Hospital Medicine

High-value care (HVC) is a philosophy and approach to medicine that focuses on achieving the best patient outcomes through evidence-based practice while minimizing harm to patients, wasted healthcare resources, and costs. Incorporating HVC principles in pediatric clinical decision-making is particularly important owing to the harms of hospitalization, overutilization, and overdiagnosis, as well as rising costs of pediatric care.^1-4 How can we maintain these principles in the face of a global pandemic and new emerging syndrome, multisystem inflammatory syndrome in children (MIS-C), which has dramatically impacted healthcare systems for children?

In this article, we discuss the barriers and opportunities around practicing HVC in our evolving approach to novel COVID-19 management in hospitalized children. We also draw lessons from our experiences on how we can respond to future events that rapidly shift our approach to care.

As children’s hospitals and pediatric providers responded to the COVID-19 pandemic, practice recommendations were implemented rapidly and changed rapidly. A major challenge with an event like this is how we respond to the unknown and uncertainty, something most healthcare workers are not comfortable doing at baseline,^5,6 particularly trainees and early-career physicians.⁷ With the benefit of hindsight, many early clinical approaches to care may now be seen as low-value care (LVC). For example, COVID-19 test availability was initially limited, and many hospitals utilized respiratory viral panels (RVPs) to potentially eliminate COVID-19 as an etiology of symptoms. RVP use increased during this time⁸; however, studies have shown that the co-infection rate of SARS-CoV2 with other respiratory viruses varies widely, so a positive RVP was of uncertain benefit.⁹ In addition, routine RVP use is often low value and may lead to overdiagnosis, additional overtesting cascades, and, at times, false reassurance and premature closure of the diagnostic workup.¹⁰

As our understanding of COVID-19 has expanded, rapid changes in treatment have also occurred. Early data were often preliminary and based on small trials of adults, and treatments ranged from inexpensive and available (dexamethasone) to quite expensive (remdesivir, monoclonal antibodies). Pragmatic randomized controlled trials (RCTs) are an important tool that may have been underutilized in pediatrics. Similar to our adult hospitalist colleagues’ experience,¹¹ the rapid rise in cases provided an opportunity to collaborate across institutions to assess which treatments were most effective. In particular, the predictable rise in rates of MIS-C after a surge in COVID-19 cases could have provided an avenue to evaluate the relative effectiveness of the various treatments used.¹² However, there were limited pediatric RCTs and thus a missed opportunity to establish an evidence-based pediatric standard of care for COVID-19 and MIS-C. This resulted in the development and dissemination of care practices before they were fully tested in children.

Similarly, the medical community has become increasingly aware of laboratory findings that may be predictive of clinical course.¹³ The outcomes of COVID and MIS-C are potentially severe, so looking for “early warning signs” with diagnostic testing is appealing. Clinicians responding to early data, and with a fear of missing something, may order a full panel of bloodwork for admitted patients to assist with decision-making and may underestimate the perceived minor harms and cost of unnecessary testing/admissions.³ However, most of the evidence regarding lab values came from the adult population. There is little understanding of how lab values impact pediatric-specific outcomes.¹⁴ Even for MIS-C, a pediatric-specific condition, early protocols emphasize broad testing approaches.¹⁵ A focus on grave (but rare) outcomes from a novel virus may also distract from more common causes of symptoms and lead to missed common diagnoses that are less severe.¹⁶ For both testing and treatment, having this early information before clear evidence on how it guided care may have caused more harm than benefit. Again, RCTs may have helped guide MIS-C therapies and protocol development.

Changing workflows may also create new barriers to HVC. One of the recommendations from Choosing Wisely^® during the COVID-19 pandemic was to batch lab draws¹⁷ to reduce the risk of exposure to healthcare workers performing phlebotomy, as well as staff who transport, handle, and process bloodwork in the lab. This may inadvertently encourage the approach of getting a lab test “in case” we need it with a single daily blood draw. In trying to avoid multiple encounters (and conserve personal protective equipment [PPE]), we may be taking a less stepwise approach than in prepandemic times.

Finally, children’s hospitals witnessed significant financial challenges and reductions in patient volume related to the pandemic.¹⁸ Reductions in patient volume could present a potential opportunity for practicing HVC (eg, more time to discuss downstream effects) or alternatively could inadvertently incentivize low-value, low-priority care via messaging around preserving financial viability.

For clinicians and healthcare systems, these examples highlight why we may be predisposed to practicing LVC during a pandemic or similar emerging threat.

In light of these challenging clinical scenarios and nonclinical factors that predispose us to LVC, how can we reinforce a high-value approach to care during a pandemic or similar emerging threat? The following five specific concepts may help providers and organizations optimize HVC during this pandemic and in future situations:

1. Utilize pediatric RCTs to provide evidence-based recommendations. In the face of a novel virus with unclear manifestations, treatment options were rapidly implemented without time for careful evaluation. In the future, collaboratively utilizing shared resources in the research community could help rapidly and rigorously evaluate outcomes in the pursuit of evidence-based practice.
2. Use standardization as a tool to mitigate uncertainty. Knowing that uncertainty can be a driver of overuse and that during emerging threats, evidence is scarce and rapidly changing, a structured method for standardizing practice across your institution or multiple institutions can be helpful in many ways. Electronic health record–based orders and guidelines provide a standard of care to relieve uncertainty and have been shown to reduce overtesting.¹⁹ These resources can also be adapted rapidly as evidence emerges, reducing the burden on providers to know the latest evolving best practice. Experts who have reviewed the literature should have a method to quickly disseminate these findings through standardized practice, providing a venue for rapid learning and implementation.²⁰
3. Plan for active deimplementation from the outset. It is inevitable that some practices implemented early in pandemic response may need to be deimplemented later as the evidence and situation evolve. However, there is ample evidence that deimplementation can be difficult.²¹ Building in deimplementation mechanisms, such as standing educational sessions or hospital committees dedicated to value that review practices, from the beginning may ease these changes.
4. Take advantage of novel opportunities to improve value. Early stop-gap interventions may be wasteful, but the upheaval from major events may also create novel opportunities to improve value in other ways. Some of these efforts, like PPE conservation and as-needed follow-up visits, may become useful methods to improve value even after the pandemic ends.^22,23 The decreased pursuit of healthcare during the pandemic may also have given us an opportunity to better define when delayed diagnosis or even nondiagnosis for certain conditions is acceptable and when it may cause harm.
5. Highlight harms of overuse. While avoiding unnecessary costs is an important aspect of reducing overuse, often the other human-centered harms of overuse are better motivators for HVC. Especially during the response to an emerging threat, the impacts of overuse may be compounded. Laboratory resources that are strained to meet COVID-19 testing demand will be further stretched by overuse of other laboratory testing. Overuse of ineffective treatments adds stress to nurses, pharmacists, and other front-line staff taking care of ill patients. Side effects of unnecessary interventions, including those that could prolong hospitalization, would also increase strain on the system. Reducing overuse is also a way to reduce workload for hospital staff during a time of crisis. Improved efficiency of practice and less time spent on practices that do not add value to patient care can insulate staff against burnout.²⁴ Hospitalization and healthcare costs can add to the stress and financial burden of patients and families.²⁵ Clinicians can highlight harms of overuse through openly talking about it on rounds with the patients, families, and entire care team and incorporating it into health system–wide messaging.

As vaccine distribution continues, like many clinicians, we are hopeful that the worst days of the pandemic are behind us. The crucible of the COVID-19 pandemic has undoubtedly changed us as clinicians and impacted our future practice patterns. We believe there is a need to challenge ourselves to continue to think from a value mindset even in times of crisis. Furthermore, there are important opportunities to learn from our response to the COVID-19 pandemic and find strategies for minimizing LVC outside the pandemic. We believe the lessons learned around improving value during this pandemic can strengthen our response to the next novel, widespread threat and reduce waste in our care systems, with a potential to increase the resilience of systems in the future.

High-value care (HVC) is a philosophy and approach to medicine that focuses on achieving the best patient outcomes through evidence-based practice while minimizing harm to patients, wasted healthcare resources, and costs. Incorporating HVC principles in pediatric clinical decision-making is particularly important owing to the harms of hospitalization, overutilization, and overdiagnosis, as well as rising costs of pediatric care.^1-4 How can we maintain these principles in the face of a global pandemic and new emerging syndrome, multisystem inflammatory syndrome in children (MIS-C), which has dramatically impacted healthcare systems for children?

In this article, we discuss the barriers and opportunities around practicing HVC in our evolving approach to novel COVID-19 management in hospitalized children. We also draw lessons from our experiences on how we can respond to future events that rapidly shift our approach to care.

As children’s hospitals and pediatric providers responded to the COVID-19 pandemic, practice recommendations were implemented rapidly and changed rapidly. A major challenge with an event like this is how we respond to the unknown and uncertainty, something most healthcare workers are not comfortable doing at baseline,^5,6 particularly trainees and early-career physicians.⁷ With the benefit of hindsight, many early clinical approaches to care may now be seen as low-value care (LVC). For example, COVID-19 test availability was initially limited, and many hospitals utilized respiratory viral panels (RVPs) to potentially eliminate COVID-19 as an etiology of symptoms. RVP use increased during this time⁸; however, studies have shown that the co-infection rate of SARS-CoV2 with other respiratory viruses varies widely, so a positive RVP was of uncertain benefit.⁹ In addition, routine RVP use is often low value and may lead to overdiagnosis, additional overtesting cascades, and, at times, false reassurance and premature closure of the diagnostic workup.¹⁰

As our understanding of COVID-19 has expanded, rapid changes in treatment have also occurred. Early data were often preliminary and based on small trials of adults, and treatments ranged from inexpensive and available (dexamethasone) to quite expensive (remdesivir, monoclonal antibodies). Pragmatic randomized controlled trials (RCTs) are an important tool that may have been underutilized in pediatrics. Similar to our adult hospitalist colleagues’ experience,¹¹ the rapid rise in cases provided an opportunity to collaborate across institutions to assess which treatments were most effective. In particular, the predictable rise in rates of MIS-C after a surge in COVID-19 cases could have provided an avenue to evaluate the relative effectiveness of the various treatments used.¹² However, there were limited pediatric RCTs and thus a missed opportunity to establish an evidence-based pediatric standard of care for COVID-19 and MIS-C. This resulted in the development and dissemination of care practices before they were fully tested in children.

Similarly, the medical community has become increasingly aware of laboratory findings that may be predictive of clinical course.¹³ The outcomes of COVID and MIS-C are potentially severe, so looking for “early warning signs” with diagnostic testing is appealing. Clinicians responding to early data, and with a fear of missing something, may order a full panel of bloodwork for admitted patients to assist with decision-making and may underestimate the perceived minor harms and cost of unnecessary testing/admissions.³ However, most of the evidence regarding lab values came from the adult population. There is little understanding of how lab values impact pediatric-specific outcomes.¹⁴ Even for MIS-C, a pediatric-specific condition, early protocols emphasize broad testing approaches.¹⁵ A focus on grave (but rare) outcomes from a novel virus may also distract from more common causes of symptoms and lead to missed common diagnoses that are less severe.¹⁶ For both testing and treatment, having this early information before clear evidence on how it guided care may have caused more harm than benefit. Again, RCTs may have helped guide MIS-C therapies and protocol development.

Changing workflows may also create new barriers to HVC. One of the recommendations from Choosing Wisely^® during the COVID-19 pandemic was to batch lab draws¹⁷ to reduce the risk of exposure to healthcare workers performing phlebotomy, as well as staff who transport, handle, and process bloodwork in the lab. This may inadvertently encourage the approach of getting a lab test “in case” we need it with a single daily blood draw. In trying to avoid multiple encounters (and conserve personal protective equipment [PPE]), we may be taking a less stepwise approach than in prepandemic times.

Finally, children’s hospitals witnessed significant financial challenges and reductions in patient volume related to the pandemic.¹⁸ Reductions in patient volume could present a potential opportunity for practicing HVC (eg, more time to discuss downstream effects) or alternatively could inadvertently incentivize low-value, low-priority care via messaging around preserving financial viability.

For clinicians and healthcare systems, these examples highlight why we may be predisposed to practicing LVC during a pandemic or similar emerging threat.

In light of these challenging clinical scenarios and nonclinical factors that predispose us to LVC, how can we reinforce a high-value approach to care during a pandemic or similar emerging threat? The following five specific concepts may help providers and organizations optimize HVC during this pandemic and in future situations:

1. Utilize pediatric RCTs to provide evidence-based recommendations. In the face of a novel virus with unclear manifestations, treatment options were rapidly implemented without time for careful evaluation. In the future, collaboratively utilizing shared resources in the research community could help rapidly and rigorously evaluate outcomes in the pursuit of evidence-based practice.
2. Use standardization as a tool to mitigate uncertainty. Knowing that uncertainty can be a driver of overuse and that during emerging threats, evidence is scarce and rapidly changing, a structured method for standardizing practice across your institution or multiple institutions can be helpful in many ways. Electronic health record–based orders and guidelines provide a standard of care to relieve uncertainty and have been shown to reduce overtesting.¹⁹ These resources can also be adapted rapidly as evidence emerges, reducing the burden on providers to know the latest evolving best practice. Experts who have reviewed the literature should have a method to quickly disseminate these findings through standardized practice, providing a venue for rapid learning and implementation.²⁰
3. Plan for active deimplementation from the outset. It is inevitable that some practices implemented early in pandemic response may need to be deimplemented later as the evidence and situation evolve. However, there is ample evidence that deimplementation can be difficult.²¹ Building in deimplementation mechanisms, such as standing educational sessions or hospital committees dedicated to value that review practices, from the beginning may ease these changes.
4. Take advantage of novel opportunities to improve value. Early stop-gap interventions may be wasteful, but the upheaval from major events may also create novel opportunities to improve value in other ways. Some of these efforts, like PPE conservation and as-needed follow-up visits, may become useful methods to improve value even after the pandemic ends.^22,23 The decreased pursuit of healthcare during the pandemic may also have given us an opportunity to better define when delayed diagnosis or even nondiagnosis for certain conditions is acceptable and when it may cause harm.
5. Highlight harms of overuse. While avoiding unnecessary costs is an important aspect of reducing overuse, often the other human-centered harms of overuse are better motivators for HVC. Especially during the response to an emerging threat, the impacts of overuse may be compounded. Laboratory resources that are strained to meet COVID-19 testing demand will be further stretched by overuse of other laboratory testing. Overuse of ineffective treatments adds stress to nurses, pharmacists, and other front-line staff taking care of ill patients. Side effects of unnecessary interventions, including those that could prolong hospitalization, would also increase strain on the system. Reducing overuse is also a way to reduce workload for hospital staff during a time of crisis. Improved efficiency of practice and less time spent on practices that do not add value to patient care can insulate staff against burnout.²⁴ Hospitalization and healthcare costs can add to the stress and financial burden of patients and families.²⁵ Clinicians can highlight harms of overuse through openly talking about it on rounds with the patients, families, and entire care team and incorporating it into health system–wide messaging.

As vaccine distribution continues, like many clinicians, we are hopeful that the worst days of the pandemic are behind us. The crucible of the COVID-19 pandemic has undoubtedly changed us as clinicians and impacted our future practice patterns. We believe there is a need to challenge ourselves to continue to think from a value mindset even in times of crisis. Furthermore, there are important opportunities to learn from our response to the COVID-19 pandemic and find strategies for minimizing LVC outside the pandemic. We believe the lessons learned around improving value during this pandemic can strengthen our response to the next novel, widespread threat and reduce waste in our care systems, with a potential to increase the resilience of systems in the future.

High-value care (HVC) is a philosophy and approach to medicine that focuses on achieving the best patient outcomes through evidence-based practice while minimizing harm to patients, wasted healthcare resources, and costs. Incorporating HVC principles in pediatric clinical decision-making is particularly important owing to the harms of hospitalization, overutilization, and overdiagnosis, as well as rising costs of pediatric care.^1-4 How can we maintain these principles in the face of a global pandemic and new emerging syndrome, multisystem inflammatory syndrome in children (MIS-C), which has dramatically impacted healthcare systems for children?

In this article, we discuss the barriers and opportunities around practicing HVC in our evolving approach to novel COVID-19 management in hospitalized children. We also draw lessons from our experiences on how we can respond to future events that rapidly shift our approach to care.

As children’s hospitals and pediatric providers responded to the COVID-19 pandemic, practice recommendations were implemented rapidly and changed rapidly. A major challenge with an event like this is how we respond to the unknown and uncertainty, something most healthcare workers are not comfortable doing at baseline,^5,6 particularly trainees and early-career physicians.⁷ With the benefit of hindsight, many early clinical approaches to care may now be seen as low-value care (LVC). For example, COVID-19 test availability was initially limited, and many hospitals utilized respiratory viral panels (RVPs) to potentially eliminate COVID-19 as an etiology of symptoms. RVP use increased during this time⁸; however, studies have shown that the co-infection rate of SARS-CoV2 with other respiratory viruses varies widely, so a positive RVP was of uncertain benefit.⁹ In addition, routine RVP use is often low value and may lead to overdiagnosis, additional overtesting cascades, and, at times, false reassurance and premature closure of the diagnostic workup.¹⁰

As our understanding of COVID-19 has expanded, rapid changes in treatment have also occurred. Early data were often preliminary and based on small trials of adults, and treatments ranged from inexpensive and available (dexamethasone) to quite expensive (remdesivir, monoclonal antibodies). Pragmatic randomized controlled trials (RCTs) are an important tool that may have been underutilized in pediatrics. Similar to our adult hospitalist colleagues’ experience,¹¹ the rapid rise in cases provided an opportunity to collaborate across institutions to assess which treatments were most effective. In particular, the predictable rise in rates of MIS-C after a surge in COVID-19 cases could have provided an avenue to evaluate the relative effectiveness of the various treatments used.¹² However, there were limited pediatric RCTs and thus a missed opportunity to establish an evidence-based pediatric standard of care for COVID-19 and MIS-C. This resulted in the development and dissemination of care practices before they were fully tested in children.

Similarly, the medical community has become increasingly aware of laboratory findings that may be predictive of clinical course.¹³ The outcomes of COVID and MIS-C are potentially severe, so looking for “early warning signs” with diagnostic testing is appealing. Clinicians responding to early data, and with a fear of missing something, may order a full panel of bloodwork for admitted patients to assist with decision-making and may underestimate the perceived minor harms and cost of unnecessary testing/admissions.³ However, most of the evidence regarding lab values came from the adult population. There is little understanding of how lab values impact pediatric-specific outcomes.¹⁴ Even for MIS-C, a pediatric-specific condition, early protocols emphasize broad testing approaches.¹⁵ A focus on grave (but rare) outcomes from a novel virus may also distract from more common causes of symptoms and lead to missed common diagnoses that are less severe.¹⁶ For both testing and treatment, having this early information before clear evidence on how it guided care may have caused more harm than benefit. Again, RCTs may have helped guide MIS-C therapies and protocol development.

Changing workflows may also create new barriers to HVC. One of the recommendations from Choosing Wisely^® during the COVID-19 pandemic was to batch lab draws¹⁷ to reduce the risk of exposure to healthcare workers performing phlebotomy, as well as staff who transport, handle, and process bloodwork in the lab. This may inadvertently encourage the approach of getting a lab test “in case” we need it with a single daily blood draw. In trying to avoid multiple encounters (and conserve personal protective equipment [PPE]), we may be taking a less stepwise approach than in prepandemic times.

Finally, children’s hospitals witnessed significant financial challenges and reductions in patient volume related to the pandemic.¹⁸ Reductions in patient volume could present a potential opportunity for practicing HVC (eg, more time to discuss downstream effects) or alternatively could inadvertently incentivize low-value, low-priority care via messaging around preserving financial viability.

For clinicians and healthcare systems, these examples highlight why we may be predisposed to practicing LVC during a pandemic or similar emerging threat.

