capacities on those services). Additionally, hospitalists can comanage surgical patients while surgeons are operating, assist intensivists with medical issues, and facilitate care of patients with minor injuries.

Using the HICS framework, each of those domains would be handled by a Strike Team led by one Team Leader whose goal is to operationalize various assets into a cohesive team specializing in those goals. Table 2 summarizes these goals, as presented in the context of patient examples.

To keep up with the ICS fundamentals, Hospitalist Unit Leaders may address a large MCI with all four strike teams or may only activate the strike teams needed for a less intensive MCI. For example, a bombing may result in a patient surge of 30% more than normal operations and thus demand a full response that includes all the strike teams noted above. By contrast, a bus accident with 20 injured patients may only require a Hospitalist Unit Leader to activate the “Admissions and Internal Transfers In” Strike Team to help offload a busy emergency room.

Emergency management is comprised of four phases: preparation, response, recovery, and mitigation. The latter two phases are beyond the scope of this paper. Although most of our review has focused on modeling disaster response, hospitalist leadership remains critical in preparing for disasters. A disaster often psychologically overwhelms care providers, who feel compelled to help but are uncertain where to begin. To aid the members of a disaster response team, a state-of-the-art hospitalist group creates Job Action Sheets (JASs) for each position in their HICS organizational chart; these sheets codify how to respond and what roles are needed. These formal, protocolized sheets provide individuals assigned to these positions a description of their roles and responsibilities, including to whom they report and over whom they supervise, and include detailed checklists to aid in reaching critical milestones during the response phase. For example, the “Surgical Comanagement and Consulting” Strike Team Leader JAS would likely include the expectations of surgeons for assisting in patient management (ie, auto-consulting on all postoperative patients) and whether nursing phone calls on surgical patients would be temporarily routed to the Strike Team during periods of OR surge.

Hospitalists are well suited as leaders in disaster preparation given their ability to coordinate care among a large spectrum of stakeholders. For example, case managers and social workers are essential members of a well-structured Discharge Strike Team. Their input is critical to ensure that disaster tactics – such as care coordination contracts with local skilled nursing facilities willing to expedite discharge in emergencies to their facilities – are in-place before a real MCI. During Hurricane Sandy, mass evacuation of affected hospitals was effective through the Healthcare Facility Evacuation Center (a healthcare coalition of the New York Hospital Association) but nevertheless plagued with issues regarding situational awareness, poor communication between facilities, and difficulty bundling patients with medical records to receiving facilities – items which can be identified, anticipated, and thoroughly vetted by hospitalists well in advance of a real-world evacuation.^{26, 27}

As the Joint Commission mandates regular exercises of the emergency plan, protocols must be drilled regularly to uncover deficiencies and areas for improvement.¹⁸ The most common failure patterns in Emergency Operation Plans (EOPs) include unrealistic and ineffective expectations and poor communication between different personnel and groups, resulting in confusion and obfuscation.^28-30 Therefore, EOPs need to be both comprehensive and realistic – characteristics that can only be tested through repeated drills. These characteristics can be tested during tabletop exercises, where hospitalists assume the role of a part of the ICS structure and with JAS in hand, attempt to reason how to respond to a given scenario.³¹ Our experience is that small-scale drills conducted more frequently than the bare minimum mandated by the Joint Commission are far more effective for success in real-life situations.

Although no hospital EOP can anticipate every contingency, hospitalists can proactively practice contingency planning for sustained system-wide mass effect incidents, in which hospitals are unable to maintain normal operations and shift from standard to crisis conventions of care. For example, mass effect incidents (ie, hospital damage from an earthquake or a massive and persistent regional power failure), require planning for how a hospital-wide mass evacuation would unfold and how efforts from multiple ancillary hospital services (engineering, nursing, security, and patient transport) would be integrated. As of 2015, over 90% of hospitals have adopted an electronic health record, but only two-thirds of hospitals feature EOPs for information technology failures.^32,33 Given the large footprint of hospitalists in clinical practice, HICS principles appear ripe for application in IT outages and through development of ICS positions structured specifically to this type of contingency.³⁴

Disasters unfold rapidly with marked patient surges and the potential to strain healthcare systems over an extended period. However, in both instances, hospitalists are possibly some of the most qualified clinicians to prepare for and respond to such events. Hospitalists need to assume a leadership role in emergency preparedness to integrate seamlessly into hospital incident command structures and to shape the interdepartmental relationships vital to success – skills at which hospitalists excel. Although no plan can address all possible disasters, familiarity with HICS and well-prepared and well-written JASs should help groups respond and succeed in almost all hazards.

Disclosures

None of the authors have any conflicts of interest to report.

Recent events, domestically and globally, have highlighted the numerous complex challenges that disasters and mass casualty incidents (MCIs) impose on hospitals. Mass casualty events result from natural phenomena (eg, hurricanes, tornadoes, and wildfires), accidents (eg, plane crashes, building collapses, and toxic waste spills), or man-made crises (eg, terrorism).^1-4 These events feature the potential to cause an acute surge of patients, which can overwhelm available hospital resources and personnel. Mass effect incidents are sustained crises, which often occur due to loss of infrastructure, epidemic infectious diseases, or need for hospital evacuations, and can completely overtax local and regional resources, thus requiring federal and state coordination.⁵

Hospital disaster response plans have traditionally centered on responses by the emergency department (ED) and associated surgical services to mass trauma-type events, without commensurate involvement of other hospital departments in either incident management operations or the planning process for mass effect incidents.^6,7 In particular, the role of hospitalists in the leadership structure of various hospital disaster command structures remains undefined.⁸ However, recent disasters suggest that hospitalist involvement will highly benefit hospital emergency preparedness.⁹ Hospitalists possess specialized expertise in patient triage and disposition; medical comanagement with surgical services; coordination of complex medical care (usually with continuous 24/7 in-house coverage); integration with the full spectrum of affiliated services, such as case management or patient rehabilitation; and quality improvement research.^10-12 At our institution, hospitalists are involved in the direct care of over 60% of the patients admitted across all medical and surgical services. Thus, we believe that hospitalists are uniquely qualified to offer leadership in disaster preparation, response, and recovery if integrated into hospitals’ incident command architectures. For example, although numerous acute patient surges are due to trauma MCIs, hospitalists may nevertheless act as the primary care providers in disasters that are medical in nature or that require rapid hospital evacuation and patient transfer (Table 1).

Effective participation in disaster response and planning requires a basic understanding of the organizational structures for incident management.^18,19 Overall disaster response within the United States is guided by the National Response Framework, a national-level strategy that directs coordination between military and civilian response efforts, the latter of which are structured by the National Incident Management System (NIMS).²⁰ NIMS organizes emergency management across all government levels (federal, state, and local) and the private sector under a common operational language and command structure. Both systems grew out of analyses of the September 11, 2001 attacks and Hurricane Katrina, indicating the need for a wider systemic organization to response efforts.¹ State-level efforts are designed to mobilize resources to assist in community-level operations. Incident management always falls to the local authorities. At this local level, discrete hospitals often take part in healthcare coalitions that act in conjunction with other health entities, local public health departments, and emergency medical services, forming a multiagency coordination system.⁵ This healthcare coalition (emergency support function #8 health and medical), in support of emergency managers of city and county governments, forms the core of the medical response. One commonality to all emergency management is the concept of an “all-hazards” approach, which aims to develop a broad and flexible strategy for efficient management of nearly any type of incident. This “all-hazards” approach allows effective management through each of the four phases of incident management: preparation, response, recovery, and ongoing mitigation.

Direct supervision over incident management is achieved through an Incident Command System (ICS), a hierarchical organization of positions involved in response. The top supervisory structure of ICS (Incident Command and General Staff) is the same regardless of the locale in which it operates, allowing coherent interoperability with other agencies. Incidents of any size are managed with a scalable approach; subordinate ICS positions, which are tailored according to specific needs, can be activated as needed. Healthcare implementation of the ICS structure led to the development of the Hospital Incident Command System (HICS), which now serves as the national standard for hospital-based incident management and facilitates the capacity of individual hospitals to coordinate with other resources regionally and is a part of NIMS for emergency response (Figure 1).²¹ The success of HICS-led regulatory agencies (namely the Centers for Medicare and Medicaid Services and the Joint Commission) to require ICS/HICS in-hospital incident response plans.²² The most recent HICS (Version V) excludes physician involvement in the overall management chart. However, we demonstrate how the inherent flexibility in ICS can adapt to involve hospitalists. Although HICS serves as a backbone that requires institutionally specific modifications, other institutions, such as ours, commonly have entire branches or positions renamed, reapportioned, or created to fill their specific needs. Specialized training in ICS, NIMS, and other aspects of hospital emergency response is beyond the scope of this article but is available for free through the Department of Homeland Security and FEMA.²³

Although incidents vary in terms of their severity, acuity of onset, duration, and composition of patients, a defining feature of MCIs is the rapid surge of patients with acute needs. Many MCIs are easily absorbed by local facilities. However, smaller hospitals or hospitals receiving patients from larger-scale incidents may become overwhelmed, in which larger incidents may result in an acute surge of over 20% of hospital capacity.¹³ Moreover, hospital surge capabilities have markedly diminished over the past decade due to overcrowding of emergency rooms, in part by admitted patients occupying the room space within the ED (“boarding”), further decreasing the hospitals’ capacities to accept new patients.²⁵

Our proposed framework for hospitalist involvement in MCI disaster response focuses on such a situation, with emphasis on augmentation of hospital surge capacity and facilitation of patient throughput and discharge. Notably, these goals are modified from the standard HICS architecture (Figures 1-2 and Table 2). In this framework, hospitalists can play a critical role in decompressing the emergency room through admitting medical patients as rapidly as possible (even if preliminary workup is still pending), facilitating rapid discharge of patients to allow newer admissions to reach the floor, and prioritizing patients that could be transferred to other facilities or services and thus opening additional beds for admission (eg, accepting patients from the ICU or surgical floors to increase capacities on those services). Additionally, hospitalists can comanage surgical patients while surgeons are operating, assist intensivists with medical issues, and facilitate care of patients with minor injuries.

Using the HICS framework, each of those domains would be handled by a Strike Team led by one Team Leader whose goal is to operationalize various assets into a cohesive team specializing in those goals. Table 2 summarizes these goals, as presented in the context of patient examples.

To keep up with the ICS fundamentals, Hospitalist Unit Leaders may address a large MCI with all four strike teams or may only activate the strike teams needed for a less intensive MCI. For example, a bombing may result in a patient surge of 30% more than normal operations and thus demand a full response that includes all the strike teams noted above. By contrast, a bus accident with 20 injured patients may only require a Hospitalist Unit Leader to activate the “Admissions and Internal Transfers In” Strike Team to help offload a busy emergency room.

Emergency management is comprised of four phases: preparation, response, recovery, and mitigation. The latter two phases are beyond the scope of this paper. Although most of our review has focused on modeling disaster response, hospitalist leadership remains critical in preparing for disasters. A disaster often psychologically overwhelms care providers, who feel compelled to help but are uncertain where to begin. To aid the members of a disaster response team, a state-of-the-art hospitalist group creates Job Action Sheets (JASs) for each position in their HICS organizational chart; these sheets codify how to respond and what roles are needed. These formal, protocolized sheets provide individuals assigned to these positions a description of their roles and responsibilities, including to whom they report and over whom they supervise, and include detailed checklists to aid in reaching critical milestones during the response phase. For example, the “Surgical Comanagement and Consulting” Strike Team Leader JAS would likely include the expectations of surgeons for assisting in patient management (ie, auto-consulting on all postoperative patients) and whether nursing phone calls on surgical patients would be temporarily routed to the Strike Team during periods of OR surge.

Hospitalists are well suited as leaders in disaster preparation given their ability to coordinate care among a large spectrum of stakeholders. For example, case managers and social workers are essential members of a well-structured Discharge Strike Team. Their input is critical to ensure that disaster tactics – such as care coordination contracts with local skilled nursing facilities willing to expedite discharge in emergencies to their facilities – are in-place before a real MCI. During Hurricane Sandy, mass evacuation of affected hospitals was effective through the Healthcare Facility Evacuation Center (a healthcare coalition of the New York Hospital Association) but nevertheless plagued with issues regarding situational awareness, poor communication between facilities, and difficulty bundling patients with medical records to receiving facilities – items which can be identified, anticipated, and thoroughly vetted by hospitalists well in advance of a real-world evacuation.^{26, 27}

As the Joint Commission mandates regular exercises of the emergency plan, protocols must be drilled regularly to uncover deficiencies and areas for improvement.¹⁸ The most common failure patterns in Emergency Operation Plans (EOPs) include unrealistic and ineffective expectations and poor communication between different personnel and groups, resulting in confusion and obfuscation.^28-30 Therefore, EOPs need to be both comprehensive and realistic – characteristics that can only be tested through repeated drills. These characteristics can be tested during tabletop exercises, where hospitalists assume the role of a part of the ICS structure and with JAS in hand, attempt to reason how to respond to a given scenario.³¹ Our experience is that small-scale drills conducted more frequently than the bare minimum mandated by the Joint Commission are far more effective for success in real-life situations.

Although no hospital EOP can anticipate every contingency, hospitalists can proactively practice contingency planning for sustained system-wide mass effect incidents, in which hospitals are unable to maintain normal operations and shift from standard to crisis conventions of care. For example, mass effect incidents (ie, hospital damage from an earthquake or a massive and persistent regional power failure), require planning for how a hospital-wide mass evacuation would unfold and how efforts from multiple ancillary hospital services (engineering, nursing, security, and patient transport) would be integrated. As of 2015, over 90% of hospitals have adopted an electronic health record, but only two-thirds of hospitals feature EOPs for information technology failures.^32,33 Given the large footprint of hospitalists in clinical practice, HICS principles appear ripe for application in IT outages and through development of ICS positions structured specifically to this type of contingency.³⁴

Disasters unfold rapidly with marked patient surges and the potential to strain healthcare systems over an extended period. However, in both instances, hospitalists are possibly some of the most qualified clinicians to prepare for and respond to such events. Hospitalists need to assume a leadership role in emergency preparedness to integrate seamlessly into hospital incident command structures and to shape the interdepartmental relationships vital to success – skills at which hospitalists excel. Although no plan can address all possible disasters, familiarity with HICS and well-prepared and well-written JASs should help groups respond and succeed in almost all hazards.

Disclosures

None of the authors have any conflicts of interest to report.

Recent events, domestically and globally, have highlighted the numerous complex challenges that disasters and mass casualty incidents (MCIs) impose on hospitals. Mass casualty events result from natural phenomena (eg, hurricanes, tornadoes, and wildfires), accidents (eg, plane crashes, building collapses, and toxic waste spills), or man-made crises (eg, terrorism).^1-4 These events feature the potential to cause an acute surge of patients, which can overwhelm available hospital resources and personnel. Mass effect incidents are sustained crises, which often occur due to loss of infrastructure, epidemic infectious diseases, or need for hospital evacuations, and can completely overtax local and regional resources, thus requiring federal and state coordination.⁵

Hospital disaster response plans have traditionally centered on responses by the emergency department (ED) and associated surgical services to mass trauma-type events, without commensurate involvement of other hospital departments in either incident management operations or the planning process for mass effect incidents.^6,7 In particular, the role of hospitalists in the leadership structure of various hospital disaster command structures remains undefined.⁸ However, recent disasters suggest that hospitalist involvement will highly benefit hospital emergency preparedness.⁹ Hospitalists possess specialized expertise in patient triage and disposition; medical comanagement with surgical services; coordination of complex medical care (usually with continuous 24/7 in-house coverage); integration with the full spectrum of affiliated services, such as case management or patient rehabilitation; and quality improvement research.^10-12 At our institution, hospitalists are involved in the direct care of over 60% of the patients admitted across all medical and surgical services. Thus, we believe that hospitalists are uniquely qualified to offer leadership in disaster preparation, response, and recovery if integrated into hospitals’ incident command architectures. For example, although numerous acute patient surges are due to trauma MCIs, hospitalists may nevertheless act as the primary care providers in disasters that are medical in nature or that require rapid hospital evacuation and patient transfer (Table 1).

Effective participation in disaster response and planning requires a basic understanding of the organizational structures for incident management.^18,19 Overall disaster response within the United States is guided by the National Response Framework, a national-level strategy that directs coordination between military and civilian response efforts, the latter of which are structured by the National Incident Management System (NIMS).²⁰ NIMS organizes emergency management across all government levels (federal, state, and local) and the private sector under a common operational language and command structure. Both systems grew out of analyses of the September 11, 2001 attacks and Hurricane Katrina, indicating the need for a wider systemic organization to response efforts.¹ State-level efforts are designed to mobilize resources to assist in community-level operations. Incident management always falls to the local authorities. At this local level, discrete hospitals often take part in healthcare coalitions that act in conjunction with other health entities, local public health departments, and emergency medical services, forming a multiagency coordination system.⁵ This healthcare coalition (emergency support function #8 health and medical), in support of emergency managers of city and county governments, forms the core of the medical response. One commonality to all emergency management is the concept of an “all-hazards” approach, which aims to develop a broad and flexible strategy for efficient management of nearly any type of incident. This “all-hazards” approach allows effective management through each of the four phases of incident management: preparation, response, recovery, and ongoing mitigation.

Direct supervision over incident management is achieved through an Incident Command System (ICS), a hierarchical organization of positions involved in response. The top supervisory structure of ICS (Incident Command and General Staff) is the same regardless of the locale in which it operates, allowing coherent interoperability with other agencies. Incidents of any size are managed with a scalable approach; subordinate ICS positions, which are tailored according to specific needs, can be activated as needed. Healthcare implementation of the ICS structure led to the development of the Hospital Incident Command System (HICS), which now serves as the national standard for hospital-based incident management and facilitates the capacity of individual hospitals to coordinate with other resources regionally and is a part of NIMS for emergency response (Figure 1).²¹ The success of HICS-led regulatory agencies (namely the Centers for Medicare and Medicaid Services and the Joint Commission) to require ICS/HICS in-hospital incident response plans.²² The most recent HICS (Version V) excludes physician involvement in the overall management chart. However, we demonstrate how the inherent flexibility in ICS can adapt to involve hospitalists. Although HICS serves as a backbone that requires institutionally specific modifications, other institutions, such as ours, commonly have entire branches or positions renamed, reapportioned, or created to fill their specific needs. Specialized training in ICS, NIMS, and other aspects of hospital emergency response is beyond the scope of this article but is available for free through the Department of Homeland Security and FEMA.²³

Although incidents vary in terms of their severity, acuity of onset, duration, and composition of patients, a defining feature of MCIs is the rapid surge of patients with acute needs. Many MCIs are easily absorbed by local facilities. However, smaller hospitals or hospitals receiving patients from larger-scale incidents may become overwhelmed, in which larger incidents may result in an acute surge of over 20% of hospital capacity.¹³ Moreover, hospital surge capabilities have markedly diminished over the past decade due to overcrowding of emergency rooms, in part by admitted patients occupying the room space within the ED (“boarding”), further decreasing the hospitals’ capacities to accept new patients.²⁵

Our proposed framework for hospitalist involvement in MCI disaster response focuses on such a situation, with emphasis on augmentation of hospital surge capacity and facilitation of patient throughput and discharge. Notably, these goals are modified from the standard HICS architecture (Figures 1-2 and Table 2). In this framework, hospitalists can play a critical role in decompressing the emergency room through admitting medical patients as rapidly as possible (even if preliminary workup is still pending), facilitating rapid discharge of patients to allow newer admissions to reach the floor, and prioritizing patients that could be transferred to other facilities or services and thus opening additional beds for admission (eg, accepting patients from the ICU or surgical floors to increase capacities on those services). Additionally, hospitalists can comanage surgical patients while surgeons are operating, assist intensivists with medical issues, and facilitate care of patients with minor injuries.

Using the HICS framework, each of those domains would be handled by a Strike Team led by one Team Leader whose goal is to operationalize various assets into a cohesive team specializing in those goals. Table 2 summarizes these goals, as presented in the context of patient examples.

To keep up with the ICS fundamentals, Hospitalist Unit Leaders may address a large MCI with all four strike teams or may only activate the strike teams needed for a less intensive MCI. For example, a bombing may result in a patient surge of 30% more than normal operations and thus demand a full response that includes all the strike teams noted above. By contrast, a bus accident with 20 injured patients may only require a Hospitalist Unit Leader to activate the “Admissions and Internal Transfers In” Strike Team to help offload a busy emergency room.

Emergency management is comprised of four phases: preparation, response, recovery, and mitigation. The latter two phases are beyond the scope of this paper. Although most of our review has focused on modeling disaster response, hospitalist leadership remains critical in preparing for disasters. A disaster often psychologically overwhelms care providers, who feel compelled to help but are uncertain where to begin. To aid the members of a disaster response team, a state-of-the-art hospitalist group creates Job Action Sheets (JASs) for each position in their HICS organizational chart; these sheets codify how to respond and what roles are needed. These formal, protocolized sheets provide individuals assigned to these positions a description of their roles and responsibilities, including to whom they report and over whom they supervise, and include detailed checklists to aid in reaching critical milestones during the response phase. For example, the “Surgical Comanagement and Consulting” Strike Team Leader JAS would likely include the expectations of surgeons for assisting in patient management (ie, auto-consulting on all postoperative patients) and whether nursing phone calls on surgical patients would be temporarily routed to the Strike Team during periods of OR surge.

Hospitalists are well suited as leaders in disaster preparation given their ability to coordinate care among a large spectrum of stakeholders. For example, case managers and social workers are essential members of a well-structured Discharge Strike Team. Their input is critical to ensure that disaster tactics – such as care coordination contracts with local skilled nursing facilities willing to expedite discharge in emergencies to their facilities – are in-place before a real MCI. During Hurricane Sandy, mass evacuation of affected hospitals was effective through the Healthcare Facility Evacuation Center (a healthcare coalition of the New York Hospital Association) but nevertheless plagued with issues regarding situational awareness, poor communication between facilities, and difficulty bundling patients with medical records to receiving facilities – items which can be identified, anticipated, and thoroughly vetted by hospitalists well in advance of a real-world evacuation.^{26, 27}

As the Joint Commission mandates regular exercises of the emergency plan, protocols must be drilled regularly to uncover deficiencies and areas for improvement.¹⁸ The most common failure patterns in Emergency Operation Plans (EOPs) include unrealistic and ineffective expectations and poor communication between different personnel and groups, resulting in confusion and obfuscation.^28-30 Therefore, EOPs need to be both comprehensive and realistic – characteristics that can only be tested through repeated drills. These characteristics can be tested during tabletop exercises, where hospitalists assume the role of a part of the ICS structure and with JAS in hand, attempt to reason how to respond to a given scenario.³¹ Our experience is that small-scale drills conducted more frequently than the bare minimum mandated by the Joint Commission are far more effective for success in real-life situations.

Although no hospital EOP can anticipate every contingency, hospitalists can proactively practice contingency planning for sustained system-wide mass effect incidents, in which hospitals are unable to maintain normal operations and shift from standard to crisis conventions of care. For example, mass effect incidents (ie, hospital damage from an earthquake or a massive and persistent regional power failure), require planning for how a hospital-wide mass evacuation would unfold and how efforts from multiple ancillary hospital services (engineering, nursing, security, and patient transport) would be integrated. As of 2015, over 90% of hospitals have adopted an electronic health record, but only two-thirds of hospitals feature EOPs for information technology failures.^32,33 Given the large footprint of hospitalists in clinical practice, HICS principles appear ripe for application in IT outages and through development of ICS positions structured specifically to this type of contingency.³⁴

Disasters unfold rapidly with marked patient surges and the potential to strain healthcare systems over an extended period. However, in both instances, hospitalists are possibly some of the most qualified clinicians to prepare for and respond to such events. Hospitalists need to assume a leadership role in emergency preparedness to integrate seamlessly into hospital incident command structures and to shape the interdepartmental relationships vital to success – skills at which hospitalists excel. Although no plan can address all possible disasters, familiarity with HICS and well-prepared and well-written JASs should help groups respond and succeed in almost all hazards.

Disclosures

None of the authors have any conflicts of interest to report.

Renin-angiotensin inhibitors, which include angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs), have demonstrated benefits in the treatment of several common cardiovascular and renal conditions. For example, they are prescribed to individuals with hypertension, heart failure with reduced ejection fraction (HFrEF), prior myocardial infarction, and chronic kidney disease with proteinuria. Perhaps unsurprisingly, many individuals presenting for surgery are already on long-term ACE inhibitor or ARB therapy. For example, such individuals comprised approximately one-third of the sample in the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) multicenter prospective cohort study of major inpatient noncardiac surgery.¹

There is considerable controversy regarding how best to manage these cardiovascular medications during the perioperative period. The critical question pertains to whether renin-angiotensin inhibitors should be temporarily withdrawn 24 hours before surgery or continued uninterrupted up to the day of surgery. The main argument for withdrawing these medications is concern that they cause perioperative hypotension. For example, a recent systematic review of randomized controlled trials (RCTs) and cohort studies found that preoperative continuation of renin-angiotensin inhibitor therapy led to a significantly increased risk of intraoperative hypotension, albeit without associated effects on rates of death, major adverse cardiac events, or postoperative hypotension.² Notably, randomized trial evidence in this meta-analysis was limited to only five trials with a total of 774 participants. Conversely, preoperative interruption of renin-angiotensin inhibitor therapy also has risks. For example, there is a potential for unintended permanent discontinuation of medications with long-term benefits.³ Furthermore, some prior cohort studies have demonstrated that the failure to resume renin-angiotensin inhibitor therapy promptly after surgery is associated with an elevated risk of postoperative mortality.^4,5 While these studies have methodological limitations related to survivorship bias and unmeasured confounders, they still raise concerns that the abrupt withdrawal of long-term cardiovascular therapy before major surgery can have adverse effects. While ACE inhibitor withdrawal has not shown adverse physiological effects in the perioperative setting, it has led to rebound myocardial ischemia in patients with prior myocardial infarction.⁶

Given this controversy, there is variation across hospitals¹ and practice guidelines with respect to perioperative management of renin-angiotensin inhibitors. For example, the 2017 Canadian Cardiovascular Society guidelines recommend that renin-angiotensin inhibitors be stopped temporarily 24 hours before major inpatient surgery,⁷ and the 2014 European guidelines recommend continuing therapy in patients with HFrEF but temporarily interrupting therapy in patients with hypertension.⁸ The 2014 American Heart Association and American College of Cardiology guidelines suggest that either continuation or interruption are reasonable options, but any interrupted therapy should be restarted postoperatively as soon as clinically feasible.⁹

In this issue of the Journal of Hospital Medicine, Shiffermiller and colleagues present a single-center RCT that provides additional high-quality data to improve our understanding of this important clinical issue.¹⁰ In a sample of 275 patients undergoing nonvascular inpatient noncardiac surgery, omission of the final dose of preoperative ACE inhibitor therapy reduced the risk of intraoperative hypotension across multiple definitions, including any episode of systolic blood pressure less than 80 mmHg (number needed to treat: 8), any episode of a systolic blood pressure less than 80 mmHg necessitating vasopressor therapy (number needed to treat: 6), and total cumulative duration of intraoperative systolic blood pressure less than 80 mmHg. In addition, the investigators found that preoperative interruption of ACE inhibitor therapy reduced the risk of postoperative hypotension (number needed to treat: 9), increased the risk of severe postoperative hypertension (number needed to harm: 9), and had no effect on clinical outcomes (eg, acute kidney injury, major adverse cardiac events). In conjunction with a recent systematic review,² these new data demonstrate that temporary preoperative discontinuation of renin-angiotensin inhibitors leads to reduced risks of intraoperative and postoperative hypotension, with the only major identified risk being episodes of postoperative hypertension.

This current evidence base suggests that, in most cases, perioperative physicians should temporarily interrupt renin-angiotensin inhibitor therapy before inpatient noncardiac surgery, provided that protocols are in place to resume treatment postoperatively as soon as clinically feasible. Nonetheless, clinicians must also be cognizant of the key limitations to current data, namely that hypotension, be it intraoperative or postoperative, remains essentially a surrogate endpoint.^11,12 Stated otherwise, the clinical importance of perioperative hypotension is largely predicated on its close association with clinically important or patient-relevant outcomes such as cardiovascular complications, acute kidney injury, and death.^13–16 There is an implicit assumption that a reduction in the risk of hypotension will necessarily lead to reduced rates of clinical adverse events. This assumption is unlikely to be true, especially since many different underlying mechanisms lead to hypotension in the dynamic perioperative environment, including decreased cardiac contractility, decreased heart rate, decreased intravascular volume status, and vasodilation. Consistent with this possibility, different perioperative interventions with similar effects on hypotension have shown quite different effects on clinical outcomes. For example, epidural analgesia invariably reduces perioperative blood pressure, yet it does not appear to increase the risk of postoperative complications.¹⁷ Similarly, both beta-blockers and clonidine increase the risk of significant perioperative hypotension and bradycardia, yet only beta-blockers appear to lead to increased rates of mortality after noncardiac surgery.^18,19 Thus, the relationship between perioperative hypotension and outcomes is clearly complex. Unless a RCT demonstrates that a hypotension-reduction strategy leads to an improvement in clinical outcomes,²⁰ perioperative physicians should not assume that prevention of hypotension will always lead to improvements in patient-relevant clinical outcomes. Similar assumptions about other surrogate endpoints in cardiovascular medicine have sometimes been spectacularly incorrect.^12,21 To more definitively address this important clinical issue, RCTs must be specifically designed to compare the effects of renin-angiotensin inhibitor therapy withdrawal versus continuation on patient-relevant and clinically important outcomes, such as death, myocardial infarction, and stroke. Fortunately, some ongoing trials will address this question, either directly (ClinicalTrials.gov NCT03374449) or as a component of a hypotension-avoidance strategy (ClinicalTrials.gov NCT03505723).

Overall, perioperative physicians should now adopt the standard approach of temporarily withdrawing renin-angiotensin inhibitor therapy 24 hours before major inpatient noncardiac surgery. Nonetheless, they should do so cautiously, recognizing that the data underpinning this strategy remain weak. As with many aspects of perioperative medicine, more research remains needed.

Disclosures

The authors have nothing to report.Funding: DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research, and a Merit Award from the Department of Anesthesia at the University of Toronto.

Renin-angiotensin inhibitors, which include angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs), have demonstrated benefits in the treatment of several common cardiovascular and renal conditions. For example, they are prescribed to individuals with hypertension, heart failure with reduced ejection fraction (HFrEF), prior myocardial infarction, and chronic kidney disease with proteinuria. Perhaps unsurprisingly, many individuals presenting for surgery are already on long-term ACE inhibitor or ARB therapy. For example, such individuals comprised approximately one-third of the sample in the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) multicenter prospective cohort study of major inpatient noncardiac surgery.¹

There is considerable controversy regarding how best to manage these cardiovascular medications during the perioperative period. The critical question pertains to whether renin-angiotensin inhibitors should be temporarily withdrawn 24 hours before surgery or continued uninterrupted up to the day of surgery. The main argument for withdrawing these medications is concern that they cause perioperative hypotension. For example, a recent systematic review of randomized controlled trials (RCTs) and cohort studies found that preoperative continuation of renin-angiotensin inhibitor therapy led to a significantly increased risk of intraoperative hypotension, albeit without associated effects on rates of death, major adverse cardiac events, or postoperative hypotension.² Notably, randomized trial evidence in this meta-analysis was limited to only five trials with a total of 774 participants. Conversely, preoperative interruption of renin-angiotensin inhibitor therapy also has risks. For example, there is a potential for unintended permanent discontinuation of medications with long-term benefits.³ Furthermore, some prior cohort studies have demonstrated that the failure to resume renin-angiotensin inhibitor therapy promptly after surgery is associated with an elevated risk of postoperative mortality.^4,5 While these studies have methodological limitations related to survivorship bias and unmeasured confounders, they still raise concerns that the abrupt withdrawal of long-term cardiovascular therapy before major surgery can have adverse effects. While ACE inhibitor withdrawal has not shown adverse physiological effects in the perioperative setting, it has led to rebound myocardial ischemia in patients with prior myocardial infarction.⁶

Given this controversy, there is variation across hospitals¹ and practice guidelines with respect to perioperative management of renin-angiotensin inhibitors. For example, the 2017 Canadian Cardiovascular Society guidelines recommend that renin-angiotensin inhibitors be stopped temporarily 24 hours before major inpatient surgery,⁷ and the 2014 European guidelines recommend continuing therapy in patients with HFrEF but temporarily interrupting therapy in patients with hypertension.⁸ The 2014 American Heart Association and American College of Cardiology guidelines suggest that either continuation or interruption are reasonable options, but any interrupted therapy should be restarted postoperatively as soon as clinically feasible.⁹

In this issue of the Journal of Hospital Medicine, Shiffermiller and colleagues present a single-center RCT that provides additional high-quality data to improve our understanding of this important clinical issue.¹⁰ In a sample of 275 patients undergoing nonvascular inpatient noncardiac surgery, omission of the final dose of preoperative ACE inhibitor therapy reduced the risk of intraoperative hypotension across multiple definitions, including any episode of systolic blood pressure less than 80 mmHg (number needed to treat: 8), any episode of a systolic blood pressure less than 80 mmHg necessitating vasopressor therapy (number needed to treat: 6), and total cumulative duration of intraoperative systolic blood pressure less than 80 mmHg. In addition, the investigators found that preoperative interruption of ACE inhibitor therapy reduced the risk of postoperative hypotension (number needed to treat: 9), increased the risk of severe postoperative hypertension (number needed to harm: 9), and had no effect on clinical outcomes (eg, acute kidney injury, major adverse cardiac events). In conjunction with a recent systematic review,² these new data demonstrate that temporary preoperative discontinuation of renin-angiotensin inhibitors leads to reduced risks of intraoperative and postoperative hypotension, with the only major identified risk being episodes of postoperative hypertension.

This current evidence base suggests that, in most cases, perioperative physicians should temporarily interrupt renin-angiotensin inhibitor therapy before inpatient noncardiac surgery, provided that protocols are in place to resume treatment postoperatively as soon as clinically feasible. Nonetheless, clinicians must also be cognizant of the key limitations to current data, namely that hypotension, be it intraoperative or postoperative, remains essentially a surrogate endpoint.^11,12 Stated otherwise, the clinical importance of perioperative hypotension is largely predicated on its close association with clinically important or patient-relevant outcomes such as cardiovascular complications, acute kidney injury, and death.^13–16 There is an implicit assumption that a reduction in the risk of hypotension will necessarily lead to reduced rates of clinical adverse events. This assumption is unlikely to be true, especially since many different underlying mechanisms lead to hypotension in the dynamic perioperative environment, including decreased cardiac contractility, decreased heart rate, decreased intravascular volume status, and vasodilation. Consistent with this possibility, different perioperative interventions with similar effects on hypotension have shown quite different effects on clinical outcomes. For example, epidural analgesia invariably reduces perioperative blood pressure, yet it does not appear to increase the risk of postoperative complications.¹⁷ Similarly, both beta-blockers and clonidine increase the risk of significant perioperative hypotension and bradycardia, yet only beta-blockers appear to lead to increased rates of mortality after noncardiac surgery.^18,19 Thus, the relationship between perioperative hypotension and outcomes is clearly complex. Unless a RCT demonstrates that a hypotension-reduction strategy leads to an improvement in clinical outcomes,²⁰ perioperative physicians should not assume that prevention of hypotension will always lead to improvements in patient-relevant clinical outcomes. Similar assumptions about other surrogate endpoints in cardiovascular medicine have sometimes been spectacularly incorrect.^12,21 To more definitively address this important clinical issue, RCTs must be specifically designed to compare the effects of renin-angiotensin inhibitor therapy withdrawal versus continuation on patient-relevant and clinically important outcomes, such as death, myocardial infarction, and stroke. Fortunately, some ongoing trials will address this question, either directly (ClinicalTrials.gov NCT03374449) or as a component of a hypotension-avoidance strategy (ClinicalTrials.gov NCT03505723).

Overall, perioperative physicians should now adopt the standard approach of temporarily withdrawing renin-angiotensin inhibitor therapy 24 hours before major inpatient noncardiac surgery. Nonetheless, they should do so cautiously, recognizing that the data underpinning this strategy remain weak. As with many aspects of perioperative medicine, more research remains needed.

Disclosures

The authors have nothing to report.Funding: DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research, and a Merit Award from the Department of Anesthesia at the University of Toronto.