In light of these challenging clinical scenarios and nonclinical factors that predispose us to LVC, how can we reinforce a high-value approach to care during a pandemic or similar emerging threat? The following five specific concepts may help providers and organizations optimize HVC during this pandemic and in future situations:

1. Utilize pediatric RCTs to provide evidence-based recommendations. In the face of a novel virus with unclear manifestations, treatment options were rapidly implemented without time for careful evaluation. In the future, collaboratively utilizing shared resources in the research community could help rapidly and rigorously evaluate outcomes in the pursuit of evidence-based practice.
2. Use standardization as a tool to mitigate uncertainty. Knowing that uncertainty can be a driver of overuse and that during emerging threats, evidence is scarce and rapidly changing, a structured method for standardizing practice across your institution or multiple institutions can be helpful in many ways. Electronic health record–based orders and guidelines provide a standard of care to relieve uncertainty and have been shown to reduce overtesting.¹⁹ These resources can also be adapted rapidly as evidence emerges, reducing the burden on providers to know the latest evolving best practice. Experts who have reviewed the literature should have a method to quickly disseminate these findings through standardized practice, providing a venue for rapid learning and implementation.²⁰
3. Plan for active deimplementation from the outset. It is inevitable that some practices implemented early in pandemic response may need to be deimplemented later as the evidence and situation evolve. However, there is ample evidence that deimplementation can be difficult.²¹ Building in deimplementation mechanisms, such as standing educational sessions or hospital committees dedicated to value that review practices, from the beginning may ease these changes.
4. Take advantage of novel opportunities to improve value. Early stop-gap interventions may be wasteful, but the upheaval from major events may also create novel opportunities to improve value in other ways. Some of these efforts, like PPE conservation and as-needed follow-up visits, may become useful methods to improve value even after the pandemic ends.^22,23 The decreased pursuit of healthcare during the pandemic may also have given us an opportunity to better define when delayed diagnosis or even nondiagnosis for certain conditions is acceptable and when it may cause harm.
5. Highlight harms of overuse. While avoiding unnecessary costs is an important aspect of reducing overuse, often the other human-centered harms of overuse are better motivators for HVC. Especially during the response to an emerging threat, the impacts of overuse may be compounded. Laboratory resources that are strained to meet COVID-19 testing demand will be further stretched by overuse of other laboratory testing. Overuse of ineffective treatments adds stress to nurses, pharmacists, and other front-line staff taking care of ill patients. Side effects of unnecessary interventions, including those that could prolong hospitalization, would also increase strain on the system. Reducing overuse is also a way to reduce workload for hospital staff during a time of crisis. Improved efficiency of practice and less time spent on practices that do not add value to patient care can insulate staff against burnout.²⁴ Hospitalization and healthcare costs can add to the stress and financial burden of patients and families.²⁵ Clinicians can highlight harms of overuse through openly talking about it on rounds with the patients, families, and entire care team and incorporating it into health system–wide messaging.

As vaccine distribution continues, like many clinicians, we are hopeful that the worst days of the pandemic are behind us. The crucible of the COVID-19 pandemic has undoubtedly changed us as clinicians and impacted our future practice patterns. We believe there is a need to challenge ourselves to continue to think from a value mindset even in times of crisis. Furthermore, there are important opportunities to learn from our response to the COVID-19 pandemic and find strategies for minimizing LVC outside the pandemic. We believe the lessons learned around improving value during this pandemic can strengthen our response to the next novel, widespread threat and reduce waste in our care systems, with a potential to increase the resilience of systems in the future.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

The hospitalist admits a 56-year-old anuric man with end-stage renal disease (ESRD) on maintenance hemodialysis (HD) for an acute coronary syndrome. He received his regularly scheduled HD the day before admission. Cardiology delays his coronary catheterization until nephrology can arrange for HD immediately after angiography. After angiography, the patient receives emergent HD even though he had acceptable metabolic parameters and did not show signs or symptoms of volume overload. The hospitalist wonders whether arranging emergent HD after the procedure with intravascular (IV) contrast was necessary for this patient.

Of the approximately 600 million radiological examinations performed annually, 75 million require iodinated contrast material (ICM).¹ ICM are small, highly diffusible, minimally protein-bound molecules. They are not metabolized by humans, with healthy kidneys excreting approximately 99.8% of the administered dose within 24 hours.² ICM has been associated with acute kidney injury (AKI), but its deleterious effects have not been thoroughly described, and the incidence and severity of contrast-associated nephropathy vary among studies.³ Not surprisingly, the strongest independent patient-related risk factor for developing contrast-induced AKI is preexisting chronic kidney disease.⁴ In patients with ESRD, the biliary system slowly clears the contrast, leading to long-standing retention. Newer low- or iso-osmolar contrast material is now used rather than older, conventional high-osmolality agents. These agents are less likely to lead to AKI.⁵

Recent studies have challenged the association between AKI and ICM administration.^6-8 In 2015, the American College of Radiology endorsed the terms contrast-associated acute kidney injury and contrast-induced acute kidney injury, instead of the contrast-induced nephropathy, to avoid the uncertainty about the causal relationship between AKI and ICM.⁹ ESRD patients have little or no functional renal tissue and are on renal replacement therapy, either HD or peritoneal dialysis. However, physicians apprehensive about the renal and cardiovascular toxicity caused by retained ICM might request postprocedural HD to promote quicker contrast clearance in patients with ESRD.

Clinicians divide patients with ESRD into two groups depending on their ability to produce urine. Those who produce urine have residual renal function (RRF), which independently predicts survival.¹⁰ Among a cohort of peritoneal and HD patients, Maiorca et al described a 40% reduction in the risk of death for each 1 mL/min increase in glomerular filtration rate (GFR).¹⁰ Therefore, patients on maintenance dialysis who have RRF are considered similar to patients with AKI and eGFR <30 mL/min/1.73 m².⁹ Clinicians might worry that contrast retention could reduce RRF by inducing AKI.^2,4,11

Volume overload is a second concern with ICM administration in ESRD patients. In mice, higher-osmolality ICM produced acute pulmonary edema, leading to death.¹² A rapid bolus of diatrizoate caused transient intravascular expansion as reflected by an average decrease in hemoglobin of 0.5 to 0.8 g/dL, depending on the osmolality of the agent.¹²

Conventional high-osmolar ICM also depresses myocardial contractile force, sinoatrial automaticity, and atrioventricular nodal conduction, resulting in bradycardia, transient heart blocks, and increased risk of ventricular fibrillation.¹² High-osmolar calcium-binding ICM transiently reduces systemic vascular resistance, resulting in transient hypotension and increased cardiac output. Researchers linked these adverse cardiac effects to the high-osmolality ionic ICM, not newer agents.¹² In one study of adverse outcomes linked to ICM, 36% of patients with normal kidney function exposed to contrast developed an adverse reaction; 2% of patients developed level 4 (severe) adverse reactions.¹³ The study noted a significantly increased risk of bradycardia (relative risk [RR], 17.9), hypotension (RR, 6.3), and angina (RR, 3.4) among those who received high-osmolality contrast agents.

HD removes 72% to 82% of ICM at 4 hours.¹⁴ Armed with data from mice or small-population studies that demonstrated the toxic effects of conventional high-osmolar ICM, many radiologists and clinicians recommend post-contrast HD for patients at high risk for contrast-induced AKI and chronic HD patients.² Moon et al suggested prophylactic HD for quicker removal of the iodinated contrast medium to prevent reduction in renal function among high-risk patients after angiographic interventions.¹⁵

Over the last 3 decades, we have transitioned from conventional radiocontrast to low-osmolality agents that are not directly toxic to the kidneys. Iodixanol, iohexol, and iopromide exposure during intravascular radiological procedures did not result in a decline of RRF among well-hydrated peritoneal dialysis patients with RRF.^16,17 The limited analysis of HD trials in the systematic review by Cruz et al concluded that periprocedural HD in patients with chronic kidney disease did not decrease the incidence of radiocontrast-associated nephropathy.¹⁸ A meta-analysis of nine studies (434 patients) concluded that ICM administration does not cause significant reduction of residual function in dialysis patients.¹⁹ Because anuric ESRD patients have no salvageable renal function and are on HD, managing AKI seems irrelevant.

Although volume overload is an important consideration, the theoretical increase in intravascular volume with administration of 100 mL of 1500 mOsm/L of conventional ICM to a 70 kg-patient is only 120 mL.¹⁴ More importantly, use of low-osmolar ICM substantially reduces any significant volume shifts.

Studies have not associated low-osmolality ICM with cardiovascular adverse effects.^20-23 A retrospective study by Takebayashi et al showed an absence of serious adverse reactions to low-osmolar contrast media when HD was performed on their regular HD schedule.²² Older, smaller prospective trials did not show a need for periprocedural HD after ICM exposure.^20,21,23 In a prospective study of 10 ESRD patients, Younathan et al assessed for postprocedural adverse effects of non-ionic contrast material and found that none required emergent HD.²³ Similarly, Hamani et al and Harasawa et al did not observe hemodynamic and cardiopulmonary effects of IV contrast in chronic HD patients (Table).^20,21 Injection of non-ionic contrast material in patients on chronic HD did not produce significant changes in blood pressure, electrocardiogram results, osmolality, extracellular fluid volume, or body weight.²³ Finally, the vasoconstrictor-mediated ischemic injury of ICM occurs within minutes of administration, making dialysis performed hours later of little benefit.

HD is associated with adverse effects, including hypotension, which can jeopardize cardiovascular recovery after a myocardial infarction.²⁴ The retrospective study performed by Fujimoto et al demonstrated dialytic complications in 24% of patients dialyzed the day of angiography.²⁵ They noted that the amount of contrast agent administered independently predicted intradialytic hypotension.^25,26

Delays in performing cardiac revascularizations are associated with an increase in 30-day mortality. The 30-day mortality rates of patients diagnosed with ST-elevation myocardial infarction who underwent revascularization in <60 minutes, 61 to 75 minutes, 76 to 90 minutes, and >90 minutes from study enrollment were 1%, 3.7%, 4%, and 6.7%, respectively.²⁷ Delayed diagnosis of pulmonary embolism or acute limb ischemia was associated with increased rates of complications and mortality.^28,29 The benefits of essential radiocontrast procedures outweigh the potential cardiovascular and cerebrovascular complications for HD patients. Considering the evidence, the American College of Radiology’s 2020 Manual on Contrast Media and the European Society for Urogenital Radiology’s 2018 guidelines on contrast medium administration in patients on HD concluded that an extra session or a change in the usual timing of HD is unnecessary.^13,30

HD performed post-contrast exposure does not provide any protective benefit, regardless of the degree of RRF (anuric ESRD or otherwise), making the timing of HD irrelevant. Do not delay studies that provide essential information for clinical management of high-risk conditions. The decision to perform HD in a patient who needs contrast-enhanced studies should be made independent of whether they will receive contrast.

• Immediate post-procedural HD after ICM exposure in ESRD patients is not required.
• Do not delay vital diagnostic or therapeutic procedures requiring ICM in ESRD patients.
• The indication for HD is independent of contrast exposure in ESRD patients.

The hospitalist did not need to arrange emergent post-procedural HD because it does not improve clinical outcomes. Delaying potentially lifesaving diagnostic and therapeutic measures involving the use of radiocontrast to secure post-radiocontrast HD could lead to worse outcomes.

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].

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

The hospitalist admits a 56-year-old anuric man with end-stage renal disease (ESRD) on maintenance hemodialysis (HD) for an acute coronary syndrome. He received his regularly scheduled HD the day before admission. Cardiology delays his coronary catheterization until nephrology can arrange for HD immediately after angiography. After angiography, the patient receives emergent HD even though he had acceptable metabolic parameters and did not show signs or symptoms of volume overload. The hospitalist wonders whether arranging emergent HD after the procedure with intravascular (IV) contrast was necessary for this patient.

Of the approximately 600 million radiological examinations performed annually, 75 million require iodinated contrast material (ICM).¹ ICM are small, highly diffusible, minimally protein-bound molecules. They are not metabolized by humans, with healthy kidneys excreting approximately 99.8% of the administered dose within 24 hours.² ICM has been associated with acute kidney injury (AKI), but its deleterious effects have not been thoroughly described, and the incidence and severity of contrast-associated nephropathy vary among studies.³ Not surprisingly, the strongest independent patient-related risk factor for developing contrast-induced AKI is preexisting chronic kidney disease.⁴ In patients with ESRD, the biliary system slowly clears the contrast, leading to long-standing retention. Newer low- or iso-osmolar contrast material is now used rather than older, conventional high-osmolality agents. These agents are less likely to lead to AKI.⁵

Recent studies have challenged the association between AKI and ICM administration.^6-8 In 2015, the American College of Radiology endorsed the terms contrast-associated acute kidney injury and contrast-induced acute kidney injury, instead of the contrast-induced nephropathy, to avoid the uncertainty about the causal relationship between AKI and ICM.⁹ ESRD patients have little or no functional renal tissue and are on renal replacement therapy, either HD or peritoneal dialysis. However, physicians apprehensive about the renal and cardiovascular toxicity caused by retained ICM might request postprocedural HD to promote quicker contrast clearance in patients with ESRD.

Clinicians divide patients with ESRD into two groups depending on their ability to produce urine. Those who produce urine have residual renal function (RRF), which independently predicts survival.¹⁰ Among a cohort of peritoneal and HD patients, Maiorca et al described a 40% reduction in the risk of death for each 1 mL/min increase in glomerular filtration rate (GFR).¹⁰ Therefore, patients on maintenance dialysis who have RRF are considered similar to patients with AKI and eGFR <30 mL/min/1.73 m².⁹ Clinicians might worry that contrast retention could reduce RRF by inducing AKI.^2,4,11

Volume overload is a second concern with ICM administration in ESRD patients. In mice, higher-osmolality ICM produced acute pulmonary edema, leading to death.¹² A rapid bolus of diatrizoate caused transient intravascular expansion as reflected by an average decrease in hemoglobin of 0.5 to 0.8 g/dL, depending on the osmolality of the agent.¹²

Conventional high-osmolar ICM also depresses myocardial contractile force, sinoatrial automaticity, and atrioventricular nodal conduction, resulting in bradycardia, transient heart blocks, and increased risk of ventricular fibrillation.¹² High-osmolar calcium-binding ICM transiently reduces systemic vascular resistance, resulting in transient hypotension and increased cardiac output. Researchers linked these adverse cardiac effects to the high-osmolality ionic ICM, not newer agents.¹² In one study of adverse outcomes linked to ICM, 36% of patients with normal kidney function exposed to contrast developed an adverse reaction; 2% of patients developed level 4 (severe) adverse reactions.¹³ The study noted a significantly increased risk of bradycardia (relative risk [RR], 17.9), hypotension (RR, 6.3), and angina (RR, 3.4) among those who received high-osmolality contrast agents.

HD removes 72% to 82% of ICM at 4 hours.¹⁴ Armed with data from mice or small-population studies that demonstrated the toxic effects of conventional high-osmolar ICM, many radiologists and clinicians recommend post-contrast HD for patients at high risk for contrast-induced AKI and chronic HD patients.² Moon et al suggested prophylactic HD for quicker removal of the iodinated contrast medium to prevent reduction in renal function among high-risk patients after angiographic interventions.¹⁵

Over the last 3 decades, we have transitioned from conventional radiocontrast to low-osmolality agents that are not directly toxic to the kidneys. Iodixanol, iohexol, and iopromide exposure during intravascular radiological procedures did not result in a decline of RRF among well-hydrated peritoneal dialysis patients with RRF.^16,17 The limited analysis of HD trials in the systematic review by Cruz et al concluded that periprocedural HD in patients with chronic kidney disease did not decrease the incidence of radiocontrast-associated nephropathy.¹⁸ A meta-analysis of nine studies (434 patients) concluded that ICM administration does not cause significant reduction of residual function in dialysis patients.¹⁹ Because anuric ESRD patients have no salvageable renal function and are on HD, managing AKI seems irrelevant.

Although volume overload is an important consideration, the theoretical increase in intravascular volume with administration of 100 mL of 1500 mOsm/L of conventional ICM to a 70 kg-patient is only 120 mL.¹⁴ More importantly, use of low-osmolar ICM substantially reduces any significant volume shifts.

Studies have not associated low-osmolality ICM with cardiovascular adverse effects.^20-23 A retrospective study by Takebayashi et al showed an absence of serious adverse reactions to low-osmolar contrast media when HD was performed on their regular HD schedule.²² Older, smaller prospective trials did not show a need for periprocedural HD after ICM exposure.^20,21,23 In a prospective study of 10 ESRD patients, Younathan et al assessed for postprocedural adverse effects of non-ionic contrast material and found that none required emergent HD.²³ Similarly, Hamani et al and Harasawa et al did not observe hemodynamic and cardiopulmonary effects of IV contrast in chronic HD patients (Table).^20,21 Injection of non-ionic contrast material in patients on chronic HD did not produce significant changes in blood pressure, electrocardiogram results, osmolality, extracellular fluid volume, or body weight.²³ Finally, the vasoconstrictor-mediated ischemic injury of ICM occurs within minutes of administration, making dialysis performed hours later of little benefit.

HD is associated with adverse effects, including hypotension, which can jeopardize cardiovascular recovery after a myocardial infarction.²⁴ The retrospective study performed by Fujimoto et al demonstrated dialytic complications in 24% of patients dialyzed the day of angiography.²⁵ They noted that the amount of contrast agent administered independently predicted intradialytic hypotension.^25,26

Delays in performing cardiac revascularizations are associated with an increase in 30-day mortality. The 30-day mortality rates of patients diagnosed with ST-elevation myocardial infarction who underwent revascularization in <60 minutes, 61 to 75 minutes, 76 to 90 minutes, and >90 minutes from study enrollment were 1%, 3.7%, 4%, and 6.7%, respectively.²⁷ Delayed diagnosis of pulmonary embolism or acute limb ischemia was associated with increased rates of complications and mortality.^28,29 The benefits of essential radiocontrast procedures outweigh the potential cardiovascular and cerebrovascular complications for HD patients. Considering the evidence, the American College of Radiology’s 2020 Manual on Contrast Media and the European Society for Urogenital Radiology’s 2018 guidelines on contrast medium administration in patients on HD concluded that an extra session or a change in the usual timing of HD is unnecessary.^13,30

HD performed post-contrast exposure does not provide any protective benefit, regardless of the degree of RRF (anuric ESRD or otherwise), making the timing of HD irrelevant. Do not delay studies that provide essential information for clinical management of high-risk conditions. The decision to perform HD in a patient who needs contrast-enhanced studies should be made independent of whether they will receive contrast.

• Immediate post-procedural HD after ICM exposure in ESRD patients is not required.
• Do not delay vital diagnostic or therapeutic procedures requiring ICM in ESRD patients.
• The indication for HD is independent of contrast exposure in ESRD patients.

The hospitalist did not need to arrange emergent post-procedural HD because it does not improve clinical outcomes. Delaying potentially lifesaving diagnostic and therapeutic measures involving the use of radiocontrast to secure post-radiocontrast HD could lead to worse outcomes.

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].

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

The hospitalist admits a 56-year-old anuric man with end-stage renal disease (ESRD) on maintenance hemodialysis (HD) for an acute coronary syndrome. He received his regularly scheduled HD the day before admission. Cardiology delays his coronary catheterization until nephrology can arrange for HD immediately after angiography. After angiography, the patient receives emergent HD even though he had acceptable metabolic parameters and did not show signs or symptoms of volume overload. The hospitalist wonders whether arranging emergent HD after the procedure with intravascular (IV) contrast was necessary for this patient.

Of the approximately 600 million radiological examinations performed annually, 75 million require iodinated contrast material (ICM).¹ ICM are small, highly diffusible, minimally protein-bound molecules. They are not metabolized by humans, with healthy kidneys excreting approximately 99.8% of the administered dose within 24 hours.² ICM has been associated with acute kidney injury (AKI), but its deleterious effects have not been thoroughly described, and the incidence and severity of contrast-associated nephropathy vary among studies.³ Not surprisingly, the strongest independent patient-related risk factor for developing contrast-induced AKI is preexisting chronic kidney disease.⁴ In patients with ESRD, the biliary system slowly clears the contrast, leading to long-standing retention. Newer low- or iso-osmolar contrast material is now used rather than older, conventional high-osmolality agents. These agents are less likely to lead to AKI.⁵

Recent studies have challenged the association between AKI and ICM administration.^6-8 In 2015, the American College of Radiology endorsed the terms contrast-associated acute kidney injury and contrast-induced acute kidney injury, instead of the contrast-induced nephropathy, to avoid the uncertainty about the causal relationship between AKI and ICM.⁹ ESRD patients have little or no functional renal tissue and are on renal replacement therapy, either HD or peritoneal dialysis. However, physicians apprehensive about the renal and cardiovascular toxicity caused by retained ICM might request postprocedural HD to promote quicker contrast clearance in patients with ESRD.

Clinicians divide patients with ESRD into two groups depending on their ability to produce urine. Those who produce urine have residual renal function (RRF), which independently predicts survival.¹⁰ Among a cohort of peritoneal and HD patients, Maiorca et al described a 40% reduction in the risk of death for each 1 mL/min increase in glomerular filtration rate (GFR).¹⁰ Therefore, patients on maintenance dialysis who have RRF are considered similar to patients with AKI and eGFR <30 mL/min/1.73 m².⁹ Clinicians might worry that contrast retention could reduce RRF by inducing AKI.^2,4,11

Volume overload is a second concern with ICM administration in ESRD patients. In mice, higher-osmolality ICM produced acute pulmonary edema, leading to death.¹² A rapid bolus of diatrizoate caused transient intravascular expansion as reflected by an average decrease in hemoglobin of 0.5 to 0.8 g/dL, depending on the osmolality of the agent.¹²

Conventional high-osmolar ICM also depresses myocardial contractile force, sinoatrial automaticity, and atrioventricular nodal conduction, resulting in bradycardia, transient heart blocks, and increased risk of ventricular fibrillation.¹² High-osmolar calcium-binding ICM transiently reduces systemic vascular resistance, resulting in transient hypotension and increased cardiac output. Researchers linked these adverse cardiac effects to the high-osmolality ionic ICM, not newer agents.¹² In one study of adverse outcomes linked to ICM, 36% of patients with normal kidney function exposed to contrast developed an adverse reaction; 2% of patients developed level 4 (severe) adverse reactions.¹³ The study noted a significantly increased risk of bradycardia (relative risk [RR], 17.9), hypotension (RR, 6.3), and angina (RR, 3.4) among those who received high-osmolality contrast agents.

HD removes 72% to 82% of ICM at 4 hours.¹⁴ Armed with data from mice or small-population studies that demonstrated the toxic effects of conventional high-osmolar ICM, many radiologists and clinicians recommend post-contrast HD for patients at high risk for contrast-induced AKI and chronic HD patients.² Moon et al suggested prophylactic HD for quicker removal of the iodinated contrast medium to prevent reduction in renal function among high-risk patients after angiographic interventions.¹⁵

Over the last 3 decades, we have transitioned from conventional radiocontrast to low-osmolality agents that are not directly toxic to the kidneys. Iodixanol, iohexol, and iopromide exposure during intravascular radiological procedures did not result in a decline of RRF among well-hydrated peritoneal dialysis patients with RRF.^16,17 The limited analysis of HD trials in the systematic review by Cruz et al concluded that periprocedural HD in patients with chronic kidney disease did not decrease the incidence of radiocontrast-associated nephropathy.¹⁸ A meta-analysis of nine studies (434 patients) concluded that ICM administration does not cause significant reduction of residual function in dialysis patients.¹⁹ Because anuric ESRD patients have no salvageable renal function and are on HD, managing AKI seems irrelevant.

Although volume overload is an important consideration, the theoretical increase in intravascular volume with administration of 100 mL of 1500 mOsm/L of conventional ICM to a 70 kg-patient is only 120 mL.¹⁴ More importantly, use of low-osmolar ICM substantially reduces any significant volume shifts.

Studies have not associated low-osmolality ICM with cardiovascular adverse effects.^20-23 A retrospective study by Takebayashi et al showed an absence of serious adverse reactions to low-osmolar contrast media when HD was performed on their regular HD schedule.²² Older, smaller prospective trials did not show a need for periprocedural HD after ICM exposure.^20,21,23 In a prospective study of 10 ESRD patients, Younathan et al assessed for postprocedural adverse effects of non-ionic contrast material and found that none required emergent HD.²³ Similarly, Hamani et al and Harasawa et al did not observe hemodynamic and cardiopulmonary effects of IV contrast in chronic HD patients (Table).^20,21 Injection of non-ionic contrast material in patients on chronic HD did not produce significant changes in blood pressure, electrocardiogram results, osmolality, extracellular fluid volume, or body weight.²³ Finally, the vasoconstrictor-mediated ischemic injury of ICM occurs within minutes of administration, making dialysis performed hours later of little benefit.

HD is associated with adverse effects, including hypotension, which can jeopardize cardiovascular recovery after a myocardial infarction.²⁴ The retrospective study performed by Fujimoto et al demonstrated dialytic complications in 24% of patients dialyzed the day of angiography.²⁵ They noted that the amount of contrast agent administered independently predicted intradialytic hypotension.^25,26

Delays in performing cardiac revascularizations are associated with an increase in 30-day mortality. The 30-day mortality rates of patients diagnosed with ST-elevation myocardial infarction who underwent revascularization in <60 minutes, 61 to 75 minutes, 76 to 90 minutes, and >90 minutes from study enrollment were 1%, 3.7%, 4%, and 6.7%, respectively.²⁷ Delayed diagnosis of pulmonary embolism or acute limb ischemia was associated with increased rates of complications and mortality.^28,29 The benefits of essential radiocontrast procedures outweigh the potential cardiovascular and cerebrovascular complications for HD patients. Considering the evidence, the American College of Radiology’s 2020 Manual on Contrast Media and the European Society for Urogenital Radiology’s 2018 guidelines on contrast medium administration in patients on HD concluded that an extra session or a change in the usual timing of HD is unnecessary.^13,30

HD performed post-contrast exposure does not provide any protective benefit, regardless of the degree of RRF (anuric ESRD or otherwise), making the timing of HD irrelevant. Do not delay studies that provide essential information for clinical management of high-risk conditions. The decision to perform HD in a patient who needs contrast-enhanced studies should be made independent of whether they will receive contrast.

• Immediate post-procedural HD after ICM exposure in ESRD patients is not required.
• Do not delay vital diagnostic or therapeutic procedures requiring ICM in ESRD patients.
• The indication for HD is independent of contrast exposure in ESRD patients.

The hospitalist did not need to arrange emergent post-procedural HD because it does not improve clinical outcomes. Delaying potentially lifesaving diagnostic and therapeutic measures involving the use of radiocontrast to secure post-radiocontrast HD could lead to worse outcomes.

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].

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

On teaching rounds, a medical student presents the following case to the attending hospitalist: “Mrs. L is a 54-year-old Black female with chronic kidney disease who was admitted with community-acquired pneumonia. She continues to improve symptomatically on ceftriaxone. Currently, she is afebrile and her vitals are stable. Supplemental oxygen has been weaned to 2 L/min by nasal cannula. Exam reveals improved crackles in the left lower chest without dullness to percussion. Labs are notable for down-trending leukocytosis and a stable serum creatinine of 2.8 mg/dL.” The hospitalist considers how including racial descriptors in clinical presentations may influence the care of the patient.

For decades, medical educators have taught learners to include sociopolitical constructs such as race in the opening sentence of the history of present illness (HPI). This practice presumably stems from the assumption that race accurately reflects biogenetic information about patients and serves as a key attribute in problem representations.¹ Proponents of including race in the HPI suggest doing so aids the clinical assessment of patients’ risks for particular diseases and may inform the selection of race-appropriate therapies.²

The construct of race does sometimes correlate with the risk of disease or response to therapies. For example, sickle cell disease (SCD) occurs more commonly among patients who identify as Black rather than White. Specifically, ancestry from African nations such as Nigeria or the Democratic Republic of Congo increases the likelihood of having the disease-associated hemoglobin gene variant HbS.¹ Popular genomic ancestry tests often report ancestral groupings that map to racial categories and may reinforce the perception that race has a genetic basis.³

Race, a construct of sociopolitical origins, incorrectly conflates skin color with genetic variation. Associations between race and disease have the potential to cause diagnostic and therapeutic errors and inequitable allocation of resources. Increased illness burden in minority populations results primarily from social factors such as environment, access to care, housing instability, food insecurity, and experiences of discrimination, rather than genetic differences. The resulting chronic and recurrent physiologic stress—known as allostatic load—also contributes to the inequitable health outcomes observed in vulnerable populations, including patients who identify as Black.⁴

Historically, race evolved as a sociopolitical framework stemming from colonialism, discrimination, and exploitation.⁵ Numerous studies reveal a lack of genetic precision in racial categories. In fact, genetic data compared across major continental groups found greater variation of microsatellite loci and restriction fragment length polymorphisms within racial groups than between them.⁶ The evidence indicates that racial categories do not reflect homogenous population groups but rather “arbitrary division[s] of continuous variation” that cannot serve as a surrogate to genetic diversity.⁵ Not only are racial categories genetically inaccurate, but data on race within the electronic health record often differ from patients’ self-description of race, underscoring the problematic nature of even identifying race.⁷ In one study, up to 41% of patients self-reported identification with at least one other racial or ethnic group than the race or ethnicity documented in their electronic health record.⁷

Additionally, conflating race with genetic variation can lead to diagnostic errors. As an example, the incidence of cystic fibrosis (CF) varies widely across populations of European ancestry. The primary focus on CF’s occurrence in patients of European descent may divert attention from the identification of mutations causing CF in populations of African descent or the decreased survival observed in the United States among CF patients of Hispanic descent.^8,9 Similarly, India represents one of the countries largely affected by SCD, suggesting that a myopic focus on SCD among those identifying as Black can lead to underdiagnosis of SCD among those with Indian ancestry.

Perhaps more insidiously, linking disease to race or other social constructs can result in differential support for affected individuals. SCD offers a striking illustration of this point. Reflecting the legacy of transatlantic slave trading, the majority of people with SCD in the United States are Black and face interpersonal and structural racism within society and healthcare that amplify the effects of this devastating illness.¹⁰ Compulsory screening programs for sickle cell trait introduced by many states in the 1970s targeted Black Americans and resulted in stigmatization and the denial of insurance, educational opportunities, and jobs for many identified with sickle cell trait. Federal funding for SCD research remains low, particularly in comparison to the tenfold higher funding for CF, which afflicts fewer, but primarily White, Americans.¹⁰

The incorporation of race into risk models and guidelines—alongside biologically relevant variables such as age and comorbid conditions—has received increasing attention for its potential to compound racial disparities in health outcomes. The American Heart Association Heart Failure Risk Score, for instance, may lead to the exclusion of some Black patients from necessary care because “Black” race, for no clear physiologic reason, serves as a protective factor against heart failure mortality.¹¹ Likewise, race adjustments in pulmonary function tests, breast cancer risk models, and estimated glomerular filtration rate calculations, among others, have limited biological basis and the potential to divert care disproportionately from minority populations.¹¹

Researchers have even called into question the application of race to pharmacotherapies. A 2001 investigation on geographic patterns of genetic variation in drug response concluded that common racial and ethnic labels were “insufficient and inaccurate representations” of the individual genetic clusters.¹² Further, numerous experts have criticized two landmark studies of vasodilators and angiotensin-converting enzyme inhibitors in Black patients with heart failure for inconsistent results and nonsignificant associations between race and major outcomes, such as the development of heart failure or death.¹³

Race-based labels can also divert attention from true causes of health inequities. The National Academy of Sciences concluded that social determinants of health and structural racism are the root causes of health inequities, rather than genetics.¹⁴ Medical professionals may perpetuate these disparities: Most US physicians demonstrate an unconscious preference—or implicit bias—for White Americans over Black Americans.¹⁵ Beyond obscuring the role of social determinants of health and structural racism in health outcomes, race-based labels may exacerbate the ways in which physicians’ implicit biases contribute to racial and ethnic health disparities, primarily affecting Black Americans.² In a recent study, clinicians documented race in the HPI for 33% of Black patients compared with 16% of White patients, and White clinicians were twice as likely to document race as Black physicians.¹⁶ Moreover, training medical students to view race as an independent risk factor of disease without discussing structural inequities can pathologize race and reinforce implicit biases linking race and disease.¹⁵

Based on the current evidence, we believe routine use of race-based labels in clinical presentations confuses providers at a minimum and potentially produces far more damage by obscuring or perpetuating the role of racism in health inequities.

Instead of routinely presenting race in the HPI, we recommend including racial or ethnic information in the social history only when the patient reports it as a meaningful identity or when it informs health disparities stemming from structural or interpersonal racism. Clinicians should include physical characteristics pertaining to race, such as skin tone, in the physical exam only if required to describe exam findings accurately. When presenting race, clinicians should explicitly justify its use and take care to avoid obfuscatory, inaccurate, or stigmatizing mention of associations between race and disease. Clinicians should not use race in clinical algorithms. Medical educators should emphasize the role of social determinants of health and structural racism in health outcomes to inform the use of race in medicine, in hopes that doing so will help students minimize implicit biases and learn to mitigate racial inequities in healthcare.^2,16 In short, clinicians and medical educators alike should ensure that clinical care and the medical curriculum avoid presenting race as a proxy for pathology.

There is little evidence to guide proper inclusion of race in clinical interviews. In the absence of clear guidance about how to approach patients about race, we suggest not asking about it unless there is a reasonable probability that doing so will improve clinical care. If a clinician decides to ask about race, it is important to provide a rationale—such as explaining that the information can be used to assure high-quality care for all patients—since many patients are uncomfortable with questions about race.¹⁷ If clinicians report information about race in the social history, we advise using the patient’s description of race rather than traditional racial categories.

Clinicians who ask their patients about race should approach every patient in a uniform manner to avoid perpetuating biases. We hope future studies will inform equitable, inclusive, and person-centered approaches to discussing race with patients and promote a shared understanding of how racism contributes to illness.

• Avoid using racial descriptors in the HPI.
• Include racial and ethnic information in the social history only when it serves as a meaningful identity or it informs disparities stemming from racism.
• If racial or ethnic information is asked for, explain to patients why and how it will be used.
• Mention physical characteristics such as skin tone, rather than race, in the physical exam if required to describe findings accurately.
• Advocate for the replacement of race or race-adjusted algorithms in patient care.
• Expand the medical curriculum in the social determinants of health and structural racism, and develop systems to avoid the use of stigmatizing, race-based labels.

Race, a sociopolitical construct, does not accurately represent genetic variation. The routine use of race in the HPI can perpetuate racial biases and muddle both diagnoses and treatment. Only mention race in the social history if it is meaningful to the patient’s self-identity or explains health disparities arising from racism. All documentation and presentations should avoid the use of stigmatizing, race-based labels.

In the clinical scenario mentioned earlier, the attending hospitalist raises the issue of race-based labels in patient care in a nonjudgmental fashion. To provide illustrative specificity, she notes how the incorporation of race in formulas of glomerular filtration rate can lead to under-referral for renal transplant. The hospitalist then facilitates an open and inclusive discussion with the team regarding the use of race in clinical presentations and its potential impact on health disparities.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason^™”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason^™” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

On teaching rounds, a medical student presents the following case to the attending hospitalist: “Mrs. L is a 54-year-old Black female with chronic kidney disease who was admitted with community-acquired pneumonia. She continues to improve symptomatically on ceftriaxone. Currently, she is afebrile and her vitals are stable. Supplemental oxygen has been weaned to 2 L/min by nasal cannula. Exam reveals improved crackles in the left lower chest without dullness to percussion. Labs are notable for down-trending leukocytosis and a stable serum creatinine of 2.8 mg/dL.” The hospitalist considers how including racial descriptors in clinical presentations may influence the care of the patient.

For decades, medical educators have taught learners to include sociopolitical constructs such as race in the opening sentence of the history of present illness (HPI). This practice presumably stems from the assumption that race accurately reflects biogenetic information about patients and serves as a key attribute in problem representations.¹ Proponents of including race in the HPI suggest doing so aids the clinical assessment of patients’ risks for particular diseases and may inform the selection of race-appropriate therapies.²

The construct of race does sometimes correlate with the risk of disease or response to therapies. For example, sickle cell disease (SCD) occurs more commonly among patients who identify as Black rather than White. Specifically, ancestry from African nations such as Nigeria or the Democratic Republic of Congo increases the likelihood of having the disease-associated hemoglobin gene variant HbS.¹ Popular genomic ancestry tests often report ancestral groupings that map to racial categories and may reinforce the perception that race has a genetic basis.³

Race, a construct of sociopolitical origins, incorrectly conflates skin color with genetic variation. Associations between race and disease have the potential to cause diagnostic and therapeutic errors and inequitable allocation of resources. Increased illness burden in minority populations results primarily from social factors such as environment, access to care, housing instability, food insecurity, and experiences of discrimination, rather than genetic differences. The resulting chronic and recurrent physiologic stress—known as allostatic load—also contributes to the inequitable health outcomes observed in vulnerable populations, including patients who identify as Black.⁴

Historically, race evolved as a sociopolitical framework stemming from colonialism, discrimination, and exploitation.⁵ Numerous studies reveal a lack of genetic precision in racial categories. In fact, genetic data compared across major continental groups found greater variation of microsatellite loci and restriction fragment length polymorphisms within racial groups than between them.⁶ The evidence indicates that racial categories do not reflect homogenous population groups but rather “arbitrary division[s] of continuous variation” that cannot serve as a surrogate to genetic diversity.⁵ Not only are racial categories genetically inaccurate, but data on race within the electronic health record often differ from patients’ self-description of race, underscoring the problematic nature of even identifying race.⁷ In one study, up to 41% of patients self-reported identification with at least one other racial or ethnic group than the race or ethnicity documented in their electronic health record.⁷

Additionally, conflating race with genetic variation can lead to diagnostic errors. As an example, the incidence of cystic fibrosis (CF) varies widely across populations of European ancestry. The primary focus on CF’s occurrence in patients of European descent may divert attention from the identification of mutations causing CF in populations of African descent or the decreased survival observed in the United States among CF patients of Hispanic descent.^8,9 Similarly, India represents one of the countries largely affected by SCD, suggesting that a myopic focus on SCD among those identifying as Black can lead to underdiagnosis of SCD among those with Indian ancestry.

Perhaps more insidiously, linking disease to race or other social constructs can result in differential support for affected individuals. SCD offers a striking illustration of this point. Reflecting the legacy of transatlantic slave trading, the majority of people with SCD in the United States are Black and face interpersonal and structural racism within society and healthcare that amplify the effects of this devastating illness.¹⁰ Compulsory screening programs for sickle cell trait introduced by many states in the 1970s targeted Black Americans and resulted in stigmatization and the denial of insurance, educational opportunities, and jobs for many identified with sickle cell trait. Federal funding for SCD research remains low, particularly in comparison to the tenfold higher funding for CF, which afflicts fewer, but primarily White, Americans.¹⁰

The incorporation of race into risk models and guidelines—alongside biologically relevant variables such as age and comorbid conditions—has received increasing attention for its potential to compound racial disparities in health outcomes. The American Heart Association Heart Failure Risk Score, for instance, may lead to the exclusion of some Black patients from necessary care because “Black” race, for no clear physiologic reason, serves as a protective factor against heart failure mortality.¹¹ Likewise, race adjustments in pulmonary function tests, breast cancer risk models, and estimated glomerular filtration rate calculations, among others, have limited biological basis and the potential to divert care disproportionately from minority populations.¹¹

Researchers have even called into question the application of race to pharmacotherapies. A 2001 investigation on geographic patterns of genetic variation in drug response concluded that common racial and ethnic labels were “insufficient and inaccurate representations” of the individual genetic clusters.¹² Further, numerous experts have criticized two landmark studies of vasodilators and angiotensin-converting enzyme inhibitors in Black patients with heart failure for inconsistent results and nonsignificant associations between race and major outcomes, such as the development of heart failure or death.¹³

Race-based labels can also divert attention from true causes of health inequities. The National Academy of Sciences concluded that social determinants of health and structural racism are the root causes of health inequities, rather than genetics.¹⁴ Medical professionals may perpetuate these disparities: Most US physicians demonstrate an unconscious preference—or implicit bias—for White Americans over Black Americans.¹⁵ Beyond obscuring the role of social determinants of health and structural racism in health outcomes, race-based labels may exacerbate the ways in which physicians’ implicit biases contribute to racial and ethnic health disparities, primarily affecting Black Americans.² In a recent study, clinicians documented race in the HPI for 33% of Black patients compared with 16% of White patients, and White clinicians were twice as likely to document race as Black physicians.¹⁶ Moreover, training medical students to view race as an independent risk factor of disease without discussing structural inequities can pathologize race and reinforce implicit biases linking race and disease.¹⁵

Based on the current evidence, we believe routine use of race-based labels in clinical presentations confuses providers at a minimum and potentially produces far more damage by obscuring or perpetuating the role of racism in health inequities.

Instead of routinely presenting race in the HPI, we recommend including racial or ethnic information in the social history only when the patient reports it as a meaningful identity or when it informs health disparities stemming from structural or interpersonal racism. Clinicians should include physical characteristics pertaining to race, such as skin tone, in the physical exam only if required to describe exam findings accurately. When presenting race, clinicians should explicitly justify its use and take care to avoid obfuscatory, inaccurate, or stigmatizing mention of associations between race and disease. Clinicians should not use race in clinical algorithms. Medical educators should emphasize the role of social determinants of health and structural racism in health outcomes to inform the use of race in medicine, in hopes that doing so will help students minimize implicit biases and learn to mitigate racial inequities in healthcare.^2,16 In short, clinicians and medical educators alike should ensure that clinical care and the medical curriculum avoid presenting race as a proxy for pathology.

There is little evidence to guide proper inclusion of race in clinical interviews. In the absence of clear guidance about how to approach patients about race, we suggest not asking about it unless there is a reasonable probability that doing so will improve clinical care. If a clinician decides to ask about race, it is important to provide a rationale—such as explaining that the information can be used to assure high-quality care for all patients—since many patients are uncomfortable with questions about race.¹⁷ If clinicians report information about race in the social history, we advise using the patient’s description of race rather than traditional racial categories.

Clinicians who ask their patients about race should approach every patient in a uniform manner to avoid perpetuating biases. We hope future studies will inform equitable, inclusive, and person-centered approaches to discussing race with patients and promote a shared understanding of how racism contributes to illness.

• Avoid using racial descriptors in the HPI.
• Include racial and ethnic information in the social history only when it serves as a meaningful identity or it informs disparities stemming from racism.
• If racial or ethnic information is asked for, explain to patients why and how it will be used.
• Mention physical characteristics such as skin tone, rather than race, in the physical exam if required to describe findings accurately.
• Advocate for the replacement of race or race-adjusted algorithms in patient care.
• Expand the medical curriculum in the social determinants of health and structural racism, and develop systems to avoid the use of stigmatizing, race-based labels.