Renin-angiotensin inhibitors, which include angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers (ARBs), have demonstrated benefits in the treatment of several common cardiovascular and renal conditions. For example, they are prescribed to individuals with hypertension, heart failure with reduced ejection fraction (HFrEF), prior myocardial infarction, and chronic kidney disease with proteinuria. Perhaps unsurprisingly, many individuals presenting for surgery are already on long-term ACE inhibitor or ARB therapy. For example, such individuals comprised approximately one-third of the sample in the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) multicenter prospective cohort study of major inpatient noncardiac surgery.¹

There is considerable controversy regarding how best to manage these cardiovascular medications during the perioperative period. The critical question pertains to whether renin-angiotensin inhibitors should be temporarily withdrawn 24 hours before surgery or continued uninterrupted up to the day of surgery. The main argument for withdrawing these medications is concern that they cause perioperative hypotension. For example, a recent systematic review of randomized controlled trials (RCTs) and cohort studies found that preoperative continuation of renin-angiotensin inhibitor therapy led to a significantly increased risk of intraoperative hypotension, albeit without associated effects on rates of death, major adverse cardiac events, or postoperative hypotension.² Notably, randomized trial evidence in this meta-analysis was limited to only five trials with a total of 774 participants. Conversely, preoperative interruption of renin-angiotensin inhibitor therapy also has risks. For example, there is a potential for unintended permanent discontinuation of medications with long-term benefits.³ Furthermore, some prior cohort studies have demonstrated that the failure to resume renin-angiotensin inhibitor therapy promptly after surgery is associated with an elevated risk of postoperative mortality.^4,5 While these studies have methodological limitations related to survivorship bias and unmeasured confounders, they still raise concerns that the abrupt withdrawal of long-term cardiovascular therapy before major surgery can have adverse effects. While ACE inhibitor withdrawal has not shown adverse physiological effects in the perioperative setting, it has led to rebound myocardial ischemia in patients with prior myocardial infarction.⁶

Given this controversy, there is variation across hospitals¹ and practice guidelines with respect to perioperative management of renin-angiotensin inhibitors. For example, the 2017 Canadian Cardiovascular Society guidelines recommend that renin-angiotensin inhibitors be stopped temporarily 24 hours before major inpatient surgery,⁷ and the 2014 European guidelines recommend continuing therapy in patients with HFrEF but temporarily interrupting therapy in patients with hypertension.⁸ The 2014 American Heart Association and American College of Cardiology guidelines suggest that either continuation or interruption are reasonable options, but any interrupted therapy should be restarted postoperatively as soon as clinically feasible.⁹

In this issue of the Journal of Hospital Medicine, Shiffermiller and colleagues present a single-center RCT that provides additional high-quality data to improve our understanding of this important clinical issue.¹⁰ In a sample of 275 patients undergoing nonvascular inpatient noncardiac surgery, omission of the final dose of preoperative ACE inhibitor therapy reduced the risk of intraoperative hypotension across multiple definitions, including any episode of systolic blood pressure less than 80 mmHg (number needed to treat: 8), any episode of a systolic blood pressure less than 80 mmHg necessitating vasopressor therapy (number needed to treat: 6), and total cumulative duration of intraoperative systolic blood pressure less than 80 mmHg. In addition, the investigators found that preoperative interruption of ACE inhibitor therapy reduced the risk of postoperative hypotension (number needed to treat: 9), increased the risk of severe postoperative hypertension (number needed to harm: 9), and had no effect on clinical outcomes (eg, acute kidney injury, major adverse cardiac events). In conjunction with a recent systematic review,² these new data demonstrate that temporary preoperative discontinuation of renin-angiotensin inhibitors leads to reduced risks of intraoperative and postoperative hypotension, with the only major identified risk being episodes of postoperative hypertension.

This current evidence base suggests that, in most cases, perioperative physicians should temporarily interrupt renin-angiotensin inhibitor therapy before inpatient noncardiac surgery, provided that protocols are in place to resume treatment postoperatively as soon as clinically feasible. Nonetheless, clinicians must also be cognizant of the key limitations to current data, namely that hypotension, be it intraoperative or postoperative, remains essentially a surrogate endpoint.^11,12 Stated otherwise, the clinical importance of perioperative hypotension is largely predicated on its close association with clinically important or patient-relevant outcomes such as cardiovascular complications, acute kidney injury, and death.^13–16 There is an implicit assumption that a reduction in the risk of hypotension will necessarily lead to reduced rates of clinical adverse events. This assumption is unlikely to be true, especially since many different underlying mechanisms lead to hypotension in the dynamic perioperative environment, including decreased cardiac contractility, decreased heart rate, decreased intravascular volume status, and vasodilation. Consistent with this possibility, different perioperative interventions with similar effects on hypotension have shown quite different effects on clinical outcomes. For example, epidural analgesia invariably reduces perioperative blood pressure, yet it does not appear to increase the risk of postoperative complications.¹⁷ Similarly, both beta-blockers and clonidine increase the risk of significant perioperative hypotension and bradycardia, yet only beta-blockers appear to lead to increased rates of mortality after noncardiac surgery.^18,19 Thus, the relationship between perioperative hypotension and outcomes is clearly complex. Unless a RCT demonstrates that a hypotension-reduction strategy leads to an improvement in clinical outcomes,²⁰ perioperative physicians should not assume that prevention of hypotension will always lead to improvements in patient-relevant clinical outcomes. Similar assumptions about other surrogate endpoints in cardiovascular medicine have sometimes been spectacularly incorrect.^12,21 To more definitively address this important clinical issue, RCTs must be specifically designed to compare the effects of renin-angiotensin inhibitor therapy withdrawal versus continuation on patient-relevant and clinically important outcomes, such as death, myocardial infarction, and stroke. Fortunately, some ongoing trials will address this question, either directly (ClinicalTrials.gov NCT03374449) or as a component of a hypotension-avoidance strategy (ClinicalTrials.gov NCT03505723).

Overall, perioperative physicians should now adopt the standard approach of temporarily withdrawing renin-angiotensin inhibitor therapy 24 hours before major inpatient noncardiac surgery. Nonetheless, they should do so cautiously, recognizing that the data underpinning this strategy remain weak. As with many aspects of perioperative medicine, more research remains needed.

Disclosures

The authors have nothing to report.Funding: DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research, and a Merit Award from the Department of Anesthesia at the University of Toronto.

Patient experience and 30-day readmission are important measures of quality of care for hospitalized patients. Performance on both of these measures impact hospitals financially. Performance on the Hospital Consumer Assessment of Healthcare Systems and Providers (HCAHPS) survey is linked to 25% of the incentive payment under Value Based Purchasing (VBP) Program.¹ Starting in 2012, the Centers for Medicare and Medicaid Services (CMS) introduced the Readmission Reduction Program, penalizing hospitals financially for excessive readmissions.²

A relationship between patient experience and readmissions has been explored at the hospital level. Studies have mostly found that higher patient experience scores are associated with lower 30-day readmission rates. In a study of the relationship between 30-day risk-standardized readmission rates for three medical conditions (acute myocardial infarction, heart failure, and pneumonia) and patient experience, the authors noted that higher experience scores for overall care and discharge planning were associated with lower readmission rates for these conditions. They also concluded that patient experience scores were more predictive of 30-day readmission than clinical performance measures. Additionally, the authors predicted that if a hospital increased its total experience scores from the 25th percentile to the 75th percentile, there would be an associated decrease in readmissions by at least 2.3% for each of these conditions.³ Practice management companies and the media have cited this finding to conclude that higher patient experience drives clinical outcomes such as 30-day readmission and that patients are often the best judges of the quality of care delivered.^4,5

Other hospital-level studies have found that high 30-day readmission rates are associated with lower overall experience scores in a mixed surgical patient population; worse reports of pain control and overall care in the colorectal surgery population; lower experience scores with discharge preparedness in vascular surgery patients; and lower experience scores with physician communication, nurse communication, and discharge preparedness.^6-9 A patient-level study noted higher readmissions are associated with worse experience with physician and nursing communication along with a paradoxically better experience with discharge information.¹⁰

Because these studies used an observational design, they demonstrated associations rather than causality. An alternative hypothesis is that readmitted patients complete their patient experience survey after readmission and the low experience is the result, rather than the cause, of their readmission. For patients who are readmitted, it is unclear whether there is an opportunity to complete the survey prior to readmission and whether being readmitted may impact patient perception of quality of care. Using patient-level data, we sought to assess HCAHPS patient-experience responses linked to the index admission of the patients who were readmitted in 30 days and compare it with those patients who were not readmitted during this time period. We paid particular attention to when the surveys were returned.

Study Design

We conducted a retrospective analysis of prospectively collected 10-year HCAHPS and Press Ganey patient survey data for a single tertiary care academic hospital.

Participants

All adult patients discharged from the hospital and who responded to the routinely sent patient-experience survey were included. Surveys were sent to a random sample of 50% of the discharged patients.

The exposure group was comprised of patients who responded to the survey and were readmitted within 30 days of discharge. After subtracting 5 days from the survey receipt date for expected delays related to mail delivery time and processing time, survey response date was calculated. The exposure group was further divided into patients who responded to the survey prior to their 30-day readmission (“Pre-readmission responders”) and those that responded to the survey after their readmission (“Postreadmission responders”). A sensitivity analysis was performed by changing the number of days subtracted from the survey receipt date by 2 days in either direction. This approach did not result in any significant changes in the results.

The control group comprised patients who were not readmitted to the hospital within 30 days of discharge and who did not have an admission in the previous 30 days as well (“Not readmitted” group). An additional comparison group for exploratory analysis included patients who had experienced an admission in the prior 30 days but were not readmitted after the admission linked to the survey. These patients responded to the patient-experience surveys that were linked to their second admission in 30 days (“2^nd-admission responders” group; Figure).

Time Periods

All survey responders from the third quarter of 2006 to the first quarter of 2016 were included in the study. Additionally, administrative data on non-responders were available from 7/2006 to 8/2012. These data were used to estimate response rates. Patient level experience and administrative data were obtained in a linked fashion for these time periods.

Instruments

Press Ganey and HCAHPS surveys were sent via mail in the same envelope. Fifty percent of the discharged patients were randomized to receive the surveys. The Press Ganey survey contained 33 items encompassing several subdomains, including room, meal, nursing, physician, ancillary staff, visitor, discharge, and overall experience.

The HCAHPS survey contained 29 CMS-mandated items, of which 21 are related to patient experience. The development, testing, and methods for administration and reporting of the HCAHPS survey have been previously described and studies using this instrument have been reported in the literature.¹¹ Press Ganey patient satisfaction survey results have also been reported in the literature.¹²

Outcome Variables and Covariates

HCAHPS and Press Ganey experience survey individual item responses were the primary outcome variables of this study. Age, self-reported health status, education, primary language spoken, service line, and time taken to respond to the surveys served as the covariates. These variables are used by CMS for patient-mix adjustment and are collected on the HCAHPS survey. Additionally, the number of days to respond to the survey were included in all regression analysis to adjust for early responder effect.^13-15

Statistical Analysis

“Percent top-box” scores were calculated for each survey item for patients in each group. The percent top-box scores were calculated as the percent of patients who responded “very good” for a given item on Press Ganey survey items and “always” or “definitely yes” or “yes” or “9” or “10” on HCAHPS survey items. CMS utilizes “percent top-box scores” to calculate payments under the VBP program and to report the results publicly. Numerous studies have also reported percent top-box scores for HCAHPS survey results.¹²

We hypothesized that whether patients complete the HCAHPS survey before or after the readmission influences their reporting of experience. To test this hypothesis, HCAHPS and Press Ganey item top-box scores of “Pre-readmission responders” and “Postreadmission responders” were compared with those of the control group using multivariate logistic regression. “Pre-readmission responders” were also compared with “Postreadmission responders”.

“2nd-admission responders” were similarly compared with the control group for an exploratory analysis. Finally, “Postreadmission responders” and “2nd-admission responders” were compared in another exploratory analysis since both these groups responded to the survey after being exposed to the readmission, even though the “Postreadmission responders” group is administratively linked to the index admission.

The Johns Hopkins Institutional Review Board approved this study.

There were 43,737 survey responders, among whom 4,707 were subsequently readmitted within 30 days of discharge. Among the readmitted patients who responded to the surveys linked to their index admission, only 15.8% returned the survey before readmission (pre-readmission responders’) and 84.2% returned the survey after readmission (postreadmission responders). Additionally, 1,663 patients responded to experience surveys linked to their readmission. There were 37,365 patients in the control arm (ie, patients who responded to the survey and were not readmitted within 30 days of discharge or in the prior 30 days; Figure 1). The readmission rate among survey responders was 10.6%. Among the readmitted patients, the median number of days to readmission was 10 days while the median number of days to respond to the survey for this group was 33 days. Among the nonreadmitted patients, the median number of days to return the survey was 29 days.

In this retrospective analysis of prospectively collected Press Ganey and HCAHPS patient-experience survey data, we found that the overwhelming majority of patients readmitted within 30 days of discharge respond to HCAHPS surveys after readmission even though the survey is sent linked to the first admission. This is not unexpected since the median time to survey response is 33 days for this group, while median time to readmission is 10 days. The dissatisfaction pattern of Postreadmission responders was similar to those who responded to the survey linked to the readmission. When a patient is readmitted prior to completing the survey, their responses appear to reflect the cumulative experience of the index admission and the readmission. The lower scores of those who respond to the survey after their readmission appear to be a driver for lower patient-experience scores related to readmissions. Overall, readmission was associated with lower scores on items in five of the nine domains used to calculate patient experience related payments under VBP.¹⁶

These findings have important implications in inferring the direction of potential causal relationship between readmissions and patient experience at the hospital level. Additionally, these patients show broad dissatisfaction with areas beyond physician communication and discharge planning. These include staff responsiveness, phlebotomy, meals, hospital cleanliness, and noise level. This pattern of dissatisfaction may represent impatience and frustration with spending additional time in the hospital environment.

Our results are consistent with findings of many of the earlier studies, but our study goes a step further by using patient-level data and incorporating survey response time in our analysis.^3,7,9,10 By separating out the readmitted patients who responded to the survey prior to admission, we attempted to address the ability of patients’ perception of care to predict future readmissions. Our results do not support this idea, since pre-readmission responders had similar experience scores to non-readmitted patients. However, because of the low numbers of pre-readmission responders, the comparison lacks precision. Current HCAHPS and Press Ganey questions may lack the ability to predict future readmissions because of the timing of the survey (postdischarge) or the questions themselves.

Overall, postreadmission responders are dissatisfied with multiple domains of hospital care. Many of these survey responses may simply be related to general frustration. Alternatively, they may represent a patient population with a high degree of needs that are not as easily met by a hospital’s routine processes of care. Even though the readmission rates were 10.6% among survey responders, 14.6% of the survey responses were associated with readmissions after accounting for those who respond to surveys linked to readmission. These patients could have significant impact on cumulative experience scores.

Our study has a few limitations. First, it involves a single tertiary care academic center study, and our results may not be generalizable. Second, we did not adjust for some of the patient characteristics associated with readmissions. Patients who were admitted within 30 days are different than those not readmitted based on payor, race, length of stay, and severity of illness, and we did not adjust for these factors in our analysis. This was intentional, however. Our goal was to better understand the relationship between 30-day readmission and patient experience scores as they are used for hospital-level studies, VBP, and public reporting. For these purposes, the scores are not adjusted for factors, such as payor and length of stay. We did adjust for patient-mix adjustment factors used by CMS. Third, the response rates to the HCAHPS were low and may have biased the scores. However, HCAHPS is widely used for comparisons between hospitals has been validated, and our study results have implications with regard to comparing hospital-level performance. HCAHPS results are relevant to policy and have financial consequences.¹⁷ Fourth, our study did not directly compare whether the relationship between patient experience for the postreadmission group and nonreadmitted group was different from the relationship between the pre-readmission group and postreadmission group. It is possible that there is no difference in relationship between the groups. However, despite the small number of pre-readmission responders, these patients tended to have more favorable experience responses than those who responded after being readmitted, even after adjusting for response time. Although the P values are nonsignificant for many comparisons, the directionality of the effect is relatively consistent. Also, the vast majority of the patients fall in the postreadmission group, and these patients appear to drive the overall experience related to readmissions. Finally, since relatively few patients turned in surveys prior to readmission, we had limited power to detect a significant difference between these pre-readmission responders and nonreadmitted patients.

Our study has implications for policy makers, researchers, and providers. The HCAHPS scores of patients who are readmitted and completed the survey after being readmitted reflects their experience of both the index admission and the readmission. We did not find evidence to support that HCAHPS survey responses predict future readmissions at the patient level. Our findings do support the concept that lower readmissions rates (whether due to the patient population or processes of care that decrease readmission rates) may improve HCAHPS scores. We suggest caution in assuming that improving patient experience is likely to reduce readmission rates.

Disclosures

The authors declare no conflicts of interest.

Patient experience and 30-day readmission are important measures of quality of care for hospitalized patients. Performance on both of these measures impact hospitals financially. Performance on the Hospital Consumer Assessment of Healthcare Systems and Providers (HCAHPS) survey is linked to 25% of the incentive payment under Value Based Purchasing (VBP) Program.¹ Starting in 2012, the Centers for Medicare and Medicaid Services (CMS) introduced the Readmission Reduction Program, penalizing hospitals financially for excessive readmissions.²

A relationship between patient experience and readmissions has been explored at the hospital level. Studies have mostly found that higher patient experience scores are associated with lower 30-day readmission rates. In a study of the relationship between 30-day risk-standardized readmission rates for three medical conditions (acute myocardial infarction, heart failure, and pneumonia) and patient experience, the authors noted that higher experience scores for overall care and discharge planning were associated with lower readmission rates for these conditions. They also concluded that patient experience scores were more predictive of 30-day readmission than clinical performance measures. Additionally, the authors predicted that if a hospital increased its total experience scores from the 25th percentile to the 75th percentile, there would be an associated decrease in readmissions by at least 2.3% for each of these conditions.³ Practice management companies and the media have cited this finding to conclude that higher patient experience drives clinical outcomes such as 30-day readmission and that patients are often the best judges of the quality of care delivered.^4,5

Other hospital-level studies have found that high 30-day readmission rates are associated with lower overall experience scores in a mixed surgical patient population; worse reports of pain control and overall care in the colorectal surgery population; lower experience scores with discharge preparedness in vascular surgery patients; and lower experience scores with physician communication, nurse communication, and discharge preparedness.^6-9 A patient-level study noted higher readmissions are associated with worse experience with physician and nursing communication along with a paradoxically better experience with discharge information.¹⁰

Because these studies used an observational design, they demonstrated associations rather than causality. An alternative hypothesis is that readmitted patients complete their patient experience survey after readmission and the low experience is the result, rather than the cause, of their readmission. For patients who are readmitted, it is unclear whether there is an opportunity to complete the survey prior to readmission and whether being readmitted may impact patient perception of quality of care. Using patient-level data, we sought to assess HCAHPS patient-experience responses linked to the index admission of the patients who were readmitted in 30 days and compare it with those patients who were not readmitted during this time period. We paid particular attention to when the surveys were returned.

Study Design

We conducted a retrospective analysis of prospectively collected 10-year HCAHPS and Press Ganey patient survey data for a single tertiary care academic hospital.

Participants

All adult patients discharged from the hospital and who responded to the routinely sent patient-experience survey were included. Surveys were sent to a random sample of 50% of the discharged patients.

The exposure group was comprised of patients who responded to the survey and were readmitted within 30 days of discharge. After subtracting 5 days from the survey receipt date for expected delays related to mail delivery time and processing time, survey response date was calculated. The exposure group was further divided into patients who responded to the survey prior to their 30-day readmission (“Pre-readmission responders”) and those that responded to the survey after their readmission (“Postreadmission responders”). A sensitivity analysis was performed by changing the number of days subtracted from the survey receipt date by 2 days in either direction. This approach did not result in any significant changes in the results.

The control group comprised patients who were not readmitted to the hospital within 30 days of discharge and who did not have an admission in the previous 30 days as well (“Not readmitted” group). An additional comparison group for exploratory analysis included patients who had experienced an admission in the prior 30 days but were not readmitted after the admission linked to the survey. These patients responded to the patient-experience surveys that were linked to their second admission in 30 days (“2^nd-admission responders” group; Figure).

Time Periods

All survey responders from the third quarter of 2006 to the first quarter of 2016 were included in the study. Additionally, administrative data on non-responders were available from 7/2006 to 8/2012. These data were used to estimate response rates. Patient level experience and administrative data were obtained in a linked fashion for these time periods.

Instruments

Press Ganey and HCAHPS surveys were sent via mail in the same envelope. Fifty percent of the discharged patients were randomized to receive the surveys. The Press Ganey survey contained 33 items encompassing several subdomains, including room, meal, nursing, physician, ancillary staff, visitor, discharge, and overall experience.

The HCAHPS survey contained 29 CMS-mandated items, of which 21 are related to patient experience. The development, testing, and methods for administration and reporting of the HCAHPS survey have been previously described and studies using this instrument have been reported in the literature.¹¹ Press Ganey patient satisfaction survey results have also been reported in the literature.¹²

Outcome Variables and Covariates

HCAHPS and Press Ganey experience survey individual item responses were the primary outcome variables of this study. Age, self-reported health status, education, primary language spoken, service line, and time taken to respond to the surveys served as the covariates. These variables are used by CMS for patient-mix adjustment and are collected on the HCAHPS survey. Additionally, the number of days to respond to the survey were included in all regression analysis to adjust for early responder effect.^13-15

Statistical Analysis

“Percent top-box” scores were calculated for each survey item for patients in each group. The percent top-box scores were calculated as the percent of patients who responded “very good” for a given item on Press Ganey survey items and “always” or “definitely yes” or “yes” or “9” or “10” on HCAHPS survey items. CMS utilizes “percent top-box scores” to calculate payments under the VBP program and to report the results publicly. Numerous studies have also reported percent top-box scores for HCAHPS survey results.¹²

We hypothesized that whether patients complete the HCAHPS survey before or after the readmission influences their reporting of experience. To test this hypothesis, HCAHPS and Press Ganey item top-box scores of “Pre-readmission responders” and “Postreadmission responders” were compared with those of the control group using multivariate logistic regression. “Pre-readmission responders” were also compared with “Postreadmission responders”.

“2nd-admission responders” were similarly compared with the control group for an exploratory analysis. Finally, “Postreadmission responders” and “2nd-admission responders” were compared in another exploratory analysis since both these groups responded to the survey after being exposed to the readmission, even though the “Postreadmission responders” group is administratively linked to the index admission.

The Johns Hopkins Institutional Review Board approved this study.

There were 43,737 survey responders, among whom 4,707 were subsequently readmitted within 30 days of discharge. Among the readmitted patients who responded to the surveys linked to their index admission, only 15.8% returned the survey before readmission (pre-readmission responders’) and 84.2% returned the survey after readmission (postreadmission responders). Additionally, 1,663 patients responded to experience surveys linked to their readmission. There were 37,365 patients in the control arm (ie, patients who responded to the survey and were not readmitted within 30 days of discharge or in the prior 30 days; Figure 1). The readmission rate among survey responders was 10.6%. Among the readmitted patients, the median number of days to readmission was 10 days while the median number of days to respond to the survey for this group was 33 days. Among the nonreadmitted patients, the median number of days to return the survey was 29 days.

In this retrospective analysis of prospectively collected Press Ganey and HCAHPS patient-experience survey data, we found that the overwhelming majority of patients readmitted within 30 days of discharge respond to HCAHPS surveys after readmission even though the survey is sent linked to the first admission. This is not unexpected since the median time to survey response is 33 days for this group, while median time to readmission is 10 days. The dissatisfaction pattern of Postreadmission responders was similar to those who responded to the survey linked to the readmission. When a patient is readmitted prior to completing the survey, their responses appear to reflect the cumulative experience of the index admission and the readmission. The lower scores of those who respond to the survey after their readmission appear to be a driver for lower patient-experience scores related to readmissions. Overall, readmission was associated with lower scores on items in five of the nine domains used to calculate patient experience related payments under VBP.¹⁶

These findings have important implications in inferring the direction of potential causal relationship between readmissions and patient experience at the hospital level. Additionally, these patients show broad dissatisfaction with areas beyond physician communication and discharge planning. These include staff responsiveness, phlebotomy, meals, hospital cleanliness, and noise level. This pattern of dissatisfaction may represent impatience and frustration with spending additional time in the hospital environment.

Our results are consistent with findings of many of the earlier studies, but our study goes a step further by using patient-level data and incorporating survey response time in our analysis.^3,7,9,10 By separating out the readmitted patients who responded to the survey prior to admission, we attempted to address the ability of patients’ perception of care to predict future readmissions. Our results do not support this idea, since pre-readmission responders had similar experience scores to non-readmitted patients. However, because of the low numbers of pre-readmission responders, the comparison lacks precision. Current HCAHPS and Press Ganey questions may lack the ability to predict future readmissions because of the timing of the survey (postdischarge) or the questions themselves.

Overall, postreadmission responders are dissatisfied with multiple domains of hospital care. Many of these survey responses may simply be related to general frustration. Alternatively, they may represent a patient population with a high degree of needs that are not as easily met by a hospital’s routine processes of care. Even though the readmission rates were 10.6% among survey responders, 14.6% of the survey responses were associated with readmissions after accounting for those who respond to surveys linked to readmission. These patients could have significant impact on cumulative experience scores.

Our study has a few limitations. First, it involves a single tertiary care academic center study, and our results may not be generalizable. Second, we did not adjust for some of the patient characteristics associated with readmissions. Patients who were admitted within 30 days are different than those not readmitted based on payor, race, length of stay, and severity of illness, and we did not adjust for these factors in our analysis. This was intentional, however. Our goal was to better understand the relationship between 30-day readmission and patient experience scores as they are used for hospital-level studies, VBP, and public reporting. For these purposes, the scores are not adjusted for factors, such as payor and length of stay. We did adjust for patient-mix adjustment factors used by CMS. Third, the response rates to the HCAHPS were low and may have biased the scores. However, HCAHPS is widely used for comparisons between hospitals has been validated, and our study results have implications with regard to comparing hospital-level performance. HCAHPS results are relevant to policy and have financial consequences.¹⁷ Fourth, our study did not directly compare whether the relationship between patient experience for the postreadmission group and nonreadmitted group was different from the relationship between the pre-readmission group and postreadmission group. It is possible that there is no difference in relationship between the groups. However, despite the small number of pre-readmission responders, these patients tended to have more favorable experience responses than those who responded after being readmitted, even after adjusting for response time. Although the P values are nonsignificant for many comparisons, the directionality of the effect is relatively consistent. Also, the vast majority of the patients fall in the postreadmission group, and these patients appear to drive the overall experience related to readmissions. Finally, since relatively few patients turned in surveys prior to readmission, we had limited power to detect a significant difference between these pre-readmission responders and nonreadmitted patients.

Our study has implications for policy makers, researchers, and providers. The HCAHPS scores of patients who are readmitted and completed the survey after being readmitted reflects their experience of both the index admission and the readmission. We did not find evidence to support that HCAHPS survey responses predict future readmissions at the patient level. Our findings do support the concept that lower readmissions rates (whether due to the patient population or processes of care that decrease readmission rates) may improve HCAHPS scores. We suggest caution in assuming that improving patient experience is likely to reduce readmission rates.

Disclosures

The authors declare no conflicts of interest.

Patient experience and 30-day readmission are important measures of quality of care for hospitalized patients. Performance on both of these measures impact hospitals financially. Performance on the Hospital Consumer Assessment of Healthcare Systems and Providers (HCAHPS) survey is linked to 25% of the incentive payment under Value Based Purchasing (VBP) Program.¹ Starting in 2012, the Centers for Medicare and Medicaid Services (CMS) introduced the Readmission Reduction Program, penalizing hospitals financially for excessive readmissions.²

A relationship between patient experience and readmissions has been explored at the hospital level. Studies have mostly found that higher patient experience scores are associated with lower 30-day readmission rates. In a study of the relationship between 30-day risk-standardized readmission rates for three medical conditions (acute myocardial infarction, heart failure, and pneumonia) and patient experience, the authors noted that higher experience scores for overall care and discharge planning were associated with lower readmission rates for these conditions. They also concluded that patient experience scores were more predictive of 30-day readmission than clinical performance measures. Additionally, the authors predicted that if a hospital increased its total experience scores from the 25th percentile to the 75th percentile, there would be an associated decrease in readmissions by at least 2.3% for each of these conditions.³ Practice management companies and the media have cited this finding to conclude that higher patient experience drives clinical outcomes such as 30-day readmission and that patients are often the best judges of the quality of care delivered.^4,5

Other hospital-level studies have found that high 30-day readmission rates are associated with lower overall experience scores in a mixed surgical patient population; worse reports of pain control and overall care in the colorectal surgery population; lower experience scores with discharge preparedness in vascular surgery patients; and lower experience scores with physician communication, nurse communication, and discharge preparedness.^6-9 A patient-level study noted higher readmissions are associated with worse experience with physician and nursing communication along with a paradoxically better experience with discharge information.¹⁰

Because these studies used an observational design, they demonstrated associations rather than causality. An alternative hypothesis is that readmitted patients complete their patient experience survey after readmission and the low experience is the result, rather than the cause, of their readmission. For patients who are readmitted, it is unclear whether there is an opportunity to complete the survey prior to readmission and whether being readmitted may impact patient perception of quality of care. Using patient-level data, we sought to assess HCAHPS patient-experience responses linked to the index admission of the patients who were readmitted in 30 days and compare it with those patients who were not readmitted during this time period. We paid particular attention to when the surveys were returned.

Study Design

We conducted a retrospective analysis of prospectively collected 10-year HCAHPS and Press Ganey patient survey data for a single tertiary care academic hospital.

Participants

All adult patients discharged from the hospital and who responded to the routinely sent patient-experience survey were included. Surveys were sent to a random sample of 50% of the discharged patients.

The exposure group was comprised of patients who responded to the survey and were readmitted within 30 days of discharge. After subtracting 5 days from the survey receipt date for expected delays related to mail delivery time and processing time, survey response date was calculated. The exposure group was further divided into patients who responded to the survey prior to their 30-day readmission (“Pre-readmission responders”) and those that responded to the survey after their readmission (“Postreadmission responders”). A sensitivity analysis was performed by changing the number of days subtracted from the survey receipt date by 2 days in either direction. This approach did not result in any significant changes in the results.

The control group comprised patients who were not readmitted to the hospital within 30 days of discharge and who did not have an admission in the previous 30 days as well (“Not readmitted” group). An additional comparison group for exploratory analysis included patients who had experienced an admission in the prior 30 days but were not readmitted after the admission linked to the survey. These patients responded to the patient-experience surveys that were linked to their second admission in 30 days (“2^nd-admission responders” group; Figure).

Time Periods

All survey responders from the third quarter of 2006 to the first quarter of 2016 were included in the study. Additionally, administrative data on non-responders were available from 7/2006 to 8/2012. These data were used to estimate response rates. Patient level experience and administrative data were obtained in a linked fashion for these time periods.

Instruments

Press Ganey and HCAHPS surveys were sent via mail in the same envelope. Fifty percent of the discharged patients were randomized to receive the surveys. The Press Ganey survey contained 33 items encompassing several subdomains, including room, meal, nursing, physician, ancillary staff, visitor, discharge, and overall experience.

The HCAHPS survey contained 29 CMS-mandated items, of which 21 are related to patient experience. The development, testing, and methods for administration and reporting of the HCAHPS survey have been previously described and studies using this instrument have been reported in the literature.¹¹ Press Ganey patient satisfaction survey results have also been reported in the literature.¹²

Outcome Variables and Covariates

HCAHPS and Press Ganey experience survey individual item responses were the primary outcome variables of this study. Age, self-reported health status, education, primary language spoken, service line, and time taken to respond to the surveys served as the covariates. These variables are used by CMS for patient-mix adjustment and are collected on the HCAHPS survey. Additionally, the number of days to respond to the survey were included in all regression analysis to adjust for early responder effect.^13-15

Statistical Analysis

“Percent top-box” scores were calculated for each survey item for patients in each group. The percent top-box scores were calculated as the percent of patients who responded “very good” for a given item on Press Ganey survey items and “always” or “definitely yes” or “yes” or “9” or “10” on HCAHPS survey items. CMS utilizes “percent top-box scores” to calculate payments under the VBP program and to report the results publicly. Numerous studies have also reported percent top-box scores for HCAHPS survey results.¹²

We hypothesized that whether patients complete the HCAHPS survey before or after the readmission influences their reporting of experience. To test this hypothesis, HCAHPS and Press Ganey item top-box scores of “Pre-readmission responders” and “Postreadmission responders” were compared with those of the control group using multivariate logistic regression. “Pre-readmission responders” were also compared with “Postreadmission responders”.

“2nd-admission responders” were similarly compared with the control group for an exploratory analysis. Finally, “Postreadmission responders” and “2nd-admission responders” were compared in another exploratory analysis since both these groups responded to the survey after being exposed to the readmission, even though the “Postreadmission responders” group is administratively linked to the index admission.

The Johns Hopkins Institutional Review Board approved this study.

There were 43,737 survey responders, among whom 4,707 were subsequently readmitted within 30 days of discharge. Among the readmitted patients who responded to the surveys linked to their index admission, only 15.8% returned the survey before readmission (pre-readmission responders’) and 84.2% returned the survey after readmission (postreadmission responders). Additionally, 1,663 patients responded to experience surveys linked to their readmission. There were 37,365 patients in the control arm (ie, patients who responded to the survey and were not readmitted within 30 days of discharge or in the prior 30 days; Figure 1). The readmission rate among survey responders was 10.6%. Among the readmitted patients, the median number of days to readmission was 10 days while the median number of days to respond to the survey for this group was 33 days. Among the nonreadmitted patients, the median number of days to return the survey was 29 days.

In this retrospective analysis of prospectively collected Press Ganey and HCAHPS patient-experience survey data, we found that the overwhelming majority of patients readmitted within 30 days of discharge respond to HCAHPS surveys after readmission even though the survey is sent linked to the first admission. This is not unexpected since the median time to survey response is 33 days for this group, while median time to readmission is 10 days. The dissatisfaction pattern of Postreadmission responders was similar to those who responded to the survey linked to the readmission. When a patient is readmitted prior to completing the survey, their responses appear to reflect the cumulative experience of the index admission and the readmission. The lower scores of those who respond to the survey after their readmission appear to be a driver for lower patient-experience scores related to readmissions. Overall, readmission was associated with lower scores on items in five of the nine domains used to calculate patient experience related payments under VBP.¹⁶

These findings have important implications in inferring the direction of potential causal relationship between readmissions and patient experience at the hospital level. Additionally, these patients show broad dissatisfaction with areas beyond physician communication and discharge planning. These include staff responsiveness, phlebotomy, meals, hospital cleanliness, and noise level. This pattern of dissatisfaction may represent impatience and frustration with spending additional time in the hospital environment.

Our results are consistent with findings of many of the earlier studies, but our study goes a step further by using patient-level data and incorporating survey response time in our analysis.^3,7,9,10 By separating out the readmitted patients who responded to the survey prior to admission, we attempted to address the ability of patients’ perception of care to predict future readmissions. Our results do not support this idea, since pre-readmission responders had similar experience scores to non-readmitted patients. However, because of the low numbers of pre-readmission responders, the comparison lacks precision. Current HCAHPS and Press Ganey questions may lack the ability to predict future readmissions because of the timing of the survey (postdischarge) or the questions themselves.

Overall, postreadmission responders are dissatisfied with multiple domains of hospital care. Many of these survey responses may simply be related to general frustration. Alternatively, they may represent a patient population with a high degree of needs that are not as easily met by a hospital’s routine processes of care. Even though the readmission rates were 10.6% among survey responders, 14.6% of the survey responses were associated with readmissions after accounting for those who respond to surveys linked to readmission. These patients could have significant impact on cumulative experience scores.

Our study has a few limitations. First, it involves a single tertiary care academic center study, and our results may not be generalizable. Second, we did not adjust for some of the patient characteristics associated with readmissions. Patients who were admitted within 30 days are different than those not readmitted based on payor, race, length of stay, and severity of illness, and we did not adjust for these factors in our analysis. This was intentional, however. Our goal was to better understand the relationship between 30-day readmission and patient experience scores as they are used for hospital-level studies, VBP, and public reporting. For these purposes, the scores are not adjusted for factors, such as payor and length of stay. We did adjust for patient-mix adjustment factors used by CMS. Third, the response rates to the HCAHPS were low and may have biased the scores. However, HCAHPS is widely used for comparisons between hospitals has been validated, and our study results have implications with regard to comparing hospital-level performance. HCAHPS results are relevant to policy and have financial consequences.¹⁷ Fourth, our study did not directly compare whether the relationship between patient experience for the postreadmission group and nonreadmitted group was different from the relationship between the pre-readmission group and postreadmission group. It is possible that there is no difference in relationship between the groups. However, despite the small number of pre-readmission responders, these patients tended to have more favorable experience responses than those who responded after being readmitted, even after adjusting for response time. Although the P values are nonsignificant for many comparisons, the directionality of the effect is relatively consistent. Also, the vast majority of the patients fall in the postreadmission group, and these patients appear to drive the overall experience related to readmissions. Finally, since relatively few patients turned in surveys prior to readmission, we had limited power to detect a significant difference between these pre-readmission responders and nonreadmitted patients.

Our study has implications for policy makers, researchers, and providers. The HCAHPS scores of patients who are readmitted and completed the survey after being readmitted reflects their experience of both the index admission and the readmission. We did not find evidence to support that HCAHPS survey responses predict future readmissions at the patient level. Our findings do support the concept that lower readmissions rates (whether due to the patient population or processes of care that decrease readmission rates) may improve HCAHPS scores. We suggest caution in assuming that improving patient experience is likely to reduce readmission rates.

Disclosures

The authors declare no conflicts of interest.

Over 7 million surgeries are performed in United States hospitals each year. Among these surgeries, approximately 85% are noncardiac, nonvascular (NCNV) procedures.^1,2 Although the preoperative use of an angiotensin-converting enzyme inhibitor (ACEI) can be expected in as many as 13% of these surgeries,³ the optimal preoperative ACEI management strategy for patients undergoing NCNV surgeries is poorly understood.

High-quality evidence suggests that renin–angiotensin–aldosterone system (RAAS) inhibitors are associated with intraoperative hypotension among patients undergoing cardiac or vascular surgeries.^4-6 Intraoperative hypotension increases the risk of 30-day mortality,⁷ and the duration of intraoperative hypotension increases the risk of end organ damage.^8,9 This body of evidence suggests that withholding ACEIs prior to cardiac and vascular surgeries is safer than continuing ACEIs without interruption.

The evidence concerning perioperative management of ACEIs is inconclusive for patients undergoing NCNV procedures. Some studies comparing patients taking or not taking a RAAS inhibitor preoperatively describe negligible differences in the frequency of intraoperative hypotensive episodes or complications.^3,10 Others have found an increased risk of intraoperative hypotension and associated postoperative adverse events in patients continuing RAAS inhibitors preoperatively.^11,12 Current guideline discrepancies reflect the uncertainty of the evidence. The guidelines set by the American College of Cardiology and American Heart Association (ACC/AHA) suggest the uninterrupted perioperative continuation of RAAS inhibitors.¹³ The guidelines provided by the European Society of Cardiology and European Society of Anaesthesiology also suggest the continuation of RAAS inhibitors throughout the perioperative period for patients with systolic heart failure but recommend transient discontinuation for patients with hypertension.¹⁴

This randomized study aimed to compare the effect of two practical strategies for preoperative ACEI management on the perioperative blood pressure of patients undergoing NCNV surgery. The two strategies studied were the omission of the final preoperative ACEI dose and the uninterrupted continuation of ACEI therapy. We hypothesized that patients randomized to ACEI omission would experience intraoperative hypotensive episodes less frequently than those randomized to ACEI continuation.

Study Design and Setting

We performed a prospective randomized controlled trial (ClinicalTrials.gov: NCT01669434). The study was carried out in a preoperative evaluation clinic and its affiliated 489-bed academic medical center. Anesthesiologists and internal medicine physicians work collaboratively in the clinic to assess more than 5,000 patients annually (one-third of the institution’s elective surgeries). Patients were randomized 1:1 in block sizes of 5 and 10 and stratified by age < 65 and ≥ 65 years to the omission or continuation of the final preoperative ACEI dose (whether that dose was scheduled for the morning of surgery or the night prior). Preoperative clinicians enrolled patients and subsequently assigned them to intervention groups on the basis of a sequentially numbered list. Patients and healthcare providers were not blinded to allocation status. Intraoperative and postoperative management was provided in accordance with usual care as decided by treatment team.

Participants

Patients who presented to the preoperative evaluation clinic between May 2015 and November 2016 and who had been taking an ACEI for at least 6 weeks were eligible for inclusion. Patients taking angiotensin receptor blockers were excluded. Enrollment was limited to patients planning NCNV surgery. Patients planning intrathoracic, major vascular, organ transplant, and oncologic surgery were excluded. Patients undergoing outpatient procedures not requiring an overnight stay in the hospital were also excluded. Patients with preoperative clinic systolic blood pressure (SBP) <90 or ≥160 or diastolic blood pressure (DBP) <60 or ≥ 95 were excluded. Patients with moderate to severe or clinically decompensated heart failure (left ventricular ejection fraction < 40% or New York Heart Association class III or IV) and those with end-stage renal disease requiring dialysis were also excluded. Patients presenting more than once during the accrual period were eligible for the initial surgery only. All participating patients provided written informed consent. This project was approved by the University of Nebraska Medical Center Institutional Review Board.

Data Collection

Baseline characteristics were recorded by study personnel at the time of enrollment. We measured serum creatinine level at the preoperative visit and on postoperative day 1. An automated anesthesia information management system was used to measure intraoperative blood pressures every three minutes. Postoperative blood pressures through discharge were measured by hospital staff per usual care. During postoperative hospitalization, we queried patients about preoperative adherence to allocation. The digital abstraction of data from the electronic medical record was supplemented by chart review when necessary.

Outcomes

The primary outcome was intraoperative hypotension defined as any SBP < 80 mm Hg occurring from the administration of the first induction agent through transfer to the postanesthesia care unit (PACU). We also examined hypotension during anesthesia induction, which we defined as the 20-minute period following the administration of the first anesthesia induction agent. Episodes of SBP < 80 were defined as being associated with vasopressor administration when any vasopressor was administered during or within 10 min of the episode.

Secondary analyses included postoperative acute kidney injury (AKI), postoperative hypotensive and hypertensive episodes, cardiac events, and mortality. When comparing postoperative day 1 creatinine levels to preoperative creatinine levels, we used the Acute Kidney Injury Network definition of AKI as an increase in creatinine of 0.3 mg/dl or 50%.¹⁵ Postoperative hypotension was defined as any SBP < 90 mm Hg and postoperative hypertension as any SBP > 180 mm Hg occurring after arrival in the PACU. Major adverse cardiac events (MACE) were defined as a composite of acute coronary syndrome, acute heart failure, or new-onset arrhythmia. Discharge from the hospital served as the study endpoint for each patient.

Analysis

Fisher’s exact test was used to compare categorical outcomes between groups. The independent sample t-test or Wilcoxon rank–sum test, as appropriate, was used to compare continuous measures. We selected Fisher’s exact test over χ²-test to produce conservative estimates. Patients were maintained in their allocated group as randomized for analytical purposes regardless of adherence to allocation. We performed all analyses using SAS version 9.4 for Windows (SAS institute, Cary, North Carolina).

We estimated that a sample size of 300 patients would achieve 80% power to detect a difference of 0.17 between the group proportions of 0.33 and 0.50 at a significance level (ɑ) of 0.05 by using a two-sided z-test with continuity correction, assuming 15% loss to follow-up. This estimate allowed for 1 interim analysis using the O’Brien-Fleming spending function truncated at three standard deviations to determine the test boundaries. The monitoring boundary P values associated with the interim analysis were .003, and the threshold P value for the final analysis was .049.

Study Flow

A total of 453 patients were screened for eligibility. Among these patients, 162 were excluded, and the remaining 291 patients were randomized (Figure 1). Surgery was cancelled in six patients allocated to omission and in four patients allocated to continuation arms, respectively. Moreover, three patients in the omission arm were excluded from the analysis following randomization. Specifically, one was excluded because of early discharge without overnight stay, one was excluded because of withdrawal of consent, and one was excluded because of missing primary outcome data. In addition, three cases in the continuation arm were excluded following randomization because of the preoperative (permanent) discontinuation of ACEI therapy in two cases and discharge without an overnight stay in one case. Finally, 275 patients were included in the analysis: 137 in the ACEI omission group and 138 in the ACEI continuation group. Adherence to allocation was 88% and 92% in the omission and continuation groups, respectively.

Baseline Characteristics

The demographic data of patients allocated to ACEI omission and those allocated to ACEI continuation were similar (Table 1). A large majority of patients in both groups took the ACEI lisinopril. Overall, 187 of 275 (68%) patients were taking at least 1 antihypertensive agent, most commonly a diuretic, in addition to an ACEI. SBP measured during the preoperative clinic visit averaged 136.5 mm Hg and did not differ significantly between groups (P = .84).

Surgical Variables

General anesthesia was the most commonly utilized technique, although spinal and regional anesthesia were also represented (Table 1). The majority of cases in both groups were planning for orthopedic and spinal surgery. The method of anesthesia or type of surgery between patients allocated to ACEI omission and those allocated to continuation did not differ (P = .61 and P = .45 respectively).

Episodes of Intraoperative Hypotension

Intraoperative SBPs are displayed in Figure 2, and hemodynamic outcomes are summarized in Table 2. Episodes of SBP < 80 mm Hg during anesthesia induction were numerically less frequent in the omission group than in the continuation group; the difference between groups, however, was not statistically significant (24 of 137 [18%] vs 38 of 138 [28%], RR: 0.64, 95% CI: 0.40 to 1.00, P = .06).

Duration of Intraoperative Hypotension

The median cumulative duration of intraoperative SBP < 80 was two minutes (range 0-41) in patients allocated to the ACEI omission group compared with seven minutes (range 0-214) in those allocated to the continuation group (P < .01). The median cumulative duration of mean arterial pressure < 55 mm Hg was also shorter in the omission group (median 0 min [range 0-39] vs 3 min [range 0-122], P < .01) than in the continuation group. The duration of surgery did not differ between groups (median 141 min [range 77-554] vs 142 min [range 57-665], P = .97).

Postoperative Outcomes

RAAS inhibitor therapy was resumed within 48 h after surgery in 122 of 137 (89%) patients allocated to the omission group and in 128 of 138 (93%) patients allocated to the continuation group (RR: 0.96, 95% CI: 0.89-1.03, P = .30).

Patients allocated to the omission group were significantly less likely to experience postoperative hypotension (15 of 137 [11%] vs 31 of 138 [22%], RR: 0.49, 95% CI: 0.28 to 0.86, P = .02) and significantly more likely to experience severe postoperative hypertension (33 of 137 [24%] vs 17 of 138 [12%], RR: 1.95, 95% CI: 1.14 to 3.34, P = .01) than those allocated to the continuation group. The occurrences of postoperative AKI (RR: 0.60, 95% CI: 0.23 to 1.60, P = .44) or MACE (RR: 4.03, 95% CI: 0.46 to 35.59, P = .21) in the omission group did not differ from the continuation group. The two groups exhibited similar PACU recovery time (mean 97.2 min) and overall hospital length of stay (mean 3.0 days) (P = .49 and P = .56 ). No episodes of inpatient mortality in either group were observed.

The omission of the final preoperative ACEI dose was associated with a significant reduction in the risk of intraoperative hypotension in patients undergoing NCNV surgery. This result confirmed our hypothesis. Coupled with the knowledge that intraoperative hypotension is associated with an increased risk of complications and mortality,^7-9,16 this study favors the omission of the final preoperative ACEI dose prior to NCNV surgeries.

Our findings are in agreement with those of previous randomized studies that explored this question^4,5 and help extend results from cardiac and vascular surgeries to NCNV surgeries. Previous studies on the use of RAAS inhibitors in NCNV surgeries did not employ randomization and yielded mixed results.^3,10-12,17 A large single-institution study (n = 18,056) noted no difference in intraoperative blood pressure between patients taking ACEIs and a matched group of non-ACEI users.³ More recently, a subgroup analysis of the international VISION study showed that omitting RAAS inhibitors on the day of surgery reduced the risk of intraoperative hypotension.¹¹ In that analysis, however, only a small amount of the variability in preoperative RAAS inhibitor management was explainable by modeling known factors, thus allowing for the possibility of unmeasured confounding. Our study, which minimized confounding through randomization, is the first to prospectively compare protocols for patients undergoing NCNV surgery. In contrast to previous studies, the present study was able to report the lack of difference in postoperative RAAS inhibitor administration between study groups. Postoperative RAAS inhibitor management affects complications and mortality.^18,19

Our present finding that preoperative ACEI management affects postoperative hypotensive and hypertensive events conflicts with some previous findings.^11,20 However, recent evidence has revealed that postoperative hypotensive episodes are associated with vascular events and mortality.^11,21 In the context of that evidence, our study lends further support to the omission of the final preoperative ACEI dose. However, we did not detect any decrease in AKI, MACE, or mortality in the ACEI omission group.

This study should be considered in light of its limitations. The pragmatic nature of the study allowed for certain potential biases. Although adherence to allocation was high, the specific ACEI agent taken and the exact timing of the final dose in relation to surgery were not controlled. Anesthetic and postoperative management decisions were made by the treatment team and may have systematically varied given that the treatment team was not blinded to allocation. Furthermore, all outcome data were collected as part of routine care and may not have captured events with great fidelity. Generalizability is limited by the execution of the study at a single academic institution, the preponderance of orthopedic and spine surgeries, and by the negligible representation of ethnicities other than Caucasian. Additionally, recruitment from the preoperative evaluation clinic likely resulted in a patient group with greater comorbidity than the overall population of patients undergoing NCNV surgery. This study was powered for intraoperative hypotension and not postoperative outcomes. Our primary outcome, intraoperative hypotension, is an intermediate measure but one that has well-established associations with adverse outcomes, including mortality. One study showed that sustaining an intraoperative SBP below 70 mm Hg for longer than 5 min increased the risk of mortality from less than 1% to nearly 6%.¹⁶ A large study detected an increase in mortality associated with SBP sustained below 80 mm Hg for 10 min or longer.⁷ Intraoperative hypotension has also been associated with postoperative AKI and myocardial injury.^8,9,12

Many of the limitations of the current study could be addressed by a large randomized controlled trial of ACEI management prior to NCNV surgeries that examines clinically important endpoints beyond intraoperative hypotension. Several specific aspects of perioperative RAAS inhibitor management also deserve further investigation. Our findings may not be generalizable to patients taking ARBs or to patients with congestive heart failure. The preoperative management of ARBs and the preoperative management of RAAS inhibitors in those with congestive heart failure are important areas of focus for future research. Lastly, our finding that preoperative ACEI management decisions can affect postoperative hypotensive and hypertensive events should be substantiated by future research, and any negative consequences of those events should be further explored.

Nonetheless, our study is the largest randomized study of preoperative RAAS inhibition published to date. More than twice as many patients were randomized in this study than in all previous randomized studies combined.^4-6 To the best of our knowledge, this is also the first randomized study evaluating NCNV surgeries. Finally, our use of a practical ACEI omission protocol based on known pharmacokinetics allows for direct application to clinical practice.

Hypertension is among the most common chronic conditions encountered in patients planning surgery, and ACEIs are among the most frequently prescribed antihypertensive medications. This study showed that ACEI continuation is associated with an increased frequency and cumulative duration of intraoperative hypotension. These findings, while at odds with current ACC/AHA guidelines, align with the findings of a meta-analysis on this subject and with recent literature.^3,11-13,22

Acknowledgments

The authors wish to thank Miranda M Fricke, MS, PA-C; Tiffany K Hillyard, APRN-FNP; and Barbara Sink, MPAS, PA-C who assisted in the design and conduct of patient enrollment and randomization procedures.

Disclosures

The authors have no relevant financial conflicts of interest to report.

Funding

This study was subsidized by a grant from the University of Nebraska Medical Center Research Support Fund. The funding source had no role in the design, conduct, analysis, or reporting of the study.

Over 7 million surgeries are performed in United States hospitals each year. Among these surgeries, approximately 85% are noncardiac, nonvascular (NCNV) procedures.^1,2 Although the preoperative use of an angiotensin-converting enzyme inhibitor (ACEI) can be expected in as many as 13% of these surgeries,³ the optimal preoperative ACEI management strategy for patients undergoing NCNV surgeries is poorly understood.

High-quality evidence suggests that renin–angiotensin–aldosterone system (RAAS) inhibitors are associated with intraoperative hypotension among patients undergoing cardiac or vascular surgeries.^4-6 Intraoperative hypotension increases the risk of 30-day mortality,⁷ and the duration of intraoperative hypotension increases the risk of end organ damage.^8,9 This body of evidence suggests that withholding ACEIs prior to cardiac and vascular surgeries is safer than continuing ACEIs without interruption.

The evidence concerning perioperative management of ACEIs is inconclusive for patients undergoing NCNV procedures. Some studies comparing patients taking or not taking a RAAS inhibitor preoperatively describe negligible differences in the frequency of intraoperative hypotensive episodes or complications.^3,10 Others have found an increased risk of intraoperative hypotension and associated postoperative adverse events in patients continuing RAAS inhibitors preoperatively.^11,12 Current guideline discrepancies reflect the uncertainty of the evidence. The guidelines set by the American College of Cardiology and American Heart Association (ACC/AHA) suggest the uninterrupted perioperative continuation of RAAS inhibitors.¹³ The guidelines provided by the European Society of Cardiology and European Society of Anaesthesiology also suggest the continuation of RAAS inhibitors throughout the perioperative period for patients with systolic heart failure but recommend transient discontinuation for patients with hypertension.¹⁴

This randomized study aimed to compare the effect of two practical strategies for preoperative ACEI management on the perioperative blood pressure of patients undergoing NCNV surgery. The two strategies studied were the omission of the final preoperative ACEI dose and the uninterrupted continuation of ACEI therapy. We hypothesized that patients randomized to ACEI omission would experience intraoperative hypotensive episodes less frequently than those randomized to ACEI continuation.

Study Design and Setting

We performed a prospective randomized controlled trial (ClinicalTrials.gov: NCT01669434). The study was carried out in a preoperative evaluation clinic and its affiliated 489-bed academic medical center. Anesthesiologists and internal medicine physicians work collaboratively in the clinic to assess more than 5,000 patients annually (one-third of the institution’s elective surgeries). Patients were randomized 1:1 in block sizes of 5 and 10 and stratified by age < 65 and ≥ 65 years to the omission or continuation of the final preoperative ACEI dose (whether that dose was scheduled for the morning of surgery or the night prior). Preoperative clinicians enrolled patients and subsequently assigned them to intervention groups on the basis of a sequentially numbered list. Patients and healthcare providers were not blinded to allocation status. Intraoperative and postoperative management was provided in accordance with usual care as decided by treatment team.

Participants

Patients who presented to the preoperative evaluation clinic between May 2015 and November 2016 and who had been taking an ACEI for at least 6 weeks were eligible for inclusion. Patients taking angiotensin receptor blockers were excluded. Enrollment was limited to patients planning NCNV surgery. Patients planning intrathoracic, major vascular, organ transplant, and oncologic surgery were excluded. Patients undergoing outpatient procedures not requiring an overnight stay in the hospital were also excluded. Patients with preoperative clinic systolic blood pressure (SBP) <90 or ≥160 or diastolic blood pressure (DBP) <60 or ≥ 95 were excluded. Patients with moderate to severe or clinically decompensated heart failure (left ventricular ejection fraction < 40% or New York Heart Association class III or IV) and those with end-stage renal disease requiring dialysis were also excluded. Patients presenting more than once during the accrual period were eligible for the initial surgery only. All participating patients provided written informed consent. This project was approved by the University of Nebraska Medical Center Institutional Review Board.

Data Collection

Baseline characteristics were recorded by study personnel at the time of enrollment. We measured serum creatinine level at the preoperative visit and on postoperative day 1. An automated anesthesia information management system was used to measure intraoperative blood pressures every three minutes. Postoperative blood pressures through discharge were measured by hospital staff per usual care. During postoperative hospitalization, we queried patients about preoperative adherence to allocation. The digital abstraction of data from the electronic medical record was supplemented by chart review when necessary.

Outcomes

The primary outcome was intraoperative hypotension defined as any SBP < 80 mm Hg occurring from the administration of the first induction agent through transfer to the postanesthesia care unit (PACU). We also examined hypotension during anesthesia induction, which we defined as the 20-minute period following the administration of the first anesthesia induction agent. Episodes of SBP < 80 were defined as being associated with vasopressor administration when any vasopressor was administered during or within 10 min of the episode.

Secondary analyses included postoperative acute kidney injury (AKI), postoperative hypotensive and hypertensive episodes, cardiac events, and mortality. When comparing postoperative day 1 creatinine levels to preoperative creatinine levels, we used the Acute Kidney Injury Network definition of AKI as an increase in creatinine of 0.3 mg/dl or 50%.¹⁵ Postoperative hypotension was defined as any SBP < 90 mm Hg and postoperative hypertension as any SBP > 180 mm Hg occurring after arrival in the PACU. Major adverse cardiac events (MACE) were defined as a composite of acute coronary syndrome, acute heart failure, or new-onset arrhythmia. Discharge from the hospital served as the study endpoint for each patient.

Analysis

Fisher’s exact test was used to compare categorical outcomes between groups. The independent sample t-test or Wilcoxon rank–sum test, as appropriate, was used to compare continuous measures. We selected Fisher’s exact test over χ²-test to produce conservative estimates. Patients were maintained in their allocated group as randomized for analytical purposes regardless of adherence to allocation. We performed all analyses using SAS version 9.4 for Windows (SAS institute, Cary, North Carolina).

We estimated that a sample size of 300 patients would achieve 80% power to detect a difference of 0.17 between the group proportions of 0.33 and 0.50 at a significance level (ɑ) of 0.05 by using a two-sided z-test with continuity correction, assuming 15% loss to follow-up. This estimate allowed for 1 interim analysis using the O’Brien-Fleming spending function truncated at three standard deviations to determine the test boundaries. The monitoring boundary P values associated with the interim analysis were .003, and the threshold P value for the final analysis was .049.

Study Flow

A total of 453 patients were screened for eligibility. Among these patients, 162 were excluded, and the remaining 291 patients were randomized (Figure 1). Surgery was cancelled in six patients allocated to omission and in four patients allocated to continuation arms, respectively. Moreover, three patients in the omission arm were excluded from the analysis following randomization. Specifically, one was excluded because of early discharge without overnight stay, one was excluded because of withdrawal of consent, and one was excluded because of missing primary outcome data. In addition, three cases in the continuation arm were excluded following randomization because of the preoperative (permanent) discontinuation of ACEI therapy in two cases and discharge without an overnight stay in one case. Finally, 275 patients were included in the analysis: 137 in the ACEI omission group and 138 in the ACEI continuation group. Adherence to allocation was 88% and 92% in the omission and continuation groups, respectively.

Baseline Characteristics

The demographic data of patients allocated to ACEI omission and those allocated to ACEI continuation were similar (Table 1). A large majority of patients in both groups took the ACEI lisinopril. Overall, 187 of 275 (68%) patients were taking at least 1 antihypertensive agent, most commonly a diuretic, in addition to an ACEI. SBP measured during the preoperative clinic visit averaged 136.5 mm Hg and did not differ significantly between groups (P = .84).

Surgical Variables

General anesthesia was the most commonly utilized technique, although spinal and regional anesthesia were also represented (Table 1). The majority of cases in both groups were planning for orthopedic and spinal surgery. The method of anesthesia or type of surgery between patients allocated to ACEI omission and those allocated to continuation did not differ (P = .61 and P = .45 respectively).

Episodes of Intraoperative Hypotension

Intraoperative SBPs are displayed in Figure 2, and hemodynamic outcomes are summarized in Table 2. Episodes of SBP < 80 mm Hg during anesthesia induction were numerically less frequent in the omission group than in the continuation group; the difference between groups, however, was not statistically significant (24 of 137 [18%] vs 38 of 138 [28%], RR: 0.64, 95% CI: 0.40 to 1.00, P = .06).

Duration of Intraoperative Hypotension

The median cumulative duration of intraoperative SBP < 80 was two minutes (range 0-41) in patients allocated to the ACEI omission group compared with seven minutes (range 0-214) in those allocated to the continuation group (P < .01). The median cumulative duration of mean arterial pressure < 55 mm Hg was also shorter in the omission group (median 0 min [range 0-39] vs 3 min [range 0-122], P < .01) than in the continuation group. The duration of surgery did not differ between groups (median 141 min [range 77-554] vs 142 min [range 57-665], P = .97).

Postoperative Outcomes

RAAS inhibitor therapy was resumed within 48 h after surgery in 122 of 137 (89%) patients allocated to the omission group and in 128 of 138 (93%) patients allocated to the continuation group (RR: 0.96, 95% CI: 0.89-1.03, P = .30).

Patients allocated to the omission group were significantly less likely to experience postoperative hypotension (15 of 137 [11%] vs 31 of 138 [22%], RR: 0.49, 95% CI: 0.28 to 0.86, P = .02) and significantly more likely to experience severe postoperative hypertension (33 of 137 [24%] vs 17 of 138 [12%], RR: 1.95, 95% CI: 1.14 to 3.34, P = .01) than those allocated to the continuation group. The occurrences of postoperative AKI (RR: 0.60, 95% CI: 0.23 to 1.60, P = .44) or MACE (RR: 4.03, 95% CI: 0.46 to 35.59, P = .21) in the omission group did not differ from the continuation group. The two groups exhibited similar PACU recovery time (mean 97.2 min) and overall hospital length of stay (mean 3.0 days) (P = .49 and P = .56 ). No episodes of inpatient mortality in either group were observed.

The omission of the final preoperative ACEI dose was associated with a significant reduction in the risk of intraoperative hypotension in patients undergoing NCNV surgery. This result confirmed our hypothesis. Coupled with the knowledge that intraoperative hypotension is associated with an increased risk of complications and mortality,^7-9,16 this study favors the omission of the final preoperative ACEI dose prior to NCNV surgeries.

Our findings are in agreement with those of previous randomized studies that explored this question^4,5 and help extend results from cardiac and vascular surgeries to NCNV surgeries. Previous studies on the use of RAAS inhibitors in NCNV surgeries did not employ randomization and yielded mixed results.^3,10-12,17 A large single-institution study (n = 18,056) noted no difference in intraoperative blood pressure between patients taking ACEIs and a matched group of non-ACEI users.³ More recently, a subgroup analysis of the international VISION study showed that omitting RAAS inhibitors on the day of surgery reduced the risk of intraoperative hypotension.¹¹ In that analysis, however, only a small amount of the variability in preoperative RAAS inhibitor management was explainable by modeling known factors, thus allowing for the possibility of unmeasured confounding. Our study, which minimized confounding through randomization, is the first to prospectively compare protocols for patients undergoing NCNV surgery. In contrast to previous studies, the present study was able to report the lack of difference in postoperative RAAS inhibitor administration between study groups. Postoperative RAAS inhibitor management affects complications and mortality.^18,19

Our present finding that preoperative ACEI management affects postoperative hypotensive and hypertensive events conflicts with some previous findings.^11,20 However, recent evidence has revealed that postoperative hypotensive episodes are associated with vascular events and mortality.^11,21 In the context of that evidence, our study lends further support to the omission of the final preoperative ACEI dose. However, we did not detect any decrease in AKI, MACE, or mortality in the ACEI omission group.

This study should be considered in light of its limitations. The pragmatic nature of the study allowed for certain potential biases. Although adherence to allocation was high, the specific ACEI agent taken and the exact timing of the final dose in relation to surgery were not controlled. Anesthetic and postoperative management decisions were made by the treatment team and may have systematically varied given that the treatment team was not blinded to allocation. Furthermore, all outcome data were collected as part of routine care and may not have captured events with great fidelity. Generalizability is limited by the execution of the study at a single academic institution, the preponderance of orthopedic and spine surgeries, and by the negligible representation of ethnicities other than Caucasian. Additionally, recruitment from the preoperative evaluation clinic likely resulted in a patient group with greater comorbidity than the overall population of patients undergoing NCNV surgery. This study was powered for intraoperative hypotension and not postoperative outcomes. Our primary outcome, intraoperative hypotension, is an intermediate measure but one that has well-established associations with adverse outcomes, including mortality. One study showed that sustaining an intraoperative SBP below 70 mm Hg for longer than 5 min increased the risk of mortality from less than 1% to nearly 6%.¹⁶ A large study detected an increase in mortality associated with SBP sustained below 80 mm Hg for 10 min or longer.⁷ Intraoperative hypotension has also been associated with postoperative AKI and myocardial injury.^8,9,12

Many of the limitations of the current study could be addressed by a large randomized controlled trial of ACEI management prior to NCNV surgeries that examines clinically important endpoints beyond intraoperative hypotension. Several specific aspects of perioperative RAAS inhibitor management also deserve further investigation. Our findings may not be generalizable to patients taking ARBs or to patients with congestive heart failure. The preoperative management of ARBs and the preoperative management of RAAS inhibitors in those with congestive heart failure are important areas of focus for future research. Lastly, our finding that preoperative ACEI management decisions can affect postoperative hypotensive and hypertensive events should be substantiated by future research, and any negative consequences of those events should be further explored.

Nonetheless, our study is the largest randomized study of preoperative RAAS inhibition published to date. More than twice as many patients were randomized in this study than in all previous randomized studies combined.^4-6 To the best of our knowledge, this is also the first randomized study evaluating NCNV surgeries. Finally, our use of a practical ACEI omission protocol based on known pharmacokinetics allows for direct application to clinical practice.

Hypertension is among the most common chronic conditions encountered in patients planning surgery, and ACEIs are among the most frequently prescribed antihypertensive medications. This study showed that ACEI continuation is associated with an increased frequency and cumulative duration of intraoperative hypotension. These findings, while at odds with current ACC/AHA guidelines, align with the findings of a meta-analysis on this subject and with recent literature.^3,11-13,22

Acknowledgments

The authors wish to thank Miranda M Fricke, MS, PA-C; Tiffany K Hillyard, APRN-FNP; and Barbara Sink, MPAS, PA-C who assisted in the design and conduct of patient enrollment and randomization procedures.

Disclosures

The authors have no relevant financial conflicts of interest to report.

Funding

This study was subsidized by a grant from the University of Nebraska Medical Center Research Support Fund. The funding source had no role in the design, conduct, analysis, or reporting of the study.

Over 7 million surgeries are performed in United States hospitals each year. Among these surgeries, approximately 85% are noncardiac, nonvascular (NCNV) procedures.^1,2 Although the preoperative use of an angiotensin-converting enzyme inhibitor (ACEI) can be expected in as many as 13% of these surgeries,³ the optimal preoperative ACEI management strategy for patients undergoing NCNV surgeries is poorly understood.

High-quality evidence suggests that renin–angiotensin–aldosterone system (RAAS) inhibitors are associated with intraoperative hypotension among patients undergoing cardiac or vascular surgeries.^4-6 Intraoperative hypotension increases the risk of 30-day mortality,⁷ and the duration of intraoperative hypotension increases the risk of end organ damage.^8,9 This body of evidence suggests that withholding ACEIs prior to cardiac and vascular surgeries is safer than continuing ACEIs without interruption.

The evidence concerning perioperative management of ACEIs is inconclusive for patients undergoing NCNV procedures. Some studies comparing patients taking or not taking a RAAS inhibitor preoperatively describe negligible differences in the frequency of intraoperative hypotensive episodes or complications.^3,10 Others have found an increased risk of intraoperative hypotension and associated postoperative adverse events in patients continuing RAAS inhibitors preoperatively.^11,12 Current guideline discrepancies reflect the uncertainty of the evidence. The guidelines set by the American College of Cardiology and American Heart Association (ACC/AHA) suggest the uninterrupted perioperative continuation of RAAS inhibitors.¹³ The guidelines provided by the European Society of Cardiology and European Society of Anaesthesiology also suggest the continuation of RAAS inhibitors throughout the perioperative period for patients with systolic heart failure but recommend transient discontinuation for patients with hypertension.¹⁴

This randomized study aimed to compare the effect of two practical strategies for preoperative ACEI management on the perioperative blood pressure of patients undergoing NCNV surgery. The two strategies studied were the omission of the final preoperative ACEI dose and the uninterrupted continuation of ACEI therapy. We hypothesized that patients randomized to ACEI omission would experience intraoperative hypotensive episodes less frequently than those randomized to ACEI continuation.

Study Design and Setting

We performed a prospective randomized controlled trial (ClinicalTrials.gov: NCT01669434). The study was carried out in a preoperative evaluation clinic and its affiliated 489-bed academic medical center. Anesthesiologists and internal medicine physicians work collaboratively in the clinic to assess more than 5,000 patients annually (one-third of the institution’s elective surgeries). Patients were randomized 1:1 in block sizes of 5 and 10 and stratified by age < 65 and ≥ 65 years to the omission or continuation of the final preoperative ACEI dose (whether that dose was scheduled for the morning of surgery or the night prior). Preoperative clinicians enrolled patients and subsequently assigned them to intervention groups on the basis of a sequentially numbered list. Patients and healthcare providers were not blinded to allocation status. Intraoperative and postoperative management was provided in accordance with usual care as decided by treatment team.

Participants

Patients who presented to the preoperative evaluation clinic between May 2015 and November 2016 and who had been taking an ACEI for at least 6 weeks were eligible for inclusion. Patients taking angiotensin receptor blockers were excluded. Enrollment was limited to patients planning NCNV surgery. Patients planning intrathoracic, major vascular, organ transplant, and oncologic surgery were excluded. Patients undergoing outpatient procedures not requiring an overnight stay in the hospital were also excluded. Patients with preoperative clinic systolic blood pressure (SBP) <90 or ≥160 or diastolic blood pressure (DBP) <60 or ≥ 95 were excluded. Patients with moderate to severe or clinically decompensated heart failure (left ventricular ejection fraction < 40% or New York Heart Association class III or IV) and those with end-stage renal disease requiring dialysis were also excluded. Patients presenting more than once during the accrual period were eligible for the initial surgery only. All participating patients provided written informed consent. This project was approved by the University of Nebraska Medical Center Institutional Review Board.

Data Collection

Baseline characteristics were recorded by study personnel at the time of enrollment. We measured serum creatinine level at the preoperative visit and on postoperative day 1. An automated anesthesia information management system was used to measure intraoperative blood pressures every three minutes. Postoperative blood pressures through discharge were measured by hospital staff per usual care. During postoperative hospitalization, we queried patients about preoperative adherence to allocation. The digital abstraction of data from the electronic medical record was supplemented by chart review when necessary.

Outcomes

The primary outcome was intraoperative hypotension defined as any SBP < 80 mm Hg occurring from the administration of the first induction agent through transfer to the postanesthesia care unit (PACU). We also examined hypotension during anesthesia induction, which we defined as the 20-minute period following the administration of the first anesthesia induction agent. Episodes of SBP < 80 were defined as being associated with vasopressor administration when any vasopressor was administered during or within 10 min of the episode.

Secondary analyses included postoperative acute kidney injury (AKI), postoperative hypotensive and hypertensive episodes, cardiac events, and mortality. When comparing postoperative day 1 creatinine levels to preoperative creatinine levels, we used the Acute Kidney Injury Network definition of AKI as an increase in creatinine of 0.3 mg/dl or 50%.¹⁵ Postoperative hypotension was defined as any SBP < 90 mm Hg and postoperative hypertension as any SBP > 180 mm Hg occurring after arrival in the PACU. Major adverse cardiac events (MACE) were defined as a composite of acute coronary syndrome, acute heart failure, or new-onset arrhythmia. Discharge from the hospital served as the study endpoint for each patient.

Analysis

Fisher’s exact test was used to compare categorical outcomes between groups. The independent sample t-test or Wilcoxon rank–sum test, as appropriate, was used to compare continuous measures. We selected Fisher’s exact test over χ²-test to produce conservative estimates. Patients were maintained in their allocated group as randomized for analytical purposes regardless of adherence to allocation. We performed all analyses using SAS version 9.4 for Windows (SAS institute, Cary, North Carolina).

We estimated that a sample size of 300 patients would achieve 80% power to detect a difference of 0.17 between the group proportions of 0.33 and 0.50 at a significance level (ɑ) of 0.05 by using a two-sided z-test with continuity correction, assuming 15% loss to follow-up. This estimate allowed for 1 interim analysis using the O’Brien-Fleming spending function truncated at three standard deviations to determine the test boundaries. The monitoring boundary P values associated with the interim analysis were .003, and the threshold P value for the final analysis was .049.