Race, a sociopolitical construct, does not accurately represent genetic variation. The routine use of race in the HPI can perpetuate racial biases and muddle both diagnoses and treatment. Only mention race in the social history if it is meaningful to the patient’s self-identity or explains health disparities arising from racism. All documentation and presentations should avoid the use of stigmatizing, race-based labels.

In the clinical scenario mentioned earlier, the attending hospitalist raises the issue of race-based labels in patient care in a nonjudgmental fashion. To provide illustrative specificity, she notes how the incorporation of race in formulas of glomerular filtration rate can lead to under-referral for renal transplant. The hospitalist then facilitates an open and inclusive discussion with the team regarding the use of race in clinical presentations and its potential impact on health disparities.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason^™”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason^™” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

On teaching rounds, a medical student presents the following case to the attending hospitalist: “Mrs. L is a 54-year-old Black female with chronic kidney disease who was admitted with community-acquired pneumonia. She continues to improve symptomatically on ceftriaxone. Currently, she is afebrile and her vitals are stable. Supplemental oxygen has been weaned to 2 L/min by nasal cannula. Exam reveals improved crackles in the left lower chest without dullness to percussion. Labs are notable for down-trending leukocytosis and a stable serum creatinine of 2.8 mg/dL.” The hospitalist considers how including racial descriptors in clinical presentations may influence the care of the patient.

For decades, medical educators have taught learners to include sociopolitical constructs such as race in the opening sentence of the history of present illness (HPI). This practice presumably stems from the assumption that race accurately reflects biogenetic information about patients and serves as a key attribute in problem representations.¹ Proponents of including race in the HPI suggest doing so aids the clinical assessment of patients’ risks for particular diseases and may inform the selection of race-appropriate therapies.²

The construct of race does sometimes correlate with the risk of disease or response to therapies. For example, sickle cell disease (SCD) occurs more commonly among patients who identify as Black rather than White. Specifically, ancestry from African nations such as Nigeria or the Democratic Republic of Congo increases the likelihood of having the disease-associated hemoglobin gene variant HbS.¹ Popular genomic ancestry tests often report ancestral groupings that map to racial categories and may reinforce the perception that race has a genetic basis.³

Race, a construct of sociopolitical origins, incorrectly conflates skin color with genetic variation. Associations between race and disease have the potential to cause diagnostic and therapeutic errors and inequitable allocation of resources. Increased illness burden in minority populations results primarily from social factors such as environment, access to care, housing instability, food insecurity, and experiences of discrimination, rather than genetic differences. The resulting chronic and recurrent physiologic stress—known as allostatic load—also contributes to the inequitable health outcomes observed in vulnerable populations, including patients who identify as Black.⁴

Historically, race evolved as a sociopolitical framework stemming from colonialism, discrimination, and exploitation.⁵ Numerous studies reveal a lack of genetic precision in racial categories. In fact, genetic data compared across major continental groups found greater variation of microsatellite loci and restriction fragment length polymorphisms within racial groups than between them.⁶ The evidence indicates that racial categories do not reflect homogenous population groups but rather “arbitrary division[s] of continuous variation” that cannot serve as a surrogate to genetic diversity.⁵ Not only are racial categories genetically inaccurate, but data on race within the electronic health record often differ from patients’ self-description of race, underscoring the problematic nature of even identifying race.⁷ In one study, up to 41% of patients self-reported identification with at least one other racial or ethnic group than the race or ethnicity documented in their electronic health record.⁷

Additionally, conflating race with genetic variation can lead to diagnostic errors. As an example, the incidence of cystic fibrosis (CF) varies widely across populations of European ancestry. The primary focus on CF’s occurrence in patients of European descent may divert attention from the identification of mutations causing CF in populations of African descent or the decreased survival observed in the United States among CF patients of Hispanic descent.^8,9 Similarly, India represents one of the countries largely affected by SCD, suggesting that a myopic focus on SCD among those identifying as Black can lead to underdiagnosis of SCD among those with Indian ancestry.

Perhaps more insidiously, linking disease to race or other social constructs can result in differential support for affected individuals. SCD offers a striking illustration of this point. Reflecting the legacy of transatlantic slave trading, the majority of people with SCD in the United States are Black and face interpersonal and structural racism within society and healthcare that amplify the effects of this devastating illness.¹⁰ Compulsory screening programs for sickle cell trait introduced by many states in the 1970s targeted Black Americans and resulted in stigmatization and the denial of insurance, educational opportunities, and jobs for many identified with sickle cell trait. Federal funding for SCD research remains low, particularly in comparison to the tenfold higher funding for CF, which afflicts fewer, but primarily White, Americans.¹⁰

The incorporation of race into risk models and guidelines—alongside biologically relevant variables such as age and comorbid conditions—has received increasing attention for its potential to compound racial disparities in health outcomes. The American Heart Association Heart Failure Risk Score, for instance, may lead to the exclusion of some Black patients from necessary care because “Black” race, for no clear physiologic reason, serves as a protective factor against heart failure mortality.¹¹ Likewise, race adjustments in pulmonary function tests, breast cancer risk models, and estimated glomerular filtration rate calculations, among others, have limited biological basis and the potential to divert care disproportionately from minority populations.¹¹

Researchers have even called into question the application of race to pharmacotherapies. A 2001 investigation on geographic patterns of genetic variation in drug response concluded that common racial and ethnic labels were “insufficient and inaccurate representations” of the individual genetic clusters.¹² Further, numerous experts have criticized two landmark studies of vasodilators and angiotensin-converting enzyme inhibitors in Black patients with heart failure for inconsistent results and nonsignificant associations between race and major outcomes, such as the development of heart failure or death.¹³

Race-based labels can also divert attention from true causes of health inequities. The National Academy of Sciences concluded that social determinants of health and structural racism are the root causes of health inequities, rather than genetics.¹⁴ Medical professionals may perpetuate these disparities: Most US physicians demonstrate an unconscious preference—or implicit bias—for White Americans over Black Americans.¹⁵ Beyond obscuring the role of social determinants of health and structural racism in health outcomes, race-based labels may exacerbate the ways in which physicians’ implicit biases contribute to racial and ethnic health disparities, primarily affecting Black Americans.² In a recent study, clinicians documented race in the HPI for 33% of Black patients compared with 16% of White patients, and White clinicians were twice as likely to document race as Black physicians.¹⁶ Moreover, training medical students to view race as an independent risk factor of disease without discussing structural inequities can pathologize race and reinforce implicit biases linking race and disease.¹⁵

Based on the current evidence, we believe routine use of race-based labels in clinical presentations confuses providers at a minimum and potentially produces far more damage by obscuring or perpetuating the role of racism in health inequities.

Instead of routinely presenting race in the HPI, we recommend including racial or ethnic information in the social history only when the patient reports it as a meaningful identity or when it informs health disparities stemming from structural or interpersonal racism. Clinicians should include physical characteristics pertaining to race, such as skin tone, in the physical exam only if required to describe exam findings accurately. When presenting race, clinicians should explicitly justify its use and take care to avoid obfuscatory, inaccurate, or stigmatizing mention of associations between race and disease. Clinicians should not use race in clinical algorithms. Medical educators should emphasize the role of social determinants of health and structural racism in health outcomes to inform the use of race in medicine, in hopes that doing so will help students minimize implicit biases and learn to mitigate racial inequities in healthcare.^2,16 In short, clinicians and medical educators alike should ensure that clinical care and the medical curriculum avoid presenting race as a proxy for pathology.

There is little evidence to guide proper inclusion of race in clinical interviews. In the absence of clear guidance about how to approach patients about race, we suggest not asking about it unless there is a reasonable probability that doing so will improve clinical care. If a clinician decides to ask about race, it is important to provide a rationale—such as explaining that the information can be used to assure high-quality care for all patients—since many patients are uncomfortable with questions about race.¹⁷ If clinicians report information about race in the social history, we advise using the patient’s description of race rather than traditional racial categories.

Clinicians who ask their patients about race should approach every patient in a uniform manner to avoid perpetuating biases. We hope future studies will inform equitable, inclusive, and person-centered approaches to discussing race with patients and promote a shared understanding of how racism contributes to illness.

• Avoid using racial descriptors in the HPI.
• Include racial and ethnic information in the social history only when it serves as a meaningful identity or it informs disparities stemming from racism.
• If racial or ethnic information is asked for, explain to patients why and how it will be used.
• Mention physical characteristics such as skin tone, rather than race, in the physical exam if required to describe findings accurately.
• Advocate for the replacement of race or race-adjusted algorithms in patient care.
• Expand the medical curriculum in the social determinants of health and structural racism, and develop systems to avoid the use of stigmatizing, race-based labels.

Race, a sociopolitical construct, does not accurately represent genetic variation. The routine use of race in the HPI can perpetuate racial biases and muddle both diagnoses and treatment. Only mention race in the social history if it is meaningful to the patient’s self-identity or explains health disparities arising from racism. All documentation and presentations should avoid the use of stigmatizing, race-based labels.

In the clinical scenario mentioned earlier, the attending hospitalist raises the issue of race-based labels in patient care in a nonjudgmental fashion. To provide illustrative specificity, she notes how the incorporation of race in formulas of glomerular filtration rate can lead to under-referral for renal transplant. The hospitalist then facilitates an open and inclusive discussion with the team regarding the use of race in clinical presentations and its potential impact on health disparities.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason^™”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason^™” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.

Within the nationwide effort to combat the opioid epidemic and reduce opioid prescribing, researchers have described different prescribing patterns for non-White racial and ethnic groups, including Black and LatinX populations. This remains a largely unexplored area within hospital medicine. Earlier studies of racial disparities demonstrate how some patients are assessed less often for pain and prescribed fewer opioids from the emergency department, surgical settings, and outpatient primary care practices. Researchers also have documented racial and ethnic disparities in analgesia for cancer pain and chronic noncancer pain.^1-11 Studies have demonstrated that White patients are more likely to receive opioid prescriptions compared with Black patients. Even with similar documented pain scores, there is evidence that Black patients receive fewer analgesics compared with White patients. For example, a recent study found that Black and Hispanic patients presenting to the emergency room for renal colic received less opioid medication compared with White patients.³ A study across 22 sites in Northern California found that racial minorities with long-bone fractures received fewer opioids at discharge than White patients.¹

It is unknown whether differential prescribing patterns by race exist among patients hospitalized on general medicine services. The objective of our study was to assess whether race and ethnicity were associated with the likelihood of opioids being prescribed and the duration of opioids prescribed when these patients are discharged from the hospital. Quantifying and seeking to understand these differences are the first steps toward ensuring racial and ethnic health equity in patient care.

Study Population and Data Sources

We identified all adults (age ≥18 years) discharged from the acute care inpatient general medicine services between June 1, 2012, and November 30, 2018, at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights, a 785-bed urban academic teaching hospital. All data were obtained from the hospital’s Epic-based electronic medical record (Epic Systems Corporation). Data elements were extracted from Clarity, the relationship database that stores Epic inpatient data. Patients discharged from the inpatient cardiology or bone marrow transplant services were not included. We excluded patients who did not receive opioids in the last 24 hours of their hospitalization. Patients with cancer-related pain diagnoses or sickle cell disease pain crises and patients who were discharged to hospice or followed by palliative care were excluded from the study based on International Classification of Diseases, Tenth Revision (ICD-10) codes (available on request) or service codes, when available, or admitting provider electronic health record documentation (Appendix Figure 1). Palliative care and hospice patients have significantly different pain needs, with management often directed by specialists. Patients with sickle cell disease are disproportionately Black and have distinct opioid prescribing patterns.^12,13 We also excluded discharge opioid prescriptions that were a resumption of the patient’s opioid prescription before admission based on medication documentation. Only new prescriptions signed by the discharging hospitalist, including different doses and formulations, were included in this study.

We performed a subgroup analysis of patients who were not prescribed opioids before their admission based on medication reconciliation but were started on opioids while hospitalized.

Primary Outcomes

We examined two primary outcomes: whether a patient received an opioid prescription at discharge, and, for patients prescribed opioids, the number of days prescribed. Days of opioids at discharge were calculated as total morphine milligram equivalents (MMEs) prescribed divided by MMEs administered during the final 24 hours of hospitalization. This metric was used as a patient-specific approach to calculating how long an opioid prescription will last after discharge, standardized according to the actual opioid requirements from hospitalization.¹⁴ If a patient was discharged with prescriptions for several opioids, the longest single prescription duration was used.

Primary Predictors

The primary predictor was the patient’s primary self-reported race/ethnicity, categorized as White, Black, LatinX, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other/unknown. Other/unknown included patients who were listed as other, declined, or who were otherwise unspecified. Self-reported race/ethnicity is obtained through reporting to the registrar. These race/ethnicity groupings were done in concordance with US Census Bureau definitions. Researchers classified patients as LatinX if they had Hispanic documented as their ethnicity, no matter their racial identification. These categorizations were chosen to be consistent with the existing literature, recognizing the role of a combined race/ethnicity definition for Hispanic or LatinX populations.¹⁵ These definitions of race/ethnicity are self-reported and reflect socially—not genetically defined—groupings.¹⁶ This variable serves as a surrogate for the structural factors that contribute to racism, the determining factor for racially disparate outcomes.¹⁷

Covariate Data Collection

Additional data were obtained regarding patient demographics, hospitalization factors, and medical diagnoses. Demographic factors included age, sex, and limited English proficiency (LEP) status. LEP was defined as having a primary language other than English and requiring an interpreter. Hospitalization factors included length of stay, whether they required intensive care unit (ICU) management, average daily MMEs administered during their entire hospitalization, MMEs administered during the final 24 hours of their hospitalization, whether the patient was on a teaching service or direct-care hospitalist service, their disposition on discharge, and year. Medical diagnosis variables included the adjusted Elixhauser Comorbidity Index based on ICD-10 codes; whether the patient was taking opioids at admission; and specific diagnoses of cancer, posttraumatic stress disorder (PTSD), and mood, anxiety, or psychotic disorder based on ICD-10 documentation.¹⁸

Statistical Analysis

All statistical analyses were performed using Stata software version 16 (StataCorp LP). Baseline demographic variables, hospitalization factors, and medical diagnosis variables were stratified by race/ethnicity. Within group comparisons were performed using chi-square or analysis of varianace (ANOVA) testing. For regression analyses, we fit two models. First, we fit a multivariable logistic regression model on all patients who received opioids during the last 24 hours of their hospitalization to examine the association between patient race/ethnicity and whether a patient received opioids at discharge, adjusting for additional patient, hospitalization, and medical covariates. Then we fit a negative binomial regression model on patients who were prescribed opioids at discharge to examine the association between patient race/ethnicity and the amount of opioids prescribed at discharge, adjusting for covariates. We used a negative binomial model because of the overdispersed distribution of discharge opioid prescriptions and only examined patients with an opioid prescription at discharge. We included the listed variables in our model because they were all found a priori to be associated with discharge opioid prescriptions.¹⁹ Instead of using days of opioids based on the last 24 hours, we performed a secondary analysis using the actual days of opioids supplied as the outcome. For example, a prescription of 12 tablets with every 6 hours dosing would be 3 days’ duration.

For both models, subgroup analyses were performed using the adjusted models restricted to patients newly prescribed opioids during their hospitalization and who were not previously taking opioids based on admission medication reconciliation. After testing for effect modification, this subgroup analysis was performed to reduce selection bias associated with earlier opioid use.

For all models, we reported predicted population opioid prescribing rates from the average marginal effects (AME).²⁰ Marginal effects were used because ours was a population level study and the outcome of interest was relatively common, limiting the effective interpretation of odds ratios.²¹ Marginal effects allow us to observe the instantaneous effect a given independent variable has on a dependent variable, while holding all other variables constant. It is implemented using the margins command in Stata. Marginal effects enable us to present our results as differences in probabilities, which is a more accurate way to describe the differences found among patient groups. Further, marginal effects are less sensitive to changes in model specifications.²²The UCSF Institutional Review Board for Human Subjects Research approved this study with a waiver of informed consent.

Unadjusted Results

We identified 10,953 patients who received opioids during the last 24 hours of hospitalization (see Appendix Figure 1 for study consort diagram). The patient population was 52.2% White, 18.4% Black, 11.5% Latinx, 10.1% Asian, 6.2% other/unknown, 0.9% Native Hawaiian/Other Pacific Islander, and 0.8% American Indian/Alaska Native (Table 1, Appendix Table 1). Black patients had fewer cancer diagnoses and the highest rate of prescribed opioids on admission. Asian patients were older and more likely to be female, and had higher rates of cancer, the highest median comorbidity index, and the smallest median daily MME during both the last 24 hours and total duration of hospitalization. Representative of general medicine patients, the most common principal discharge diagnoses in our dataset were pneumonia, cellulitis, altered mental status, sepsis, and abdominal pain.

Overall, 5541 (50.6%) patients who received opioids in the last 24 hours of their hospitalization received an opioid prescription at discharge. There were significant differences among racial/ethnic groups receiving an opioid prescription at discharge. Black patients were less likely to be discharged with an opioid compared with White patients (47.7% vs 50.3%; P < .001) (Table 2). The median discharge prescription duration for all patients was 9.3 days (interquartile range [IQR], 3.8-20.0). Black patients received the fewest median days of opioids at 7.5 days (IQR, 3.2-16.7) compared with White patients at 8.8 days (IQR, 3.7-20.0; P < .001) (Table 2).

Adjusted Regression Results

Demographic, clinical, and diagnosis specific factors were significantly associated with opioid prescriptions, including previous opioid use, sex, and a concurrent cancer diagnosis. There were fewer opioid prescriptions over time (Figure).

Following multivariable logistic regression for the association between race/ethnicity and opioid on discharge and controlling for covariates, we found that Black patients were less likely to receive an opioid prescription on discharge compared with White patients (predicted population rate, 47.6% vs 50.7%; AME −3.1%; 95% CI, −5.5% to −0.8%). Asian patients were more likely to receive a prescription on discharge compared with White patients (predicted population rate, 55.6% vs 50.7%; AME +4.9; 95% CI, 1.5%-8.3%).

Following multivariable negative binomial regression for the association between race/ethnicity and the number of opioid days on discharge, we found that Black patients received a shorter duration of opioid days compared with White patients (predicted days of opioids on discharge, 15.7 days vs 17.8 days; AME −2.1 days; 95% CI, −3.3 to −0.87) (Table 3). There were no significant differences among patients and the other racial/ethnic groups.

Our secondary analysis from the negative binomial regression with the days of opioids supplied metric yielded similar results to our primary analysis showing that Black patients received statically significantly fewer days of opioid therapy compared with White patients (Appendix Table 2).

Subgroup Regression Results

After testing for effect modification, which was negative, we examined the relationships for patients started on opioids during their hospitalization (Appendix Table 3 and Appendix Table 4). There were 5101 patients with newly prescribed opioids during their hospitalization. Adjusting for covariates, we found that Black patients were less likely to receive opioids at discharge compared with White patients (predicted population rate, 34.9% vs 40.4%; AME −5.5%; 95% CI, −9.2% to −1.9%). American Indian or Alaska Native patients were more likely to receive opioids on discharge (predicted population rate, 58.3% vs 40.4%; AME +17.9%; 95% CI, 1.0%-34.8%). We also found that Asian patients received more days of opioids on discharge (predicted days of opioid on discharge, 16.7 vs 13.7 days; AME +3.0 days; 95% CI, 0.6-5.3 days) (Appendix Table 4, Appendix Figure 2).

We found that Black patients discharged from the general medicine service were less likely to receive opioids and received shorter courses on discharge compared with White patients, adjusting for demographic, hospitalization, and medical diagnosis variables. Asian patients were more likely to receive an opioid prescription at discharge—a finding not reported in the literature on opioid prescribing disparities in most other practice settings.¹

Previous studies have shown racial disparities in pain management in emergency and surgical settings, but these relationships have not been characterized in an inpatient medicine population. Medicine patients comprise the majority of admitted patients in the United States and reflect a wide diversity of medical conditions, many requiring opioids for pain management. Determining the etiology of these differential prescribing patterns was not within the scope of our study, but earlier studies demonstrate a number of reasons why these patterns exist across racial and ethnic groups in other practice settings.^23,24 These reports give us insight into potential mechanisms for our study population.

Differences in pain management likely represent the multiple structural mechanisms by which racism operates.¹⁷ Drawing from the existing literature and the socioecological model, we hypothesize the ways that individual, interpersonal relationships, organizations, communities, and public policy impact opioid prescribing.^25,26 Using this model and considering the framework of Critical Race Theory (CRT), we can work towards understanding how race and ethnicity stand in as surrogates for racism and how this manifests in different outcomes and identify areas for intervention. CRT draws attention to race consciousness, contemporary orientation, centering in the margins, and praxis. In the context of this analysis, we recognize race consciousness and the interactions among factors such as race/ethnicity, language, and diagnoses such as PTSD.²⁷ This approach is necessary because racism is a multilevel construct influenced by macrolevel factors.²⁸

Individually and interpersonally, there is clinician-driven bias in pain assessment, which is activated under times of stress and diagnostic uncertainty and is amplified by a lack of clear guidelines for pain management prescriptions.^23,29-32 Institutional and organizational culture contribute to disparities through ingrained culture, practice patterns, and resource allocation.^29,33 Last, public policy and the larger sociopolitical environment worsen disparities through nondiverse workforces, state and federal guidelines, criminal justice policy, supply chain regulation, and access to care.

As individual clinicians, departments, and health systems leaders, we must identify areas for intervention. At the individual and interpersonal levels, there is evidence that taking implicit association tests could help clinicians become more aware of their negative associations, and empathy-inducing, perspective-taking interventions can reduce pain treatment bias.^31,34 At the institutional level, we must report data on disparities, create guidelines for pain management, and reevaluate the educational curriculum and culture to assess how certain biases could be propagated. The lack of straightforward guidelines leads to unclear indications for opioid prescriptions, exacerbating provider-level differences in prescribing. At the policy level, legislation that promotes workplace diversity, increases training for and access to pain specialists, and incentivizes data collection and reporting could help reduce disparities.³⁵ Equitable access to prescriptions and care is essential. Pharmacies often understock opioids in minority neighborhoods, meaning that even if a patient is prescribed an opioid on discharge, he or she might have difficulty filling the prescription.³⁶

One could question whether fewer opioid prescriptions for Black patients protects against the harms of opioid overprescribing, and therefore is not reflective of harmful inequity.³⁷ Ongoing national programs aim to reduce the harmful effects of opioids, which is reflected in the reduction in opioid prescribing over time in our institution. Our point is that differences in prescribing could reflect practices that do result in patient harm, such as less adequately controlled pain among Black patients.^1,3 Undertreated pain has negative health and social consequences and further contributes to substance-use stigma within minority communities.³⁸ Moreover, Black people who describe more discrimination in medical settings were more likely to report subsequent opioid misuse.³⁹

Although the above mechanisms might partially explain our findings among Black patients, the higher rate of prescribing for Asian patients is more challenging to explain. Our models adjusted for clinical factors. Notably, our Asian patients had the highest baseline comorbidity index, oldest mean age, and highest cancer rates, and it is possible that we were unable to fully account for illness severity or related pain needs (Table 1). It also is possible—although speculative—that factors such as language, provider concordance, and the type of disease process all contribute.⁴⁰ Some researchers have proposed a “stereotype content model” that seeks to establish a pathway among social structure (status of a patient) to clinician stereotypes (is this patient warm and/or competent) to emotional prejudices (envy, pride) and ultimately to discrimination (active/passive, help/harm).²³Our study has limitations. Our model was limited by the available data collected on our patients. Covariates including primary care follow-up, pain scores, and overdose history were not available. Furthermore, our categorization of race/ethnicity was based on self-reported data. We had 676 patients with race/ethnicity specified as other/unknown. We recognize the heterogeneity within these racial/ethnic categorizations. For example, within the LatinX or Asian communities, there are large differences based on region, country, ethnic, or cultural groups. Our study only included patients presenting to a hospital in San Francisco, which is different from the racial/ethnic makeup of other cities across the nation. Our electronic health record capture of history of opioid use disorder and mood disorders is contingent on individual clinician documentation. We did not account for provider-level differences, which is an important part of variation in prescribing differences. We also did not examine differences at the diagnosis-specific level. Finally, we could not determine the indication or appropriateness of opioid prescriptions.

Future studies will be necessary to characterize this relationship at a diagnosis-specific level and to describe causal pathways. Within our own institution, these findings present an opportunity for positive change. We hope to continue to explore the etiology of these disparities and identify areas where differences could impact patient outcomes, such as pain control. It is essential to develop appropriate recommendations for inpatient and discharge opioid prescribing to help minimize disparities and to mitigate potential harms of overprescribing. All health systems should continue to collect data on their own disparities in opioid prescribing and educate clinicians on promoting more equitable practices.

The authors thank Sneha Daya, MD, Sachin Shah, MD, MPH, and the UCSF Division of Hospital Medicine Data Core.

Within the nationwide effort to combat the opioid epidemic and reduce opioid prescribing, researchers have described different prescribing patterns for non-White racial and ethnic groups, including Black and LatinX populations. This remains a largely unexplored area within hospital medicine. Earlier studies of racial disparities demonstrate how some patients are assessed less often for pain and prescribed fewer opioids from the emergency department, surgical settings, and outpatient primary care practices. Researchers also have documented racial and ethnic disparities in analgesia for cancer pain and chronic noncancer pain.^1-11 Studies have demonstrated that White patients are more likely to receive opioid prescriptions compared with Black patients. Even with similar documented pain scores, there is evidence that Black patients receive fewer analgesics compared with White patients. For example, a recent study found that Black and Hispanic patients presenting to the emergency room for renal colic received less opioid medication compared with White patients.³ A study across 22 sites in Northern California found that racial minorities with long-bone fractures received fewer opioids at discharge than White patients.¹

It is unknown whether differential prescribing patterns by race exist among patients hospitalized on general medicine services. The objective of our study was to assess whether race and ethnicity were associated with the likelihood of opioids being prescribed and the duration of opioids prescribed when these patients are discharged from the hospital. Quantifying and seeking to understand these differences are the first steps toward ensuring racial and ethnic health equity in patient care.

Study Population and Data Sources

We identified all adults (age ≥18 years) discharged from the acute care inpatient general medicine services between June 1, 2012, and November 30, 2018, at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights, a 785-bed urban academic teaching hospital. All data were obtained from the hospital’s Epic-based electronic medical record (Epic Systems Corporation). Data elements were extracted from Clarity, the relationship database that stores Epic inpatient data. Patients discharged from the inpatient cardiology or bone marrow transplant services were not included. We excluded patients who did not receive opioids in the last 24 hours of their hospitalization. Patients with cancer-related pain diagnoses or sickle cell disease pain crises and patients who were discharged to hospice or followed by palliative care were excluded from the study based on International Classification of Diseases, Tenth Revision (ICD-10) codes (available on request) or service codes, when available, or admitting provider electronic health record documentation (Appendix Figure 1). Palliative care and hospice patients have significantly different pain needs, with management often directed by specialists. Patients with sickle cell disease are disproportionately Black and have distinct opioid prescribing patterns.^12,13 We also excluded discharge opioid prescriptions that were a resumption of the patient’s opioid prescription before admission based on medication documentation. Only new prescriptions signed by the discharging hospitalist, including different doses and formulations, were included in this study.

We performed a subgroup analysis of patients who were not prescribed opioids before their admission based on medication reconciliation but were started on opioids while hospitalized.

Primary Outcomes

We examined two primary outcomes: whether a patient received an opioid prescription at discharge, and, for patients prescribed opioids, the number of days prescribed. Days of opioids at discharge were calculated as total morphine milligram equivalents (MMEs) prescribed divided by MMEs administered during the final 24 hours of hospitalization. This metric was used as a patient-specific approach to calculating how long an opioid prescription will last after discharge, standardized according to the actual opioid requirements from hospitalization.¹⁴ If a patient was discharged with prescriptions for several opioids, the longest single prescription duration was used.

Primary Predictors

The primary predictor was the patient’s primary self-reported race/ethnicity, categorized as White, Black, LatinX, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other/unknown. Other/unknown included patients who were listed as other, declined, or who were otherwise unspecified. Self-reported race/ethnicity is obtained through reporting to the registrar. These race/ethnicity groupings were done in concordance with US Census Bureau definitions. Researchers classified patients as LatinX if they had Hispanic documented as their ethnicity, no matter their racial identification. These categorizations were chosen to be consistent with the existing literature, recognizing the role of a combined race/ethnicity definition for Hispanic or LatinX populations.¹⁵ These definitions of race/ethnicity are self-reported and reflect socially—not genetically defined—groupings.¹⁶ This variable serves as a surrogate for the structural factors that contribute to racism, the determining factor for racially disparate outcomes.¹⁷

Covariate Data Collection

Additional data were obtained regarding patient demographics, hospitalization factors, and medical diagnoses. Demographic factors included age, sex, and limited English proficiency (LEP) status. LEP was defined as having a primary language other than English and requiring an interpreter. Hospitalization factors included length of stay, whether they required intensive care unit (ICU) management, average daily MMEs administered during their entire hospitalization, MMEs administered during the final 24 hours of their hospitalization, whether the patient was on a teaching service or direct-care hospitalist service, their disposition on discharge, and year. Medical diagnosis variables included the adjusted Elixhauser Comorbidity Index based on ICD-10 codes; whether the patient was taking opioids at admission; and specific diagnoses of cancer, posttraumatic stress disorder (PTSD), and mood, anxiety, or psychotic disorder based on ICD-10 documentation.¹⁸

Statistical Analysis

All statistical analyses were performed using Stata software version 16 (StataCorp LP). Baseline demographic variables, hospitalization factors, and medical diagnosis variables were stratified by race/ethnicity. Within group comparisons were performed using chi-square or analysis of varianace (ANOVA) testing. For regression analyses, we fit two models. First, we fit a multivariable logistic regression model on all patients who received opioids during the last 24 hours of their hospitalization to examine the association between patient race/ethnicity and whether a patient received opioids at discharge, adjusting for additional patient, hospitalization, and medical covariates. Then we fit a negative binomial regression model on patients who were prescribed opioids at discharge to examine the association between patient race/ethnicity and the amount of opioids prescribed at discharge, adjusting for covariates. We used a negative binomial model because of the overdispersed distribution of discharge opioid prescriptions and only examined patients with an opioid prescription at discharge. We included the listed variables in our model because they were all found a priori to be associated with discharge opioid prescriptions.¹⁹ Instead of using days of opioids based on the last 24 hours, we performed a secondary analysis using the actual days of opioids supplied as the outcome. For example, a prescription of 12 tablets with every 6 hours dosing would be 3 days’ duration.

For both models, subgroup analyses were performed using the adjusted models restricted to patients newly prescribed opioids during their hospitalization and who were not previously taking opioids based on admission medication reconciliation. After testing for effect modification, this subgroup analysis was performed to reduce selection bias associated with earlier opioid use.

For all models, we reported predicted population opioid prescribing rates from the average marginal effects (AME).²⁰ Marginal effects were used because ours was a population level study and the outcome of interest was relatively common, limiting the effective interpretation of odds ratios.²¹ Marginal effects allow us to observe the instantaneous effect a given independent variable has on a dependent variable, while holding all other variables constant. It is implemented using the margins command in Stata. Marginal effects enable us to present our results as differences in probabilities, which is a more accurate way to describe the differences found among patient groups. Further, marginal effects are less sensitive to changes in model specifications.²²The UCSF Institutional Review Board for Human Subjects Research approved this study with a waiver of informed consent.

Unadjusted Results

We identified 10,953 patients who received opioids during the last 24 hours of hospitalization (see Appendix Figure 1 for study consort diagram). The patient population was 52.2% White, 18.4% Black, 11.5% Latinx, 10.1% Asian, 6.2% other/unknown, 0.9% Native Hawaiian/Other Pacific Islander, and 0.8% American Indian/Alaska Native (Table 1, Appendix Table 1). Black patients had fewer cancer diagnoses and the highest rate of prescribed opioids on admission. Asian patients were older and more likely to be female, and had higher rates of cancer, the highest median comorbidity index, and the smallest median daily MME during both the last 24 hours and total duration of hospitalization. Representative of general medicine patients, the most common principal discharge diagnoses in our dataset were pneumonia, cellulitis, altered mental status, sepsis, and abdominal pain.

Overall, 5541 (50.6%) patients who received opioids in the last 24 hours of their hospitalization received an opioid prescription at discharge. There were significant differences among racial/ethnic groups receiving an opioid prescription at discharge. Black patients were less likely to be discharged with an opioid compared with White patients (47.7% vs 50.3%; P < .001) (Table 2). The median discharge prescription duration for all patients was 9.3 days (interquartile range [IQR], 3.8-20.0). Black patients received the fewest median days of opioids at 7.5 days (IQR, 3.2-16.7) compared with White patients at 8.8 days (IQR, 3.7-20.0; P < .001) (Table 2).

Adjusted Regression Results

Demographic, clinical, and diagnosis specific factors were significantly associated with opioid prescriptions, including previous opioid use, sex, and a concurrent cancer diagnosis. There were fewer opioid prescriptions over time (Figure).

Following multivariable logistic regression for the association between race/ethnicity and opioid on discharge and controlling for covariates, we found that Black patients were less likely to receive an opioid prescription on discharge compared with White patients (predicted population rate, 47.6% vs 50.7%; AME −3.1%; 95% CI, −5.5% to −0.8%). Asian patients were more likely to receive a prescription on discharge compared with White patients (predicted population rate, 55.6% vs 50.7%; AME +4.9; 95% CI, 1.5%-8.3%).

Following multivariable negative binomial regression for the association between race/ethnicity and the number of opioid days on discharge, we found that Black patients received a shorter duration of opioid days compared with White patients (predicted days of opioids on discharge, 15.7 days vs 17.8 days; AME −2.1 days; 95% CI, −3.3 to −0.87) (Table 3). There were no significant differences among patients and the other racial/ethnic groups.

Our secondary analysis from the negative binomial regression with the days of opioids supplied metric yielded similar results to our primary analysis showing that Black patients received statically significantly fewer days of opioid therapy compared with White patients (Appendix Table 2).

Subgroup Regression Results

After testing for effect modification, which was negative, we examined the relationships for patients started on opioids during their hospitalization (Appendix Table 3 and Appendix Table 4). There were 5101 patients with newly prescribed opioids during their hospitalization. Adjusting for covariates, we found that Black patients were less likely to receive opioids at discharge compared with White patients (predicted population rate, 34.9% vs 40.4%; AME −5.5%; 95% CI, −9.2% to −1.9%). American Indian or Alaska Native patients were more likely to receive opioids on discharge (predicted population rate, 58.3% vs 40.4%; AME +17.9%; 95% CI, 1.0%-34.8%). We also found that Asian patients received more days of opioids on discharge (predicted days of opioid on discharge, 16.7 vs 13.7 days; AME +3.0 days; 95% CI, 0.6-5.3 days) (Appendix Table 4, Appendix Figure 2).

We found that Black patients discharged from the general medicine service were less likely to receive opioids and received shorter courses on discharge compared with White patients, adjusting for demographic, hospitalization, and medical diagnosis variables. Asian patients were more likely to receive an opioid prescription at discharge—a finding not reported in the literature on opioid prescribing disparities in most other practice settings.¹

Previous studies have shown racial disparities in pain management in emergency and surgical settings, but these relationships have not been characterized in an inpatient medicine population. Medicine patients comprise the majority of admitted patients in the United States and reflect a wide diversity of medical conditions, many requiring opioids for pain management. Determining the etiology of these differential prescribing patterns was not within the scope of our study, but earlier studies demonstrate a number of reasons why these patterns exist across racial and ethnic groups in other practice settings.^23,24 These reports give us insight into potential mechanisms for our study population.

Differences in pain management likely represent the multiple structural mechanisms by which racism operates.¹⁷ Drawing from the existing literature and the socioecological model, we hypothesize the ways that individual, interpersonal relationships, organizations, communities, and public policy impact opioid prescribing.^25,26 Using this model and considering the framework of Critical Race Theory (CRT), we can work towards understanding how race and ethnicity stand in as surrogates for racism and how this manifests in different outcomes and identify areas for intervention. CRT draws attention to race consciousness, contemporary orientation, centering in the margins, and praxis. In the context of this analysis, we recognize race consciousness and the interactions among factors such as race/ethnicity, language, and diagnoses such as PTSD.²⁷ This approach is necessary because racism is a multilevel construct influenced by macrolevel factors.²⁸

Individually and interpersonally, there is clinician-driven bias in pain assessment, which is activated under times of stress and diagnostic uncertainty and is amplified by a lack of clear guidelines for pain management prescriptions.^23,29-32 Institutional and organizational culture contribute to disparities through ingrained culture, practice patterns, and resource allocation.^29,33 Last, public policy and the larger sociopolitical environment worsen disparities through nondiverse workforces, state and federal guidelines, criminal justice policy, supply chain regulation, and access to care.

As individual clinicians, departments, and health systems leaders, we must identify areas for intervention. At the individual and interpersonal levels, there is evidence that taking implicit association tests could help clinicians become more aware of their negative associations, and empathy-inducing, perspective-taking interventions can reduce pain treatment bias.^31,34 At the institutional level, we must report data on disparities, create guidelines for pain management, and reevaluate the educational curriculum and culture to assess how certain biases could be propagated. The lack of straightforward guidelines leads to unclear indications for opioid prescriptions, exacerbating provider-level differences in prescribing. At the policy level, legislation that promotes workplace diversity, increases training for and access to pain specialists, and incentivizes data collection and reporting could help reduce disparities.³⁵ Equitable access to prescriptions and care is essential. Pharmacies often understock opioids in minority neighborhoods, meaning that even if a patient is prescribed an opioid on discharge, he or she might have difficulty filling the prescription.³⁶

One could question whether fewer opioid prescriptions for Black patients protects against the harms of opioid overprescribing, and therefore is not reflective of harmful inequity.³⁷ Ongoing national programs aim to reduce the harmful effects of opioids, which is reflected in the reduction in opioid prescribing over time in our institution. Our point is that differences in prescribing could reflect practices that do result in patient harm, such as less adequately controlled pain among Black patients.^1,3 Undertreated pain has negative health and social consequences and further contributes to substance-use stigma within minority communities.³⁸ Moreover, Black people who describe more discrimination in medical settings were more likely to report subsequent opioid misuse.³⁹

Although the above mechanisms might partially explain our findings among Black patients, the higher rate of prescribing for Asian patients is more challenging to explain. Our models adjusted for clinical factors. Notably, our Asian patients had the highest baseline comorbidity index, oldest mean age, and highest cancer rates, and it is possible that we were unable to fully account for illness severity or related pain needs (Table 1). It also is possible—although speculative—that factors such as language, provider concordance, and the type of disease process all contribute.⁴⁰ Some researchers have proposed a “stereotype content model” that seeks to establish a pathway among social structure (status of a patient) to clinician stereotypes (is this patient warm and/or competent) to emotional prejudices (envy, pride) and ultimately to discrimination (active/passive, help/harm).²³Our study has limitations. Our model was limited by the available data collected on our patients. Covariates including primary care follow-up, pain scores, and overdose history were not available. Furthermore, our categorization of race/ethnicity was based on self-reported data. We had 676 patients with race/ethnicity specified as other/unknown. We recognize the heterogeneity within these racial/ethnic categorizations. For example, within the LatinX or Asian communities, there are large differences based on region, country, ethnic, or cultural groups. Our study only included patients presenting to a hospital in San Francisco, which is different from the racial/ethnic makeup of other cities across the nation. Our electronic health record capture of history of opioid use disorder and mood disorders is contingent on individual clinician documentation. We did not account for provider-level differences, which is an important part of variation in prescribing differences. We also did not examine differences at the diagnosis-specific level. Finally, we could not determine the indication or appropriateness of opioid prescriptions.

Future studies will be necessary to characterize this relationship at a diagnosis-specific level and to describe causal pathways. Within our own institution, these findings present an opportunity for positive change. We hope to continue to explore the etiology of these disparities and identify areas where differences could impact patient outcomes, such as pain control. It is essential to develop appropriate recommendations for inpatient and discharge opioid prescribing to help minimize disparities and to mitigate potential harms of overprescribing. All health systems should continue to collect data on their own disparities in opioid prescribing and educate clinicians on promoting more equitable practices.

The authors thank Sneha Daya, MD, Sachin Shah, MD, MPH, and the UCSF Division of Hospital Medicine Data Core.

Within the nationwide effort to combat the opioid epidemic and reduce opioid prescribing, researchers have described different prescribing patterns for non-White racial and ethnic groups, including Black and LatinX populations. This remains a largely unexplored area within hospital medicine. Earlier studies of racial disparities demonstrate how some patients are assessed less often for pain and prescribed fewer opioids from the emergency department, surgical settings, and outpatient primary care practices. Researchers also have documented racial and ethnic disparities in analgesia for cancer pain and chronic noncancer pain.^1-11 Studies have demonstrated that White patients are more likely to receive opioid prescriptions compared with Black patients. Even with similar documented pain scores, there is evidence that Black patients receive fewer analgesics compared with White patients. For example, a recent study found that Black and Hispanic patients presenting to the emergency room for renal colic received less opioid medication compared with White patients.³ A study across 22 sites in Northern California found that racial minorities with long-bone fractures received fewer opioids at discharge than White patients.¹

It is unknown whether differential prescribing patterns by race exist among patients hospitalized on general medicine services. The objective of our study was to assess whether race and ethnicity were associated with the likelihood of opioids being prescribed and the duration of opioids prescribed when these patients are discharged from the hospital. Quantifying and seeking to understand these differences are the first steps toward ensuring racial and ethnic health equity in patient care.