Study Flow

A total of 453 patients were screened for eligibility. Among these patients, 162 were excluded, and the remaining 291 patients were randomized (Figure 1). Surgery was cancelled in six patients allocated to omission and in four patients allocated to continuation arms, respectively. Moreover, three patients in the omission arm were excluded from the analysis following randomization. Specifically, one was excluded because of early discharge without overnight stay, one was excluded because of withdrawal of consent, and one was excluded because of missing primary outcome data. In addition, three cases in the continuation arm were excluded following randomization because of the preoperative (permanent) discontinuation of ACEI therapy in two cases and discharge without an overnight stay in one case. Finally, 275 patients were included in the analysis: 137 in the ACEI omission group and 138 in the ACEI continuation group. Adherence to allocation was 88% and 92% in the omission and continuation groups, respectively.

Baseline Characteristics

The demographic data of patients allocated to ACEI omission and those allocated to ACEI continuation were similar (Table 1). A large majority of patients in both groups took the ACEI lisinopril. Overall, 187 of 275 (68%) patients were taking at least 1 antihypertensive agent, most commonly a diuretic, in addition to an ACEI. SBP measured during the preoperative clinic visit averaged 136.5 mm Hg and did not differ significantly between groups (P = .84).

Surgical Variables

General anesthesia was the most commonly utilized technique, although spinal and regional anesthesia were also represented (Table 1). The majority of cases in both groups were planning for orthopedic and spinal surgery. The method of anesthesia or type of surgery between patients allocated to ACEI omission and those allocated to continuation did not differ (P = .61 and P = .45 respectively).

Episodes of Intraoperative Hypotension

Intraoperative SBPs are displayed in Figure 2, and hemodynamic outcomes are summarized in Table 2. Episodes of SBP < 80 mm Hg during anesthesia induction were numerically less frequent in the omission group than in the continuation group; the difference between groups, however, was not statistically significant (24 of 137 [18%] vs 38 of 138 [28%], RR: 0.64, 95% CI: 0.40 to 1.00, P = .06).

Duration of Intraoperative Hypotension

The median cumulative duration of intraoperative SBP < 80 was two minutes (range 0-41) in patients allocated to the ACEI omission group compared with seven minutes (range 0-214) in those allocated to the continuation group (P < .01). The median cumulative duration of mean arterial pressure < 55 mm Hg was also shorter in the omission group (median 0 min [range 0-39] vs 3 min [range 0-122], P < .01) than in the continuation group. The duration of surgery did not differ between groups (median 141 min [range 77-554] vs 142 min [range 57-665], P = .97).

Postoperative Outcomes

RAAS inhibitor therapy was resumed within 48 h after surgery in 122 of 137 (89%) patients allocated to the omission group and in 128 of 138 (93%) patients allocated to the continuation group (RR: 0.96, 95% CI: 0.89-1.03, P = .30).

Patients allocated to the omission group were significantly less likely to experience postoperative hypotension (15 of 137 [11%] vs 31 of 138 [22%], RR: 0.49, 95% CI: 0.28 to 0.86, P = .02) and significantly more likely to experience severe postoperative hypertension (33 of 137 [24%] vs 17 of 138 [12%], RR: 1.95, 95% CI: 1.14 to 3.34, P = .01) than those allocated to the continuation group. The occurrences of postoperative AKI (RR: 0.60, 95% CI: 0.23 to 1.60, P = .44) or MACE (RR: 4.03, 95% CI: 0.46 to 35.59, P = .21) in the omission group did not differ from the continuation group. The two groups exhibited similar PACU recovery time (mean 97.2 min) and overall hospital length of stay (mean 3.0 days) (P = .49 and P = .56 ). No episodes of inpatient mortality in either group were observed.

The omission of the final preoperative ACEI dose was associated with a significant reduction in the risk of intraoperative hypotension in patients undergoing NCNV surgery. This result confirmed our hypothesis. Coupled with the knowledge that intraoperative hypotension is associated with an increased risk of complications and mortality,^7-9,16 this study favors the omission of the final preoperative ACEI dose prior to NCNV surgeries.

Our findings are in agreement with those of previous randomized studies that explored this question^4,5 and help extend results from cardiac and vascular surgeries to NCNV surgeries. Previous studies on the use of RAAS inhibitors in NCNV surgeries did not employ randomization and yielded mixed results.^3,10-12,17 A large single-institution study (n = 18,056) noted no difference in intraoperative blood pressure between patients taking ACEIs and a matched group of non-ACEI users.³ More recently, a subgroup analysis of the international VISION study showed that omitting RAAS inhibitors on the day of surgery reduced the risk of intraoperative hypotension.¹¹ In that analysis, however, only a small amount of the variability in preoperative RAAS inhibitor management was explainable by modeling known factors, thus allowing for the possibility of unmeasured confounding. Our study, which minimized confounding through randomization, is the first to prospectively compare protocols for patients undergoing NCNV surgery. In contrast to previous studies, the present study was able to report the lack of difference in postoperative RAAS inhibitor administration between study groups. Postoperative RAAS inhibitor management affects complications and mortality.^18,19

Our present finding that preoperative ACEI management affects postoperative hypotensive and hypertensive events conflicts with some previous findings.^11,20 However, recent evidence has revealed that postoperative hypotensive episodes are associated with vascular events and mortality.^11,21 In the context of that evidence, our study lends further support to the omission of the final preoperative ACEI dose. However, we did not detect any decrease in AKI, MACE, or mortality in the ACEI omission group.

This study should be considered in light of its limitations. The pragmatic nature of the study allowed for certain potential biases. Although adherence to allocation was high, the specific ACEI agent taken and the exact timing of the final dose in relation to surgery were not controlled. Anesthetic and postoperative management decisions were made by the treatment team and may have systematically varied given that the treatment team was not blinded to allocation. Furthermore, all outcome data were collected as part of routine care and may not have captured events with great fidelity. Generalizability is limited by the execution of the study at a single academic institution, the preponderance of orthopedic and spine surgeries, and by the negligible representation of ethnicities other than Caucasian. Additionally, recruitment from the preoperative evaluation clinic likely resulted in a patient group with greater comorbidity than the overall population of patients undergoing NCNV surgery. This study was powered for intraoperative hypotension and not postoperative outcomes. Our primary outcome, intraoperative hypotension, is an intermediate measure but one that has well-established associations with adverse outcomes, including mortality. One study showed that sustaining an intraoperative SBP below 70 mm Hg for longer than 5 min increased the risk of mortality from less than 1% to nearly 6%.¹⁶ A large study detected an increase in mortality associated with SBP sustained below 80 mm Hg for 10 min or longer.⁷ Intraoperative hypotension has also been associated with postoperative AKI and myocardial injury.^8,9,12

Many of the limitations of the current study could be addressed by a large randomized controlled trial of ACEI management prior to NCNV surgeries that examines clinically important endpoints beyond intraoperative hypotension. Several specific aspects of perioperative RAAS inhibitor management also deserve further investigation. Our findings may not be generalizable to patients taking ARBs or to patients with congestive heart failure. The preoperative management of ARBs and the preoperative management of RAAS inhibitors in those with congestive heart failure are important areas of focus for future research. Lastly, our finding that preoperative ACEI management decisions can affect postoperative hypotensive and hypertensive events should be substantiated by future research, and any negative consequences of those events should be further explored.

Nonetheless, our study is the largest randomized study of preoperative RAAS inhibition published to date. More than twice as many patients were randomized in this study than in all previous randomized studies combined.^4-6 To the best of our knowledge, this is also the first randomized study evaluating NCNV surgeries. Finally, our use of a practical ACEI omission protocol based on known pharmacokinetics allows for direct application to clinical practice.

Hypertension is among the most common chronic conditions encountered in patients planning surgery, and ACEIs are among the most frequently prescribed antihypertensive medications. This study showed that ACEI continuation is associated with an increased frequency and cumulative duration of intraoperative hypotension. These findings, while at odds with current ACC/AHA guidelines, align with the findings of a meta-analysis on this subject and with recent literature.^3,11-13,22

Acknowledgments

The authors wish to thank Miranda M Fricke, MS, PA-C; Tiffany K Hillyard, APRN-FNP; and Barbara Sink, MPAS, PA-C who assisted in the design and conduct of patient enrollment and randomization procedures.

Disclosures

The authors have no relevant financial conflicts of interest to report.

Funding

This study was subsidized by a grant from the University of Nebraska Medical Center Research Support Fund. The funding source had no role in the design, conduct, analysis, or reporting of the study.

High hospital occupancy is a fundamental challenge faced by healthcare systems in the United States.^1-3 However, few studies have examined the effect of high occupancy on outcomes in the inpatient setting,^4-9 and these showed mixed results. Hospital-acquired conditions (HACs), such as Clostridium difficile infection (CDI), are quality indicators for inpatient care and part of the Centers for Medicare and Medicaid Services’ Hospital-Acquired Conditions Reductions Program.^10-12 However, few studies—largely conducted outside of the US—have evaluated the association between inpatient occupancy and HACs. These studies showed increasing hospital-acquired infection rates with increasing occupancy.^13-15 Past studies of hospital occupancy have relied on annual average licensed bed counts, which are not a reliable measure of available and staffed beds and do not account for variations in patient volume and bed supply.¹⁶ Using a novel measure of inpatient occupancy, we tested the hypothesis that increasing inpatient occupancy is associated with a greater likelihood of CDI.

We performed a retrospective analysis of administrative data from non-federal, acute care hospitals in California during 2008–2012 using the Office of Statewide Health Planning and Development (OSHPD) Patient Discharge Data set, a complete census of all CA licensed general acute care hospital discharge records. This study was approved by the OSHPD Committee for the Protection of Human Subjects and was deemed exempt by our institution’s Institutional Review Board.

Selection of Participants

The study population consisted of fee-for-service Medicare enrollees ≥65 years admitted through the emergency department (ED) with a hospital length of stay (HLOS) <50 days and a primary discharge diagnosis of acute myocardial infarction (MI), pneumonia (PNA), or heart failure (HF; [identified through the respective Clinical Classification Software [CCS]).

The sample was restricted to discharges with a HLOS of <50 days, because those with longer HLOS (0.01% of study sample) were likely different in ways that may bias our findings (eg, they will likely be sicker). We limited our study to admissions through the ED to reduce potential selection bias by excluding elective admissions and hospital-to-hospital transfers, which are likely dependent on occupancy. MI, HF, and PNA diagnoses were selected because they are prevalent and have high inpatient mortality, allowing us to examine the effect of occupancy on some of the sickest inpatients.¹⁷

Hospital-acquired cases of CDI were identified as discharges (using ICD-9 code 008.45 for CDI) that were not marked as present-on-admission (POA) using the method described by Zhan et al.¹⁸ To avoid small facility outlying effects, we included hospitals that had 100 or more MI, HF, and PNA discharges that met the inclusion criteria over the study years.

OSHPD inpatient data were combined with OSHPD hospital annual financial data that contain hospital-level variables including ownership (City/County, District, Investor, and Non-Profit), geography (based on health services area), teaching status, urbanicity, and size based on the number of average annual licensed beds. If characteristics were not available for a given hospital for 1 or more years, the information from the closest available year was used for that hospital (replacement required for 10,504 (1.5%) cases; 4,856 otherwise eligible cases (0.7%) were dropped because the hospital was not included in the annual financial data for any year. Approximately 0.2% of records had invalid values for disposition, payer, or admission route, and were therefore dropped. Patient residence zip code-level socioeconomic status was measured using the percentage of families living below the poverty line, median family income, and the percentage of individuals with less than a high school degree among those aged ≥ 25 years¹⁹; these measures were divided into 3 groups (bottom quartile, top quartile, and middle 50%) for analysis.

Measure of Occupancy

Calculating Daily Census and Bed Capacity

We calculated the daily census using admission date and HLOS for each observation in our dataset. We approximated the bed capacity as the maximum daily census in the 121-day window (+/- 60 days) around each census day in each hospital. The 121-day window was chosen to increase the likelihood of capturing changes in bed availability (eg, due to unit closures) and seasonal variability. Our daily census does not include patients admitted with psychiatric and obstetrics diagnoses and long-term care/rehabilitation stays (identified through CCS categories and excluded) because these patients are not likely to compete for the same hospital resources as those receiving care for MI, HF, and PNA. See Appendix Table 1 for definition of the occupancy terms.

Calculating Relative Daily Occupancy

We developed a raw hospital-specific occupancy measure by dividing the daily census by the maximum census in each 121-day window for each hospital. We converted these raw measures to percentiles within the 121-day window to create a daily relative occupancy measure. For example, median level occupancy day would correspond to an occupancy of 0.5; a minimum or maximum occupancy day would correspond to 0 or 1, respectively. We preferred a relative occupancy measure because it assumes that what constitutes “high occupancy” likely depends on the usual occupancy level of the facility.

Measuring Admission Day Occupancy and Average Occupancy over Hospitalization

Using the relative daily occupancy values, we constructed patient-level variables representing occupancy on admission day and average occupancy during hospitalization.

Data Analysis

First, we estimated descriptive statistics of the sample for occupancy, patient-level (eg, age, race, gender, and severity of illness), hospital-level (eg, size, teaching status, and urbanicity), and incident-level (day-of-the-week and season) variables. Next, we used logistic regression with cluster standard errors to estimate the adjusted and unadjusted association of occupancy with CDI. For this analysis, occupancy was broken into 4 groups: 0.00-0.25 (low occupancy); 0.26-0.50; 0.51-0.75; and 0.76-1.00 (high occupancy), with the 0.0-0.25 group treated as the reference level. We fit separate models for admission and average occupancy and re-ran the latter model including HLOS as a sensitivity analysis.

Study Population and Hospitals

Across 327 hospitals, 558,829 discharges (including deaths) met our inclusion criteria and there were 2045 admissions with CDI. The hospital and discharge characteristics are reported in Appendix Table 2.

Relationship of Occupancy with CDI

With regard to admission occupancy, the 0.26-0.50 group did not have a significantly higher rate of CDI than the low occupancy group. Both the 0.51-0.75 and the 0.76-1.00 occupancy groups had 15% lower odds of CDI compared to the low occupancy group (Table). The adjusted results were similar, although the comparison between the low and high occupancy groups was marginally nonsignificant.

With regard to average occupancy, intermediate levels of occupancy (ie, 0.26-0.50 and 0.51-0.75 groups) had over 3-fold increased odds of CDI relative to the low occupancy group; the high occupancy group did not have significantly different odds of CDI compared to the low occupancy group (Table 1). The adjusted results were similar with no changes in statistical significance. Including HLOS tempered the adjusted odds of CDI to 1.6 for intermediate levels of occupancy, but these remained significantly higher than high or low occupancy.

Hospital occupancy is related to CDI. However, contrary to expectation, we found that higher admission and average occupancy over hospitalization were not related to more hospital-acquired CDI. CDI rates were highest for intermediate levels of average occupancy with lower CDI rates at high and low occupancy. CDI had an inverse relationship with admission occupancy.

These findings suggest that an exploration of the processes associated with hospitals accommodating higher occupancy might elucidate measures to reduce CDI. How do staffing, implementation of policies, and routine procedures vary when hospitals are busy or quiet? What aspects of care delivery that function well during high and low occupancy periods breakdown during intermediate occupancy? Hospital policies, practices, and procedures during different phases of occupancy might inform best practices. These data suggest that hospital occupancy level should be a routinely collected data element by infection control officers and that this should be linked with protocols triggered or modified with high or low occupancy that might affect HACs.

Previous studies in Europe found increasing hospital-acquired infection rates with increasing occupancy.^13-15 The authors postulated that increasing occupancy may limit available resources and increase nursing workloads, negatively impacting adherence to hand hygiene and cleaning protocols .⁸ However, these studies did not account for infections that were POA. In addition, our study examined hospitals in California after the 2006 implementation of the minimum nurse staffing policy, which means that staff to patient ratios could not fall below fixed thresholds that were typically higher than pre-policy ratios.¹⁹

This study had limitations pertaining to coded administrative data, including quality of coding and data validity. However, OSHPD has strict data reporting processes.²⁰ This study focused on 1 state; however, California is large with a demographically diverse population and hospital types, characteristics that would help generalize findings. Furthermore, when using the average occupancy measure, we could not determine whether the complication was acquired during the high occupancy period of the hospitalization.

Higher admission day occupancy was associated with lower likelihood of CDI, and CDI rates were lower at high and low average occupancy. These findings should prompt exploration of how hospitals react to occupancy changes and how those care processes translate into HACs in order to inform best practices for hospital care.

Acknowledgments

The authors would like to thank Ms. Amanda Kogowski, MPH and Mr. Rekar Taymour, MS for their editorial assistance with drafting the manuscript.

Disclosures

The authors have no conflicts to disclose.

Funding 

This study was funded by the National Institute on Aging.

High hospital occupancy is a fundamental challenge faced by healthcare systems in the United States.^1-3 However, few studies have examined the effect of high occupancy on outcomes in the inpatient setting,^4-9 and these showed mixed results. Hospital-acquired conditions (HACs), such as Clostridium difficile infection (CDI), are quality indicators for inpatient care and part of the Centers for Medicare and Medicaid Services’ Hospital-Acquired Conditions Reductions Program.^10-12 However, few studies—largely conducted outside of the US—have evaluated the association between inpatient occupancy and HACs. These studies showed increasing hospital-acquired infection rates with increasing occupancy.^13-15 Past studies of hospital occupancy have relied on annual average licensed bed counts, which are not a reliable measure of available and staffed beds and do not account for variations in patient volume and bed supply.¹⁶ Using a novel measure of inpatient occupancy, we tested the hypothesis that increasing inpatient occupancy is associated with a greater likelihood of CDI.

We performed a retrospective analysis of administrative data from non-federal, acute care hospitals in California during 2008–2012 using the Office of Statewide Health Planning and Development (OSHPD) Patient Discharge Data set, a complete census of all CA licensed general acute care hospital discharge records. This study was approved by the OSHPD Committee for the Protection of Human Subjects and was deemed exempt by our institution’s Institutional Review Board.

Selection of Participants

The study population consisted of fee-for-service Medicare enrollees ≥65 years admitted through the emergency department (ED) with a hospital length of stay (HLOS) <50 days and a primary discharge diagnosis of acute myocardial infarction (MI), pneumonia (PNA), or heart failure (HF; [identified through the respective Clinical Classification Software [CCS]).

The sample was restricted to discharges with a HLOS of <50 days, because those with longer HLOS (0.01% of study sample) were likely different in ways that may bias our findings (eg, they will likely be sicker). We limited our study to admissions through the ED to reduce potential selection bias by excluding elective admissions and hospital-to-hospital transfers, which are likely dependent on occupancy. MI, HF, and PNA diagnoses were selected because they are prevalent and have high inpatient mortality, allowing us to examine the effect of occupancy on some of the sickest inpatients.¹⁷

Hospital-acquired cases of CDI were identified as discharges (using ICD-9 code 008.45 for CDI) that were not marked as present-on-admission (POA) using the method described by Zhan et al.¹⁸ To avoid small facility outlying effects, we included hospitals that had 100 or more MI, HF, and PNA discharges that met the inclusion criteria over the study years.

OSHPD inpatient data were combined with OSHPD hospital annual financial data that contain hospital-level variables including ownership (City/County, District, Investor, and Non-Profit), geography (based on health services area), teaching status, urbanicity, and size based on the number of average annual licensed beds. If characteristics were not available for a given hospital for 1 or more years, the information from the closest available year was used for that hospital (replacement required for 10,504 (1.5%) cases; 4,856 otherwise eligible cases (0.7%) were dropped because the hospital was not included in the annual financial data for any year. Approximately 0.2% of records had invalid values for disposition, payer, or admission route, and were therefore dropped. Patient residence zip code-level socioeconomic status was measured using the percentage of families living below the poverty line, median family income, and the percentage of individuals with less than a high school degree among those aged ≥ 25 years¹⁹; these measures were divided into 3 groups (bottom quartile, top quartile, and middle 50%) for analysis.

Measure of Occupancy

Calculating Daily Census and Bed Capacity

We calculated the daily census using admission date and HLOS for each observation in our dataset. We approximated the bed capacity as the maximum daily census in the 121-day window (+/- 60 days) around each census day in each hospital. The 121-day window was chosen to increase the likelihood of capturing changes in bed availability (eg, due to unit closures) and seasonal variability. Our daily census does not include patients admitted with psychiatric and obstetrics diagnoses and long-term care/rehabilitation stays (identified through CCS categories and excluded) because these patients are not likely to compete for the same hospital resources as those receiving care for MI, HF, and PNA. See Appendix Table 1 for definition of the occupancy terms.

Calculating Relative Daily Occupancy

We developed a raw hospital-specific occupancy measure by dividing the daily census by the maximum census in each 121-day window for each hospital. We converted these raw measures to percentiles within the 121-day window to create a daily relative occupancy measure. For example, median level occupancy day would correspond to an occupancy of 0.5; a minimum or maximum occupancy day would correspond to 0 or 1, respectively. We preferred a relative occupancy measure because it assumes that what constitutes “high occupancy” likely depends on the usual occupancy level of the facility.

Measuring Admission Day Occupancy and Average Occupancy over Hospitalization

Using the relative daily occupancy values, we constructed patient-level variables representing occupancy on admission day and average occupancy during hospitalization.

Data Analysis

First, we estimated descriptive statistics of the sample for occupancy, patient-level (eg, age, race, gender, and severity of illness), hospital-level (eg, size, teaching status, and urbanicity), and incident-level (day-of-the-week and season) variables. Next, we used logistic regression with cluster standard errors to estimate the adjusted and unadjusted association of occupancy with CDI. For this analysis, occupancy was broken into 4 groups: 0.00-0.25 (low occupancy); 0.26-0.50; 0.51-0.75; and 0.76-1.00 (high occupancy), with the 0.0-0.25 group treated as the reference level. We fit separate models for admission and average occupancy and re-ran the latter model including HLOS as a sensitivity analysis.

Study Population and Hospitals

Across 327 hospitals, 558,829 discharges (including deaths) met our inclusion criteria and there were 2045 admissions with CDI. The hospital and discharge characteristics are reported in Appendix Table 2.

Relationship of Occupancy with CDI

With regard to admission occupancy, the 0.26-0.50 group did not have a significantly higher rate of CDI than the low occupancy group. Both the 0.51-0.75 and the 0.76-1.00 occupancy groups had 15% lower odds of CDI compared to the low occupancy group (Table). The adjusted results were similar, although the comparison between the low and high occupancy groups was marginally nonsignificant.

With regard to average occupancy, intermediate levels of occupancy (ie, 0.26-0.50 and 0.51-0.75 groups) had over 3-fold increased odds of CDI relative to the low occupancy group; the high occupancy group did not have significantly different odds of CDI compared to the low occupancy group (Table 1). The adjusted results were similar with no changes in statistical significance. Including HLOS tempered the adjusted odds of CDI to 1.6 for intermediate levels of occupancy, but these remained significantly higher than high or low occupancy.

Hospital occupancy is related to CDI. However, contrary to expectation, we found that higher admission and average occupancy over hospitalization were not related to more hospital-acquired CDI. CDI rates were highest for intermediate levels of average occupancy with lower CDI rates at high and low occupancy. CDI had an inverse relationship with admission occupancy.

These findings suggest that an exploration of the processes associated with hospitals accommodating higher occupancy might elucidate measures to reduce CDI. How do staffing, implementation of policies, and routine procedures vary when hospitals are busy or quiet? What aspects of care delivery that function well during high and low occupancy periods breakdown during intermediate occupancy? Hospital policies, practices, and procedures during different phases of occupancy might inform best practices. These data suggest that hospital occupancy level should be a routinely collected data element by infection control officers and that this should be linked with protocols triggered or modified with high or low occupancy that might affect HACs.

Previous studies in Europe found increasing hospital-acquired infection rates with increasing occupancy.^13-15 The authors postulated that increasing occupancy may limit available resources and increase nursing workloads, negatively impacting adherence to hand hygiene and cleaning protocols .⁸ However, these studies did not account for infections that were POA. In addition, our study examined hospitals in California after the 2006 implementation of the minimum nurse staffing policy, which means that staff to patient ratios could not fall below fixed thresholds that were typically higher than pre-policy ratios.¹⁹

This study had limitations pertaining to coded administrative data, including quality of coding and data validity. However, OSHPD has strict data reporting processes.²⁰ This study focused on 1 state; however, California is large with a demographically diverse population and hospital types, characteristics that would help generalize findings. Furthermore, when using the average occupancy measure, we could not determine whether the complication was acquired during the high occupancy period of the hospitalization.

Higher admission day occupancy was associated with lower likelihood of CDI, and CDI rates were lower at high and low average occupancy. These findings should prompt exploration of how hospitals react to occupancy changes and how those care processes translate into HACs in order to inform best practices for hospital care.

Acknowledgments

The authors would like to thank Ms. Amanda Kogowski, MPH and Mr. Rekar Taymour, MS for their editorial assistance with drafting the manuscript.

Disclosures

The authors have no conflicts to disclose.

Funding 

This study was funded by the National Institute on Aging.

High hospital occupancy is a fundamental challenge faced by healthcare systems in the United States.^1-3 However, few studies have examined the effect of high occupancy on outcomes in the inpatient setting,^4-9 and these showed mixed results. Hospital-acquired conditions (HACs), such as Clostridium difficile infection (CDI), are quality indicators for inpatient care and part of the Centers for Medicare and Medicaid Services’ Hospital-Acquired Conditions Reductions Program.^10-12 However, few studies—largely conducted outside of the US—have evaluated the association between inpatient occupancy and HACs. These studies showed increasing hospital-acquired infection rates with increasing occupancy.^13-15 Past studies of hospital occupancy have relied on annual average licensed bed counts, which are not a reliable measure of available and staffed beds and do not account for variations in patient volume and bed supply.¹⁶ Using a novel measure of inpatient occupancy, we tested the hypothesis that increasing inpatient occupancy is associated with a greater likelihood of CDI.

We performed a retrospective analysis of administrative data from non-federal, acute care hospitals in California during 2008–2012 using the Office of Statewide Health Planning and Development (OSHPD) Patient Discharge Data set, a complete census of all CA licensed general acute care hospital discharge records. This study was approved by the OSHPD Committee for the Protection of Human Subjects and was deemed exempt by our institution’s Institutional Review Board.

Selection of Participants

The study population consisted of fee-for-service Medicare enrollees ≥65 years admitted through the emergency department (ED) with a hospital length of stay (HLOS) <50 days and a primary discharge diagnosis of acute myocardial infarction (MI), pneumonia (PNA), or heart failure (HF; [identified through the respective Clinical Classification Software [CCS]).

The sample was restricted to discharges with a HLOS of <50 days, because those with longer HLOS (0.01% of study sample) were likely different in ways that may bias our findings (eg, they will likely be sicker). We limited our study to admissions through the ED to reduce potential selection bias by excluding elective admissions and hospital-to-hospital transfers, which are likely dependent on occupancy. MI, HF, and PNA diagnoses were selected because they are prevalent and have high inpatient mortality, allowing us to examine the effect of occupancy on some of the sickest inpatients.¹⁷

Hospital-acquired cases of CDI were identified as discharges (using ICD-9 code 008.45 for CDI) that were not marked as present-on-admission (POA) using the method described by Zhan et al.¹⁸ To avoid small facility outlying effects, we included hospitals that had 100 or more MI, HF, and PNA discharges that met the inclusion criteria over the study years.

OSHPD inpatient data were combined with OSHPD hospital annual financial data that contain hospital-level variables including ownership (City/County, District, Investor, and Non-Profit), geography (based on health services area), teaching status, urbanicity, and size based on the number of average annual licensed beds. If characteristics were not available for a given hospital for 1 or more years, the information from the closest available year was used for that hospital (replacement required for 10,504 (1.5%) cases; 4,856 otherwise eligible cases (0.7%) were dropped because the hospital was not included in the annual financial data for any year. Approximately 0.2% of records had invalid values for disposition, payer, or admission route, and were therefore dropped. Patient residence zip code-level socioeconomic status was measured using the percentage of families living below the poverty line, median family income, and the percentage of individuals with less than a high school degree among those aged ≥ 25 years¹⁹; these measures were divided into 3 groups (bottom quartile, top quartile, and middle 50%) for analysis.

Measure of Occupancy

Calculating Daily Census and Bed Capacity

We calculated the daily census using admission date and HLOS for each observation in our dataset. We approximated the bed capacity as the maximum daily census in the 121-day window (+/- 60 days) around each census day in each hospital. The 121-day window was chosen to increase the likelihood of capturing changes in bed availability (eg, due to unit closures) and seasonal variability. Our daily census does not include patients admitted with psychiatric and obstetrics diagnoses and long-term care/rehabilitation stays (identified through CCS categories and excluded) because these patients are not likely to compete for the same hospital resources as those receiving care for MI, HF, and PNA. See Appendix Table 1 for definition of the occupancy terms.

Calculating Relative Daily Occupancy

We developed a raw hospital-specific occupancy measure by dividing the daily census by the maximum census in each 121-day window for each hospital. We converted these raw measures to percentiles within the 121-day window to create a daily relative occupancy measure. For example, median level occupancy day would correspond to an occupancy of 0.5; a minimum or maximum occupancy day would correspond to 0 or 1, respectively. We preferred a relative occupancy measure because it assumes that what constitutes “high occupancy” likely depends on the usual occupancy level of the facility.

Measuring Admission Day Occupancy and Average Occupancy over Hospitalization

Using the relative daily occupancy values, we constructed patient-level variables representing occupancy on admission day and average occupancy during hospitalization.

Data Analysis

First, we estimated descriptive statistics of the sample for occupancy, patient-level (eg, age, race, gender, and severity of illness), hospital-level (eg, size, teaching status, and urbanicity), and incident-level (day-of-the-week and season) variables. Next, we used logistic regression with cluster standard errors to estimate the adjusted and unadjusted association of occupancy with CDI. For this analysis, occupancy was broken into 4 groups: 0.00-0.25 (low occupancy); 0.26-0.50; 0.51-0.75; and 0.76-1.00 (high occupancy), with the 0.0-0.25 group treated as the reference level. We fit separate models for admission and average occupancy and re-ran the latter model including HLOS as a sensitivity analysis.

Study Population and Hospitals

Across 327 hospitals, 558,829 discharges (including deaths) met our inclusion criteria and there were 2045 admissions with CDI. The hospital and discharge characteristics are reported in Appendix Table 2.

Relationship of Occupancy with CDI

With regard to admission occupancy, the 0.26-0.50 group did not have a significantly higher rate of CDI than the low occupancy group. Both the 0.51-0.75 and the 0.76-1.00 occupancy groups had 15% lower odds of CDI compared to the low occupancy group (Table). The adjusted results were similar, although the comparison between the low and high occupancy groups was marginally nonsignificant.

With regard to average occupancy, intermediate levels of occupancy (ie, 0.26-0.50 and 0.51-0.75 groups) had over 3-fold increased odds of CDI relative to the low occupancy group; the high occupancy group did not have significantly different odds of CDI compared to the low occupancy group (Table 1). The adjusted results were similar with no changes in statistical significance. Including HLOS tempered the adjusted odds of CDI to 1.6 for intermediate levels of occupancy, but these remained significantly higher than high or low occupancy.

Hospital occupancy is related to CDI. However, contrary to expectation, we found that higher admission and average occupancy over hospitalization were not related to more hospital-acquired CDI. CDI rates were highest for intermediate levels of average occupancy with lower CDI rates at high and low occupancy. CDI had an inverse relationship with admission occupancy.

These findings suggest that an exploration of the processes associated with hospitals accommodating higher occupancy might elucidate measures to reduce CDI. How do staffing, implementation of policies, and routine procedures vary when hospitals are busy or quiet? What aspects of care delivery that function well during high and low occupancy periods breakdown during intermediate occupancy? Hospital policies, practices, and procedures during different phases of occupancy might inform best practices. These data suggest that hospital occupancy level should be a routinely collected data element by infection control officers and that this should be linked with protocols triggered or modified with high or low occupancy that might affect HACs.

Previous studies in Europe found increasing hospital-acquired infection rates with increasing occupancy.^13-15 The authors postulated that increasing occupancy may limit available resources and increase nursing workloads, negatively impacting adherence to hand hygiene and cleaning protocols .⁸ However, these studies did not account for infections that were POA. In addition, our study examined hospitals in California after the 2006 implementation of the minimum nurse staffing policy, which means that staff to patient ratios could not fall below fixed thresholds that were typically higher than pre-policy ratios.¹⁹

This study had limitations pertaining to coded administrative data, including quality of coding and data validity. However, OSHPD has strict data reporting processes.²⁰ This study focused on 1 state; however, California is large with a demographically diverse population and hospital types, characteristics that would help generalize findings. Furthermore, when using the average occupancy measure, we could not determine whether the complication was acquired during the high occupancy period of the hospitalization.

Higher admission day occupancy was associated with lower likelihood of CDI, and CDI rates were lower at high and low average occupancy. These findings should prompt exploration of how hospitals react to occupancy changes and how those care processes translate into HACs in order to inform best practices for hospital care.

Acknowledgments

The authors would like to thank Ms. Amanda Kogowski, MPH and Mr. Rekar Taymour, MS for their editorial assistance with drafting the manuscript.

Disclosures

The authors have no conflicts to disclose.

Funding 

This study was funded by the National Institute on Aging.

Pediatric hospital medicine (PHM) has grown tremendously since Wachter first described the specialty in 1996.¹ Evidence of this growth is seen most markedly at the annual Pediatric Hospitalist Meeting, which has experienced an increase in attendance from 700 in 2013 to over 1,200 in 2017². Although the exact number of pediatric hospitalists in the United States is unknown, the American Academy of Pediatrics Section on Hospital Medicine (AAP SOHM) estimates that approximately 3,000-5,000 pediatric hospitalists currently practice in the country (personal communication).

As PHM programs have grown, variability has been reported in the roles, responsibilities, and workload among practitioners. Gosdin et al.³ reported large ranges and standard deviations in workload among full-time equivalents (FTEs) in academic PHM programs. However, this study’s ability to account for important nuances in program description was limited given that its data were obtained from an online survey.

Program variability, particularly regarding clinical hours and overall clinical burden (eg, in-house hours, census caps, and weekend coverage), is concerning given the well-reported increase in physician burn-out.^4,5 Benchmarking data regarding the overall workload of pediatric hospitalists can offer nationally recognized guidance to assist program leaders in building successful programs. With this goal in mind, we sought to obtain data on university-based PHM programs to describe the current average workload for a 1.0 clinical FTE pediatric hospitalist and to assess the perceptions of program directors regarding the sustainability of the current workload.

Study Design and Population

To obtain data with sufficient detail to compare programs, the authors, all of whom are practicing pediatric hospitalists at university-based programs, conducted structured interviews of PHM leaders in the United States. Given the absence of a single database for all PHM programs in the United States, the clinical division/program leaders of university-based programs were invited to participate through a post (with 2 reminders) to the AAP SOHM Listserv for PHM Division Leaders in May of 2017. To encourage participation, respondents were promised a summary of aggregate data. The study was exempted by the IRB of the University of Chicago.

Interview Content and Administration

The authors designed an 18-question structured interview regarding the current state of staffing in university-based PHM programs, with a focus on current descriptions of FTE, patient volume, and workload. Utilizing prior surveys³ as a basis, the authors iteratively determined the questions essential to understanding the programs’ current staffing models and ideal models. Considering the diversity of program models, interviews allowed for the clarification of questions and answers. A question regarding employment models was included to determine whether hospitalists were university-employed, hospital-employed, or a hybrid of the 2 modes of employment. The interview was also designed to establish a common language for work metrics (hours per year) for comparative purposes and to assess the perceived sustainability of the workload. Questions were provided in advance to provide respondents with sufficient time to collect data, thus increasing the accuracy of estimates. Respondents were asked, “Do you or your hospitalists have concerns about the sustainability of the model?” Sustainability was intentionally undefined to prevent limiting respondent perspective. For clarification, however, a follow-up comment that included examples was provided: “Faculty departures, reduction in total effort, and/or significant burn out.” The authors piloted the interview protocol by interviewing the division leaders of their own programs, and revisions were made based on feedback on feasibility and clarity. Finally, the AAP SOHM Subcommittee on Division Leaders provided feedback, which was incorporated.

Each author then interviewed 10-12 leaders (or designee) during May and June of 2017. Answers were recorded in REDCAP, an online survey and database tool that contains largely numeric data fields and has 1 field for narrative comments.

Data Analysis

Descriptive statistics were used to summarize interview responses, including median values with interquartile range. Data were compared between programs with models that were self-identified as either sustainable or unsustainable, with P-values in categorical variables from χ²-test or Fischer’s exact test and in continuous variables from Wilcoxon rank-sum test. 

Spearman correlation coefficient was used to evaluate the association between average protected time (defined as the percent of funded time for nonclinical roles) and percentage working full-time clinical effort. It was also used to evaluate hours per year per 1.0 FTE and total weekends per year per 1.0 FTE and perceived sustainability. Linear regression was used to determine whether associations differed between groups identifying as sustainable versus unsustainable.