Study Population and Data Sources

We identified all adults (age ≥18 years) discharged from the acute care inpatient general medicine services between June 1, 2012, and November 30, 2018, at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights, a 785-bed urban academic teaching hospital. All data were obtained from the hospital’s Epic-based electronic medical record (Epic Systems Corporation). Data elements were extracted from Clarity, the relationship database that stores Epic inpatient data. Patients discharged from the inpatient cardiology or bone marrow transplant services were not included. We excluded patients who did not receive opioids in the last 24 hours of their hospitalization. Patients with cancer-related pain diagnoses or sickle cell disease pain crises and patients who were discharged to hospice or followed by palliative care were excluded from the study based on International Classification of Diseases, Tenth Revision (ICD-10) codes (available on request) or service codes, when available, or admitting provider electronic health record documentation (Appendix Figure 1). Palliative care and hospice patients have significantly different pain needs, with management often directed by specialists. Patients with sickle cell disease are disproportionately Black and have distinct opioid prescribing patterns.^12,13 We also excluded discharge opioid prescriptions that were a resumption of the patient’s opioid prescription before admission based on medication documentation. Only new prescriptions signed by the discharging hospitalist, including different doses and formulations, were included in this study.

We performed a subgroup analysis of patients who were not prescribed opioids before their admission based on medication reconciliation but were started on opioids while hospitalized.

Primary Outcomes

We examined two primary outcomes: whether a patient received an opioid prescription at discharge, and, for patients prescribed opioids, the number of days prescribed. Days of opioids at discharge were calculated as total morphine milligram equivalents (MMEs) prescribed divided by MMEs administered during the final 24 hours of hospitalization. This metric was used as a patient-specific approach to calculating how long an opioid prescription will last after discharge, standardized according to the actual opioid requirements from hospitalization.¹⁴ If a patient was discharged with prescriptions for several opioids, the longest single prescription duration was used.

Primary Predictors

The primary predictor was the patient’s primary self-reported race/ethnicity, categorized as White, Black, LatinX, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other/unknown. Other/unknown included patients who were listed as other, declined, or who were otherwise unspecified. Self-reported race/ethnicity is obtained through reporting to the registrar. These race/ethnicity groupings were done in concordance with US Census Bureau definitions. Researchers classified patients as LatinX if they had Hispanic documented as their ethnicity, no matter their racial identification. These categorizations were chosen to be consistent with the existing literature, recognizing the role of a combined race/ethnicity definition for Hispanic or LatinX populations.¹⁵ These definitions of race/ethnicity are self-reported and reflect socially—not genetically defined—groupings.¹⁶ This variable serves as a surrogate for the structural factors that contribute to racism, the determining factor for racially disparate outcomes.¹⁷

Covariate Data Collection

Additional data were obtained regarding patient demographics, hospitalization factors, and medical diagnoses. Demographic factors included age, sex, and limited English proficiency (LEP) status. LEP was defined as having a primary language other than English and requiring an interpreter. Hospitalization factors included length of stay, whether they required intensive care unit (ICU) management, average daily MMEs administered during their entire hospitalization, MMEs administered during the final 24 hours of their hospitalization, whether the patient was on a teaching service or direct-care hospitalist service, their disposition on discharge, and year. Medical diagnosis variables included the adjusted Elixhauser Comorbidity Index based on ICD-10 codes; whether the patient was taking opioids at admission; and specific diagnoses of cancer, posttraumatic stress disorder (PTSD), and mood, anxiety, or psychotic disorder based on ICD-10 documentation.¹⁸

Statistical Analysis

All statistical analyses were performed using Stata software version 16 (StataCorp LP). Baseline demographic variables, hospitalization factors, and medical diagnosis variables were stratified by race/ethnicity. Within group comparisons were performed using chi-square or analysis of varianace (ANOVA) testing. For regression analyses, we fit two models. First, we fit a multivariable logistic regression model on all patients who received opioids during the last 24 hours of their hospitalization to examine the association between patient race/ethnicity and whether a patient received opioids at discharge, adjusting for additional patient, hospitalization, and medical covariates. Then we fit a negative binomial regression model on patients who were prescribed opioids at discharge to examine the association between patient race/ethnicity and the amount of opioids prescribed at discharge, adjusting for covariates. We used a negative binomial model because of the overdispersed distribution of discharge opioid prescriptions and only examined patients with an opioid prescription at discharge. We included the listed variables in our model because they were all found a priori to be associated with discharge opioid prescriptions.¹⁹ Instead of using days of opioids based on the last 24 hours, we performed a secondary analysis using the actual days of opioids supplied as the outcome. For example, a prescription of 12 tablets with every 6 hours dosing would be 3 days’ duration.

For both models, subgroup analyses were performed using the adjusted models restricted to patients newly prescribed opioids during their hospitalization and who were not previously taking opioids based on admission medication reconciliation. After testing for effect modification, this subgroup analysis was performed to reduce selection bias associated with earlier opioid use.

For all models, we reported predicted population opioid prescribing rates from the average marginal effects (AME).²⁰ Marginal effects were used because ours was a population level study and the outcome of interest was relatively common, limiting the effective interpretation of odds ratios.²¹ Marginal effects allow us to observe the instantaneous effect a given independent variable has on a dependent variable, while holding all other variables constant. It is implemented using the margins command in Stata. Marginal effects enable us to present our results as differences in probabilities, which is a more accurate way to describe the differences found among patient groups. Further, marginal effects are less sensitive to changes in model specifications.²²The UCSF Institutional Review Board for Human Subjects Research approved this study with a waiver of informed consent.

Unadjusted Results

We identified 10,953 patients who received opioids during the last 24 hours of hospitalization (see Appendix Figure 1 for study consort diagram). The patient population was 52.2% White, 18.4% Black, 11.5% Latinx, 10.1% Asian, 6.2% other/unknown, 0.9% Native Hawaiian/Other Pacific Islander, and 0.8% American Indian/Alaska Native (Table 1, Appendix Table 1). Black patients had fewer cancer diagnoses and the highest rate of prescribed opioids on admission. Asian patients were older and more likely to be female, and had higher rates of cancer, the highest median comorbidity index, and the smallest median daily MME during both the last 24 hours and total duration of hospitalization. Representative of general medicine patients, the most common principal discharge diagnoses in our dataset were pneumonia, cellulitis, altered mental status, sepsis, and abdominal pain.

Overall, 5541 (50.6%) patients who received opioids in the last 24 hours of their hospitalization received an opioid prescription at discharge. There were significant differences among racial/ethnic groups receiving an opioid prescription at discharge. Black patients were less likely to be discharged with an opioid compared with White patients (47.7% vs 50.3%; P < .001) (Table 2). The median discharge prescription duration for all patients was 9.3 days (interquartile range [IQR], 3.8-20.0). Black patients received the fewest median days of opioids at 7.5 days (IQR, 3.2-16.7) compared with White patients at 8.8 days (IQR, 3.7-20.0; P < .001) (Table 2).

Adjusted Regression Results

Demographic, clinical, and diagnosis specific factors were significantly associated with opioid prescriptions, including previous opioid use, sex, and a concurrent cancer diagnosis. There were fewer opioid prescriptions over time (Figure).

Following multivariable logistic regression for the association between race/ethnicity and opioid on discharge and controlling for covariates, we found that Black patients were less likely to receive an opioid prescription on discharge compared with White patients (predicted population rate, 47.6% vs 50.7%; AME −3.1%; 95% CI, −5.5% to −0.8%). Asian patients were more likely to receive a prescription on discharge compared with White patients (predicted population rate, 55.6% vs 50.7%; AME +4.9; 95% CI, 1.5%-8.3%).

Following multivariable negative binomial regression for the association between race/ethnicity and the number of opioid days on discharge, we found that Black patients received a shorter duration of opioid days compared with White patients (predicted days of opioids on discharge, 15.7 days vs 17.8 days; AME −2.1 days; 95% CI, −3.3 to −0.87) (Table 3). There were no significant differences among patients and the other racial/ethnic groups.

Our secondary analysis from the negative binomial regression with the days of opioids supplied metric yielded similar results to our primary analysis showing that Black patients received statically significantly fewer days of opioid therapy compared with White patients (Appendix Table 2).

Subgroup Regression Results

After testing for effect modification, which was negative, we examined the relationships for patients started on opioids during their hospitalization (Appendix Table 3 and Appendix Table 4). There were 5101 patients with newly prescribed opioids during their hospitalization. Adjusting for covariates, we found that Black patients were less likely to receive opioids at discharge compared with White patients (predicted population rate, 34.9% vs 40.4%; AME −5.5%; 95% CI, −9.2% to −1.9%). American Indian or Alaska Native patients were more likely to receive opioids on discharge (predicted population rate, 58.3% vs 40.4%; AME +17.9%; 95% CI, 1.0%-34.8%). We also found that Asian patients received more days of opioids on discharge (predicted days of opioid on discharge, 16.7 vs 13.7 days; AME +3.0 days; 95% CI, 0.6-5.3 days) (Appendix Table 4, Appendix Figure 2).

We found that Black patients discharged from the general medicine service were less likely to receive opioids and received shorter courses on discharge compared with White patients, adjusting for demographic, hospitalization, and medical diagnosis variables. Asian patients were more likely to receive an opioid prescription at discharge—a finding not reported in the literature on opioid prescribing disparities in most other practice settings.¹

Previous studies have shown racial disparities in pain management in emergency and surgical settings, but these relationships have not been characterized in an inpatient medicine population. Medicine patients comprise the majority of admitted patients in the United States and reflect a wide diversity of medical conditions, many requiring opioids for pain management. Determining the etiology of these differential prescribing patterns was not within the scope of our study, but earlier studies demonstrate a number of reasons why these patterns exist across racial and ethnic groups in other practice settings.^23,24 These reports give us insight into potential mechanisms for our study population.

Differences in pain management likely represent the multiple structural mechanisms by which racism operates.¹⁷ Drawing from the existing literature and the socioecological model, we hypothesize the ways that individual, interpersonal relationships, organizations, communities, and public policy impact opioid prescribing.^25,26 Using this model and considering the framework of Critical Race Theory (CRT), we can work towards understanding how race and ethnicity stand in as surrogates for racism and how this manifests in different outcomes and identify areas for intervention. CRT draws attention to race consciousness, contemporary orientation, centering in the margins, and praxis. In the context of this analysis, we recognize race consciousness and the interactions among factors such as race/ethnicity, language, and diagnoses such as PTSD.²⁷ This approach is necessary because racism is a multilevel construct influenced by macrolevel factors.²⁸

Individually and interpersonally, there is clinician-driven bias in pain assessment, which is activated under times of stress and diagnostic uncertainty and is amplified by a lack of clear guidelines for pain management prescriptions.^23,29-32 Institutional and organizational culture contribute to disparities through ingrained culture, practice patterns, and resource allocation.^29,33 Last, public policy and the larger sociopolitical environment worsen disparities through nondiverse workforces, state and federal guidelines, criminal justice policy, supply chain regulation, and access to care.

As individual clinicians, departments, and health systems leaders, we must identify areas for intervention. At the individual and interpersonal levels, there is evidence that taking implicit association tests could help clinicians become more aware of their negative associations, and empathy-inducing, perspective-taking interventions can reduce pain treatment bias.^31,34 At the institutional level, we must report data on disparities, create guidelines for pain management, and reevaluate the educational curriculum and culture to assess how certain biases could be propagated. The lack of straightforward guidelines leads to unclear indications for opioid prescriptions, exacerbating provider-level differences in prescribing. At the policy level, legislation that promotes workplace diversity, increases training for and access to pain specialists, and incentivizes data collection and reporting could help reduce disparities.³⁵ Equitable access to prescriptions and care is essential. Pharmacies often understock opioids in minority neighborhoods, meaning that even if a patient is prescribed an opioid on discharge, he or she might have difficulty filling the prescription.³⁶

One could question whether fewer opioid prescriptions for Black patients protects against the harms of opioid overprescribing, and therefore is not reflective of harmful inequity.³⁷ Ongoing national programs aim to reduce the harmful effects of opioids, which is reflected in the reduction in opioid prescribing over time in our institution. Our point is that differences in prescribing could reflect practices that do result in patient harm, such as less adequately controlled pain among Black patients.^1,3 Undertreated pain has negative health and social consequences and further contributes to substance-use stigma within minority communities.³⁸ Moreover, Black people who describe more discrimination in medical settings were more likely to report subsequent opioid misuse.³⁹

Although the above mechanisms might partially explain our findings among Black patients, the higher rate of prescribing for Asian patients is more challenging to explain. Our models adjusted for clinical factors. Notably, our Asian patients had the highest baseline comorbidity index, oldest mean age, and highest cancer rates, and it is possible that we were unable to fully account for illness severity or related pain needs (Table 1). It also is possible—although speculative—that factors such as language, provider concordance, and the type of disease process all contribute.⁴⁰ Some researchers have proposed a “stereotype content model” that seeks to establish a pathway among social structure (status of a patient) to clinician stereotypes (is this patient warm and/or competent) to emotional prejudices (envy, pride) and ultimately to discrimination (active/passive, help/harm).²³Our study has limitations. Our model was limited by the available data collected on our patients. Covariates including primary care follow-up, pain scores, and overdose history were not available. Furthermore, our categorization of race/ethnicity was based on self-reported data. We had 676 patients with race/ethnicity specified as other/unknown. We recognize the heterogeneity within these racial/ethnic categorizations. For example, within the LatinX or Asian communities, there are large differences based on region, country, ethnic, or cultural groups. Our study only included patients presenting to a hospital in San Francisco, which is different from the racial/ethnic makeup of other cities across the nation. Our electronic health record capture of history of opioid use disorder and mood disorders is contingent on individual clinician documentation. We did not account for provider-level differences, which is an important part of variation in prescribing differences. We also did not examine differences at the diagnosis-specific level. Finally, we could not determine the indication or appropriateness of opioid prescriptions.

Future studies will be necessary to characterize this relationship at a diagnosis-specific level and to describe causal pathways. Within our own institution, these findings present an opportunity for positive change. We hope to continue to explore the etiology of these disparities and identify areas where differences could impact patient outcomes, such as pain control. It is essential to develop appropriate recommendations for inpatient and discharge opioid prescribing to help minimize disparities and to mitigate potential harms of overprescribing. All health systems should continue to collect data on their own disparities in opioid prescribing and educate clinicians on promoting more equitable practices.

The authors thank Sneha Daya, MD, Sachin Shah, MD, MPH, and the UCSF Division of Hospital Medicine Data Core.

Communication has always played a central role in facilitating technological advances and social progress. The printing press, mail, telegraph, radio, television, electronic mail, and social media have all allowed for the exchange of ideas that led to progress, and have done so with increasing speed. But some people are beginning to question whether we are experiencing diminishing returns from making such communication easier, faster, and more widespread. Disinformation, conspiracies, inappropriate messages, and personal attacks are just as easy to communicate as truth, good ideas, and empathy. In many cases, truth and falsehood are nearly indistinguishable. Raw, nasty emotions contained in personal attacks are often provocative, thus generating even more engagement, which many people view as the purpose of social media. In this context, it is more important than ever for trusted voices, such as those of scientists and physicians, to play a role in the public sphere.

In this essay, we offer our personal recommendations on how healthcare professionals, who in our view have outsized authority and responsibility on healthcare topics, might improve communication on social media. We focus particularly on Twitter given its prominent role in the public exchange of ideas and its recognized benefits (and challenges) for scientific communication.¹ We make these recommendations with some trepidation because we are sure readers will be able to find times when we have not followed our own advice. And we are sure many will disagree or feel that our advice raises the bar too high. We divide our recommendations into lists of Do’s and Don’ts. Let’s start with the Do’s.

DO separate facts from inferences, ideally labeling them as such. For example, you can report that public health has found five cases of the delta variant in people in a specific nursing home as a fact. You might then infer that the variant is widespread in that facility, and that community spread in the region is likely. Stating the source of your facts helps the reader evaluate their reliability and precision.

DO state when you are quoting preliminary evidence. If posting a preprint, press release, or other non-peer-reviewed paper (even if it is your own!), make its preliminary status clear to the reader (Figure, part A).

DO read the full article before posting. If you are posting an article, make sure you understand the whole context of any results you are highlighting. Avoid exaggerating, fear-mongering, or selectively picking facts or results to bolster your opinion.DO seek to add value to the public discourse. Rather than simply retweeting popular posts, consider taking the time to collate evidence (including contrary evidence) into a thread if seeking to prove a point or to teach, especially when it relates to something in your field. You likely are more knowledgeable about topics in your field than 99% of readers; use Twitter to spread your expertise. Clinical “tweetorials,” such as those popularized by @tony_breu, can be very effective teaching tools (Figure, part B).

DO make recommendations as specific as possible. If your goal is to improve adherence to evidence-based medicine or support disadvantaged people, be explicit about how you would achieve these goals. Tell readers exactly what you have in mind so that individuals and leaders can operationalize the recommendations. Use threads to expand on your advice and its rationale.

DO consider engaging with misinformation. We suggest doing so if the misinformation is posted by someone prominent who is likely to have broad reach, but only once per post and in a factual manner. You are unlikely to convince persons who post disinformation that they are wrong; extended arguments are unhelpful. Your role here is simply to inform readers of the post, who may be more open to reason. Occasionally, you may even convince the initial poster, as seen in Figure, part C, to delete certain misinformation. But don’t count on it.

DO consider your obligation to the general public. It is fine to engage explicitly with the medical community (ie, through tweetchats),² but also consider that your comments will be accessible to everyone. Now more than ever the public is looking to healthcare professionals for clarity, reassurance, and evidence about medical matters.

DO acknowledge when you were wrong. Update your opinions as facts change or when you realize you made a mistake. The COVID-19 pandemic has brought home the rapidity with which we can gain scientific knowledge. Many of the things we thought were right early on—and posted about on Twitter—we now know to be wrong. Be forthright about this, while making it clear that the fact that we know more now doesn’t mean no information can be trusted. (A corollary: Don’t overstate what we know to be true at any time, so that it does not feel as much of a surprise if we later learn more and need to revise an opinion or a statement.)

DO be kind. This is perhaps the most important thing. We are all experiencing stress as physicians, parents, children, and colleagues. Spend your time focusing on people’s actions rather than impugning their motives or intelligence. Most of the time you don’t really know what their motives are. We recognize that kindness may not generate the same amount of engagement as sarcasm, but at least take time to consider whether you want to be seen as mean-spirited forever.

DO pause before sending. Twitter creates a false perception of the need for speed (and doesn’t really lend itself to revising drafts). But in reality, there is no rush. The torrent of Twitter posts means that people typically only see what has been posted around the time they log in; an early post is not necessarily more likely to be noticed. So, take your time and avoid falling into a trap of writing something you will regret, or, in extreme cases, that will get you fired or otherwise ruin your career. There is no rush to be first; Twitter will still be there tomorrow.

For some time, mentors have warned physicians (and others whose careers depend on their reputation) to be careful in their use of social media. Electronic dissemination of inappropriate words or images can come back to haunt people—sometimes immediately, sometimes many years later.³ Physicians are also at risk of falling into some pitfalls specific to the profession. That said, here are some Don’ts to avoid or be cautious about.

DON’T reveal information about patients in a recognizable fashion. Journals ask for written consent from patients when authors submit a manuscript about individual cases so readers can be sure consent has been obtained. The same standard should apply to social media; if not, you are clearly violating a professional standard. Yet, on Twitter, people may assume you have not obtained consent, conveying a false sense of invasion of privacy and undermining confidence in the profession. The safest thing to do is not tell stories about patients, or to completely disguise the story so even the patients can’t recognize themselves. If you do choose to post about a patient, obtain written permission that you save, and clearly indicate that you have that permission in the Tweet.

DON’T claim to have expertise in areas where you have little training or education. For example, just because you are an expert in critical care and have seen the ravages of COVID-19 on your patients doesn’t mean you are an expert in how to stop a pandemic, though your observations may be helpful to those who are. This does not mean you shouldn’t speak out on important moral issues like climate change, nuclear war, or injustices, which clearly reflect personal opinion and values. Rather, be cautious about commenting authoritatively on areas in which the lay reader might mistakenly think you have specific expertise.

DON’T make yourself the hero of every story. Implicitly seeking praise for doing your job (Look at me, I’m working on Christmas!) may breed resentment and undercut professionalism. Rather, state what it is about your job that works well and what doesn’t (for example, teaching tips, wellness advice, and organizational strategies) in a way that helps others emulate your successes.

DON’T let emotions get the better of you. This past year has been full of outrageous and appalling events and behavior. We do not suggest that you ignore these. Rather, make sure that if you are blaming an individual for something that it really was their fault, because they had control of the factors that led to the disastrous outcome. Consider focusing on systemic and structural explanations for unacceptable phenomena to minimize defensiveness and maximize the potential for identifying solutions. And yes, sometimes you just have to let it rip, but be selective—maybe show your post to someone else and sleep on it before you send it.

We hope that you will find these suggestions helpful in both creating and reading social media posts on important topics. We recognize that some people like the spontaneity of the social media platform and will thus find our suggestions stunting. But at least everyone ought to consider what they are trying to achieve when they make public statements. The exchange of ideas has always been a key ingredient in creating progress. Let’s optimize the usefulness of those exchanges for that purpose, and to promote knowledge and science in a way that helps us all live healthier and happier lives.

Communication has always played a central role in facilitating technological advances and social progress. The printing press, mail, telegraph, radio, television, electronic mail, and social media have all allowed for the exchange of ideas that led to progress, and have done so with increasing speed. But some people are beginning to question whether we are experiencing diminishing returns from making such communication easier, faster, and more widespread. Disinformation, conspiracies, inappropriate messages, and personal attacks are just as easy to communicate as truth, good ideas, and empathy. In many cases, truth and falsehood are nearly indistinguishable. Raw, nasty emotions contained in personal attacks are often provocative, thus generating even more engagement, which many people view as the purpose of social media. In this context, it is more important than ever for trusted voices, such as those of scientists and physicians, to play a role in the public sphere.

In this essay, we offer our personal recommendations on how healthcare professionals, who in our view have outsized authority and responsibility on healthcare topics, might improve communication on social media. We focus particularly on Twitter given its prominent role in the public exchange of ideas and its recognized benefits (and challenges) for scientific communication.¹ We make these recommendations with some trepidation because we are sure readers will be able to find times when we have not followed our own advice. And we are sure many will disagree or feel that our advice raises the bar too high. We divide our recommendations into lists of Do’s and Don’ts. Let’s start with the Do’s.

DO separate facts from inferences, ideally labeling them as such. For example, you can report that public health has found five cases of the delta variant in people in a specific nursing home as a fact. You might then infer that the variant is widespread in that facility, and that community spread in the region is likely. Stating the source of your facts helps the reader evaluate their reliability and precision.

DO state when you are quoting preliminary evidence. If posting a preprint, press release, or other non-peer-reviewed paper (even if it is your own!), make its preliminary status clear to the reader (Figure, part A).

DO read the full article before posting. If you are posting an article, make sure you understand the whole context of any results you are highlighting. Avoid exaggerating, fear-mongering, or selectively picking facts or results to bolster your opinion.DO seek to add value to the public discourse. Rather than simply retweeting popular posts, consider taking the time to collate evidence (including contrary evidence) into a thread if seeking to prove a point or to teach, especially when it relates to something in your field. You likely are more knowledgeable about topics in your field than 99% of readers; use Twitter to spread your expertise. Clinical “tweetorials,” such as those popularized by @tony_breu, can be very effective teaching tools (Figure, part B).

DO make recommendations as specific as possible. If your goal is to improve adherence to evidence-based medicine or support disadvantaged people, be explicit about how you would achieve these goals. Tell readers exactly what you have in mind so that individuals and leaders can operationalize the recommendations. Use threads to expand on your advice and its rationale.

DO consider engaging with misinformation. We suggest doing so if the misinformation is posted by someone prominent who is likely to have broad reach, but only once per post and in a factual manner. You are unlikely to convince persons who post disinformation that they are wrong; extended arguments are unhelpful. Your role here is simply to inform readers of the post, who may be more open to reason. Occasionally, you may even convince the initial poster, as seen in Figure, part C, to delete certain misinformation. But don’t count on it.

DO consider your obligation to the general public. It is fine to engage explicitly with the medical community (ie, through tweetchats),² but also consider that your comments will be accessible to everyone. Now more than ever the public is looking to healthcare professionals for clarity, reassurance, and evidence about medical matters.

DO acknowledge when you were wrong. Update your opinions as facts change or when you realize you made a mistake. The COVID-19 pandemic has brought home the rapidity with which we can gain scientific knowledge. Many of the things we thought were right early on—and posted about on Twitter—we now know to be wrong. Be forthright about this, while making it clear that the fact that we know more now doesn’t mean no information can be trusted. (A corollary: Don’t overstate what we know to be true at any time, so that it does not feel as much of a surprise if we later learn more and need to revise an opinion or a statement.)

DO be kind. This is perhaps the most important thing. We are all experiencing stress as physicians, parents, children, and colleagues. Spend your time focusing on people’s actions rather than impugning their motives or intelligence. Most of the time you don’t really know what their motives are. We recognize that kindness may not generate the same amount of engagement as sarcasm, but at least take time to consider whether you want to be seen as mean-spirited forever.

DO pause before sending. Twitter creates a false perception of the need for speed (and doesn’t really lend itself to revising drafts). But in reality, there is no rush. The torrent of Twitter posts means that people typically only see what has been posted around the time they log in; an early post is not necessarily more likely to be noticed. So, take your time and avoid falling into a trap of writing something you will regret, or, in extreme cases, that will get you fired or otherwise ruin your career. There is no rush to be first; Twitter will still be there tomorrow.

For some time, mentors have warned physicians (and others whose careers depend on their reputation) to be careful in their use of social media. Electronic dissemination of inappropriate words or images can come back to haunt people—sometimes immediately, sometimes many years later.³ Physicians are also at risk of falling into some pitfalls specific to the profession. That said, here are some Don’ts to avoid or be cautious about.

DON’T reveal information about patients in a recognizable fashion. Journals ask for written consent from patients when authors submit a manuscript about individual cases so readers can be sure consent has been obtained. The same standard should apply to social media; if not, you are clearly violating a professional standard. Yet, on Twitter, people may assume you have not obtained consent, conveying a false sense of invasion of privacy and undermining confidence in the profession. The safest thing to do is not tell stories about patients, or to completely disguise the story so even the patients can’t recognize themselves. If you do choose to post about a patient, obtain written permission that you save, and clearly indicate that you have that permission in the Tweet.

DON’T claim to have expertise in areas where you have little training or education. For example, just because you are an expert in critical care and have seen the ravages of COVID-19 on your patients doesn’t mean you are an expert in how to stop a pandemic, though your observations may be helpful to those who are. This does not mean you shouldn’t speak out on important moral issues like climate change, nuclear war, or injustices, which clearly reflect personal opinion and values. Rather, be cautious about commenting authoritatively on areas in which the lay reader might mistakenly think you have specific expertise.

DON’T make yourself the hero of every story. Implicitly seeking praise for doing your job (Look at me, I’m working on Christmas!) may breed resentment and undercut professionalism. Rather, state what it is about your job that works well and what doesn’t (for example, teaching tips, wellness advice, and organizational strategies) in a way that helps others emulate your successes.

DON’T let emotions get the better of you. This past year has been full of outrageous and appalling events and behavior. We do not suggest that you ignore these. Rather, make sure that if you are blaming an individual for something that it really was their fault, because they had control of the factors that led to the disastrous outcome. Consider focusing on systemic and structural explanations for unacceptable phenomena to minimize defensiveness and maximize the potential for identifying solutions. And yes, sometimes you just have to let it rip, but be selective—maybe show your post to someone else and sleep on it before you send it.

We hope that you will find these suggestions helpful in both creating and reading social media posts on important topics. We recognize that some people like the spontaneity of the social media platform and will thus find our suggestions stunting. But at least everyone ought to consider what they are trying to achieve when they make public statements. The exchange of ideas has always been a key ingredient in creating progress. Let’s optimize the usefulness of those exchanges for that purpose, and to promote knowledge and science in a way that helps us all live healthier and happier lives.

Communication has always played a central role in facilitating technological advances and social progress. The printing press, mail, telegraph, radio, television, electronic mail, and social media have all allowed for the exchange of ideas that led to progress, and have done so with increasing speed. But some people are beginning to question whether we are experiencing diminishing returns from making such communication easier, faster, and more widespread. Disinformation, conspiracies, inappropriate messages, and personal attacks are just as easy to communicate as truth, good ideas, and empathy. In many cases, truth and falsehood are nearly indistinguishable. Raw, nasty emotions contained in personal attacks are often provocative, thus generating even more engagement, which many people view as the purpose of social media. In this context, it is more important than ever for trusted voices, such as those of scientists and physicians, to play a role in the public sphere.

In this essay, we offer our personal recommendations on how healthcare professionals, who in our view have outsized authority and responsibility on healthcare topics, might improve communication on social media. We focus particularly on Twitter given its prominent role in the public exchange of ideas and its recognized benefits (and challenges) for scientific communication.¹ We make these recommendations with some trepidation because we are sure readers will be able to find times when we have not followed our own advice. And we are sure many will disagree or feel that our advice raises the bar too high. We divide our recommendations into lists of Do’s and Don’ts. Let’s start with the Do’s.

DO separate facts from inferences, ideally labeling them as such. For example, you can report that public health has found five cases of the delta variant in people in a specific nursing home as a fact. You might then infer that the variant is widespread in that facility, and that community spread in the region is likely. Stating the source of your facts helps the reader evaluate their reliability and precision.

DO state when you are quoting preliminary evidence. If posting a preprint, press release, or other non-peer-reviewed paper (even if it is your own!), make its preliminary status clear to the reader (Figure, part A).

DO read the full article before posting. If you are posting an article, make sure you understand the whole context of any results you are highlighting. Avoid exaggerating, fear-mongering, or selectively picking facts or results to bolster your opinion.DO seek to add value to the public discourse. Rather than simply retweeting popular posts, consider taking the time to collate evidence (including contrary evidence) into a thread if seeking to prove a point or to teach, especially when it relates to something in your field. You likely are more knowledgeable about topics in your field than 99% of readers; use Twitter to spread your expertise. Clinical “tweetorials,” such as those popularized by @tony_breu, can be very effective teaching tools (Figure, part B).

DO make recommendations as specific as possible. If your goal is to improve adherence to evidence-based medicine or support disadvantaged people, be explicit about how you would achieve these goals. Tell readers exactly what you have in mind so that individuals and leaders can operationalize the recommendations. Use threads to expand on your advice and its rationale.

DO consider engaging with misinformation. We suggest doing so if the misinformation is posted by someone prominent who is likely to have broad reach, but only once per post and in a factual manner. You are unlikely to convince persons who post disinformation that they are wrong; extended arguments are unhelpful. Your role here is simply to inform readers of the post, who may be more open to reason. Occasionally, you may even convince the initial poster, as seen in Figure, part C, to delete certain misinformation. But don’t count on it.

DO consider your obligation to the general public. It is fine to engage explicitly with the medical community (ie, through tweetchats),² but also consider that your comments will be accessible to everyone. Now more than ever the public is looking to healthcare professionals for clarity, reassurance, and evidence about medical matters.

DO acknowledge when you were wrong. Update your opinions as facts change or when you realize you made a mistake. The COVID-19 pandemic has brought home the rapidity with which we can gain scientific knowledge. Many of the things we thought were right early on—and posted about on Twitter—we now know to be wrong. Be forthright about this, while making it clear that the fact that we know more now doesn’t mean no information can be trusted. (A corollary: Don’t overstate what we know to be true at any time, so that it does not feel as much of a surprise if we later learn more and need to revise an opinion or a statement.)

DO be kind. This is perhaps the most important thing. We are all experiencing stress as physicians, parents, children, and colleagues. Spend your time focusing on people’s actions rather than impugning their motives or intelligence. Most of the time you don’t really know what their motives are. We recognize that kindness may not generate the same amount of engagement as sarcasm, but at least take time to consider whether you want to be seen as mean-spirited forever.

DO pause before sending. Twitter creates a false perception of the need for speed (and doesn’t really lend itself to revising drafts). But in reality, there is no rush. The torrent of Twitter posts means that people typically only see what has been posted around the time they log in; an early post is not necessarily more likely to be noticed. So, take your time and avoid falling into a trap of writing something you will regret, or, in extreme cases, that will get you fired or otherwise ruin your career. There is no rush to be first; Twitter will still be there tomorrow.

For some time, mentors have warned physicians (and others whose careers depend on their reputation) to be careful in their use of social media. Electronic dissemination of inappropriate words or images can come back to haunt people—sometimes immediately, sometimes many years later.³ Physicians are also at risk of falling into some pitfalls specific to the profession. That said, here are some Don’ts to avoid or be cautious about.

DON’T reveal information about patients in a recognizable fashion. Journals ask for written consent from patients when authors submit a manuscript about individual cases so readers can be sure consent has been obtained. The same standard should apply to social media; if not, you are clearly violating a professional standard. Yet, on Twitter, people may assume you have not obtained consent, conveying a false sense of invasion of privacy and undermining confidence in the profession. The safest thing to do is not tell stories about patients, or to completely disguise the story so even the patients can’t recognize themselves. If you do choose to post about a patient, obtain written permission that you save, and clearly indicate that you have that permission in the Tweet.

DON’T claim to have expertise in areas where you have little training or education. For example, just because you are an expert in critical care and have seen the ravages of COVID-19 on your patients doesn’t mean you are an expert in how to stop a pandemic, though your observations may be helpful to those who are. This does not mean you shouldn’t speak out on important moral issues like climate change, nuclear war, or injustices, which clearly reflect personal opinion and values. Rather, be cautious about commenting authoritatively on areas in which the lay reader might mistakenly think you have specific expertise.

DON’T make yourself the hero of every story. Implicitly seeking praise for doing your job (Look at me, I’m working on Christmas!) may breed resentment and undercut professionalism. Rather, state what it is about your job that works well and what doesn’t (for example, teaching tips, wellness advice, and organizational strategies) in a way that helps others emulate your successes.

DON’T let emotions get the better of you. This past year has been full of outrageous and appalling events and behavior. We do not suggest that you ignore these. Rather, make sure that if you are blaming an individual for something that it really was their fault, because they had control of the factors that led to the disastrous outcome. Consider focusing on systemic and structural explanations for unacceptable phenomena to minimize defensiveness and maximize the potential for identifying solutions. And yes, sometimes you just have to let it rip, but be selective—maybe show your post to someone else and sleep on it before you send it.

We hope that you will find these suggestions helpful in both creating and reading social media posts on important topics. We recognize that some people like the spontaneity of the social media platform and will thus find our suggestions stunting. But at least everyone ought to consider what they are trying to achieve when they make public statements. The exchange of ideas has always been a key ingredient in creating progress. Let’s optimize the usefulness of those exchanges for that purpose, and to promote knowledge and science in a way that helps us all live healthier and happier lives.

This icon represents the patient’s case. Each paragraph that follows represents the discussant’s thoughts.

A 32-year-old, previously healthy woman presented to the emergency department (ED) with 3 days of nasal pain, congestion, and cough. A day prior, she had consulted with her primary care provider by phone and had been prescribed amoxicillin-clavulanate for presumed bacterial sinusitis. She subsequently developed fever (39 oC) and pleuritic, left-upper-quadrant abdominal pain. In the ED, chest radiograph demonstrated right hilar opacification. Laboratory studies and computed tomography (CT) of the abdomen and pelvis did not identify a cause for her pain. Given the pleuritic nature of her left-upper-quadrant pain, CT pulmonary angiography was ordered. The CT revealed “mass-like” right hilar opacification and lymphadenopathy. No pulmonary emboli were identified. Levofloxacin was prescribed for presumed pneumonia, and the patient was discharged home. The following week, mediastinal biopsy was arranged for evaluation of the right hilar abnormality.

This is a young woman presenting with upper respiratory symptoms, abdominal pain, fever, and hilar lymphadenopathy. Upper respiratory symptoms are common and usually indicate an inflammatory response to allergens or infection, though autoimmune disorders may affect the upper airways. Fever and hilar lymphadenopathy likely also signify an inflammatory response. Taken together, these findings can be associated with mycobacterial or fungal infection, malignancy, and, particularly in a young woman, sarcoidosis, which could explain her abdominal pain if her presentation included splenomegaly. At this point she likely has a systemic illness involving at least the upper, and possibly the lower, respiratory tract.

Within days, her symptoms resolved. Mediastinal biopsy of the hilar node revealed scant pus. Pathology demonstrated suppurative granulomata. Gram stain; bacterial, mycobacterial, and fungal cultures; and 16S ribosomal analyses for bacteria and fungi from the biopsy were unrevealing. For unclear reasons, prior to the biopsy, she was given intramuscular Haemophilus influenzae type B and tetanus, diphtheria, and pertussis vaccines. Two weeks later, she presented again with fever and left-upper-quadrant pain as well as painful skin nodules at her biopsy and vaccination sites. She was admitted for further evaluation. Chest CT showed expansion of the mediastinal lesion and splenic enlargement. Biopsy of a skin lesion revealed suppurative granulomatous dermatitis and panniculitis. Repeat blood cultures were negative, though serum β-D-glucan was weakly positive at 173 pg/mL (reference range, <60 pg/mL). Tissue cultures and Gram, acid-fast, Fite, and Warthin-Starry stains from the skin biopsy were negative. She was discharged on fluconazole and then readmitted 2 days later with dyspnea, fever, and leukocytosis.

The young woman’s symptoms resolved, only to recur days later; her granulomatous hilar lesions grew larger, and new cutaneous and splenic findings appeared. The granulomatous lesions prompt consideration of infectious, malignant, and immune-mediated processes. The negative cultures make infection less likely, although the elevated β-D-glucan may suggest fungal infection. By description, the skin lesions are consistent with pathergy, a phenomenon characterized by trauma-provoked cutaneous lesions or ulcers, which is associated with numerous syndromes, including Behçet syndrome, inflammatory bowel disease, and neutrophilic dermatoses such as pyoderma gangrenosum (PG) and Sweet syndrome. In addition to details about her medical history, it is important to seek evidence of oral ulcers or vasculitis, as Behçet syndrome may be associated with cutaneous, visceral, and ophthalmologic vasculitis.

Her medical history included hypertension and active, 10-pack-year cigarette use. During childhood, she had occasional ingrown hairs and folliculitis. She did not take medications prior to this acute illness. Family history was notable for cardiovascular disease. She rarely consumed alcohol and did not use illicit drugs. She lived in a rural town in the mid–Willamette Valley of Oregon and worked as an administrative assistant. She spent time outdoors, including trail running and golfing. A case of tularemia was recently reported in an area near her home. Her only travel outside of Oregon was to Puerto Vallarta, Mexico, 16 years previously. She grew up on a farm and had no known tuberculosis exposure.

Tularemia is an interesting diagnostic consideration and could explain her fever, cutaneous lesions, and hilar adenopathy. It is plausible that she had clinically mild pneumonic tularemia at the outset and that her cutaneous lesions are variants of ulceroglandular tularemia. Positive antibodies for Francisella tularensis would be expected if this were the cause of her illness. The ingrown hairs raise the possibility of a primary immune deficiency syndrome predisposing her to abscesses. However, they seem to have been of trivial significance to her, making an immune deficiency syndrome unlikely.

On readmission, she was afebrile, normotensive, and tachycardic (114 beats/min), with a normal respiratory rate and oxygen saturation. She was not ill appearing. She had noninjected conjunctiva and no oral lesions. Apart from tachycardia, cardiovascular examination was unremarkable. Abdominal examination was notable for mild distension and a palpable, tender spleen. Musculoskeletal and neurologic examinations were normal. Her skin was notable for various sized (8 cm × 4 cm to 10 cm × 15 cm) painful ulcers with violaceous, friable borders—some with fluctuance and purulent drainage—on her right hand, bilateral arms, right axilla, sternum, and legs (Figure 1).

Laboratory studies were notable for normocytic anemia (hemoglobin, 8.9 g/dL; range, 12.0-16.0 g/dL), leukocytosis (white blood cells, 24,900/µL; range, 4500-11,000/µL), thrombocytosis (platelet count, 690,000/µL; range, 150,000-400,000/µL), and elevated inflammatory markers (C-reactive protein, 33 mg/dL; range, <0.5 mg/dL; erythrocyte sedimentation rate, 78 mm/h; range, <20 mm/h). A complete metabolic panel was within normal limits. Repeat blood cultures and β -D-glucan and 16S ribosomal assays were negative. Polymerase chain reaction testing for Bartonella henselae was negative. Urine probes for Neisseria gonorrhoeae and Chlamydia trachomatis were negative. Rapid plasma regain (RPR) was negative. Antibodies to toxoplasmosis, histoplasmosis, blastomycosis, and aspergillosis were unrevealing. A Coccidioides test by immunodiffusion was negative. Serum antigen tests for Cryptococcus and Epstein-Barr virus (EBV) were negative. EBV, HIV, and hepatitis antibody tests were negative. Rheumatologic studies, including antinuclear, anti-double-stranded DNA, anti-Smith, anti–Sjögren syndrome antigens A and B, anticentromere, anti-topoisomerase (anti-Scl-70), anti-histidyl-transfer-RNA-synthetase (anti-Jo-1), and anti-nucleosome (anti-chromatic) antibodies, were unrevealing. Levels of angiotensin-converting enzyme, rheumatoid factor, complement, cytoplasmic, and perinuclear antineutrophil cytoplasmic antibodies were also normal. A neutrophil oxidative burst test was negative. In addition, peripheral flow cytology and serum and urine protein electrophoresis were negative. Chest CT revealed bilateral lower lobe consolidations concerning for necrotizing pneumonia, splenic enlargement, numerous hypodense splenic lesions, and a 1.3-cm right hilar node, which had decreased in size compared with 1 month prior.