Participation and Program Characteristics

Of the 143 subscribers to the listserv, which includes community and university-based programs, 62 division leaders/directors that self-identified by university-based hospitalist programs initially responded, and 56 completed phone interviews. Of these 56 respondents, 48% were university employed. The remainder were hospital employed (27%), had joint university/hospital appointments (13%), practiced in a private group (5%), or other models (7%).

Administration

A wide variation was reported in the clinical time expected of a 1.0 FTE hospitalist. Clinical time for 1.0 FTE was defined as the amount of clinical service a full-time hospitalist is expected to complete in 12 months (Table 1). The median hours worked per year were 1800 (Interquartile range [IQR] 1620,1975; mean 1796). The median number of weekends worked per year was 15.0 (IQR 12.5, 21; mean 16.8). Only 30% of pediatric hospitalists were full-time clinicians, whereas the rest had protected time for nonclinical duties. The average amount of protected time was 20% per full-time hospitalist.

Sustainability and Ideal FTE

Half of the division leaders reported that they or their hospitalists have concerns about the sustainability of the current workload. Programs perceived as sustainable required significantly fewer weekends per year (13 vs. 16, P < .02; Table 2) than those perceived as unsustainable. University-employed programs were more likely to be perceived as unsustainable (64% unsustainable vs. 32% unsustainable, P < .048), whereas programs with other employment models were more likely to be perceived as sustainable (Table 2). Total hours currently worked did not differ significantly between programs perceived as sustainable and unsustainable. Respondents reported an ideal workload for a 1.0 FTE of 1700 clinical hours (median). The hours worked per year for programs perceived as sustainable were statistically closer to their ideal than those perceived as unsustainable (P = .46; Table 2).

This study updates what has been previously reported about the structure and characteristics of university-based pediatric hospitalist programs.³ It also deepens our understanding of a relatively new field and the evolution of clinical coverage models. This evolution has been impacted by decreased resident work hours, increased patient complexity and acuity,⁶ and a broadened focus on care coordination and communication,⁷ while attempting to build and sustain a high-quality workforce.

This study is the first to use an interview-based method to determine the current PHM workload and to focus exclusively on university-based programs. Compared with the study by Gosdin et al,³ our study, which utilized interviews instead of surveys, was able to clarify questions and obtain workload data with a common language of hours per year. This approach allowed interviewees to incorporate subtleties, such as clinical vs. total FTE, in their responses. Our study found a slightly narrower range of clinical hours per year and extended the understanding of nonclinical duties by finding that university-based hospitalists have an average of 20% protected time from clinical duties.

In this study, we also explored the perceived sustainability of current clinical models and the ideal clinical model in hours per year. Half of respondents felt their current model was unsustainable. This result suggested that the field must continue to mitigate attrition and burnout.

Interestingly, the total number of clinical hours did not significantly differ in programs perceived to be unsustainable. Instead, a higher number of weekends worked and university employment were associated with lack of sustainability. We hypothesize that weekends have a disproportionate impact on work-life balance as compared with total hours, and that employment by a university may be a proxy for the increased academic and teaching demands of hospitalists without protected time. Future studies may better elucidate these findings and inform programmatic efforts to address sustainability.

Given that PHM is a relatively young field, considering the evolution of our clinical work model within the context of pediatric emergency medicine (PEM), a field that faces similar challenges in overnight and weekend staffing requirements, may be helpful. Gorelick et al.⁸ reported that total clinical work hours in PEM (combined academic and nonacademic programs) has decreased from 35.3 hours per week in 1998 to 26.7 in 2013. Extrapolating these numbers to an annual position with 5 weeks PTO/CME, the average PEM attending physician works 1254 clinical hours. These numbers demonstrate a marked difference compared with the average 1800 clinical work hours for PHM found in our study.

Although total hours trend lower in PEM, the authors noted continued challenges in sustainability with an estimated half of all PEM respondents indicating a plan to reduce hours or leave the field in the next 5 years and endorsing symptoms of burnout.⁶ These findings from PEM may motivate PHM leaders to be more aggressive in adjusting work models toward sustainability in the future.

Our study has several limitations. We utilized a convenience sampling approach that requires the voluntary participation of division directors. Although we had robust interest from respondents representing all major geographic areas, the respondent pool might conceivably over-represent those most interested in understanding and/or changing PHM clinical models. Overall, our sample size was smaller than that achieved by a survey approach. Nevertheless, this limitation was offset by controlling respondent type and clarifying questions, thus improving the quality of our obtained data.

This interview-based study of PHM directors describes the current state of clinical work models for university-based hospitalists. University-based PHM programs have similar mean and median total clinical hours per year. However, these hours are higher than those considered ideal by PHM directors, and many are concerned about the sustainability of current work models. Notably, programs that are university-employed or have higher weekends worked per year are more likely to be perceived as unsustainable. Future studies should explore differences between programs with sustainable work models and those with high levels of attrition and burnout.

Disclosures

The authors have no other conflicts to report.

Funding

A grant from the American Academy of Pediatrics Section on Hospital Medicine funded this study through the Subcommittee on Division and Program Leaders.

Pediatric hospital medicine (PHM) has grown tremendously since Wachter first described the specialty in 1996.¹ Evidence of this growth is seen most markedly at the annual Pediatric Hospitalist Meeting, which has experienced an increase in attendance from 700 in 2013 to over 1,200 in 2017². Although the exact number of pediatric hospitalists in the United States is unknown, the American Academy of Pediatrics Section on Hospital Medicine (AAP SOHM) estimates that approximately 3,000-5,000 pediatric hospitalists currently practice in the country (personal communication).

As PHM programs have grown, variability has been reported in the roles, responsibilities, and workload among practitioners. Gosdin et al.³ reported large ranges and standard deviations in workload among full-time equivalents (FTEs) in academic PHM programs. However, this study’s ability to account for important nuances in program description was limited given that its data were obtained from an online survey.

Program variability, particularly regarding clinical hours and overall clinical burden (eg, in-house hours, census caps, and weekend coverage), is concerning given the well-reported increase in physician burn-out.^4,5 Benchmarking data regarding the overall workload of pediatric hospitalists can offer nationally recognized guidance to assist program leaders in building successful programs. With this goal in mind, we sought to obtain data on university-based PHM programs to describe the current average workload for a 1.0 clinical FTE pediatric hospitalist and to assess the perceptions of program directors regarding the sustainability of the current workload.

Study Design and Population

To obtain data with sufficient detail to compare programs, the authors, all of whom are practicing pediatric hospitalists at university-based programs, conducted structured interviews of PHM leaders in the United States. Given the absence of a single database for all PHM programs in the United States, the clinical division/program leaders of university-based programs were invited to participate through a post (with 2 reminders) to the AAP SOHM Listserv for PHM Division Leaders in May of 2017. To encourage participation, respondents were promised a summary of aggregate data. The study was exempted by the IRB of the University of Chicago.

Interview Content and Administration

The authors designed an 18-question structured interview regarding the current state of staffing in university-based PHM programs, with a focus on current descriptions of FTE, patient volume, and workload. Utilizing prior surveys³ as a basis, the authors iteratively determined the questions essential to understanding the programs’ current staffing models and ideal models. Considering the diversity of program models, interviews allowed for the clarification of questions and answers. A question regarding employment models was included to determine whether hospitalists were university-employed, hospital-employed, or a hybrid of the 2 modes of employment. The interview was also designed to establish a common language for work metrics (hours per year) for comparative purposes and to assess the perceived sustainability of the workload. Questions were provided in advance to provide respondents with sufficient time to collect data, thus increasing the accuracy of estimates. Respondents were asked, “Do you or your hospitalists have concerns about the sustainability of the model?” Sustainability was intentionally undefined to prevent limiting respondent perspective. For clarification, however, a follow-up comment that included examples was provided: “Faculty departures, reduction in total effort, and/or significant burn out.” The authors piloted the interview protocol by interviewing the division leaders of their own programs, and revisions were made based on feedback on feasibility and clarity. Finally, the AAP SOHM Subcommittee on Division Leaders provided feedback, which was incorporated.

Each author then interviewed 10-12 leaders (or designee) during May and June of 2017. Answers were recorded in REDCAP, an online survey and database tool that contains largely numeric data fields and has 1 field for narrative comments.

Data Analysis

Descriptive statistics were used to summarize interview responses, including median values with interquartile range. Data were compared between programs with models that were self-identified as either sustainable or unsustainable, with P-values in categorical variables from χ²-test or Fischer’s exact test and in continuous variables from Wilcoxon rank-sum test. 

Spearman correlation coefficient was used to evaluate the association between average protected time (defined as the percent of funded time for nonclinical roles) and percentage working full-time clinical effort. It was also used to evaluate hours per year per 1.0 FTE and total weekends per year per 1.0 FTE and perceived sustainability. Linear regression was used to determine whether associations differed between groups identifying as sustainable versus unsustainable.

Participation and Program Characteristics

Of the 143 subscribers to the listserv, which includes community and university-based programs, 62 division leaders/directors that self-identified by university-based hospitalist programs initially responded, and 56 completed phone interviews. Of these 56 respondents, 48% were university employed. The remainder were hospital employed (27%), had joint university/hospital appointments (13%), practiced in a private group (5%), or other models (7%).

Administration

A wide variation was reported in the clinical time expected of a 1.0 FTE hospitalist. Clinical time for 1.0 FTE was defined as the amount of clinical service a full-time hospitalist is expected to complete in 12 months (Table 1). The median hours worked per year were 1800 (Interquartile range [IQR] 1620,1975; mean 1796). The median number of weekends worked per year was 15.0 (IQR 12.5, 21; mean 16.8). Only 30% of pediatric hospitalists were full-time clinicians, whereas the rest had protected time for nonclinical duties. The average amount of protected time was 20% per full-time hospitalist.

Sustainability and Ideal FTE

Half of the division leaders reported that they or their hospitalists have concerns about the sustainability of the current workload. Programs perceived as sustainable required significantly fewer weekends per year (13 vs. 16, P < .02; Table 2) than those perceived as unsustainable. University-employed programs were more likely to be perceived as unsustainable (64% unsustainable vs. 32% unsustainable, P < .048), whereas programs with other employment models were more likely to be perceived as sustainable (Table 2). Total hours currently worked did not differ significantly between programs perceived as sustainable and unsustainable. Respondents reported an ideal workload for a 1.0 FTE of 1700 clinical hours (median). The hours worked per year for programs perceived as sustainable were statistically closer to their ideal than those perceived as unsustainable (P = .46; Table 2).

This study updates what has been previously reported about the structure and characteristics of university-based pediatric hospitalist programs.³ It also deepens our understanding of a relatively new field and the evolution of clinical coverage models. This evolution has been impacted by decreased resident work hours, increased patient complexity and acuity,⁶ and a broadened focus on care coordination and communication,⁷ while attempting to build and sustain a high-quality workforce.

This study is the first to use an interview-based method to determine the current PHM workload and to focus exclusively on university-based programs. Compared with the study by Gosdin et al,³ our study, which utilized interviews instead of surveys, was able to clarify questions and obtain workload data with a common language of hours per year. This approach allowed interviewees to incorporate subtleties, such as clinical vs. total FTE, in their responses. Our study found a slightly narrower range of clinical hours per year and extended the understanding of nonclinical duties by finding that university-based hospitalists have an average of 20% protected time from clinical duties.

In this study, we also explored the perceived sustainability of current clinical models and the ideal clinical model in hours per year. Half of respondents felt their current model was unsustainable. This result suggested that the field must continue to mitigate attrition and burnout.

Interestingly, the total number of clinical hours did not significantly differ in programs perceived to be unsustainable. Instead, a higher number of weekends worked and university employment were associated with lack of sustainability. We hypothesize that weekends have a disproportionate impact on work-life balance as compared with total hours, and that employment by a university may be a proxy for the increased academic and teaching demands of hospitalists without protected time. Future studies may better elucidate these findings and inform programmatic efforts to address sustainability.

Given that PHM is a relatively young field, considering the evolution of our clinical work model within the context of pediatric emergency medicine (PEM), a field that faces similar challenges in overnight and weekend staffing requirements, may be helpful. Gorelick et al.⁸ reported that total clinical work hours in PEM (combined academic and nonacademic programs) has decreased from 35.3 hours per week in 1998 to 26.7 in 2013. Extrapolating these numbers to an annual position with 5 weeks PTO/CME, the average PEM attending physician works 1254 clinical hours. These numbers demonstrate a marked difference compared with the average 1800 clinical work hours for PHM found in our study.

Although total hours trend lower in PEM, the authors noted continued challenges in sustainability with an estimated half of all PEM respondents indicating a plan to reduce hours or leave the field in the next 5 years and endorsing symptoms of burnout.⁶ These findings from PEM may motivate PHM leaders to be more aggressive in adjusting work models toward sustainability in the future.

Our study has several limitations. We utilized a convenience sampling approach that requires the voluntary participation of division directors. Although we had robust interest from respondents representing all major geographic areas, the respondent pool might conceivably over-represent those most interested in understanding and/or changing PHM clinical models. Overall, our sample size was smaller than that achieved by a survey approach. Nevertheless, this limitation was offset by controlling respondent type and clarifying questions, thus improving the quality of our obtained data.

This interview-based study of PHM directors describes the current state of clinical work models for university-based hospitalists. University-based PHM programs have similar mean and median total clinical hours per year. However, these hours are higher than those considered ideal by PHM directors, and many are concerned about the sustainability of current work models. Notably, programs that are university-employed or have higher weekends worked per year are more likely to be perceived as unsustainable. Future studies should explore differences between programs with sustainable work models and those with high levels of attrition and burnout.

Disclosures

The authors have no other conflicts to report.

Funding

A grant from the American Academy of Pediatrics Section on Hospital Medicine funded this study through the Subcommittee on Division and Program Leaders.

Pediatric hospital medicine (PHM) has grown tremendously since Wachter first described the specialty in 1996.¹ Evidence of this growth is seen most markedly at the annual Pediatric Hospitalist Meeting, which has experienced an increase in attendance from 700 in 2013 to over 1,200 in 2017². Although the exact number of pediatric hospitalists in the United States is unknown, the American Academy of Pediatrics Section on Hospital Medicine (AAP SOHM) estimates that approximately 3,000-5,000 pediatric hospitalists currently practice in the country (personal communication).

As PHM programs have grown, variability has been reported in the roles, responsibilities, and workload among practitioners. Gosdin et al.³ reported large ranges and standard deviations in workload among full-time equivalents (FTEs) in academic PHM programs. However, this study’s ability to account for important nuances in program description was limited given that its data were obtained from an online survey.

Program variability, particularly regarding clinical hours and overall clinical burden (eg, in-house hours, census caps, and weekend coverage), is concerning given the well-reported increase in physician burn-out.^4,5 Benchmarking data regarding the overall workload of pediatric hospitalists can offer nationally recognized guidance to assist program leaders in building successful programs. With this goal in mind, we sought to obtain data on university-based PHM programs to describe the current average workload for a 1.0 clinical FTE pediatric hospitalist and to assess the perceptions of program directors regarding the sustainability of the current workload.

Study Design and Population

To obtain data with sufficient detail to compare programs, the authors, all of whom are practicing pediatric hospitalists at university-based programs, conducted structured interviews of PHM leaders in the United States. Given the absence of a single database for all PHM programs in the United States, the clinical division/program leaders of university-based programs were invited to participate through a post (with 2 reminders) to the AAP SOHM Listserv for PHM Division Leaders in May of 2017. To encourage participation, respondents were promised a summary of aggregate data. The study was exempted by the IRB of the University of Chicago.

Interview Content and Administration

The authors designed an 18-question structured interview regarding the current state of staffing in university-based PHM programs, with a focus on current descriptions of FTE, patient volume, and workload. Utilizing prior surveys³ as a basis, the authors iteratively determined the questions essential to understanding the programs’ current staffing models and ideal models. Considering the diversity of program models, interviews allowed for the clarification of questions and answers. A question regarding employment models was included to determine whether hospitalists were university-employed, hospital-employed, or a hybrid of the 2 modes of employment. The interview was also designed to establish a common language for work metrics (hours per year) for comparative purposes and to assess the perceived sustainability of the workload. Questions were provided in advance to provide respondents with sufficient time to collect data, thus increasing the accuracy of estimates. Respondents were asked, “Do you or your hospitalists have concerns about the sustainability of the model?” Sustainability was intentionally undefined to prevent limiting respondent perspective. For clarification, however, a follow-up comment that included examples was provided: “Faculty departures, reduction in total effort, and/or significant burn out.” The authors piloted the interview protocol by interviewing the division leaders of their own programs, and revisions were made based on feedback on feasibility and clarity. Finally, the AAP SOHM Subcommittee on Division Leaders provided feedback, which was incorporated.

Each author then interviewed 10-12 leaders (or designee) during May and June of 2017. Answers were recorded in REDCAP, an online survey and database tool that contains largely numeric data fields and has 1 field for narrative comments.

Data Analysis

Descriptive statistics were used to summarize interview responses, including median values with interquartile range. Data were compared between programs with models that were self-identified as either sustainable or unsustainable, with P-values in categorical variables from χ²-test or Fischer’s exact test and in continuous variables from Wilcoxon rank-sum test. 

Spearman correlation coefficient was used to evaluate the association between average protected time (defined as the percent of funded time for nonclinical roles) and percentage working full-time clinical effort. It was also used to evaluate hours per year per 1.0 FTE and total weekends per year per 1.0 FTE and perceived sustainability. Linear regression was used to determine whether associations differed between groups identifying as sustainable versus unsustainable.

Participation and Program Characteristics

Of the 143 subscribers to the listserv, which includes community and university-based programs, 62 division leaders/directors that self-identified by university-based hospitalist programs initially responded, and 56 completed phone interviews. Of these 56 respondents, 48% were university employed. The remainder were hospital employed (27%), had joint university/hospital appointments (13%), practiced in a private group (5%), or other models (7%).

Administration

A wide variation was reported in the clinical time expected of a 1.0 FTE hospitalist. Clinical time for 1.0 FTE was defined as the amount of clinical service a full-time hospitalist is expected to complete in 12 months (Table 1). The median hours worked per year were 1800 (Interquartile range [IQR] 1620,1975; mean 1796). The median number of weekends worked per year was 15.0 (IQR 12.5, 21; mean 16.8). Only 30% of pediatric hospitalists were full-time clinicians, whereas the rest had protected time for nonclinical duties. The average amount of protected time was 20% per full-time hospitalist.

Sustainability and Ideal FTE

Half of the division leaders reported that they or their hospitalists have concerns about the sustainability of the current workload. Programs perceived as sustainable required significantly fewer weekends per year (13 vs. 16, P < .02; Table 2) than those perceived as unsustainable. University-employed programs were more likely to be perceived as unsustainable (64% unsustainable vs. 32% unsustainable, P < .048), whereas programs with other employment models were more likely to be perceived as sustainable (Table 2). Total hours currently worked did not differ significantly between programs perceived as sustainable and unsustainable. Respondents reported an ideal workload for a 1.0 FTE of 1700 clinical hours (median). The hours worked per year for programs perceived as sustainable were statistically closer to their ideal than those perceived as unsustainable (P = .46; Table 2).

This study updates what has been previously reported about the structure and characteristics of university-based pediatric hospitalist programs.³ It also deepens our understanding of a relatively new field and the evolution of clinical coverage models. This evolution has been impacted by decreased resident work hours, increased patient complexity and acuity,⁶ and a broadened focus on care coordination and communication,⁷ while attempting to build and sustain a high-quality workforce.

This study is the first to use an interview-based method to determine the current PHM workload and to focus exclusively on university-based programs. Compared with the study by Gosdin et al,³ our study, which utilized interviews instead of surveys, was able to clarify questions and obtain workload data with a common language of hours per year. This approach allowed interviewees to incorporate subtleties, such as clinical vs. total FTE, in their responses. Our study found a slightly narrower range of clinical hours per year and extended the understanding of nonclinical duties by finding that university-based hospitalists have an average of 20% protected time from clinical duties.

In this study, we also explored the perceived sustainability of current clinical models and the ideal clinical model in hours per year. Half of respondents felt their current model was unsustainable. This result suggested that the field must continue to mitigate attrition and burnout.

Interestingly, the total number of clinical hours did not significantly differ in programs perceived to be unsustainable. Instead, a higher number of weekends worked and university employment were associated with lack of sustainability. We hypothesize that weekends have a disproportionate impact on work-life balance as compared with total hours, and that employment by a university may be a proxy for the increased academic and teaching demands of hospitalists without protected time. Future studies may better elucidate these findings and inform programmatic efforts to address sustainability.

Given that PHM is a relatively young field, considering the evolution of our clinical work model within the context of pediatric emergency medicine (PEM), a field that faces similar challenges in overnight and weekend staffing requirements, may be helpful. Gorelick et al.⁸ reported that total clinical work hours in PEM (combined academic and nonacademic programs) has decreased from 35.3 hours per week in 1998 to 26.7 in 2013. Extrapolating these numbers to an annual position with 5 weeks PTO/CME, the average PEM attending physician works 1254 clinical hours. These numbers demonstrate a marked difference compared with the average 1800 clinical work hours for PHM found in our study.

Although total hours trend lower in PEM, the authors noted continued challenges in sustainability with an estimated half of all PEM respondents indicating a plan to reduce hours or leave the field in the next 5 years and endorsing symptoms of burnout.⁶ These findings from PEM may motivate PHM leaders to be more aggressive in adjusting work models toward sustainability in the future.

Our study has several limitations. We utilized a convenience sampling approach that requires the voluntary participation of division directors. Although we had robust interest from respondents representing all major geographic areas, the respondent pool might conceivably over-represent those most interested in understanding and/or changing PHM clinical models. Overall, our sample size was smaller than that achieved by a survey approach. Nevertheless, this limitation was offset by controlling respondent type and clarifying questions, thus improving the quality of our obtained data.

This interview-based study of PHM directors describes the current state of clinical work models for university-based hospitalists. University-based PHM programs have similar mean and median total clinical hours per year. However, these hours are higher than those considered ideal by PHM directors, and many are concerned about the sustainability of current work models. Notably, programs that are university-employed or have higher weekends worked per year are more likely to be perceived as unsustainable. Future studies should explore differences between programs with sustainable work models and those with high levels of attrition and burnout.

Disclosures

The authors have no other conflicts to report.

Funding

A grant from the American Academy of Pediatrics Section on Hospital Medicine funded this study through the Subcommittee on Division and Program Leaders.

Hospital stays for pulmonary embolism (PE) represent a significant cost burden to the United States healthcare system.¹ The mean total hospitalization costs for treating a patient with PE ranges widely from $8,764 to $37,006, with an average reported length of stay between 4 and 5 days.^2,3 This cost range is attributed to many factors, including type of PE, therapy-induced bleeding risk requiring close monitoring, comorbidities, and social determinants of health. Given that patients with low-risk PE represent the majority of the cases, changes in approaches to care for this population can significantly impact the overall healthcare costs for PE. The European Society of Cardiology (ESC) guidelines incorporate well-validated risk scores, known as the pulmonary embolism severity index (PESI) and the simplified PESI (sPESI) score, and diagnostic test recommendations, including troponin test, echocardiography, and computed tomography, to evaluate patients with PE at varying risk for mortality.⁴ In these guidelines, the risk stratification algorithm for patients with a low PESI score or a sPESI score of zero does not include checking for the presence of troponin. In reality, practicing hospitalists frequently find that patients receiving a workup in the emergency department for suspected PE undergo troponin test. The ESC guidelines categorize patients with a low-risk score on PESI/sPESI, who subsequently have a positive troponin status, as intermediate low-risk and suggest consideration of hospitalization. The guidelines recommend patients with positive cardiac biomarkers to undergo assessment of right ventricular function through echocardiogram or computed tomography analysis. Moreover, the guidelines support early discharge or ambulatory treatment for low-risk patients who have a negative troponin status.⁴

The American College of Chest Physicians (ACCP) guidelines on venous thromboembolism (VTE) recommend that cardiac biomarkers should not be measured routinely in all patients with PE and that positive troponin status should discourage physicians from pursuing ambulatory treatment.⁵ Therefore, ambiguity lies within both guidelines with regard to how hospitalists should interpret a positive troponin status in patients with low risk, which in turn may lead to unnecessary hospitalizations and further imaging. This systematic review and meta-analysis aims to provide clarity, both about gaps in literature and about how practicing hospitalists should interpret troponins in patients with low-risk PE.

Data Sources and Searches

This systematic review and meta-analysis was performed in accordance with the established methods and Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We searched MEDLINE, SCOPUS, and Cochrane Controlled Trial Registry databases for studies published from inception to December 2016 by using the following key words: pulmonary embolism AND PESI OR “pulmonary embolism severity index.” Only articles written in English language were included. The full articles of potentially eligible studies were reviewed, and articles published only in abstract form were excluded.

Study Selection

Two investigators independently assessed the abstract of each article, and the full article was assessed if it fulfilled the following criteria: (1) the publication must be original; (2) inclusion of objectively diagnosed, hemodynamically stable patients (normotensive patients) with acute PE in the inpatient or outpatient setting; (3) inclusion of patients>19 years old; (4) use of the PESI or sPESI model to stratify patients into a low-risk group irrespective of any evidence of right ventricular dysfunction; and (5) testing of cardiac troponin levels (TnI-troponin I, TnT-troponin T, or hs-TnI/TnT-high sensitivity troponin I/T) in patients. Study design, sample size, duration of follow-up, type of troponin used, definition of hemodynamic stability, and specific type of outcome measured (endpoint) did not affect the study eligibility.

Data Extraction and Risk of Bias Assessment

Statistical Analysis

Data were summarized by using 30-day all-cause mortality only because it is the most consistent endpoint reported by all of the included studies. For each study, 30-day all-cause mortality was analyzed across the 2 troponin groups, and the results were summarized in terms of positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and odds ratio (OR). To quantify the uncertainty in the LRs and ORs, we calculated 95% confidence intervals (CI).

Overall measures of PPV, NPV, PLR, and NLR were calculated on the pooled collection of data from the studies. LRs are one of the best measures of diagnostic accuracy; therefore, we defined the degree of probability of disease based on simple estimations that were reported by McGee.⁶ These estimations are independent of pretest probability and include the following: PLR 5.0 increases the probability of the outcome by about 30%, whereas NLR 0.20 decreases the probability of the outcome by 30%. To identify reasonable performance, we defined a PLR > 5 as an increase in moderate to high probability and a NLR < 0.20 as a decrease in moderate to high probability.⁶

The overall association between 30-day all-cause mortality and troponin classification among patients with low-risk PE was assessed using a mixed effects logistic regression model. The model included a random intercept to account for the correlation among the measurements for patients within a study. The exponentiated regression coefficient for troponin classification is the OR for 30-day all-cause mortality, comparing troponin-positive patients to troponin-negative patients. OR is reported with a 95% CI and a P value. A continuity correction (correction = 0.5) was applied to zero cells. Heterogeneity was measured using Cochran Q statistic and Higgins I² statistic.

Search Results

Figure 1 represents the PRISMA flow diagram for literature search and selection process to identify eligible studies for inclusion.

Study Characteristics

The abstracts of 117 articles were initially identified using the search strategy described above. Of these, 18 articles were deemed appropriate for review based on the criteria outlined in “Study Selection.” The full-text articles of the selected studies were obtained. Upon further evaluation, we identified 16 articles (Figure 1) eligible for the systematic review. Two studies were excluded because they did not provide the number of study participants that met the primary endpoints. The included studies were published from 2009–2016 (Table 1). For patients with low-risk PE, the number of patients with right ventricle dysfunction was either difficult to determine or not reported in all the studies.

Regarding study design, 11 studies were described as prospective cohorts and the remaining 5 studies were identified as retrospective (Table 1). Seven studies stratified participants’ risk of mortality by using sPESI, and 8 studies employed the PESI score. A total of 6952 participants diagnosed with PE were obtained, and 2662 (38%) were recognized as being low-risk based on either the PESI or sPESI. The sample sizes of the individual studies ranged from 121 to 1,291. The studies used either hs-cTnT, hs-cTnI, cTnT, cTnI, or a combination of hs-cTnT and cTnI or cTnT for troponin assay. Most studies used a pre-defined cut-off value to determine positive or negative troponin status.

Thirteen studies reported 30-day event rate as one of the primary endpoints. The 3 other studies included 90-day all-cause mortality, and 2 of them included in-hospital events. Secondary event rates were only reported in 4 studies and consisted of nonfatal PE, nonfatal major bleeding, and PE-related mortality.

Our systematic review revealed that 5 of the 16 studies used either hemodynamic decompensation, cardiopulmonary resuscitation, mechanical ventilation, or a combination of any of these parameters as part of their primary or secondary endpoint. However, none of the studies specified the number of patients that reached any of these endpoints. Furthermore, 10 of the 16 studies did not specify 30-day PE-related mortality outcomes. The most common endpoint was 30-day all-cause mortality, and only 7 studies reported outcomes with positive or negative troponin status.

Outcome Data of All Studies

A total of 2662 participants were categorized as being low risk based on the PESI or sPESI risk score. The pooled rate of PE-related mortality (specified and inferred) was 5 (0.46%) from 6 studies (1,093 patients), in which only 2 studies specified PE-related mortality as the primary endpoint (Vanni [2011]¹⁹ and Jimenez [2011]²⁰). The pooled rate of 30-day all-cause mortality was 24 (1.3%) from 12 studies (1882 patients). In 14 studies (2163 patients), the rates of recurrence of PE and major bleeding were 3 (0.14%) and 6 (0.28%), respectively.

Outcomes of Studies with Corresponding Troponin+ and Troponin –

Seven studies used positive or negative troponin status as endpoint to assess low-risk participants (Table 2). However, only 5 studies were included in the final meta-analysis because some data were missing in the Sanchez¹⁴ study and the Oszu⁸ study’s mortality endpoint was more than 30 days. The risk of bias within the studies was evaluated, and for most studies, the quality was of moderate degree (Supplementary Table 1). Table 2 shows the results for the overall pooled data stratified by study. In the pooled data, 463 (67%) patients tested negative for troponin and 228 (33%) tested positive. The overall mortality (from sensitivity analysis) including in-hospital, 30-day, and 90-day mortalities was 1.2%. The NPVs for all individual studies and the overall NPV are 1 or approximately 1. The overall PPVs and by study were low, ranging from 0 to 0.60. The PLRs and NLRs were not estimated for an outcome within an individual study if none of the patients experienced the outcome. When outcomes were only observed among troponin-negative patients, such as in the study of Moores (2009)²² who used 30-day all-cause mortality, the PLR had a value of zero. When outcomes were only observed among troponin-positive patients, as for 30-day all-cause mortality in the Hakemi(2015)⁹, Lauque (2014)¹⁰, and Lankeit(2011)¹⁶ studies, the NLR had a value of zero. For zero cells, a continuity correction of 0.5 was applied. The pooled likelihood ratios (LRs) for all-cause mortality were positive LR 2.04 (95% CI, 1.53 to 2.72) and negative LR 0.72 (95% CI, 0.37 to 1.40). The OR for all-cause mortality was 4.79 (95% CI 1.11 to 20.68, P = .0357).

A forest plot was created to visualize the PLR from each study included in the main analysis (Figure 2).

In this meta-analysis of 5 studies, which included 691 patients with low-risk PESI or sPESI scores, those tested positive for troponin had nearly a fivefold increased risk of 30-day all-cause mortality compared with patients who tested negative. However, the clinical significance of this association is unclear given that the CI is quite wide and mortality could be associated with PE versus other causes. Similar results were reported by other meta-analyses that consisted of patients with normotensive PE.^23-25 To our knowledge, the present meta-analysis is the first to report outcomes in patients with low-risk PE stratified by the presence of cardiac troponin.

A published paper on simplifying the clinical interpretation of LRs state that a positive LR of greater than 5 and a negative LR of less than 0.20 provide dependable evidence regarding reasonable prognostic performance.⁶ In our analysis, the positive LR was less than 5 and the negative LR’s CI included one. These results suggest a small statistical probability that a patient with a low PESI/sPESI score and a positive troponin status would benefit from inpatient monitoring; simultaneously, a negative troponin does not necessarily translate to safe outpatient therapy, based on our statistical analysis. Previous studies also reported nonextreme positive LRs.^23,24 We therefore conclude that low-risk PE patients with positive troponins may be eligible for safe ambulatory treatment or early discharge. However, the number of outcomes of interest (mortality) occurred in only 6 patients among the 228 patients who had positive troponin status. The majority of deaths were reported by Hakemi et al.⁹ in their retrospective cohort study; as such, drawing conclusions is difficult. Furthermore, the low 30-day all-cause mortality rate of 2.6% in the positive troponin group may have been affected by close monitoring of the patients, who commonly received hemodynamic and oxygen support. Based on these factors, our conclusion is relatively weak, and we cannot recommend a change in practice compared to existing guidelines. In general, additional prospective research is needed to determine whether patients with low-risk PE tested positive for troponin can receive care safely outside the hospital or, rather, require hospitalization similar to patients with intermediate-high risk PE.

We identified a number of other limitations in our analysis. First, aside from the relatively small number of pertinent studies in the literature, most of the studies are of low-moderate quality. Second, the troponin classification in various studies was not conducted using the same assay, and the cut-off value determining positive versus negative results in each case may have differed. These differences may have created some ambiguity or misclassification when the data were pooled together. Third, although the mixed effects logistic regression model controls for some of the variations among patients enrolled in different studies, significant differences exist in terms of patient characteristics or the protocol for follow-up care. This aspect was unaccounted for in this analysis. Lastly, pooled outcome events could not be retrieved from all of the included studies, which would have resulted in a misrepresentation of the true outcomes.

The ESC guidelines suggest avoiding cardiac biomarker testing in patients with low-risk PE because this practice does not have therapeutic implications. Moreover, ESC and ACCP guidelines both state that a positive cardiac biomarker should discourage treatment out of the hospital. The ACCP guidelines further encourage testing of cardiac biomarkers and/or evaluating right ventricular function via echocardiography when uncertainty exists regarding whether patients may require close in-hospital monitoring or not. Although no resounding evidence suggests that troponins have therapeutic implications in patients with low-risk PE, the current guidelines and our meta-analysis cannot offer an overwhelmingly convincing recommendation about whether or not patients with low-risk PE and positive cardiac biomarkers are best treated in the ambulatory or inpatient setting. Such patients may benefit from monitoring in an observation unit (eg, less than 24 or 48 hours), rather than requiring a full admission to the hospital. Nevertheless, our analysis shows that making this determination will require prospective studies that will utilize cardiac troponin status in predicting PE-related events, such as arrhythmia, acute respiratory failure, and hemodynamic decompensation, rather than all-cause mortality.

Until further studies, hospitalists should integrate the use of cardiac troponin and other clinical data, including those available from patient history, physical exam, and other laboratory testing, in determining whether or not to admit, observe, or discharge patients with low-risk PE. As the current guidelines recommend, we support consideration of right ventricular function assessment, via echocardiogram or computed tomography, in patients with positive cardiac troponins even when their PESI/sPESI score is low.

The authors would like to thank Megan Therese Smith, PhD and Lishi Zhang, MS for their contribution in providing a comprehensive statistical analysis of this meta-analysis.

Disclosures

The authors declare no conflicts of interest in the work under consideration for publication. Abdullah Mahayni and Mukti Patel, MD also declared no conflicts of interest with regard to the relevant financial activities outside the submitted work. Omar Darwish, DO and Alpesh Amin, MD also declared no relevant financial activities outside the submitted work; they are speakers for Bristol Myer Squibb and Pfizer regarding the anticoagulant, Apixaban, for treatment of venous thromboembolism and atrial fibrillation.

Hospital stays for pulmonary embolism (PE) represent a significant cost burden to the United States healthcare system.¹ The mean total hospitalization costs for treating a patient with PE ranges widely from $8,764 to $37,006, with an average reported length of stay between 4 and 5 days.^2,3 This cost range is attributed to many factors, including type of PE, therapy-induced bleeding risk requiring close monitoring, comorbidities, and social determinants of health. Given that patients with low-risk PE represent the majority of the cases, changes in approaches to care for this population can significantly impact the overall healthcare costs for PE. The European Society of Cardiology (ESC) guidelines incorporate well-validated risk scores, known as the pulmonary embolism severity index (PESI) and the simplified PESI (sPESI) score, and diagnostic test recommendations, including troponin test, echocardiography, and computed tomography, to evaluate patients with PE at varying risk for mortality.⁴ In these guidelines, the risk stratification algorithm for patients with a low PESI score or a sPESI score of zero does not include checking for the presence of troponin. In reality, practicing hospitalists frequently find that patients receiving a workup in the emergency department for suspected PE undergo troponin test. The ESC guidelines categorize patients with a low-risk score on PESI/sPESI, who subsequently have a positive troponin status, as intermediate low-risk and suggest consideration of hospitalization. The guidelines recommend patients with positive cardiac biomarkers to undergo assessment of right ventricular function through echocardiogram or computed tomography analysis. Moreover, the guidelines support early discharge or ambulatory treatment for low-risk patients who have a negative troponin status.⁴

The American College of Chest Physicians (ACCP) guidelines on venous thromboembolism (VTE) recommend that cardiac biomarkers should not be measured routinely in all patients with PE and that positive troponin status should discourage physicians from pursuing ambulatory treatment.⁵ Therefore, ambiguity lies within both guidelines with regard to how hospitalists should interpret a positive troponin status in patients with low risk, which in turn may lead to unnecessary hospitalizations and further imaging. This systematic review and meta-analysis aims to provide clarity, both about gaps in literature and about how practicing hospitalists should interpret troponins in patients with low-risk PE.

Data Sources and Searches

This systematic review and meta-analysis was performed in accordance with the established methods and Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We searched MEDLINE, SCOPUS, and Cochrane Controlled Trial Registry databases for studies published from inception to December 2016 by using the following key words: pulmonary embolism AND PESI OR “pulmonary embolism severity index.” Only articles written in English language were included. The full articles of potentially eligible studies were reviewed, and articles published only in abstract form were excluded.

Study Selection

Two investigators independently assessed the abstract of each article, and the full article was assessed if it fulfilled the following criteria: (1) the publication must be original; (2) inclusion of objectively diagnosed, hemodynamically stable patients (normotensive patients) with acute PE in the inpatient or outpatient setting; (3) inclusion of patients>19 years old; (4) use of the PESI or sPESI model to stratify patients into a low-risk group irrespective of any evidence of right ventricular dysfunction; and (5) testing of cardiac troponin levels (TnI-troponin I, TnT-troponin T, or hs-TnI/TnT-high sensitivity troponin I/T) in patients. Study design, sample size, duration of follow-up, type of troponin used, definition of hemodynamic stability, and specific type of outcome measured (endpoint) did not affect the study eligibility.

Data Extraction and Risk of Bias Assessment

Statistical Analysis

Data were summarized by using 30-day all-cause mortality only because it is the most consistent endpoint reported by all of the included studies. For each study, 30-day all-cause mortality was analyzed across the 2 troponin groups, and the results were summarized in terms of positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and odds ratio (OR). To quantify the uncertainty in the LRs and ORs, we calculated 95% confidence intervals (CI).

Overall measures of PPV, NPV, PLR, and NLR were calculated on the pooled collection of data from the studies. LRs are one of the best measures of diagnostic accuracy; therefore, we defined the degree of probability of disease based on simple estimations that were reported by McGee.⁶ These estimations are independent of pretest probability and include the following: PLR 5.0 increases the probability of the outcome by about 30%, whereas NLR 0.20 decreases the probability of the outcome by 30%. To identify reasonable performance, we defined a PLR > 5 as an increase in moderate to high probability and a NLR < 0.20 as a decrease in moderate to high probability.⁶

The overall association between 30-day all-cause mortality and troponin classification among patients with low-risk PE was assessed using a mixed effects logistic regression model. The model included a random intercept to account for the correlation among the measurements for patients within a study. The exponentiated regression coefficient for troponin classification is the OR for 30-day all-cause mortality, comparing troponin-positive patients to troponin-negative patients. OR is reported with a 95% CI and a P value. A continuity correction (correction = 0.5) was applied to zero cells. Heterogeneity was measured using Cochran Q statistic and Higgins I² statistic.

Search Results

Figure 1 represents the PRISMA flow diagram for literature search and selection process to identify eligible studies for inclusion.

Study Characteristics

The abstracts of 117 articles were initially identified using the search strategy described above. Of these, 18 articles were deemed appropriate for review based on the criteria outlined in “Study Selection.” The full-text articles of the selected studies were obtained. Upon further evaluation, we identified 16 articles (Figure 1) eligible for the systematic review. Two studies were excluded because they did not provide the number of study participants that met the primary endpoints. The included studies were published from 2009–2016 (Table 1). For patients with low-risk PE, the number of patients with right ventricle dysfunction was either difficult to determine or not reported in all the studies.

Regarding study design, 11 studies were described as prospective cohorts and the remaining 5 studies were identified as retrospective (Table 1). Seven studies stratified participants’ risk of mortality by using sPESI, and 8 studies employed the PESI score. A total of 6952 participants diagnosed with PE were obtained, and 2662 (38%) were recognized as being low-risk based on either the PESI or sPESI. The sample sizes of the individual studies ranged from 121 to 1,291. The studies used either hs-cTnT, hs-cTnI, cTnT, cTnI, or a combination of hs-cTnT and cTnI or cTnT for troponin assay. Most studies used a pre-defined cut-off value to determine positive or negative troponin status.

Thirteen studies reported 30-day event rate as one of the primary endpoints. The 3 other studies included 90-day all-cause mortality, and 2 of them included in-hospital events. Secondary event rates were only reported in 4 studies and consisted of nonfatal PE, nonfatal major bleeding, and PE-related mortality.

Our systematic review revealed that 5 of the 16 studies used either hemodynamic decompensation, cardiopulmonary resuscitation, mechanical ventilation, or a combination of any of these parameters as part of their primary or secondary endpoint. However, none of the studies specified the number of patients that reached any of these endpoints. Furthermore, 10 of the 16 studies did not specify 30-day PE-related mortality outcomes. The most common endpoint was 30-day all-cause mortality, and only 7 studies reported outcomes with positive or negative troponin status.

Outcome Data of All Studies

A total of 2662 participants were categorized as being low risk based on the PESI or sPESI risk score. The pooled rate of PE-related mortality (specified and inferred) was 5 (0.46%) from 6 studies (1,093 patients), in which only 2 studies specified PE-related mortality as the primary endpoint (Vanni [2011]¹⁹ and Jimenez [2011]²⁰). The pooled rate of 30-day all-cause mortality was 24 (1.3%) from 12 studies (1882 patients). In 14 studies (2163 patients), the rates of recurrence of PE and major bleeding were 3 (0.14%) and 6 (0.28%), respectively.

Outcomes of Studies with Corresponding Troponin+ and Troponin –

Seven studies used positive or negative troponin status as endpoint to assess low-risk participants (Table 2). However, only 5 studies were included in the final meta-analysis because some data were missing in the Sanchez¹⁴ study and the Oszu⁸ study’s mortality endpoint was more than 30 days. The risk of bias within the studies was evaluated, and for most studies, the quality was of moderate degree (Supplementary Table 1). Table 2 shows the results for the overall pooled data stratified by study. In the pooled data, 463 (67%) patients tested negative for troponin and 228 (33%) tested positive. The overall mortality (from sensitivity analysis) including in-hospital, 30-day, and 90-day mortalities was 1.2%. The NPVs for all individual studies and the overall NPV are 1 or approximately 1. The overall PPVs and by study were low, ranging from 0 to 0.60. The PLRs and NLRs were not estimated for an outcome within an individual study if none of the patients experienced the outcome. When outcomes were only observed among troponin-negative patients, such as in the study of Moores (2009)²² who used 30-day all-cause mortality, the PLR had a value of zero. When outcomes were only observed among troponin-positive patients, as for 30-day all-cause mortality in the Hakemi(2015)⁹, Lauque (2014)¹⁰, and Lankeit(2011)¹⁶ studies, the NLR had a value of zero. For zero cells, a continuity correction of 0.5 was applied. The pooled likelihood ratios (LRs) for all-cause mortality were positive LR 2.04 (95% CI, 1.53 to 2.72) and negative LR 0.72 (95% CI, 0.37 to 1.40). The OR for all-cause mortality was 4.79 (95% CI 1.11 to 20.68, P = .0357).

A forest plot was created to visualize the PLR from each study included in the main analysis (Figure 2).

In this meta-analysis of 5 studies, which included 691 patients with low-risk PESI or sPESI scores, those tested positive for troponin had nearly a fivefold increased risk of 30-day all-cause mortality compared with patients who tested negative. However, the clinical significance of this association is unclear given that the CI is quite wide and mortality could be associated with PE versus other causes. Similar results were reported by other meta-analyses that consisted of patients with normotensive PE.^23-25 To our knowledge, the present meta-analysis is the first to report outcomes in patients with low-risk PE stratified by the presence of cardiac troponin.

A published paper on simplifying the clinical interpretation of LRs state that a positive LR of greater than 5 and a negative LR of less than 0.20 provide dependable evidence regarding reasonable prognostic performance.⁶ In our analysis, the positive LR was less than 5 and the negative LR’s CI included one. These results suggest a small statistical probability that a patient with a low PESI/sPESI score and a positive troponin status would benefit from inpatient monitoring; simultaneously, a negative troponin does not necessarily translate to safe outpatient therapy, based on our statistical analysis. Previous studies also reported nonextreme positive LRs.^23,24 We therefore conclude that low-risk PE patients with positive troponins may be eligible for safe ambulatory treatment or early discharge. However, the number of outcomes of interest (mortality) occurred in only 6 patients among the 228 patients who had positive troponin status. The majority of deaths were reported by Hakemi et al.⁹ in their retrospective cohort study; as such, drawing conclusions is difficult. Furthermore, the low 30-day all-cause mortality rate of 2.6% in the positive troponin group may have been affected by close monitoring of the patients, who commonly received hemodynamic and oxygen support. Based on these factors, our conclusion is relatively weak, and we cannot recommend a change in practice compared to existing guidelines. In general, additional prospective research is needed to determine whether patients with low-risk PE tested positive for troponin can receive care safely outside the hospital or, rather, require hospitalization similar to patients with intermediate-high risk PE.

We identified a number of other limitations in our analysis. First, aside from the relatively small number of pertinent studies in the literature, most of the studies are of low-moderate quality. Second, the troponin classification in various studies was not conducted using the same assay, and the cut-off value determining positive versus negative results in each case may have differed. These differences may have created some ambiguity or misclassification when the data were pooled together. Third, although the mixed effects logistic regression model controls for some of the variations among patients enrolled in different studies, significant differences exist in terms of patient characteristics or the protocol for follow-up care. This aspect was unaccounted for in this analysis. Lastly, pooled outcome events could not be retrieved from all of the included studies, which would have resulted in a misrepresentation of the true outcomes.

The ESC guidelines suggest avoiding cardiac biomarker testing in patients with low-risk PE because this practice does not have therapeutic implications. Moreover, ESC and ACCP guidelines both state that a positive cardiac biomarker should discourage treatment out of the hospital. The ACCP guidelines further encourage testing of cardiac biomarkers and/or evaluating right ventricular function via echocardiography when uncertainty exists regarding whether patients may require close in-hospital monitoring or not. Although no resounding evidence suggests that troponins have therapeutic implications in patients with low-risk PE, the current guidelines and our meta-analysis cannot offer an overwhelmingly convincing recommendation about whether or not patients with low-risk PE and positive cardiac biomarkers are best treated in the ambulatory or inpatient setting. Such patients may benefit from monitoring in an observation unit (eg, less than 24 or 48 hours), rather than requiring a full admission to the hospital. Nevertheless, our analysis shows that making this determination will require prospective studies that will utilize cardiac troponin status in predicting PE-related events, such as arrhythmia, acute respiratory failure, and hemodynamic decompensation, rather than all-cause mortality.

Until further studies, hospitalists should integrate the use of cardiac troponin and other clinical data, including those available from patient history, physical exam, and other laboratory testing, in determining whether or not to admit, observe, or discharge patients with low-risk PE. As the current guidelines recommend, we support consideration of right ventricular function assessment, via echocardiogram or computed tomography, in patients with positive cardiac troponins even when their PESI/sPESI score is low.

The authors would like to thank Megan Therese Smith, PhD and Lishi Zhang, MS for their contribution in providing a comprehensive statistical analysis of this meta-analysis.

Disclosures

The authors declare no conflicts of interest in the work under consideration for publication. Abdullah Mahayni and Mukti Patel, MD also declared no conflicts of interest with regard to the relevant financial activities outside the submitted work. Omar Darwish, DO and Alpesh Amin, MD also declared no relevant financial activities outside the submitted work; they are speakers for Bristol Myer Squibb and Pfizer regarding the anticoagulant, Apixaban, for treatment of venous thromboembolism and atrial fibrillation.

Hospital stays for pulmonary embolism (PE) represent a significant cost burden to the United States healthcare system.¹ The mean total hospitalization costs for treating a patient with PE ranges widely from $8,764 to $37,006, with an average reported length of stay between 4 and 5 days.^2,3 This cost range is attributed to many factors, including type of PE, therapy-induced bleeding risk requiring close monitoring, comorbidities, and social determinants of health. Given that patients with low-risk PE represent the majority of the cases, changes in approaches to care for this population can significantly impact the overall healthcare costs for PE. The European Society of Cardiology (ESC) guidelines incorporate well-validated risk scores, known as the pulmonary embolism severity index (PESI) and the simplified PESI (sPESI) score, and diagnostic test recommendations, including troponin test, echocardiography, and computed tomography, to evaluate patients with PE at varying risk for mortality.⁴ In these guidelines, the risk stratification algorithm for patients with a low PESI score or a sPESI score of zero does not include checking for the presence of troponin. In reality, practicing hospitalists frequently find that patients receiving a workup in the emergency department for suspected PE undergo troponin test. The ESC guidelines categorize patients with a low-risk score on PESI/sPESI, who subsequently have a positive troponin status, as intermediate low-risk and suggest consideration of hospitalization. The guidelines recommend patients with positive cardiac biomarkers to undergo assessment of right ventricular function through echocardiogram or computed tomography analysis. Moreover, the guidelines support early discharge or ambulatory treatment for low-risk patients who have a negative troponin status.⁴

The American College of Chest Physicians (ACCP) guidelines on venous thromboembolism (VTE) recommend that cardiac biomarkers should not be measured routinely in all patients with PE and that positive troponin status should discourage physicians from pursuing ambulatory treatment.⁵ Therefore, ambiguity lies within both guidelines with regard to how hospitalists should interpret a positive troponin status in patients with low risk, which in turn may lead to unnecessary hospitalizations and further imaging. This systematic review and meta-analysis aims to provide clarity, both about gaps in literature and about how practicing hospitalists should interpret troponins in patients with low-risk PE.

Data Sources and Searches

This systematic review and meta-analysis was performed in accordance with the established methods and Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We searched MEDLINE, SCOPUS, and Cochrane Controlled Trial Registry databases for studies published from inception to December 2016 by using the following key words: pulmonary embolism AND PESI OR “pulmonary embolism severity index.” Only articles written in English language were included. The full articles of potentially eligible studies were reviewed, and articles published only in abstract form were excluded.

Study Selection

Two investigators independently assessed the abstract of each article, and the full article was assessed if it fulfilled the following criteria: (1) the publication must be original; (2) inclusion of objectively diagnosed, hemodynamically stable patients (normotensive patients) with acute PE in the inpatient or outpatient setting; (3) inclusion of patients>19 years old; (4) use of the PESI or sPESI model to stratify patients into a low-risk group irrespective of any evidence of right ventricular dysfunction; and (5) testing of cardiac troponin levels (TnI-troponin I, TnT-troponin T, or hs-TnI/TnT-high sensitivity troponin I/T) in patients. Study design, sample size, duration of follow-up, type of troponin used, definition of hemodynamic stability, and specific type of outcome measured (endpoint) did not affect the study eligibility.

Data Extraction and Risk of Bias Assessment

Statistical Analysis

Data were summarized by using 30-day all-cause mortality only because it is the most consistent endpoint reported by all of the included studies. For each study, 30-day all-cause mortality was analyzed across the 2 troponin groups, and the results were summarized in terms of positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and odds ratio (OR). To quantify the uncertainty in the LRs and ORs, we calculated 95% confidence intervals (CI).

Overall measures of PPV, NPV, PLR, and NLR were calculated on the pooled collection of data from the studies. LRs are one of the best measures of diagnostic accuracy; therefore, we defined the degree of probability of disease based on simple estimations that were reported by McGee.⁶ These estimations are independent of pretest probability and include the following: PLR 5.0 increases the probability of the outcome by about 30%, whereas NLR 0.20 decreases the probability of the outcome by 30%. To identify reasonable performance, we defined a PLR > 5 as an increase in moderate to high probability and a NLR < 0.20 as a decrease in moderate to high probability.⁶

The overall association between 30-day all-cause mortality and troponin classification among patients with low-risk PE was assessed using a mixed effects logistic regression model. The model included a random intercept to account for the correlation among the measurements for patients within a study. The exponentiated regression coefficient for troponin classification is the OR for 30-day all-cause mortality, comparing troponin-positive patients to troponin-negative patients. OR is reported with a 95% CI and a P value. A continuity correction (correction = 0.5) was applied to zero cells. Heterogeneity was measured using Cochran Q statistic and Higgins I² statistic.

Search Results

Figure 1 represents the PRISMA flow diagram for literature search and selection process to identify eligible studies for inclusion.

Study Characteristics

The abstracts of 117 articles were initially identified using the search strategy described above. Of these, 18 articles were deemed appropriate for review based on the criteria outlined in “Study Selection.” The full-text articles of the selected studies were obtained. Upon further evaluation, we identified 16 articles (Figure 1) eligible for the systematic review. Two studies were excluded because they did not provide the number of study participants that met the primary endpoints. The included studies were published from 2009–2016 (Table 1). For patients with low-risk PE, the number of patients with right ventricle dysfunction was either difficult to determine or not reported in all the studies.

Regarding study design, 11 studies were described as prospective cohorts and the remaining 5 studies were identified as retrospective (Table 1). Seven studies stratified participants’ risk of mortality by using sPESI, and 8 studies employed the PESI score. A total of 6952 participants diagnosed with PE were obtained, and 2662 (38%) were recognized as being low-risk based on either the PESI or sPESI. The sample sizes of the individual studies ranged from 121 to 1,291. The studies used either hs-cTnT, hs-cTnI, cTnT, cTnI, or a combination of hs-cTnT and cTnI or cTnT for troponin assay. Most studies used a pre-defined cut-off value to determine positive or negative troponin status.

Thirteen studies reported 30-day event rate as one of the primary endpoints. The 3 other studies included 90-day all-cause mortality, and 2 of them included in-hospital events. Secondary event rates were only reported in 4 studies and consisted of nonfatal PE, nonfatal major bleeding, and PE-related mortality.

Our systematic review revealed that 5 of the 16 studies used either hemodynamic decompensation, cardiopulmonary resuscitation, mechanical ventilation, or a combination of any of these parameters as part of their primary or secondary endpoint. However, none of the studies specified the number of patients that reached any of these endpoints. Furthermore, 10 of the 16 studies did not specify 30-day PE-related mortality outcomes. The most common endpoint was 30-day all-cause mortality, and only 7 studies reported outcomes with positive or negative troponin status.

Outcome Data of All Studies

A total of 2662 participants were categorized as being low risk based on the PESI or sPESI risk score. The pooled rate of PE-related mortality (specified and inferred) was 5 (0.46%) from 6 studies (1,093 patients), in which only 2 studies specified PE-related mortality as the primary endpoint (Vanni [2011]¹⁹ and Jimenez [2011]²⁰). The pooled rate of 30-day all-cause mortality was 24 (1.3%) from 12 studies (1882 patients). In 14 studies (2163 patients), the rates of recurrence of PE and major bleeding were 3 (0.14%) and 6 (0.28%), respectively.

Outcomes of Studies with Corresponding Troponin+ and Troponin –

Seven studies used positive or negative troponin status as endpoint to assess low-risk participants (Table 2). However, only 5 studies were included in the final meta-analysis because some data were missing in the Sanchez¹⁴ study and the Oszu⁸ study’s mortality endpoint was more than 30 days. The risk of bias within the studies was evaluated, and for most studies, the quality was of moderate degree (Supplementary Table 1). Table 2 shows the results for the overall pooled data stratified by study. In the pooled data, 463 (67%) patients tested negative for troponin and 228 (33%) tested positive. The overall mortality (from sensitivity analysis) including in-hospital, 30-day, and 90-day mortalities was 1.2%. The NPVs for all individual studies and the overall NPV are 1 or approximately 1. The overall PPVs and by study were low, ranging from 0 to 0.60. The PLRs and NLRs were not estimated for an outcome within an individual study if none of the patients experienced the outcome. When outcomes were only observed among troponin-negative patients, such as in the study of Moores (2009)²² who used 30-day all-cause mortality, the PLR had a value of zero. When outcomes were only observed among troponin-positive patients, as for 30-day all-cause mortality in the Hakemi(2015)⁹, Lauque (2014)¹⁰, and Lankeit(2011)¹⁶ studies, the NLR had a value of zero. For zero cells, a continuity correction of 0.5 was applied. The pooled likelihood ratios (LRs) for all-cause mortality were positive LR 2.04 (95% CI, 1.53 to 2.72) and negative LR 0.72 (95% CI, 0.37 to 1.40). The OR for all-cause mortality was 4.79 (95% CI 1.11 to 20.68, P = .0357).

A forest plot was created to visualize the PLR from each study included in the main analysis (Figure 2).

In this meta-analysis of 5 studies, which included 691 patients with low-risk PESI or sPESI scores, those tested positive for troponin had nearly a fivefold increased risk of 30-day all-cause mortality compared with patients who tested negative. However, the clinical significance of this association is unclear given that the CI is quite wide and mortality could be associated with PE versus other causes. Similar results were reported by other meta-analyses that consisted of patients with normotensive PE.^23-25 To our knowledge, the present meta-analysis is the first to report outcomes in patients with low-risk PE stratified by the presence of cardiac troponin.

A published paper on simplifying the clinical interpretation of LRs state that a positive LR of greater than 5 and a negative LR of less than 0.20 provide dependable evidence regarding reasonable prognostic performance.⁶ In our analysis, the positive LR was less than 5 and the negative LR’s CI included one. These results suggest a small statistical probability that a patient with a low PESI/sPESI score and a positive troponin status would benefit from inpatient monitoring; simultaneously, a negative troponin does not necessarily translate to safe outpatient therapy, based on our statistical analysis. Previous studies also reported nonextreme positive LRs.^23,24 We therefore conclude that low-risk PE patients with positive troponins may be eligible for safe ambulatory treatment or early discharge. However, the number of outcomes of interest (mortality) occurred in only 6 patients among the 228 patients who had positive troponin status. The majority of deaths were reported by Hakemi et al.⁹ in their retrospective cohort study; as such, drawing conclusions is difficult. Furthermore, the low 30-day all-cause mortality rate of 2.6% in the positive troponin group may have been affected by close monitoring of the patients, who commonly received hemodynamic and oxygen support. Based on these factors, our conclusion is relatively weak, and we cannot recommend a change in practice compared to existing guidelines. In general, additional prospective research is needed to determine whether patients with low-risk PE tested positive for troponin can receive care safely outside the hospital or, rather, require hospitalization similar to patients with intermediate-high risk PE.

We identified a number of other limitations in our analysis. First, aside from the relatively small number of pertinent studies in the literature, most of the studies are of low-moderate quality. Second, the troponin classification in various studies was not conducted using the same assay, and the cut-off value determining positive versus negative results in each case may have differed. These differences may have created some ambiguity or misclassification when the data were pooled together. Third, although the mixed effects logistic regression model controls for some of the variations among patients enrolled in different studies, significant differences exist in terms of patient characteristics or the protocol for follow-up care. This aspect was unaccounted for in this analysis. Lastly, pooled outcome events could not be retrieved from all of the included studies, which would have resulted in a misrepresentation of the true outcomes.

The ESC guidelines suggest avoiding cardiac biomarker testing in patients with low-risk PE because this practice does not have therapeutic implications. Moreover, ESC and ACCP guidelines both state that a positive cardiac biomarker should discourage treatment out of the hospital. The ACCP guidelines further encourage testing of cardiac biomarkers and/or evaluating right ventricular function via echocardiography when uncertainty exists regarding whether patients may require close in-hospital monitoring or not. Although no resounding evidence suggests that troponins have therapeutic implications in patients with low-risk PE, the current guidelines and our meta-analysis cannot offer an overwhelmingly convincing recommendation about whether or not patients with low-risk PE and positive cardiac biomarkers are best treated in the ambulatory or inpatient setting. Such patients may benefit from monitoring in an observation unit (eg, less than 24 or 48 hours), rather than requiring a full admission to the hospital. Nevertheless, our analysis shows that making this determination will require prospective studies that will utilize cardiac troponin status in predicting PE-related events, such as arrhythmia, acute respiratory failure, and hemodynamic decompensation, rather than all-cause mortality.

Until further studies, hospitalists should integrate the use of cardiac troponin and other clinical data, including those available from patient history, physical exam, and other laboratory testing, in determining whether or not to admit, observe, or discharge patients with low-risk PE. As the current guidelines recommend, we support consideration of right ventricular function assessment, via echocardiogram or computed tomography, in patients with positive cardiac troponins even when their PESI/sPESI score is low.

The authors would like to thank Megan Therese Smith, PhD and Lishi Zhang, MS for their contribution in providing a comprehensive statistical analysis of this meta-analysis.

Disclosures

The authors declare no conflicts of interest in the work under consideration for publication. Abdullah Mahayni and Mukti Patel, MD also declared no conflicts of interest with regard to the relevant financial activities outside the submitted work. Omar Darwish, DO and Alpesh Amin, MD also declared no relevant financial activities outside the submitted work; they are speakers for Bristol Myer Squibb and Pfizer regarding the anticoagulant, Apixaban, for treatment of venous thromboembolism and atrial fibrillation.

Approximately 14% of children who sustain a concussion are admitted to the hospital,¹ although admission rates reportedly vary substantially among pediatric hospitals.² Children hospitalized for concussion may be at a higher risk for persistent postconcussive symptoms,^3,4 yet little is known about this subset of children and how they are managed while in the hospital. Characterizing children hospitalized for concussion and describing the inpatient care they received will promote hypothesis generation for further inquiry into indications for admission, as well as the relationship between inpatient management and concussion recovery.

We described a cohort of children admitted to 40 pediatric hospitals primarily for concussion and detailed care delivered during hospitalization. We explored individual-level factors and their association with prolonged length of stay (LOS) and emergency department (ED) readmission. Finally, we evaluated if there had been changes in inpatient care over the 8-year study period.

Study Design

The Institutional Review Board determined that this retrospective cohort study was exempt from review.

Data Source

The Children’s Hospital Association’s Pediatric Health Information System (PHIS) is an administrative database from pediatric hospitals located within 17 major metropolitan areas in the United States. Data include: service dates, patient demographics, payer type, diagnosis codes, resource utilization information (eg, medications), and hospital characteristics.^1,5 De-identified data undergo reliability and validity checks prior to inclusion.^1,5 We analyzed data from 40 of 43 hospitals that contributed inpatient data during our study period. 2 hospitals were excluded due to inconsistent data submission, and 1 removed their data.

Study Population

Data were extracted for children 0 to 17 years old who were admitted to an inpatient or observational unit between January 1, 2007 and December 31, 2014 for traumatic brain injury (TBI). Children were identified using International Classification of Diseases, Clinical Modification, Ninth Revision (ICD-9-CM) diagnosis codes that denote TBI per the Centers for Disease Control (CDC): 800.0–801.9, 803.0–804.9, 850–854.1, and 959.01.^6–8 To examine inpatient care for concussion, we only retained children with a primary (ie, first) concussion-related diagnosis code (850.0–850.9⁹) for analyses. For patients with multiple visits during our study period, only the index admission was analyzed. We refined our cohort using 2 injury scores calculated from ICD-9-CM diagnosis codes using validated ICDMAP-90 injury coding software.^6,10–12 The Abbreviated Injury Scale (AIS) ranges from 1 (minor injury) to 6 (not survivable). The total Injury Severity Score (ISS) is based on 6 body regions (head/neck, face, chest, abdomen, extremity, and external) and calculated by summing the squares of the 3 worst AIS scores.¹³ A concussion receives a head AIS score of 2 if there is an associated loss of consciousness or a score of 1 if there is not; therefore, children were excluded if the head AIS score was >2. We also excluded children with the following features, as they may be indicative of more severe injuries that were likely the cause of admission: ISS > 6, secondary diagnosis code of skull fracture or intracranial injury, intensive care unit (ICU) or operating room (OR) charges, or a LOS > 7 days. Because some children are hospitalized for potentially abusive minor head trauma pending a safe discharge plan, we excluded children 0 to 4 years of age with child abuse, which was determined using a specific set of diagnosis codes (E960-E96820, 995.54, and 995.55) similar to previous research.¹⁴

Data Elements and Outcomes

Outcomes

Based on previous reports,^1,15 a LOS ≥ 2 days distinguished a typical hospitalization from a prolonged one. ED revisit was identified when a child had a visit with a TBI-related primary diagnosis code at a PHIS hospital within 30 days of initial admission and was discharged home. We limited analyses to children discharged, as children readmitted may have had an initially missed intracranial injury.

Patient Characteristics

We examined the following patient variables: age, race, sex, presence of chronic medical condition, payer type, household income, area of residence (eg, rural versus urban), and mechanism of injury. Age was categorized to represent early childhood (0 to 4 years), school age (5 to 12 years), and adolescence (12 to 17 years). Race was grouped as white, black, or other (Asian, Pacific Islander, American Indian, and “other” per PHIS). Ethnicity was described as Hispanic/Latino or not Hispanic/Latino. Children with medical conditions lasting at least 12 months and comorbidities that may impact TBI recovery were identified using a subgrouping of ICD-9-CM codes for children with “complex chronic conditions”.¹⁶ Payer type was categorized as government, private, and self-pay. We extracted a PHIS variable representing the 2010 median household income for the child’s home zip code and categorized it into quartiles based on the Federal Poverty Level for a family of 4.^17,18 Area of residence was defined using a Rural–Urban Commuting Area (RUCA) classification system¹⁹ and grouped into large urban core, suburban area, large rural town, or small rural town/isolated rural area.¹⁷ Mechanism of injury was determined using E-codes and categorized using the CDC injury framework,²⁰ with sports-related injuries identified using a previously described set of E-codes.¹ Mechanisms of injury included fall, motor vehicle collision, other motorized transport (eg, all-terrain vehicles), sports-related, struck by or against (ie, objects), and all others (eg, cyclists).

Hospital Characteristics

Hospitals were characterized by region (Northeast, Central, South, and West) and size (small <200, medium 200–400, and large >400 beds). The trauma-level accreditation was identified with Level 1 reflecting the highest possible trauma resources.

Medical Care Variables

Care variables included medications, neuroimaging, and cost of stay. Medication classes included oral non-narcotic analgesics [acetaminophen, ibuprofen, and others (aspirin, tramadol, and naproxen)], oral narcotics (codeine, oxycodone, and narcotic–non-narcotic combinations), intravenous (IV) non-narcotics (ketorolac), IV narcotics (morphine, fentanyl, and hydromorphone), antiemetics [ondansetron, metoclopramide, and phenothiazines (prochlorperazine, chlorpromazine, and promethazine)], maintenance IV fluids (dextrose with electrolytes or 0.45% sodium chloride), and resuscitation IV fluids (0.9% sodium chloride or lactated Ringer’s solution). Receipt of neuroimaging was determined if head computed tomography (CT) had been conducted at the admitting hospital. Adjusted cost of stay was calculated using a hospital-specific cost-to-charge ratio with additional adjustments using the Center for Medicare & Medicaid’s Wage Index.

Statistical Analyses

Descriptive statistics were calculated for individual, injury, and hospital, and care data elements, LOS, and ED readmissions. The number of children admitted with TBI was used as the denominator to assess the proportion of pediatric TBI admissions that were due to concussions. To identify factors associated with prolonged LOS (ie, ≥2 days) and ED readmission, we employed a mixed models approach that accounted for clustering of observations within hospitals. Independent variables included age, sex, race, ethnicity, payer type, household income, RUCA code, chronic medical condition, and injury mechanism. Models were adjusted for hospital location, size, and trauma-level accreditation. The binary distribution was specified along with a logit link function. A 2-phase process determined factors associated with each outcome. First, bivariable models were developed, followed by multivariable models that included independent variables with P values < .25 in the bivariable analysis. Backward step-wise elimination was performed, deleting variables with the highest P value one at a time. After each deletion, the percentage change in odds ratios was examined; if variable removal resulted in >10% change, the variable was retained as a potential confounder. This process was repeated until all remaining variables were significant (P < .05) with the exception of potential confounders. Finally, we examined the proportion of children receiving selected care practices annually. Descriptive and trend analyses were used to analyze adjusted median cost of stay. Analyses were performed using SAS software (Version 9.3, SAS Institute Inc., Cary, North Carolina).

Over 8 years, 88,526 children were admitted to 40 PHIS hospitals with a TBI-related diagnosis, among whom 13,708 had a primary diagnosis of concussion. We excluded 2,973 children with 1 or more of the following characteristics: a secondary diagnosis of intracranial injury (n = 58), head AIS score > 2 (n = 218), LOS > 7 days (n = 50), OR charges (n = 132), ICU charges (n = 1947), and ISS > 6 (n = 568). Six additional children aging 0 to 4 years were excluded due to child abuse. The remaining 10,729 children, averaging 1300 hospitalizations annually, were identified as being hospitalized primarily for concussion.

Table 1 summarizes the individual characteristics for this cohort. The average (standard deviation) age was 9.5 (5.1) years. Ethnicity was missing for 25.3% and therefore excluded from the multivariable models. Almost all children had a head AIS score of 2 (99.2%), and the majority had a total ISS ≤ 4 (73.4%). The majority of admissions were admitted to Level 1 trauma-accredited hospitals (78.7%) and medium-sized hospitals (63.9%).

From 2007 to 2014, approximately 15% of children admitted to PHIS hospitals for TBI were admitted primarily for concussion. Since almost all children had a head AIS score of 2 and an ISS ≤ 4, our data suggest that most children had an associated loss of consciousness and that concussion was the only injury sustained, respectively. This study identified important subgroups that necessitated inpatient care but are rarely the focus of concussion research (eg, toddlers and those injured due to a motor vehicle collision). Most children (83.3%) received medications to treat common postconcussive symptoms (eg, pain and nausea), with almost half receiving 3 or more medication classes. Factors associated with the development of postconcussive syndrome (eg, female sex and adolescent age)^4,21 were significantly associated with hospitalization of 2 or more days and ED revisit within 30 days of admission. In the absence of evidenced-based guidelines for inpatient concussion management, we identified significant trends in care, including increased use of specific pain [ie, oral and IV nonsteroidal anti-inflammatory drugs (NSAIDs)] and antiemetic (ie, ondansetron) medications and decreased use of head CT. Given the number of children admitted and receiving treatment for concussion symptomatology, influences on the decision to deliver specific care practices, as well as the impact and benefit of hospitalization, require closer examination.

Our study extends previous reports from the PHIS database by characterizing children admitted for concussion.¹ We found that children admitted for concussion had similar characteristics to the broader population of children who sustain concussion (eg, school-aged children, male, and injured due to a fall or during sports).^1,3,22 However, approximately 20% of the cohort were less than 5 years old, and less is known regarding appropriate treatment and outcomes of concussion in this age group.²³ Uncertainty regarding optimal management and a young child’s inability to articulate symptoms may contribute to a physician’s decision to admit for close observation. Similar to Blinman et al., we found that a substantial proportion of children admitted with concussion were injured due to a motor vehicle collision,³ suggesting that although sports-related injuries are responsible for a significant proportion of pediatric concussions, children injured by other preventable mechanisms may also be incurring significant concussive injuries. Finally, the majority of our cohort was from an urban core, relative to a rural area, which is likely a reflection of the regionalization of trauma care, as well as variations in access to health care.

Although most children recover fully from concussion without specific interventions, 20%-30% may remain symptomatic at 1 month,^3,4,21,24 and children who are hospitalized with concussion may be at higher risk for protracted symptoms. While specific individual or injury-related factors (eg, female sex, adolescent age, and injury due to motor vehicle collision) may contribute to more significant postconcussive symptoms, it is unclear how inpatient management affects recovery trajectory. Frequent sleep disruptions associated with inpatient care²⁵ contradict current acute concussion management recommendations for physical and cognitive rest²⁶ and could potentially impair symptom recovery. Additionally, we found widespread use of NSAIDs, although there is evidence suggesting that NSAIDs may potentially worsen concussive symptoms.²⁶ We identified an increase in medication usage over time despite limited evidence of their effectiveness for pediatric concussion.^27–29 This change may reflect improved symptom screening^4,30 and/or increased awareness of specific medication safety profiles in pediatric trauma patients, especially for NSAIDs and ondansetron. Although we saw an increase in NSAID use, we did not see a proportional decrease in narcotic use. Similarly, while two-thirds of our cohort received IV medications, there is controversy about the need for IV fluids and medications for other pediatric illnesses, with research demonstrating that IV treatment may not reduce recovery time and may contribute to prolonged hospitalization and phlebitis.^31,32 Thus, there is a need to understand the therapeutic effectiveness and benefits of medications and fluids on postconcussion recovery.

Neuroimaging rates for children receiving ED evaluation for concussion have been reported to be up to 60%-70%,^1,22 although a more recent study spanning 2006 to 2011 found a 35-%–40% head CT rate in pediatric patients by hospital-based EDs in the United States.³³ Our results appear to support decreasing head CT use over time in pediatric hospitals. Hospitalization for observation is costly¹ but could decrease a child’s risk of malignancy from radiation exposure. Further work on balancing cost, risk, and shared decision-making with parents could guide decisions regarding emergent neuroimaging versus admission.

This study has limitations inherent to the use of an administrative dataset, including lack of information regarding why the child was admitted. Since the focus was to describe inpatient care of children with concussion, those discharged home from the ED were not included in this dataset. Consequently, we could not contrast the ED care of those discharged home with those who were admitted or assess trends in admission rates for concussion. Although the overall number of concussion admissions has continued to remain stable over time,¹ due to a lack of prospectively collected clinical information, we are unable to determine whether observed trends in care are secondary to changes in practice or changes in concussion severity. However, there has been no research to date supporting the latter. Ethnicity was excluded due to high levels of missing data. Cost of stay was not extensively analyzed given hospital variation in designation of observational or inpatient status, which subsequently affects billing.³⁴ Rates of neuroimaging and ED revisit may have been underestimated since children could have received care at a non-PHIS hospital. Similarly, the decrease in the proportion of children receiving neuroimaging over time may have been associated with an increase in children being transferred from a non-PHIS hospital for admission, although with increased regionalization in trauma care, we would not expect transfers of children with only concussion to have significantly increased. Finally, data were limited to the pediatric tertiary care centers participating in PHIS, thereby reducing generalizability and introducing selection bias by only including children who were able to access care at PHIS hospitals. Although the care practices we evaluated (eg, NSAIDs and head CT) are available at all hospitals, our analyses only reflect care delivered within the PHIS.

Concussion accounted for 15% of all pediatric TBI admissions during our study period. Further investigation of potential factors associated with admission and protracted recovery (eg, adolescent females needing treatment for severe symptomatology) could facilitate better understanding of how hospitalization affects recovery. Additionally, research on acute pharmacotherapies (eg, IV therapies and/or inpatient treatment until symptoms resolve) is needed to fully elucidate the acute and long-term benefits of interventions delivered to children.

Colleen Mangeot: Biostatistician with extensive PHIS knowledge who contributed to database creation and statistical analysis. Yanhong (Amy) Liu: Research database programmer who developed the database, ran quality assurance measures, and cleaned all study data.

Disclosures

The authors have nothing to disclose.

Funding

This study was supported by grant R40 MC 268060102 from the Maternal and Child Health Research Program, Maternal and Child Health Bureau (Title V, Social Security Act), Health Resources and Services Administration, Department of Health and Human Services. The funding source was not involved in development of the study design; in the collection, analysis and interpretation of data; or in the writing of this report.

Approximately 14% of children who sustain a concussion are admitted to the hospital,¹ although admission rates reportedly vary substantially among pediatric hospitals.² Children hospitalized for concussion may be at a higher risk for persistent postconcussive symptoms,^3,4 yet little is known about this subset of children and how they are managed while in the hospital. Characterizing children hospitalized for concussion and describing the inpatient care they received will promote hypothesis generation for further inquiry into indications for admission, as well as the relationship between inpatient management and concussion recovery.

We described a cohort of children admitted to 40 pediatric hospitals primarily for concussion and detailed care delivered during hospitalization. We explored individual-level factors and their association with prolonged length of stay (LOS) and emergency department (ED) readmission. Finally, we evaluated if there had been changes in inpatient care over the 8-year study period.

Study Design

The Institutional Review Board determined that this retrospective cohort study was exempt from review.

Data Source

The Children’s Hospital Association’s Pediatric Health Information System (PHIS) is an administrative database from pediatric hospitals located within 17 major metropolitan areas in the United States. Data include: service dates, patient demographics, payer type, diagnosis codes, resource utilization information (eg, medications), and hospital characteristics.^1,5 De-identified data undergo reliability and validity checks prior to inclusion.^1,5 We analyzed data from 40 of 43 hospitals that contributed inpatient data during our study period. 2 hospitals were excluded due to inconsistent data submission, and 1 removed their data.

Study Population

Data were extracted for children 0 to 17 years old who were admitted to an inpatient or observational unit between January 1, 2007 and December 31, 2014 for traumatic brain injury (TBI). Children were identified using International Classification of Diseases, Clinical Modification, Ninth Revision (ICD-9-CM) diagnosis codes that denote TBI per the Centers for Disease Control (CDC): 800.0–801.9, 803.0–804.9, 850–854.1, and 959.01.^6–8 To examine inpatient care for concussion, we only retained children with a primary (ie, first) concussion-related diagnosis code (850.0–850.9⁹) for analyses. For patients with multiple visits during our study period, only the index admission was analyzed. We refined our cohort using 2 injury scores calculated from ICD-9-CM diagnosis codes using validated ICDMAP-90 injury coding software.^6,10–12 The Abbreviated Injury Scale (AIS) ranges from 1 (minor injury) to 6 (not survivable). The total Injury Severity Score (ISS) is based on 6 body regions (head/neck, face, chest, abdomen, extremity, and external) and calculated by summing the squares of the 3 worst AIS scores.¹³ A concussion receives a head AIS score of 2 if there is an associated loss of consciousness or a score of 1 if there is not; therefore, children were excluded if the head AIS score was >2. We also excluded children with the following features, as they may be indicative of more severe injuries that were likely the cause of admission: ISS > 6, secondary diagnosis code of skull fracture or intracranial injury, intensive care unit (ICU) or operating room (OR) charges, or a LOS > 7 days. Because some children are hospitalized for potentially abusive minor head trauma pending a safe discharge plan, we excluded children 0 to 4 years of age with child abuse, which was determined using a specific set of diagnosis codes (E960-E96820, 995.54, and 995.55) similar to previous research.¹⁴

Data Elements and Outcomes

Outcomes

Based on previous reports,^1,15 a LOS ≥ 2 days distinguished a typical hospitalization from a prolonged one. ED revisit was identified when a child had a visit with a TBI-related primary diagnosis code at a PHIS hospital within 30 days of initial admission and was discharged home. We limited analyses to children discharged, as children readmitted may have had an initially missed intracranial injury.

Patient Characteristics

We examined the following patient variables: age, race, sex, presence of chronic medical condition, payer type, household income, area of residence (eg, rural versus urban), and mechanism of injury. Age was categorized to represent early childhood (0 to 4 years), school age (5 to 12 years), and adolescence (12 to 17 years). Race was grouped as white, black, or other (Asian, Pacific Islander, American Indian, and “other” per PHIS). Ethnicity was described as Hispanic/Latino or not Hispanic/Latino. Children with medical conditions lasting at least 12 months and comorbidities that may impact TBI recovery were identified using a subgrouping of ICD-9-CM codes for children with “complex chronic conditions”.¹⁶ Payer type was categorized as government, private, and self-pay. We extracted a PHIS variable representing the 2010 median household income for the child’s home zip code and categorized it into quartiles based on the Federal Poverty Level for a family of 4.^17,18 Area of residence was defined using a Rural–Urban Commuting Area (RUCA) classification system¹⁹ and grouped into large urban core, suburban area, large rural town, or small rural town/isolated rural area.¹⁷ Mechanism of injury was determined using E-codes and categorized using the CDC injury framework,²⁰ with sports-related injuries identified using a previously described set of E-codes.¹ Mechanisms of injury included fall, motor vehicle collision, other motorized transport (eg, all-terrain vehicles), sports-related, struck by or against (ie, objects), and all others (eg, cyclists).

Hospital Characteristics

Hospitals were characterized by region (Northeast, Central, South, and West) and size (small <200, medium 200–400, and large >400 beds). The trauma-level accreditation was identified with Level 1 reflecting the highest possible trauma resources.

Medical Care Variables

Care variables included medications, neuroimaging, and cost of stay. Medication classes included oral non-narcotic analgesics [acetaminophen, ibuprofen, and others (aspirin, tramadol, and naproxen)], oral narcotics (codeine, oxycodone, and narcotic–non-narcotic combinations), intravenous (IV) non-narcotics (ketorolac), IV narcotics (morphine, fentanyl, and hydromorphone), antiemetics [ondansetron, metoclopramide, and phenothiazines (prochlorperazine, chlorpromazine, and promethazine)], maintenance IV fluids (dextrose with electrolytes or 0.45% sodium chloride), and resuscitation IV fluids (0.9% sodium chloride or lactated Ringer’s solution). Receipt of neuroimaging was determined if head computed tomography (CT) had been conducted at the admitting hospital. Adjusted cost of stay was calculated using a hospital-specific cost-to-charge ratio with additional adjustments using the Center for Medicare & Medicaid’s Wage Index.

Statistical Analyses

Descriptive statistics were calculated for individual, injury, and hospital, and care data elements, LOS, and ED readmissions. The number of children admitted with TBI was used as the denominator to assess the proportion of pediatric TBI admissions that were due to concussions. To identify factors associated with prolonged LOS (ie, ≥2 days) and ED readmission, we employed a mixed models approach that accounted for clustering of observations within hospitals. Independent variables included age, sex, race, ethnicity, payer type, household income, RUCA code, chronic medical condition, and injury mechanism. Models were adjusted for hospital location, size, and trauma-level accreditation. The binary distribution was specified along with a logit link function. A 2-phase process determined factors associated with each outcome. First, bivariable models were developed, followed by multivariable models that included independent variables with P values < .25 in the bivariable analysis. Backward step-wise elimination was performed, deleting variables with the highest P value one at a time. After each deletion, the percentage change in odds ratios was examined; if variable removal resulted in >10% change, the variable was retained as a potential confounder. This process was repeated until all remaining variables were significant (P < .05) with the exception of potential confounders. Finally, we examined the proportion of children receiving selected care practices annually. Descriptive and trend analyses were used to analyze adjusted median cost of stay. Analyses were performed using SAS software (Version 9.3, SAS Institute Inc., Cary, North Carolina).

Over 8 years, 88,526 children were admitted to 40 PHIS hospitals with a TBI-related diagnosis, among whom 13,708 had a primary diagnosis of concussion. We excluded 2,973 children with 1 or more of the following characteristics: a secondary diagnosis of intracranial injury (n = 58), head AIS score > 2 (n = 218), LOS > 7 days (n = 50), OR charges (n = 132), ICU charges (n = 1947), and ISS > 6 (n = 568). Six additional children aging 0 to 4 years were excluded due to child abuse. The remaining 10,729 children, averaging 1300 hospitalizations annually, were identified as being hospitalized primarily for concussion.

Table 1 summarizes the individual characteristics for this cohort. The average (standard deviation) age was 9.5 (5.1) years. Ethnicity was missing for 25.3% and therefore excluded from the multivariable models. Almost all children had a head AIS score of 2 (99.2%), and the majority had a total ISS ≤ 4 (73.4%). The majority of admissions were admitted to Level 1 trauma-accredited hospitals (78.7%) and medium-sized hospitals (63.9%).

From 2007 to 2014, approximately 15% of children admitted to PHIS hospitals for TBI were admitted primarily for concussion. Since almost all children had a head AIS score of 2 and an ISS ≤ 4, our data suggest that most children had an associated loss of consciousness and that concussion was the only injury sustained, respectively. This study identified important subgroups that necessitated inpatient care but are rarely the focus of concussion research (eg, toddlers and those injured due to a motor vehicle collision). Most children (83.3%) received medications to treat common postconcussive symptoms (eg, pain and nausea), with almost half receiving 3 or more medication classes. Factors associated with the development of postconcussive syndrome (eg, female sex and adolescent age)^4,21 were significantly associated with hospitalization of 2 or more days and ED revisit within 30 days of admission. In the absence of evidenced-based guidelines for inpatient concussion management, we identified significant trends in care, including increased use of specific pain [ie, oral and IV nonsteroidal anti-inflammatory drugs (NSAIDs)] and antiemetic (ie, ondansetron) medications and decreased use of head CT. Given the number of children admitted and receiving treatment for concussion symptomatology, influences on the decision to deliver specific care practices, as well as the impact and benefit of hospitalization, require closer examination.

Our study extends previous reports from the PHIS database by characterizing children admitted for concussion.¹ We found that children admitted for concussion had similar characteristics to the broader population of children who sustain concussion (eg, school-aged children, male, and injured due to a fall or during sports).^1,3,22 However, approximately 20% of the cohort were less than 5 years old, and less is known regarding appropriate treatment and outcomes of concussion in this age group.²³ Uncertainty regarding optimal management and a young child’s inability to articulate symptoms may contribute to a physician’s decision to admit for close observation. Similar to Blinman et al., we found that a substantial proportion of children admitted with concussion were injured due to a motor vehicle collision,³ suggesting that although sports-related injuries are responsible for a significant proportion of pediatric concussions, children injured by other preventable mechanisms may also be incurring significant concussive injuries. Finally, the majority of our cohort was from an urban core, relative to a rural area, which is likely a reflection of the regionalization of trauma care, as well as variations in access to health care.

Although most children recover fully from concussion without specific interventions, 20%-30% may remain symptomatic at 1 month,^3,4,21,24 and children who are hospitalized with concussion may be at higher risk for protracted symptoms. While specific individual or injury-related factors (eg, female sex, adolescent age, and injury due to motor vehicle collision) may contribute to more significant postconcussive symptoms, it is unclear how inpatient management affects recovery trajectory. Frequent sleep disruptions associated with inpatient care²⁵ contradict current acute concussion management recommendations for physical and cognitive rest²⁶ and could potentially impair symptom recovery. Additionally, we found widespread use of NSAIDs, although there is evidence suggesting that NSAIDs may potentially worsen concussive symptoms.²⁶ We identified an increase in medication usage over time despite limited evidence of their effectiveness for pediatric concussion.^27–29 This change may reflect improved symptom screening^4,30 and/or increased awareness of specific medication safety profiles in pediatric trauma patients, especially for NSAIDs and ondansetron. Although we saw an increase in NSAID use, we did not see a proportional decrease in narcotic use. Similarly, while two-thirds of our cohort received IV medications, there is controversy about the need for IV fluids and medications for other pediatric illnesses, with research demonstrating that IV treatment may not reduce recovery time and may contribute to prolonged hospitalization and phlebitis.^31,32 Thus, there is a need to understand the therapeutic effectiveness and benefits of medications and fluids on postconcussion recovery.

Neuroimaging rates for children receiving ED evaluation for concussion have been reported to be up to 60%-70%,^1,22 although a more recent study spanning 2006 to 2011 found a 35-%–40% head CT rate in pediatric patients by hospital-based EDs in the United States.³³ Our results appear to support decreasing head CT use over time in pediatric hospitals. Hospitalization for observation is costly¹ but could decrease a child’s risk of malignancy from radiation exposure. Further work on balancing cost, risk, and shared decision-making with parents could guide decisions regarding emergent neuroimaging versus admission.

This study has limitations inherent to the use of an administrative dataset, including lack of information regarding why the child was admitted. Since the focus was to describe inpatient care of children with concussion, those discharged home from the ED were not included in this dataset. Consequently, we could not contrast the ED care of those discharged home with those who were admitted or assess trends in admission rates for concussion. Although the overall number of concussion admissions has continued to remain stable over time,¹ due to a lack of prospectively collected clinical information, we are unable to determine whether observed trends in care are secondary to changes in practice or changes in concussion severity. However, there has been no research to date supporting the latter. Ethnicity was excluded due to high levels of missing data. Cost of stay was not extensively analyzed given hospital variation in designation of observational or inpatient status, which subsequently affects billing.³⁴ Rates of neuroimaging and ED revisit may have been underestimated since children could have received care at a non-PHIS hospital. Similarly, the decrease in the proportion of children receiving neuroimaging over time may have been associated with an increase in children being transferred from a non-PHIS hospital for admission, although with increased regionalization in trauma care, we would not expect transfers of children with only concussion to have significantly increased. Finally, data were limited to the pediatric tertiary care centers participating in PHIS, thereby reducing generalizability and introducing selection bias by only including children who were able to access care at PHIS hospitals. Although the care practices we evaluated (eg, NSAIDs and head CT) are available at all hospitals, our analyses only reflect care delivered within the PHIS.

Concussion accounted for 15% of all pediatric TBI admissions during our study period. Further investigation of potential factors associated with admission and protracted recovery (eg, adolescent females needing treatment for severe symptomatology) could facilitate better understanding of how hospitalization affects recovery. Additionally, research on acute pharmacotherapies (eg, IV therapies and/or inpatient treatment until symptoms resolve) is needed to fully elucidate the acute and long-term benefits of interventions delivered to children.

Colleen Mangeot: Biostatistician with extensive PHIS knowledge who contributed to database creation and statistical analysis. Yanhong (Amy) Liu: Research database programmer who developed the database, ran quality assurance measures, and cleaned all study data.

Disclosures

The authors have nothing to disclose.

Funding

This study was supported by grant R40 MC 268060102 from the Maternal and Child Health Research Program, Maternal and Child Health Bureau (Title V, Social Security Act), Health Resources and Services Administration, Department of Health and Human Services. The funding source was not involved in development of the study design; in the collection, analysis and interpretation of data; or in the writing of this report.

Approximately 14% of children who sustain a concussion are admitted to the hospital,¹ although admission rates reportedly vary substantially among pediatric hospitals.² Children hospitalized for concussion may be at a higher risk for persistent postconcussive symptoms,^3,4 yet little is known about this subset of children and how they are managed while in the hospital. Characterizing children hospitalized for concussion and describing the inpatient care they received will promote hypothesis generation for further inquiry into indications for admission, as well as the relationship between inpatient management and concussion recovery.

We described a cohort of children admitted to 40 pediatric hospitals primarily for concussion and detailed care delivered during hospitalization. We explored individual-level factors and their association with prolonged length of stay (LOS) and emergency department (ED) readmission. Finally, we evaluated if there had been changes in inpatient care over the 8-year study period.

Study Design

The Institutional Review Board determined that this retrospective cohort study was exempt from review.

Data Source

The Children’s Hospital Association’s Pediatric Health Information System (PHIS) is an administrative database from pediatric hospitals located within 17 major metropolitan areas in the United States. Data include: service dates, patient demographics, payer type, diagnosis codes, resource utilization information (eg, medications), and hospital characteristics.^1,5 De-identified data undergo reliability and validity checks prior to inclusion.^1,5 We analyzed data from 40 of 43 hospitals that contributed inpatient data during our study period. 2 hospitals were excluded due to inconsistent data submission, and 1 removed their data.

Study Population

Data were extracted for children 0 to 17 years old who were admitted to an inpatient or observational unit between January 1, 2007 and December 31, 2014 for traumatic brain injury (TBI). Children were identified using International Classification of Diseases, Clinical Modification, Ninth Revision (ICD-9-CM) diagnosis codes that denote TBI per the Centers for Disease Control (CDC): 800.0–801.9, 803.0–804.9, 850–854.1, and 959.01.^6–8 To examine inpatient care for concussion, we only retained children with a primary (ie, first) concussion-related diagnosis code (850.0–850.9⁹) for analyses. For patients with multiple visits during our study period, only the index admission was analyzed. We refined our cohort using 2 injury scores calculated from ICD-9-CM diagnosis codes using validated ICDMAP-90 injury coding software.^6,10–12 The Abbreviated Injury Scale (AIS) ranges from 1 (minor injury) to 6 (not survivable). The total Injury Severity Score (ISS) is based on 6 body regions (head/neck, face, chest, abdomen, extremity, and external) and calculated by summing the squares of the 3 worst AIS scores.¹³ A concussion receives a head AIS score of 2 if there is an associated loss of consciousness or a score of 1 if there is not; therefore, children were excluded if the head AIS score was >2. We also excluded children with the following features, as they may be indicative of more severe injuries that were likely the cause of admission: ISS > 6, secondary diagnosis code of skull fracture or intracranial injury, intensive care unit (ICU) or operating room (OR) charges, or a LOS > 7 days. Because some children are hospitalized for potentially abusive minor head trauma pending a safe discharge plan, we excluded children 0 to 4 years of age with child abuse, which was determined using a specific set of diagnosis codes (E960-E96820, 995.54, and 995.55) similar to previous research.¹⁴

Data Elements and Outcomes

Outcomes

Based on previous reports,^1,15 a LOS ≥ 2 days distinguished a typical hospitalization from a prolonged one. ED revisit was identified when a child had a visit with a TBI-related primary diagnosis code at a PHIS hospital within 30 days of initial admission and was discharged home. We limited analyses to children discharged, as children readmitted may have had an initially missed intracranial injury.

Patient Characteristics

We examined the following patient variables: age, race, sex, presence of chronic medical condition, payer type, household income, area of residence (eg, rural versus urban), and mechanism of injury. Age was categorized to represent early childhood (0 to 4 years), school age (5 to 12 years), and adolescence (12 to 17 years). Race was grouped as white, black, or other (Asian, Pacific Islander, American Indian, and “other” per PHIS). Ethnicity was described as Hispanic/Latino or not Hispanic/Latino. Children with medical conditions lasting at least 12 months and comorbidities that may impact TBI recovery were identified using a subgrouping of ICD-9-CM codes for children with “complex chronic conditions”.¹⁶ Payer type was categorized as government, private, and self-pay. We extracted a PHIS variable representing the 2010 median household income for the child’s home zip code and categorized it into quartiles based on the Federal Poverty Level for a family of 4.^17,18 Area of residence was defined using a Rural–Urban Commuting Area (RUCA) classification system¹⁹ and grouped into large urban core, suburban area, large rural town, or small rural town/isolated rural area.¹⁷ Mechanism of injury was determined using E-codes and categorized using the CDC injury framework,²⁰ with sports-related injuries identified using a previously described set of E-codes.¹ Mechanisms of injury included fall, motor vehicle collision, other motorized transport (eg, all-terrain vehicles), sports-related, struck by or against (ie, objects), and all others (eg, cyclists).

Hospital Characteristics

Hospitals were characterized by region (Northeast, Central, South, and West) and size (small <200, medium 200–400, and large >400 beds). The trauma-level accreditation was identified with Level 1 reflecting the highest possible trauma resources.

Medical Care Variables

Care variables included medications, neuroimaging, and cost of stay. Medication classes included oral non-narcotic analgesics [acetaminophen, ibuprofen, and others (aspirin, tramadol, and naproxen)], oral narcotics (codeine, oxycodone, and narcotic–non-narcotic combinations), intravenous (IV) non-narcotics (ketorolac), IV narcotics (morphine, fentanyl, and hydromorphone), antiemetics [ondansetron, metoclopramide, and phenothiazines (prochlorperazine, chlorpromazine, and promethazine)], maintenance IV fluids (dextrose with electrolytes or 0.45% sodium chloride), and resuscitation IV fluids (0.9% sodium chloride or lactated Ringer’s solution). Receipt of neuroimaging was determined if head computed tomography (CT) had been conducted at the admitting hospital. Adjusted cost of stay was calculated using a hospital-specific cost-to-charge ratio with additional adjustments using the Center for Medicare & Medicaid’s Wage Index.

Statistical Analyses

Descriptive statistics were calculated for individual, injury, and hospital, and care data elements, LOS, and ED readmissions. The number of children admitted with TBI was used as the denominator to assess the proportion of pediatric TBI admissions that were due to concussions. To identify factors associated with prolonged LOS (ie, ≥2 days) and ED readmission, we employed a mixed models approach that accounted for clustering of observations within hospitals. Independent variables included age, sex, race, ethnicity, payer type, household income, RUCA code, chronic medical condition, and injury mechanism. Models were adjusted for hospital location, size, and trauma-level accreditation. The binary distribution was specified along with a logit link function. A 2-phase process determined factors associated with each outcome. First, bivariable models were developed, followed by multivariable models that included independent variables with P values < .25 in the bivariable analysis. Backward step-wise elimination was performed, deleting variables with the highest P value one at a time. After each deletion, the percentage change in odds ratios was examined; if variable removal resulted in >10% change, the variable was retained as a potential confounder. This process was repeated until all remaining variables were significant (P < .05) with the exception of potential confounders. Finally, we examined the proportion of children receiving selected care practices annually. Descriptive and trend analyses were used to analyze adjusted median cost of stay. Analyses were performed using SAS software (Version 9.3, SAS Institute Inc., Cary, North Carolina).

Over 8 years, 88,526 children were admitted to 40 PHIS hospitals with a TBI-related diagnosis, among whom 13,708 had a primary diagnosis of concussion. We excluded 2,973 children with 1 or more of the following characteristics: a secondary diagnosis of intracranial injury (n = 58), head AIS score > 2 (n = 218), LOS > 7 days (n = 50), OR charges (n = 132), ICU charges (n = 1947), and ISS > 6 (n = 568). Six additional children aging 0 to 4 years were excluded due to child abuse. The remaining 10,729 children, averaging 1300 hospitalizations annually, were identified as being hospitalized primarily for concussion.

Table 1 summarizes the individual characteristics for this cohort. The average (standard deviation) age was 9.5 (5.1) years. Ethnicity was missing for 25.3% and therefore excluded from the multivariable models. Almost all children had a head AIS score of 2 (99.2%), and the majority had a total ISS ≤ 4 (73.4%). The majority of admissions were admitted to Level 1 trauma-accredited hospitals (78.7%) and medium-sized hospitals (63.9%).

From 2007 to 2014, approximately 15% of children admitted to PHIS hospitals for TBI were admitted primarily for concussion. Since almost all children had a head AIS score of 2 and an ISS ≤ 4, our data suggest that most children had an associated loss of consciousness and that concussion was the only injury sustained, respectively. This study identified important subgroups that necessitated inpatient care but are rarely the focus of concussion research (eg, toddlers and those injured due to a motor vehicle collision). Most children (83.3%) received medications to treat common postconcussive symptoms (eg, pain and nausea), with almost half receiving 3 or more medication classes. Factors associated with the development of postconcussive syndrome (eg, female sex and adolescent age)^4,21 were significantly associated with hospitalization of 2 or more days and ED revisit within 30 days of admission. In the absence of evidenced-based guidelines for inpatient concussion management, we identified significant trends in care, including increased use of specific pain [ie, oral and IV nonsteroidal anti-inflammatory drugs (NSAIDs)] and antiemetic (ie, ondansetron) medications and decreased use of head CT. Given the number of children admitted and receiving treatment for concussion symptomatology, influences on the decision to deliver specific care practices, as well as the impact and benefit of hospitalization, require closer examination.

Our study extends previous reports from the PHIS database by characterizing children admitted for concussion.¹ We found that children admitted for concussion had similar characteristics to the broader population of children who sustain concussion (eg, school-aged children, male, and injured due to a fall or during sports).^1,3,22 However, approximately 20% of the cohort were less than 5 years old, and less is known regarding appropriate treatment and outcomes of concussion in this age group.²³ Uncertainty regarding optimal management and a young child’s inability to articulate symptoms may contribute to a physician’s decision to admit for close observation. Similar to Blinman et al., we found that a substantial proportion of children admitted with concussion were injured due to a motor vehicle collision,³ suggesting that although sports-related injuries are responsible for a significant proportion of pediatric concussions, children injured by other preventable mechanisms may also be incurring significant concussive injuries. Finally, the majority of our cohort was from an urban core, relative to a rural area, which is likely a reflection of the regionalization of trauma care, as well as variations in access to health care.

Although most children recover fully from concussion without specific interventions, 20%-30% may remain symptomatic at 1 month,^3,4,21,24 and children who are hospitalized with concussion may be at higher risk for protracted symptoms. While specific individual or injury-related factors (eg, female sex, adolescent age, and injury due to motor vehicle collision) may contribute to more significant postconcussive symptoms, it is unclear how inpatient management affects recovery trajectory. Frequent sleep disruptions associated with inpatient care²⁵ contradict current acute concussion management recommendations for physical and cognitive rest²⁶ and could potentially impair symptom recovery. Additionally, we found widespread use of NSAIDs, although there is evidence suggesting that NSAIDs may potentially worsen concussive symptoms.²⁶ We identified an increase in medication usage over time despite limited evidence of their effectiveness for pediatric concussion.^27–29 This change may reflect improved symptom screening^4,30 and/or increased awareness of specific medication safety profiles in pediatric trauma patients, especially for NSAIDs and ondansetron. Although we saw an increase in NSAID use, we did not see a proportional decrease in narcotic use. Similarly, while two-thirds of our cohort received IV medications, there is controversy about the need for IV fluids and medications for other pediatric illnesses, with research demonstrating that IV treatment may not reduce recovery time and may contribute to prolonged hospitalization and phlebitis.^31,32 Thus, there is a need to understand the therapeutic effectiveness and benefits of medications and fluids on postconcussion recovery.

Neuroimaging rates for children receiving ED evaluation for concussion have been reported to be up to 60%-70%,^1,22 although a more recent study spanning 2006 to 2011 found a 35-%–40% head CT rate in pediatric patients by hospital-based EDs in the United States.³³ Our results appear to support decreasing head CT use over time in pediatric hospitals. Hospitalization for observation is costly¹ but could decrease a child’s risk of malignancy from radiation exposure. Further work on balancing cost, risk, and shared decision-making with parents could guide decisions regarding emergent neuroimaging versus admission.

This study has limitations inherent to the use of an administrative dataset, including lack of information regarding why the child was admitted. Since the focus was to describe inpatient care of children with concussion, those discharged home from the ED were not included in this dataset. Consequently, we could not contrast the ED care of those discharged home with those who were admitted or assess trends in admission rates for concussion. Although the overall number of concussion admissions has continued to remain stable over time,¹ due to a lack of prospectively collected clinical information, we are unable to determine whether observed trends in care are secondary to changes in practice or changes in concussion severity. However, there has been no research to date supporting the latter. Ethnicity was excluded due to high levels of missing data. Cost of stay was not extensively analyzed given hospital variation in designation of observational or inpatient status, which subsequently affects billing.³⁴ Rates of neuroimaging and ED revisit may have been underestimated since children could have received care at a non-PHIS hospital. Similarly, the decrease in the proportion of children receiving neuroimaging over time may have been associated with an increase in children being transferred from a non-PHIS hospital for admission, although with increased regionalization in trauma care, we would not expect transfers of children with only concussion to have significantly increased. Finally, data were limited to the pediatric tertiary care centers participating in PHIS, thereby reducing generalizability and introducing selection bias by only including children who were able to access care at PHIS hospitals. Although the care practices we evaluated (eg, NSAIDs and head CT) are available at all hospitals, our analyses only reflect care delivered within the PHIS.

Concussion accounted for 15% of all pediatric TBI admissions during our study period. Further investigation of potential factors associated with admission and protracted recovery (eg, adolescent females needing treatment for severe symptomatology) could facilitate better understanding of how hospitalization affects recovery. Additionally, research on acute pharmacotherapies (eg, IV therapies and/or inpatient treatment until symptoms resolve) is needed to fully elucidate the acute and long-term benefits of interventions delivered to children.

Colleen Mangeot: Biostatistician with extensive PHIS knowledge who contributed to database creation and statistical analysis. Yanhong (Amy) Liu: Research database programmer who developed the database, ran quality assurance measures, and cleaned all study data.

Disclosures

The authors have nothing to disclose.

Funding

This study was supported by grant R40 MC 268060102 from the Maternal and Child Health Research Program, Maternal and Child Health Bureau (Title V, Social Security Act), Health Resources and Services Administration, Department of Health and Human Services. The funding source was not involved in development of the study design; in the collection, analysis and interpretation of data; or in the writing of this report.

Diagnostic error—defined as a failure to establish an accurate and timely explanation of the patient’s health problem—is an important source of patient harm.¹ Data suggest that all patients will experience at least 1 diagnostic error in their lifetime.^2-4 Not surprisingly, diagnostic errors are among the leading categories of paid malpractice claims in the United States.⁵

Despite diagnostic errors being morbid and sometimes deadly in the hospital,^6,7 little is known about how residents and learners approach diagnostic decision making. Errors in diagnosis are believed to stem from cognitive or system failures,⁸ with errors in cognition believed to occur due to rapid, reflexive thinking operating in the absence of a more analytical, deliberate process. System-based problems (eg, lack of expert availability, technology barriers, and access to data) have also been cited as contributors.⁹ However, whether and how these apply to trainees is not known.

Therefore, we conducted a focused ethnography of inpatient medicine teams (ie, attendings, residents, interns, and medical students) in 2 affiliated teaching hospitals, aiming to (a) observe the process of diagnosis by trainees and (b) identify methods to improve the diagnostic process and prevent errors.

We designed a multimethod, focused ethnographic study to examine diagnostic decision making in hospital settings.^10,11 In contrast to anthropologic ethnographies that study entire fields using open-ended questions, our study was designed to examine the process of diagnosis from the perspective of clinicians engaged in this activity.¹¹ This approach allowed us to capture diagnostic decisions and cognitive and system-based factors in a manner currently lacking in the literature.¹²

Setting and Participants

Between January 2016 and May 2016, we observed the members of four inpatient internal medicine teaching teams at 2 affiliated teaching hospitals. We purposefully selected teaching teams for observation because they are the primary model of care in academic settings and we have expertise in carrying out similar studies.^13,14 Teaching teams typically consisted of a medical attending (senior-level physician), 1 senior resident (a second- or third-year postgraduate trainee), two interns (a trainee in their first postgraduate year), and two to four  medical students. Teams were selected at random using existing schedules and followed Monday to Friday so as to permit observation of work on call and noncall days. Owing to manpower limitations, weekend and night shifts were not observed. However, overnight events were captured during morning rounds.

Most of the teams began rounds at 8:30 AM. Typically, rounds lasted for 90–120 min and concluded with a recap (ie, “running the list”) with a review of explicit plans for patients after they had been evaluated by the attending. This discussion often occurred in the team rooms, with the attending leading the discussion with the trainees.

Data Collection

A multidisciplinary team, including clinicians (eg, physicians, nurses), nonclinicians (eg, qualitative researchers, social scientists), and healthcare engineers, conducted the observations. We observed preround activities of interns and residents before arrival of the attending (7:00 AM - 8:30 AM), followed by morning rounds with the entire team, and afternoon work that included senior residents, interns, and students.

To capture multiple aspects of the diagnostic process, we collected data using field notes modeled on components of the National Academy of Science model for diagnosis (Appendix).^1,15 This model encompasses phases of the diagnostic process (eg, data gathering, integration, formulation of a working diagnosis, treatment delivery, and outcomes) and the work system (team members, organization, technology and tools, physical environment, tasks).

Focus Groups and Interviews

At the end of weekly observations, we conducted focus groups with the residents and one-on- one interviews with the attendings. Focus groups with the residents were conducted to encourage a group discussion about the diagnostic process. Separate interviews with the attendings were performed to ensure that power differentials did not influence discussions. During focus groups, we specifically asked about challenges and possible solutions to improve diagnosis. Experienced qualitative methodologists (J.F., M.H., M.Q.) used semistructured interview guides for discussions (Appendix).

Data Analysis

After aggregating and reading the data, three reviewers (V.C., S.K., S.S.) began inductive analysis by handwriting notes and initial reflective thoughts to create preliminary codes. Multiple team members then reread the original field notes and the focus group/interview data to refine the preliminary codes and develop additional codes. Next, relationships between codes were identified and used to develop key themes. Triangulation of data collected from observations and interview/focus group sessions was carried out to compare data that we surmised with data that were verbalized by the team. The developed themes were discussed as a group to ensure consistency of major findings.

Ethical and Regulatory Oversight

This study was reviewed and approved by the Institutional Review Boards at the University of Michigan Health System (HUM-00106657) and the VA Ann Arbor Healthcare System (1-2016-010040).

Four teaching teams (4 attendings, 4 senior residents, 9 interns, and 14 medical students) were observed over 33 distinct shifts and 168 hours. Observations included morning rounds (96 h), postround call days (52 h), and postround non-call days (20 h). Morning rounds lasted an average of 127 min (range: 48-232 min) and included an average of 9 patients (range: 4-16 patients).

Themes Regarding the Diagnostic Process

We identified the following 4 primary themes related to the diagnostic process in teaching hospitals: (1) diagnosis is a social phenomenon; (2) data necessary to make diagnoses are fragmented; (3) distractions undermine the diagnostic process; and (4) time pressures interfere with diagnostic decision making (Appendix Table 1).

(1) Diagnosis is a Social Phenomenon.

Team members viewed the process of diagnosis as a social exchange of facts, findings, and strategies within a defined structure. The opportunity to discuss impressions with others was valued as a means to share, test, and process assumptions.

“Rounds are the most important part of the process. That is where we make most decisions in a collective, collaborative way with the attending present. We bounce ideas off each other.” (Intern)

Typical of social processes, variations based on time of day and schedule were observed. For instance, during call days, learners gathered data and formed working diagnosis and treatment plans with minimal attending interaction. This separation of roles and responsibilities introduced a hierarchy within diagnosis as follows:

“The interns would not call me first; they would talk to the senior resident and then if the senior thought he should chat with me, then they would call. But for the most part, they gather information and come up with the plan.” (Attending).

The work system was suited to facilitate social interactions. For instance, designated rooms (with team members informally assigned to a computer) provided physical proximity of the resident to interns and medical students. In this space, numerous informal discussions between team members (eg, “What do you think about this test?” “I’m not sure what to do about this finding.” “Should I call a [consult] on this patient?”) were observed. Although proximity to each other was viewed as beneficial, dangers to the social nature of diagnosis in the form of anchoring (ie, a cognitive bias where emphasis is placed on the first piece of data)¹⁶ were also mentioned. Similarly, the paradox associated with social proof (ie, the pressure to assume conformity within a group) was also observed as disagreement between team members and attendings rarely occurred during observations.

“I mean, they’re the attending, right? It’s hard to argue with them when they want a test or something done. When I do push back, it’s rare that others will support me–so it’s usually me and the attending.” (Resident)

“I would push back if I think it’s really bad for the patient or could cause harm–but the truth is, it doesn’t happen much.” (Intern)

(2) Data Necessary to Make Diagnoses are Fragmented

Team members universally cited fragmentation in data delivery, retrieval, and processing as a barrier to diagnosis. Team members indicated that test results might not be looked at or acted upon in a timely manner, and participants pointed to the electronic medical record as a source of this challenge.

“Before I knew about [the app for Epic], I would literally sit on the computer to get all the information we would need on rounds. Its key to making decisions. We often say we will do something, only to find the test result doesn’t support it–and then we’re back to square 1.” (Intern)

Information used by teams came from myriad sources (eg, patients, family members, electronic records) and from various settings (eg, emergency department, patient rooms, discussions with consultants). Additionally, test results often appeared without warning. Thus, availability of information was poorly aligned with clinical duties.

“They (the lab) will call us when a blood culture is positive or something is off. That is very helpful but it often comes later in the day, when we’re done with rounds.” (Resident)

The work system was highlighted as a key contributor to data fragmentation. Peculiarities of our electronic medical record (EMR) and how data were collected, stored, or presented were described as “frustrating,” and “unsafe,” by team members. Correspondingly, we frequently observed interns asking for assistance for tasks such as ordering tests or finding information despite being “trained” to use the EMR.

“People have to learn how to filter, how to recognize the most important points and link data streams together in terms of causality. But we assume they know where to find that information. It’s actually a very hard thing to do, for both the house staff and me.” (Attending)

(3) Distractions Undermine the Diagnostic Process

Distractions often created cognitive difficulties. For example, ambient noise and interruptions from neighbors working on other teams were cited as barriers to diagnosis. In addition, we observed several team members using headphones to drown out ambient noise while working on the computer.

“I know I shouldn’t do it (wear headphones), but I have no other way of turning down the noise so I can concentrate.” (Intern)

Similarly, the unpredictable nature and the volume of pages often interrupted thinking about diagnosis.

“Sometimes the pager just goes off all the time and (after making sure its not an urgent issue), I will just ignore it for a bit, especially if I am in the middle of something. It would be great if I could finish my thought process knowing I would not be interrupted.” (Resident)

To mitigate this problem, 1 attending described how he would proactively seek out nurses caring for his patients to “head off” questions (eg, “I will renew the restraints and medications this morning,” and “Is there anything you need in terms of orders for this patient that I can take care of now?”) that might lead to pages. Another resident described his approach as follows:

“I make it a point to tell the nurses where I will be hanging out and where they can find me if they have any questions. I tell them to come talk to me rather than page me since that will be less distracting.” (Resident).

Most of the interns described documentation work such as writing admission and progress notes in negative terms (“an academic exercise,” “part of the billing activity”). However, in the context of interruptions, some described this as helpful.

“The most valuable part of the thinking process was writing the assessment and plan because that’s actually my schema for all problems. It literally is the only time where I can sit and collect my thoughts to formulate a diagnosis and plan.” (Intern)

(4) Time Pressures Interfere With Diagnostic Decision Making

All team members spoke about the challenge of finding time for diagnosis during the workday. Often, they had to skip learning sessions for this purpose.

“They tell us we should go to morning report or noon conference but when I’m running around trying to get things done. I hate having to choose between my education and doing what’s best for the patient–but that’s often what it comes down to.” (Intern)

When specifically asked whether setting aside dedicated time to specifically review and formulate diagnoses would be valuable, respondents were uniformly enthusiastic. Team members described attentional conflicts as being the worst when “cross covering” other teams on call days, as their patient load effectively doubled during this time. Of note, cross-covering occurred when teams were also on call—and thus took them away from important diagnostic activities such as data gathering or synthesis for patients they were admitting.

“If you were to ever design a system where errors were likely–this is how you would design it: take a team with little supervision, double their patient load, keep them busy with new challenging cases and then ask questions about patients they know little about.” (Resident)

Although diagnostic errors have been called “the next frontier for patient safety,”¹⁷ little is known about the process, barriers, and facilitators to diagnosis in teaching hospitals. In this focused ethnography conducted at 2 academic medical centers, we identified multiple cognitive and system-level challenges and potential strategies to improve diagnosis from trainees engaged in this activity. Key themes identified by those we observed included the social nature of diagnosis, fragmented information delivery, constant distractions and interruptions, and time pressures. In turn, these insights allow us to generate strategies that can be applied to improve the diagnostic process in teaching hospitals.

Our study underscores the importance of social interactions in diagnosis. In contrast, most of the interventions to prevent diagnostic errors target individual providers through practices such as metacognition and “thinking about thinking.”^18-20 These interventions are based on Daniel Kahnemann’s work on dual thought process. Type 1 thought processes are fast, subconscious, reflexive, largely intuitive, and more vulnerable to error. In contrast, Type 2 processes are slower, deliberate, analytic, and less prone to error.²¹ Although an individual’s Type 2 thought capacity is limited, a major goal of cognitive interventions is to encourage Type 2 over Type 1 thinking, an approach termed “de-biasing.”^22-24 Unfortunately, cognitive interventions testing such approaches have suffered mixed results–perhaps because of lack of focus on collective wisdom or group thinking, which may be key to diagnosis from our findings.^9,25 In this sense, morning rounds were a social gathering used to strategize and develop care plans, but with limited time to think about diagnosis.²⁶ Introduction of defined periods for individuals to engage in diagnostic activities such as de-biasing (ie, asking “what else could this be)²⁷ before or after rounds may provide an opportunity for reflection and improving diagnosis. In addition, embedding tools such as diagnosis expanders and checklists within these defined time slots^28,29 may prove to be useful in reflecting on diagnosis and preventing diagnostic errors.

An unexpected yet important finding from this study were the challenges posed by distractions and the physical environment. Potentially maladaptive workarounds to these interruptions included use of headphones; more productive strategies included updating nurses with plans to avert pages and creating a list of activities to ensure that key tasks were not forgotten.^30,31 Applying lessons from aviation, a focused effort to limit distractions during key portions of the day, might be worth considering for diagnostic safety.³² Similarly, improving the environment in which diagnosis occurs—including creating spaces that are quiet, orderly, and optimized for thinking—may be valuable.³³Our study has limitations. First, our findings are limited to direct observations; we are thus unable to comment on how unobserved aspects of care (eg, cognitive processes) might have influenced our findings. Our observations of clinical care might also have introduced a Hawthorne effect. However, because we were closely integrated with teams and conducted focus groups to corroborate our assessments, we believe that this was not the case. Second, we did not identify diagnostic errors or link processes we observed to errors. Third, our approach is limited to 2 teaching centers, thereby limiting the generalizability of findings. Relatedly, we were only able to conduct observations during weekdays; differences in weekend and night resources might affect our insights.

The cognitive and system-based barriers faced by clinicians in teaching hospitals suggest that new methods to improve diagnosis are needed. Future interventions such as defined “time-outs” for diagnosis, strategies focused on limiting distractions, and methods to improve communication between team members are novel and have parallels in other industries. As challenges to quantify diagnostic errors abound,³⁴ improving cognitive- and system-based factors via reflection through communication, concentration, and organization is necessary to improve medical decision making in academic medical centers.

Disclosures

None declared for all coauthors.

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. Dr. Chopra is supported by a career development award from the Agency of Healthcare Research and Quality (1-K08-HS022835-01). Dr. Krein is supported by a VA Health Services Research and Development Research Career Scientist Award (RCS 11-222). Dr. Singh is partially supported by Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality or the Department of Veterans Affairs.

Diagnostic error—defined as a failure to establish an accurate and timely explanation of the patient’s health problem—is an important source of patient harm.¹ Data suggest that all patients will experience at least 1 diagnostic error in their lifetime.^2-4 Not surprisingly, diagnostic errors are among the leading categories of paid malpractice claims in the United States.⁵

Despite diagnostic errors being morbid and sometimes deadly in the hospital,^6,7 little is known about how residents and learners approach diagnostic decision making. Errors in diagnosis are believed to stem from cognitive or system failures,⁸ with errors in cognition believed to occur due to rapid, reflexive thinking operating in the absence of a more analytical, deliberate process. System-based problems (eg, lack of expert availability, technology barriers, and access to data) have also been cited as contributors.⁹ However, whether and how these apply to trainees is not known.

Therefore, we conducted a focused ethnography of inpatient medicine teams (ie, attendings, residents, interns, and medical students) in 2 affiliated teaching hospitals, aiming to (a) observe the process of diagnosis by trainees and (b) identify methods to improve the diagnostic process and prevent errors.

We designed a multimethod, focused ethnographic study to examine diagnostic decision making in hospital settings.^10,11 In contrast to anthropologic ethnographies that study entire fields using open-ended questions, our study was designed to examine the process of diagnosis from the perspective of clinicians engaged in this activity.¹¹ This approach allowed us to capture diagnostic decisions and cognitive and system-based factors in a manner currently lacking in the literature.¹²

Setting and Participants

Between January 2016 and May 2016, we observed the members of four inpatient internal medicine teaching teams at 2 affiliated teaching hospitals. We purposefully selected teaching teams for observation because they are the primary model of care in academic settings and we have expertise in carrying out similar studies.^13,14 Teaching teams typically consisted of a medical attending (senior-level physician), 1 senior resident (a second- or third-year postgraduate trainee), two interns (a trainee in their first postgraduate year), and two to four  medical students. Teams were selected at random using existing schedules and followed Monday to Friday so as to permit observation of work on call and noncall days. Owing to manpower limitations, weekend and night shifts were not observed. However, overnight events were captured during morning rounds.

Most of the teams began rounds at 8:30 AM. Typically, rounds lasted for 90–120 min and concluded with a recap (ie, “running the list”) with a review of explicit plans for patients after they had been evaluated by the attending. This discussion often occurred in the team rooms, with the attending leading the discussion with the trainees.

Data Collection

A multidisciplinary team, including clinicians (eg, physicians, nurses), nonclinicians (eg, qualitative researchers, social scientists), and healthcare engineers, conducted the observations. We observed preround activities of interns and residents before arrival of the attending (7:00 AM - 8:30 AM), followed by morning rounds with the entire team, and afternoon work that included senior residents, interns, and students.

To capture multiple aspects of the diagnostic process, we collected data using field notes modeled on components of the National Academy of Science model for diagnosis (Appendix).^1,15 This model encompasses phases of the diagnostic process (eg, data gathering, integration, formulation of a working diagnosis, treatment delivery, and outcomes) and the work system (team members, organization, technology and tools, physical environment, tasks).

Focus Groups and Interviews

At the end of weekly observations, we conducted focus groups with the residents and one-on- one interviews with the attendings. Focus groups with the residents were conducted to encourage a group discussion about the diagnostic process. Separate interviews with the attendings were performed to ensure that power differentials did not influence discussions. During focus groups, we specifically asked about challenges and possible solutions to improve diagnosis. Experienced qualitative methodologists (J.F., M.H., M.Q.) used semistructured interview guides for discussions (Appendix).

Data Analysis

After aggregating and reading the data, three reviewers (V.C., S.K., S.S.) began inductive analysis by handwriting notes and initial reflective thoughts to create preliminary codes. Multiple team members then reread the original field notes and the focus group/interview data to refine the preliminary codes and develop additional codes. Next, relationships between codes were identified and used to develop key themes. Triangulation of data collected from observations and interview/focus group sessions was carried out to compare data that we surmised with data that were verbalized by the team. The developed themes were discussed as a group to ensure consistency of major findings.

Ethical and Regulatory Oversight

This study was reviewed and approved by the Institutional Review Boards at the University of Michigan Health System (HUM-00106657) and the VA Ann Arbor Healthcare System (1-2016-010040).

Four teaching teams (4 attendings, 4 senior residents, 9 interns, and 14 medical students) were observed over 33 distinct shifts and 168 hours. Observations included morning rounds (96 h), postround call days (52 h), and postround non-call days (20 h). Morning rounds lasted an average of 127 min (range: 48-232 min) and included an average of 9 patients (range: 4-16 patients).

Themes Regarding the Diagnostic Process

We identified the following 4 primary themes related to the diagnostic process in teaching hospitals: (1) diagnosis is a social phenomenon; (2) data necessary to make diagnoses are fragmented; (3) distractions undermine the diagnostic process; and (4) time pressures interfere with diagnostic decision making (Appendix Table 1).

(1) Diagnosis is a Social Phenomenon.

Team members viewed the process of diagnosis as a social exchange of facts, findings, and strategies within a defined structure. The opportunity to discuss impressions with others was valued as a means to share, test, and process assumptions.

“Rounds are the most important part of the process. That is where we make most decisions in a collective, collaborative way with the attending present. We bounce ideas off each other.” (Intern)

Typical of social processes, variations based on time of day and schedule were observed. For instance, during call days, learners gathered data and formed working diagnosis and treatment plans with minimal attending interaction. This separation of roles and responsibilities introduced a hierarchy within diagnosis as follows:

“The interns would not call me first; they would talk to the senior resident and then if the senior thought he should chat with me, then they would call. But for the most part, they gather information and come up with the plan.” (Attending).

The work system was suited to facilitate social interactions. For instance, designated rooms (with team members informally assigned to a computer) provided physical proximity of the resident to interns and medical students. In this space, numerous informal discussions between team members (eg, “What do you think about this test?” “I’m not sure what to do about this finding.” “Should I call a [consult] on this patient?”) were observed. Although proximity to each other was viewed as beneficial, dangers to the social nature of diagnosis in the form of anchoring (ie, a cognitive bias where emphasis is placed on the first piece of data)¹⁶ were also mentioned. Similarly, the paradox associated with social proof (ie, the pressure to assume conformity within a group) was also observed as disagreement between team members and attendings rarely occurred during observations.

“I mean, they’re the attending, right? It’s hard to argue with them when they want a test or something done. When I do push back, it’s rare that others will support me–so it’s usually me and the attending.” (Resident)

“I would push back if I think it’s really bad for the patient or could cause harm–but the truth is, it doesn’t happen much.” (Intern)

(2) Data Necessary to Make Diagnoses are Fragmented

Team members universally cited fragmentation in data delivery, retrieval, and processing as a barrier to diagnosis. Team members indicated that test results might not be looked at or acted upon in a timely manner, and participants pointed to the electronic medical record as a source of this challenge.

“Before I knew about [the app for Epic], I would literally sit on the computer to get all the information we would need on rounds. Its key to making decisions. We often say we will do something, only to find the test result doesn’t support it–and then we’re back to square 1.” (Intern)

Information used by teams came from myriad sources (eg, patients, family members, electronic records) and from various settings (eg, emergency department, patient rooms, discussions with consultants). Additionally, test results often appeared without warning. Thus, availability of information was poorly aligned with clinical duties.

“They (the lab) will call us when a blood culture is positive or something is off. That is very helpful but it often comes later in the day, when we’re done with rounds.” (Resident)

The work system was highlighted as a key contributor to data fragmentation. Peculiarities of our electronic medical record (EMR) and how data were collected, stored, or presented were described as “frustrating,” and “unsafe,” by team members. Correspondingly, we frequently observed interns asking for assistance for tasks such as ordering tests or finding information despite being “trained” to use the EMR.

“People have to learn how to filter, how to recognize the most important points and link data streams together in terms of causality. But we assume they know where to find that information. It’s actually a very hard thing to do, for both the house staff and me.” (Attending)

(3) Distractions Undermine the Diagnostic Process

Distractions often created cognitive difficulties. For example, ambient noise and interruptions from neighbors working on other teams were cited as barriers to diagnosis. In addition, we observed several team members using headphones to drown out ambient noise while working on the computer.

“I know I shouldn’t do it (wear headphones), but I have no other way of turning down the noise so I can concentrate.” (Intern)

Similarly, the unpredictable nature and the volume of pages often interrupted thinking about diagnosis.

“Sometimes the pager just goes off all the time and (after making sure its not an urgent issue), I will just ignore it for a bit, especially if I am in the middle of something. It would be great if I could finish my thought process knowing I would not be interrupted.” (Resident)

To mitigate this problem, 1 attending described how he would proactively seek out nurses caring for his patients to “head off” questions (eg, “I will renew the restraints and medications this morning,” and “Is there anything you need in terms of orders for this patient that I can take care of now?”) that might lead to pages. Another resident described his approach as follows:

“I make it a point to tell the nurses where I will be hanging out and where they can find me if they have any questions. I tell them to come talk to me rather than page me since that will be less distracting.” (Resident).

Most of the interns described documentation work such as writing admission and progress notes in negative terms (“an academic exercise,” “part of the billing activity”). However, in the context of interruptions, some described this as helpful.

“The most valuable part of the thinking process was writing the assessment and plan because that’s actually my schema for all problems. It literally is the only time where I can sit and collect my thoughts to formulate a diagnosis and plan.” (Intern)

(4) Time Pressures Interfere With Diagnostic Decision Making

All team members spoke about the challenge of finding time for diagnosis during the workday. Often, they had to skip learning sessions for this purpose.

“They tell us we should go to morning report or noon conference but when I’m running around trying to get things done. I hate having to choose between my education and doing what’s best for the patient–but that’s often what it comes down to.” (Intern)

When specifically asked whether setting aside dedicated time to specifically review and formulate diagnoses would be valuable, respondents were uniformly enthusiastic. Team members described attentional conflicts as being the worst when “cross covering” other teams on call days, as their patient load effectively doubled during this time. Of note, cross-covering occurred when teams were also on call—and thus took them away from important diagnostic activities such as data gathering or synthesis for patients they were admitting.

“If you were to ever design a system where errors were likely–this is how you would design it: take a team with little supervision, double their patient load, keep them busy with new challenging cases and then ask questions about patients they know little about.” (Resident)

Although diagnostic errors have been called “the next frontier for patient safety,”¹⁷ little is known about the process, barriers, and facilitators to diagnosis in teaching hospitals. In this focused ethnography conducted at 2 academic medical centers, we identified multiple cognitive and system-level challenges and potential strategies to improve diagnosis from trainees engaged in this activity. Key themes identified by those we observed included the social nature of diagnosis, fragmented information delivery, constant distractions and interruptions, and time pressures. In turn, these insights allow us to generate strategies that can be applied to improve the diagnostic process in teaching hospitals.

Our study underscores the importance of social interactions in diagnosis. In contrast, most of the interventions to prevent diagnostic errors target individual providers through practices such as metacognition and “thinking about thinking.”^18-20 These interventions are based on Daniel Kahnemann’s work on dual thought process. Type 1 thought processes are fast, subconscious, reflexive, largely intuitive, and more vulnerable to error. In contrast, Type 2 processes are slower, deliberate, analytic, and less prone to error.²¹ Although an individual’s Type 2 thought capacity is limited, a major goal of cognitive interventions is to encourage Type 2 over Type 1 thinking, an approach termed “de-biasing.”^22-24 Unfortunately, cognitive interventions testing such approaches have suffered mixed results–perhaps because of lack of focus on collective wisdom or group thinking, which may be key to diagnosis from our findings.^9,25 In this sense, morning rounds were a social gathering used to strategize and develop care plans, but with limited time to think about diagnosis.²⁶ Introduction of defined periods for individuals to engage in diagnostic activities such as de-biasing (ie, asking “what else could this be)²⁷ before or after rounds may provide an opportunity for reflection and improving diagnosis. In addition, embedding tools such as diagnosis expanders and checklists within these defined time slots^28,29 may prove to be useful in reflecting on diagnosis and preventing diagnostic errors.

An unexpected yet important finding from this study were the challenges posed by distractions and the physical environment. Potentially maladaptive workarounds to these interruptions included use of headphones; more productive strategies included updating nurses with plans to avert pages and creating a list of activities to ensure that key tasks were not forgotten.^30,31 Applying lessons from aviation, a focused effort to limit distractions during key portions of the day, might be worth considering for diagnostic safety.³² Similarly, improving the environment in which diagnosis occurs—including creating spaces that are quiet, orderly, and optimized for thinking—may be valuable.³³Our study has limitations. First, our findings are limited to direct observations; we are thus unable to comment on how unobserved aspects of care (eg, cognitive processes) might have influenced our findings. Our observations of clinical care might also have introduced a Hawthorne effect. However, because we were closely integrated with teams and conducted focus groups to corroborate our assessments, we believe that this was not the case. Second, we did not identify diagnostic errors or link processes we observed to errors. Third, our approach is limited to 2 teaching centers, thereby limiting the generalizability of findings. Relatedly, we were only able to conduct observations during weekdays; differences in weekend and night resources might affect our insights.

The cognitive and system-based barriers faced by clinicians in teaching hospitals suggest that new methods to improve diagnosis are needed. Future interventions such as defined “time-outs” for diagnosis, strategies focused on limiting distractions, and methods to improve communication between team members are novel and have parallels in other industries. As challenges to quantify diagnostic errors abound,³⁴ improving cognitive- and system-based factors via reflection through communication, concentration, and organization is necessary to improve medical decision making in academic medical centers.

Disclosures

None declared for all coauthors.

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. Dr. Chopra is supported by a career development award from the Agency of Healthcare Research and Quality (1-K08-HS022835-01). Dr. Krein is supported by a VA Health Services Research and Development Research Career Scientist Award (RCS 11-222). Dr. Singh is partially supported by Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality or the Department of Veterans Affairs.

Diagnostic error—defined as a failure to establish an accurate and timely explanation of the patient’s health problem—is an important source of patient harm.¹ Data suggest that all patients will experience at least 1 diagnostic error in their lifetime.^2-4 Not surprisingly, diagnostic errors are among the leading categories of paid malpractice claims in the United States.⁵

Despite diagnostic errors being morbid and sometimes deadly in the hospital,^6,7 little is known about how residents and learners approach diagnostic decision making. Errors in diagnosis are believed to stem from cognitive or system failures,⁸ with errors in cognition believed to occur due to rapid, reflexive thinking operating in the absence of a more analytical, deliberate process. System-based problems (eg, lack of expert availability, technology barriers, and access to data) have also been cited as contributors.⁹ However, whether and how these apply to trainees is not known.

Therefore, we conducted a focused ethnography of inpatient medicine teams (ie, attendings, residents, interns, and medical students) in 2 affiliated teaching hospitals, aiming to (a) observe the process of diagnosis by trainees and (b) identify methods to improve the diagnostic process and prevent errors.

We designed a multimethod, focused ethnographic study to examine diagnostic decision making in hospital settings.^10,11 In contrast to anthropologic ethnographies that study entire fields using open-ended questions, our study was designed to examine the process of diagnosis from the perspective of clinicians engaged in this activity.¹¹ This approach allowed us to capture diagnostic decisions and cognitive and system-based factors in a manner currently lacking in the literature.¹²

Setting and Participants

Between January 2016 and May 2016, we observed the members of four inpatient internal medicine teaching teams at 2 affiliated teaching hospitals. We purposefully selected teaching teams for observation because they are the primary model of care in academic settings and we have expertise in carrying out similar studies.^13,14 Teaching teams typically consisted of a medical attending (senior-level physician), 1 senior resident (a second- or third-year postgraduate trainee), two interns (a trainee in their first postgraduate year), and two to four  medical students. Teams were selected at random using existing schedules and followed Monday to Friday so as to permit observation of work on call and noncall days. Owing to manpower limitations, weekend and night shifts were not observed. However, overnight events were captured during morning rounds.

Most of the teams began rounds at 8:30 AM. Typically, rounds lasted for 90–120 min and concluded with a recap (ie, “running the list”) with a review of explicit plans for patients after they had been evaluated by the attending. This discussion often occurred in the team rooms, with the attending leading the discussion with the trainees.

Data Collection

A multidisciplinary team, including clinicians (eg, physicians, nurses), nonclinicians (eg, qualitative researchers, social scientists), and healthcare engineers, conducted the observations. We observed preround activities of interns and residents before arrival of the attending (7:00 AM - 8:30 AM), followed by morning rounds with the entire team, and afternoon work that included senior residents, interns, and students.

To capture multiple aspects of the diagnostic process, we collected data using field notes modeled on components of the National Academy of Science model for diagnosis (Appendix).^1,15 This model encompasses phases of the diagnostic process (eg, data gathering, integration, formulation of a working diagnosis, treatment delivery, and outcomes) and the work system (team members, organization, technology and tools, physical environment, tasks).

Focus Groups and Interviews

At the end of weekly observations, we conducted focus groups with the residents and one-on- one interviews with the attendings. Focus groups with the residents were conducted to encourage a group discussion about the diagnostic process. Separate interviews with the attendings were performed to ensure that power differentials did not influence discussions. During focus groups, we specifically asked about challenges and possible solutions to improve diagnosis. Experienced qualitative methodologists (J.F., M.H., M.Q.) used semistructured interview guides for discussions (Appendix).

Data Analysis

After aggregating and reading the data, three reviewers (V.C., S.K., S.S.) began inductive analysis by handwriting notes and initial reflective thoughts to create preliminary codes. Multiple team members then reread the original field notes and the focus group/interview data to refine the preliminary codes and develop additional codes. Next, relationships between codes were identified and used to develop key themes. Triangulation of data collected from observations and interview/focus group sessions was carried out to compare data that we surmised with data that were verbalized by the team. The developed themes were discussed as a group to ensure consistency of major findings.

Ethical and Regulatory Oversight

This study was reviewed and approved by the Institutional Review Boards at the University of Michigan Health System (HUM-00106657) and the VA Ann Arbor Healthcare System (1-2016-010040).

Four teaching teams (4 attendings, 4 senior residents, 9 interns, and 14 medical students) were observed over 33 distinct shifts and 168 hours. Observations included morning rounds (96 h), postround call days (52 h), and postround non-call days (20 h). Morning rounds lasted an average of 127 min (range: 48-232 min) and included an average of 9 patients (range: 4-16 patients).

Themes Regarding the Diagnostic Process

We identified the following 4 primary themes related to the diagnostic process in teaching hospitals: (1) diagnosis is a social phenomenon; (2) data necessary to make diagnoses are fragmented; (3) distractions undermine the diagnostic process; and (4) time pressures interfere with diagnostic decision making (Appendix Table 1).

(1) Diagnosis is a Social Phenomenon.

Team members viewed the process of diagnosis as a social exchange of facts, findings, and strategies within a defined structure. The opportunity to discuss impressions with others was valued as a means to share, test, and process assumptions.

“Rounds are the most important part of the process. That is where we make most decisions in a collective, collaborative way with the attending present. We bounce ideas off each other.” (Intern)

Typical of social processes, variations based on time of day and schedule were observed. For instance, during call days, learners gathered data and formed working diagnosis and treatment plans with minimal attending interaction. This separation of roles and responsibilities introduced a hierarchy within diagnosis as follows:

“The interns would not call me first; they would talk to the senior resident and then if the senior thought he should chat with me, then they would call. But for the most part, they gather information and come up with the plan.” (Attending).

The work system was suited to facilitate social interactions. For instance, designated rooms (with team members informally assigned to a computer) provided physical proximity of the resident to interns and medical students. In this space, numerous informal discussions between team members (eg, “What do you think about this test?” “I’m not sure what to do about this finding.” “Should I call a [consult] on this patient?”) were observed. Although proximity to each other was viewed as beneficial, dangers to the social nature of diagnosis in the form of anchoring (ie, a cognitive bias where emphasis is placed on the first piece of data)¹⁶ were also mentioned. Similarly, the paradox associated with social proof (ie, the pressure to assume conformity within a group) was also observed as disagreement between team members and attendings rarely occurred during observations.

“I mean, they’re the attending, right? It’s hard to argue with them when they want a test or something done. When I do push back, it’s rare that others will support me–so it’s usually me and the attending.” (Resident)

“I would push back if I think it’s really bad for the patient or could cause harm–but the truth is, it doesn’t happen much.” (Intern)

(2) Data Necessary to Make Diagnoses are Fragmented

Team members universally cited fragmentation in data delivery, retrieval, and processing as a barrier to diagnosis. Team members indicated that test results might not be looked at or acted upon in a timely manner, and participants pointed to the electronic medical record as a source of this challenge.

“Before I knew about [the app for Epic], I would literally sit on the computer to get all the information we would need on rounds. Its key to making decisions. We often say we will do something, only to find the test result doesn’t support it–and then we’re back to square 1.” (Intern)

Information used by teams came from myriad sources (eg, patients, family members, electronic records) and from various settings (eg, emergency department, patient rooms, discussions with consultants). Additionally, test results often appeared without warning. Thus, availability of information was poorly aligned with clinical duties.

“They (the lab) will call us when a blood culture is positive or something is off. That is very helpful but it often comes later in the day, when we’re done with rounds.” (Resident)

The work system was highlighted as a key contributor to data fragmentation. Peculiarities of our electronic medical record (EMR) and how data were collected, stored, or presented were described as “frustrating,” and “unsafe,” by team members. Correspondingly, we frequently observed interns asking for assistance for tasks such as ordering tests or finding information despite being “trained” to use the EMR.

“People have to learn how to filter, how to recognize the most important points and link data streams together in terms of causality. But we assume they know where to find that information. It’s actually a very hard thing to do, for both the house staff and me.” (Attending)

(3) Distractions Undermine the Diagnostic Process

Distractions often created cognitive difficulties. For example, ambient noise and interruptions from neighbors working on other teams were cited as barriers to diagnosis. In addition, we observed several team members using headphones to drown out ambient noise while working on the computer.

“I know I shouldn’t do it (wear headphones), but I have no other way of turning down the noise so I can concentrate.” (Intern)

Similarly, the unpredictable nature and the volume of pages often interrupted thinking about diagnosis.

“Sometimes the pager just goes off all the time and (after making sure its not an urgent issue), I will just ignore it for a bit, especially if I am in the middle of something. It would be great if I could finish my thought process knowing I would not be interrupted.” (Resident)

To mitigate this problem, 1 attending described how he would proactively seek out nurses caring for his patients to “head off” questions (eg, “I will renew the restraints and medications this morning,” and “Is there anything you need in terms of orders for this patient that I can take care of now?”) that might lead to pages. Another resident described his approach as follows:

“I make it a point to tell the nurses where I will be hanging out and where they can find me if they have any questions. I tell them to come talk to me rather than page me since that will be less distracting.” (Resident).

Most of the interns described documentation work such as writing admission and progress notes in negative terms (“an academic exercise,” “part of the billing activity”). However, in the context of interruptions, some described this as helpful.

“The most valuable part of the thinking process was writing the assessment and plan because that’s actually my schema for all problems. It literally is the only time where I can sit and collect my thoughts to formulate a diagnosis and plan.” (Intern)

(4) Time Pressures Interfere With Diagnostic Decision Making

All team members spoke about the challenge of finding time for diagnosis during the workday. Often, they had to skip learning sessions for this purpose.

“They tell us we should go to morning report or noon conference but when I’m running around trying to get things done. I hate having to choose between my education and doing what’s best for the patient–but that’s often what it comes down to.” (Intern)

When specifically asked whether setting aside dedicated time to specifically review and formulate diagnoses would be valuable, respondents were uniformly enthusiastic. Team members described attentional conflicts as being the worst when “cross covering” other teams on call days, as their patient load effectively doubled during this time. Of note, cross-covering occurred when teams were also on call—and thus took them away from important diagnostic activities such as data gathering or synthesis for patients they were admitting.

“If you were to ever design a system where errors were likely–this is how you would design it: take a team with little supervision, double their patient load, keep them busy with new challenging cases and then ask questions about patients they know little about.” (Resident)

Although diagnostic errors have been called “the next frontier for patient safety,”¹⁷ little is known about the process, barriers, and facilitators to diagnosis in teaching hospitals. In this focused ethnography conducted at 2 academic medical centers, we identified multiple cognitive and system-level challenges and potential strategies to improve diagnosis from trainees engaged in this activity. Key themes identified by those we observed included the social nature of diagnosis, fragmented information delivery, constant distractions and interruptions, and time pressures. In turn, these insights allow us to generate strategies that can be applied to improve the diagnostic process in teaching hospitals.

Our study underscores the importance of social interactions in diagnosis. In contrast, most of the interventions to prevent diagnostic errors target individual providers through practices such as metacognition and “thinking about thinking.”^18-20 These interventions are based on Daniel Kahnemann’s work on dual thought process. Type 1 thought processes are fast, subconscious, reflexive, largely intuitive, and more vulnerable to error. In contrast, Type 2 processes are slower, deliberate, analytic, and less prone to error.²¹ Although an individual’s Type 2 thought capacity is limited, a major goal of cognitive interventions is to encourage Type 2 over Type 1 thinking, an approach termed “de-biasing.”^22-24 Unfortunately, cognitive interventions testing such approaches have suffered mixed results–perhaps because of lack of focus on collective wisdom or group thinking, which may be key to diagnosis from our findings.^9,25 In this sense, morning rounds were a social gathering used to strategize and develop care plans, but with limited time to think about diagnosis.²⁶ Introduction of defined periods for individuals to engage in diagnostic activities such as de-biasing (ie, asking “what else could this be)²⁷ before or after rounds may provide an opportunity for reflection and improving diagnosis. In addition, embedding tools such as diagnosis expanders and checklists within these defined time slots^28,29 may prove to be useful in reflecting on diagnosis and preventing diagnostic errors.

An unexpected yet important finding from this study were the challenges posed by distractions and the physical environment. Potentially maladaptive workarounds to these interruptions included use of headphones; more productive strategies included updating nurses with plans to avert pages and creating a list of activities to ensure that key tasks were not forgotten.^30,31 Applying lessons from aviation, a focused effort to limit distractions during key portions of the day, might be worth considering for diagnostic safety.³² Similarly, improving the environment in which diagnosis occurs—including creating spaces that are quiet, orderly, and optimized for thinking—may be valuable.³³Our study has limitations. First, our findings are limited to direct observations; we are thus unable to comment on how unobserved aspects of care (eg, cognitive processes) might have influenced our findings. Our observations of clinical care might also have introduced a Hawthorne effect. However, because we were closely integrated with teams and conducted focus groups to corroborate our assessments, we believe that this was not the case. Second, we did not identify diagnostic errors or link processes we observed to errors. Third, our approach is limited to 2 teaching centers, thereby limiting the generalizability of findings. Relatedly, we were only able to conduct observations during weekdays; differences in weekend and night resources might affect our insights.

The cognitive and system-based barriers faced by clinicians in teaching hospitals suggest that new methods to improve diagnosis are needed. Future interventions such as defined “time-outs” for diagnosis, strategies focused on limiting distractions, and methods to improve communication between team members are novel and have parallels in other industries. As challenges to quantify diagnostic errors abound,³⁴ improving cognitive- and system-based factors via reflection through communication, concentration, and organization is necessary to improve medical decision making in academic medical centers.

Disclosures

None declared for all coauthors.

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. Dr. Chopra is supported by a career development award from the Agency of Healthcare Research and Quality (1-K08-HS022835-01). Dr. Krein is supported by a VA Health Services Research and Development Research Career Scientist Award (RCS 11-222). Dr. Singh is partially supported by Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality or the Department of Veterans Affairs.