In summary, the patient presented with recurrent upper respiratory symptoms, fever, and abdominal pain; expanding granulomatous hilar lesions, splenomegaly, and cutaneous lesions consistent with pathergy; elevated inflammatory markers and leukocytosis; and a possible exposure to F tularensis. She has had extensive negative infectious workups, except for a weakly positive β-D-glucan, and completed several courses of apparently unhelpful antimicrobials. At this point, the most notable findings are her splenomegaly and inflammatory masses suggesting an inflammatory process, which may be autoimmune in nature. Both vasculitis and sarcoidosis remain possibilities, and malignancy is possible. Given her possible exposure to F tularensis, obtaining serum antibodies to F tularensis, in addition to biopsies of the skin lesions, is advisable.

Laboratory studies revealed a positive F tularensis antibody with a titer of 1:320 and an IgM of 7 U/mL and IgG of 30 U/mL. This was repeated, revealing a titer of 1:540 and an IgM and IgG of 5 U/mL and 20 U/mL, respectively. Given the potential exposure history, the clinical syndrome compatible with tularemia, and an otherwise extensive yet unrevealing evaluation, she was treated with a 10-day course of streptomycin. Her fever persisted, and the splenic lesions increased in size and number, prompting addition of moxifloxacin without apparent benefit. Skin biopsies taken from the patient’s arm were notable for nodular, suppurative, neutrophilic infiltrates and histiocytes in the medium and deep dermis without multinucleated histiocytes or evidence of vasculitis. Fungal, mycobacterial, and bacterial stains from the biopsy were negative. The findings were consistent with but not diagnostic of an acute neutrophilic dermatosis.

At this point, the patient has a confirmed exposure to F tularensis; she also has persistent fever, progressive splenomegaly, and new skin biopsies consistent with neutrophilic dermatosis. Despite the F tularensis antibody positivity, her negative cultures and lack of improvement with multiple courses of antimicrobials argue against an infectious etiology. Accordingly, malignancy should be considered but seems less likely given that no laboratory, imaging, or tissue samples support it. This leaves immune-mediated etiologies, especially autoimmune conditions associated with neutrophilic dermatoses, as the most likely explanation of her inflammatory syndrome. Neutrophilic dermatoses include some vasculitides, Sweet syndrome, PG, Behçet syndrome, and other inflammatory entities. She has no evidence of vasculitis on biopsy. Given the evidence of inflammation and the history of pathergy, Behçet syndrome and PG should be seriously considered.

She underwent incision and drainage of the left leg and mediastinal lesions. A follow-up chest CT revealed stable cutaneous and deep tissue lesions and continued splenic enlargement. She was started on prednisone and dapsone for presumed cutaneous and visceral PG. The lesions improved dramatically and, following a month-long hospitalization, she was discharged on dapsone and a slow prednisone taper. Three weeks after discharge, while on dapsone and prednisone, she developed a new skin lesion. Cyclosporine was added, with improvement. Eight weeks after discharge, she developed fever, acute left-upper-quadrant pain, and marked splenomegaly with abscesses seen on CT imaging (Figure 2).

This continues to be a very puzzling case, and it is worth revisiting her clinical course once again. This is a previously healthy 32-year-old woman with multiple hospital presentations for upper-respiratory symptoms, persistent fever, abdominal pain, and painful cutaneous lesions consistent with pathergy; she was found to have granulomatous hilar lesions, progressive splenomegaly, and skin biopsies consistent with neutrophilic dermatosis. Exhaustive infectious and rheumatologic workup was negative, and no evident malignancy was found. Finally, despite multiple courses of antimicrobials, including standard treatments for tularemia (for which she had positive antibodies), her clinical course failed to improve until the addition of systemic anti-inflammatory agents, which resulted in rapid improvement. She then presented 8 weeks later with recurrent fever and splenomegaly. Given the recurrence and the severity of the splenic pathology, a diagnostic splenectomy is advisable for what appears to be visceral PG. In addition, attempting to identify a trigger of her syndrome is important. PG can be associated with inflammatory bowel disease, hematologic disorders (eg, leukemia, myeloma, myelodysplastic syndrome, and myelofibrosis), and autoimmune diseases, especially inflammatory arthritis.¹ Therefore, a diagnostic colonoscopy and bone marrow biopsy should be considered. With no history or examination supporting inflammatory arthritis and a broad, unrevealing workup, her rheumatologic evaluation is sufficient.

The patient underwent splenectomy. Gross description of the spleen was notable for multiple abscesses, consisting on microscopy of large areas of necrosis with islands of dense neutrophil collections (Figure 3). Microscopic examination failed to demonstrate microorganisms on multiple stains, and there was no microscopic or flow cytometric evidence of lymphoma. The final pathologic diagnosis was multiple sterile splenic abscesses with siderosis, which, in the context of her overall syndrome, was consistent with an entity termed aseptic abscess syndrome (AAS). After discharge, she underwent a slow steroid taper and was ultimately maintained on daily low-dose prednisone. Cyclosporine and dapsone were discontinued in favor of infliximab infusions. She underwent additional diagnostic workup, including an unremarkable colonoscopy and a bone marrow biopsy, which showed monoclonal gammopathy of undetermined significance (MGUS) with an insignificant IgA monoclonal gammopathy. All cutaneous lesions healed. Three years after the splenectomy, while still on infliximab and prednisone, she developed a new aseptic lung abscess, which resolved after increasing her prednisone dose. Six years after splenectomy, she developed an aseptic liver abscess, which resolved after again increasing the frequency of her infliximab infusions.

Diagnostic uncertainty is an intrinsic feature of medical practice—in part because patients often present with undifferentiated and evolving symptoms.² When faced with uncertainty, clinicians are well served by prioritizing a thoughtful differential diagnosis, adopting a stepwise management strategy, and engaging in iterative reassessments of the patient. In this case, a 32-year-old, previously healthy woman presented with an array of symptoms, including abdominal pain, fever, leukocytosis, necrotic skin lesions, necrotizing mediastinal lymphadenitis, pathergy, and splenomegaly. Elements of the history, examination, and diagnostic studies supported a differential diagnosis of tularemia, PG, and AAS. Through stepwise management and ongoing reassessment, she was ultimately diagnosed with AAS.

Tularemia was initially an important diagnostic consideration in this patient, given her potential exposure and positive F tularensis serum antibodies. Francisella tularensis is a Gram-negative coccobacillus found in more than 250 species of fish, ticks, birds, and mammals. In humans, an incubation period of 3 to 5 days is typical. Although clinical manifestations vary, they often include fever, headache, and malaise.³ Other findings may include lymphadenopathy with or without ulcerative cutaneous lesions (glandular or ulceroglandular tularemia) and cough, dyspnea, pleuritic chest pain, and hilar adenopathy (pneumonic tularemia). As noted by the discussant, a pneumonic tularemia syndrome could have explained this patient’s fever, respiratory symptoms, and hilar adenopathy; ulceroglandular tularemia might have explained her cutaneous lesions. Since splenomegaly may be seen in tularemia, this finding was also consistent with the diagnosis. Serum antibody testing is supportive of the diagnosis, while culture confirms it. Standard treatment consists of a 10- to 14-day course of streptomycin, and combination therapy with a fluoroquinolone is recommended in severe cases.⁴ In this patient, however, F tularensis was not demonstrated on culture. Furthermore, she did not experience the expected clinical improvement with treatment. Finally, because both IgG and IgM tularemia antibodies may co-occur up to 10 years following infection, her positive F tularensis serum antibodies did not provide evidence of acute infection.⁵

Recognizing inconsistencies in the diagnosis of tularemia, the focus shifted to PG owing to the patient’s neutrophilic cutaneous lesions, negative infectious workup, and pathergy. Pyoderma gangrenosum is a neutrophilic dermatosis—one of a heterogeneous group of skin conditions characterized by perivascular and diffuse neutrophilic infiltrates without an identifiable infectious agent.⁶ It is a chronic, recurrent cutaneous disease with several variants.⁷ The classic presentation includes painful lower-extremity ulcers with violaceous undermined borders and may be associated with pathergy. Guiding principles for the management of PG include controlling inflammation, optimizing wound healing, and minimizing exacerbating factors.¹ As such, treatment mainstays include local and systemic anti-inflammatory agents and wound care. As the discussant highlighted, in this case the inflammatory skin lesions were suggestive of PG. However, other features of the case, notably, splenomegaly, splenic abscesses, and necrotizing mediastinal lymphadenitis, were more consistent with another diagnosis: AAS. Aseptic abscess syndrome is an autoinflammatory disorder defined by deep, noninfectious abscesses that preferentially affect the spleen.⁸ Additional clinical manifestations include weight loss, fever, abdominal pain, and leukocytosis. Lesions may also affect bone, kidney, liver, lung, lymph node, and skin. In one case series, neutrophilic dermatoses were seen in 20% of AAS cases.⁸ In all cases of AAS, extensive infectious workup is unrevealing, and antibiotics are ineffective. The pathophysiology of AAS is unknown.

Similar to PG, the majority of AAS cases are associated with inflammatory bowel disease, especially Crohn disease.⁹ However, AAS also has associations with conditions such as MGUS, rheumatoid arthritis, spondyloarthritis, and relapsing polychondritis. Histologically, early lesions demonstrate a necrotic core of neutrophils, with or without surrounding palisading histiocytes, and giant cells. In older lesions, neutrophils may be absent; fibrous tissue may be present.⁸ Treatment regimens include splenectomy, corticosteroids, colchicine, thalidomide, tumor necrosis factor (TNF) antagonists, and cyclophosphamide. The discussant astutely recommended a splenectomy for this patient, which was both diagnostic and therapeutic. As in this case, relapse is common. Optimal maintenance therapy is yet to be determined.⁹

Given the overlapping clinical manifestations, shared disease associations, and similar responsiveness to immunosuppression, it is unclear whether AAS represents a new disease entity or a variant of known autoinflammatory disorders. Aseptic abscess syndrome is likely part of a spectrum of autoinflammatory disorders with inflammatory bowel diseases, neutrophilic dermatoses, and other similar diseases.⁸ While infectious visceral abscesses remain more common, this case highlights the clinical manifestation of an emerging and likely underrecognized entity.

• Aseptic abscess syndrome should be considered in patients who present with visceral (particularly splenic) abscesses and negative infectious workup.
• Aseptic abscess syndrome is commonly associated with other autoinflammatory disorders; the majority of reported cases are associated with inflammatory bowel disease, especially Crohn disease.
• Up to 20% of AAS cases are associated with neutrophilic dermatoses such as PG.
• The initial treatment for this syndrome is high-dose intravenous glucocorticoids; maintenance treatment regimens include corticosteroids, colchicine, thalidomide, TNF antagonists, and cyclophosphamide.

The authors would thank Dr Bob Pelz and Dr John Townes for their contributions to the case.

This icon represents the patient’s case. Each paragraph that follows represents the discussant’s thoughts.

A 32-year-old, previously healthy woman presented to the emergency department (ED) with 3 days of nasal pain, congestion, and cough. A day prior, she had consulted with her primary care provider by phone and had been prescribed amoxicillin-clavulanate for presumed bacterial sinusitis. She subsequently developed fever (39 oC) and pleuritic, left-upper-quadrant abdominal pain. In the ED, chest radiograph demonstrated right hilar opacification. Laboratory studies and computed tomography (CT) of the abdomen and pelvis did not identify a cause for her pain. Given the pleuritic nature of her left-upper-quadrant pain, CT pulmonary angiography was ordered. The CT revealed “mass-like” right hilar opacification and lymphadenopathy. No pulmonary emboli were identified. Levofloxacin was prescribed for presumed pneumonia, and the patient was discharged home. The following week, mediastinal biopsy was arranged for evaluation of the right hilar abnormality.

This is a young woman presenting with upper respiratory symptoms, abdominal pain, fever, and hilar lymphadenopathy. Upper respiratory symptoms are common and usually indicate an inflammatory response to allergens or infection, though autoimmune disorders may affect the upper airways. Fever and hilar lymphadenopathy likely also signify an inflammatory response. Taken together, these findings can be associated with mycobacterial or fungal infection, malignancy, and, particularly in a young woman, sarcoidosis, which could explain her abdominal pain if her presentation included splenomegaly. At this point she likely has a systemic illness involving at least the upper, and possibly the lower, respiratory tract.

Within days, her symptoms resolved. Mediastinal biopsy of the hilar node revealed scant pus. Pathology demonstrated suppurative granulomata. Gram stain; bacterial, mycobacterial, and fungal cultures; and 16S ribosomal analyses for bacteria and fungi from the biopsy were unrevealing. For unclear reasons, prior to the biopsy, she was given intramuscular Haemophilus influenzae type B and tetanus, diphtheria, and pertussis vaccines. Two weeks later, she presented again with fever and left-upper-quadrant pain as well as painful skin nodules at her biopsy and vaccination sites. She was admitted for further evaluation. Chest CT showed expansion of the mediastinal lesion and splenic enlargement. Biopsy of a skin lesion revealed suppurative granulomatous dermatitis and panniculitis. Repeat blood cultures were negative, though serum β-D-glucan was weakly positive at 173 pg/mL (reference range, <60 pg/mL). Tissue cultures and Gram, acid-fast, Fite, and Warthin-Starry stains from the skin biopsy were negative. She was discharged on fluconazole and then readmitted 2 days later with dyspnea, fever, and leukocytosis.

The young woman’s symptoms resolved, only to recur days later; her granulomatous hilar lesions grew larger, and new cutaneous and splenic findings appeared. The granulomatous lesions prompt consideration of infectious, malignant, and immune-mediated processes. The negative cultures make infection less likely, although the elevated β-D-glucan may suggest fungal infection. By description, the skin lesions are consistent with pathergy, a phenomenon characterized by trauma-provoked cutaneous lesions or ulcers, which is associated with numerous syndromes, including Behçet syndrome, inflammatory bowel disease, and neutrophilic dermatoses such as pyoderma gangrenosum (PG) and Sweet syndrome. In addition to details about her medical history, it is important to seek evidence of oral ulcers or vasculitis, as Behçet syndrome may be associated with cutaneous, visceral, and ophthalmologic vasculitis.

Her medical history included hypertension and active, 10-pack-year cigarette use. During childhood, she had occasional ingrown hairs and folliculitis. She did not take medications prior to this acute illness. Family history was notable for cardiovascular disease. She rarely consumed alcohol and did not use illicit drugs. She lived in a rural town in the mid–Willamette Valley of Oregon and worked as an administrative assistant. She spent time outdoors, including trail running and golfing. A case of tularemia was recently reported in an area near her home. Her only travel outside of Oregon was to Puerto Vallarta, Mexico, 16 years previously. She grew up on a farm and had no known tuberculosis exposure.

Tularemia is an interesting diagnostic consideration and could explain her fever, cutaneous lesions, and hilar adenopathy. It is plausible that she had clinically mild pneumonic tularemia at the outset and that her cutaneous lesions are variants of ulceroglandular tularemia. Positive antibodies for Francisella tularensis would be expected if this were the cause of her illness. The ingrown hairs raise the possibility of a primary immune deficiency syndrome predisposing her to abscesses. However, they seem to have been of trivial significance to her, making an immune deficiency syndrome unlikely.

On readmission, she was afebrile, normotensive, and tachycardic (114 beats/min), with a normal respiratory rate and oxygen saturation. She was not ill appearing. She had noninjected conjunctiva and no oral lesions. Apart from tachycardia, cardiovascular examination was unremarkable. Abdominal examination was notable for mild distension and a palpable, tender spleen. Musculoskeletal and neurologic examinations were normal. Her skin was notable for various sized (8 cm × 4 cm to 10 cm × 15 cm) painful ulcers with violaceous, friable borders—some with fluctuance and purulent drainage—on her right hand, bilateral arms, right axilla, sternum, and legs (Figure 1).

Laboratory studies were notable for normocytic anemia (hemoglobin, 8.9 g/dL; range, 12.0-16.0 g/dL), leukocytosis (white blood cells, 24,900/µL; range, 4500-11,000/µL), thrombocytosis (platelet count, 690,000/µL; range, 150,000-400,000/µL), and elevated inflammatory markers (C-reactive protein, 33 mg/dL; range, <0.5 mg/dL; erythrocyte sedimentation rate, 78 mm/h; range, <20 mm/h). A complete metabolic panel was within normal limits. Repeat blood cultures and β -D-glucan and 16S ribosomal assays were negative. Polymerase chain reaction testing for Bartonella henselae was negative. Urine probes for Neisseria gonorrhoeae and Chlamydia trachomatis were negative. Rapid plasma regain (RPR) was negative. Antibodies to toxoplasmosis, histoplasmosis, blastomycosis, and aspergillosis were unrevealing. A Coccidioides test by immunodiffusion was negative. Serum antigen tests for Cryptococcus and Epstein-Barr virus (EBV) were negative. EBV, HIV, and hepatitis antibody tests were negative. Rheumatologic studies, including antinuclear, anti-double-stranded DNA, anti-Smith, anti–Sjögren syndrome antigens A and B, anticentromere, anti-topoisomerase (anti-Scl-70), anti-histidyl-transfer-RNA-synthetase (anti-Jo-1), and anti-nucleosome (anti-chromatic) antibodies, were unrevealing. Levels of angiotensin-converting enzyme, rheumatoid factor, complement, cytoplasmic, and perinuclear antineutrophil cytoplasmic antibodies were also normal. A neutrophil oxidative burst test was negative. In addition, peripheral flow cytology and serum and urine protein electrophoresis were negative. Chest CT revealed bilateral lower lobe consolidations concerning for necrotizing pneumonia, splenic enlargement, numerous hypodense splenic lesions, and a 1.3-cm right hilar node, which had decreased in size compared with 1 month prior.

In summary, the patient presented with recurrent upper respiratory symptoms, fever, and abdominal pain; expanding granulomatous hilar lesions, splenomegaly, and cutaneous lesions consistent with pathergy; elevated inflammatory markers and leukocytosis; and a possible exposure to F tularensis. She has had extensive negative infectious workups, except for a weakly positive β-D-glucan, and completed several courses of apparently unhelpful antimicrobials. At this point, the most notable findings are her splenomegaly and inflammatory masses suggesting an inflammatory process, which may be autoimmune in nature. Both vasculitis and sarcoidosis remain possibilities, and malignancy is possible. Given her possible exposure to F tularensis, obtaining serum antibodies to F tularensis, in addition to biopsies of the skin lesions, is advisable.

Laboratory studies revealed a positive F tularensis antibody with a titer of 1:320 and an IgM of 7 U/mL and IgG of 30 U/mL. This was repeated, revealing a titer of 1:540 and an IgM and IgG of 5 U/mL and 20 U/mL, respectively. Given the potential exposure history, the clinical syndrome compatible with tularemia, and an otherwise extensive yet unrevealing evaluation, she was treated with a 10-day course of streptomycin. Her fever persisted, and the splenic lesions increased in size and number, prompting addition of moxifloxacin without apparent benefit. Skin biopsies taken from the patient’s arm were notable for nodular, suppurative, neutrophilic infiltrates and histiocytes in the medium and deep dermis without multinucleated histiocytes or evidence of vasculitis. Fungal, mycobacterial, and bacterial stains from the biopsy were negative. The findings were consistent with but not diagnostic of an acute neutrophilic dermatosis.

At this point, the patient has a confirmed exposure to F tularensis; she also has persistent fever, progressive splenomegaly, and new skin biopsies consistent with neutrophilic dermatosis. Despite the F tularensis antibody positivity, her negative cultures and lack of improvement with multiple courses of antimicrobials argue against an infectious etiology. Accordingly, malignancy should be considered but seems less likely given that no laboratory, imaging, or tissue samples support it. This leaves immune-mediated etiologies, especially autoimmune conditions associated with neutrophilic dermatoses, as the most likely explanation of her inflammatory syndrome. Neutrophilic dermatoses include some vasculitides, Sweet syndrome, PG, Behçet syndrome, and other inflammatory entities. She has no evidence of vasculitis on biopsy. Given the evidence of inflammation and the history of pathergy, Behçet syndrome and PG should be seriously considered.

She underwent incision and drainage of the left leg and mediastinal lesions. A follow-up chest CT revealed stable cutaneous and deep tissue lesions and continued splenic enlargement. She was started on prednisone and dapsone for presumed cutaneous and visceral PG. The lesions improved dramatically and, following a month-long hospitalization, she was discharged on dapsone and a slow prednisone taper. Three weeks after discharge, while on dapsone and prednisone, she developed a new skin lesion. Cyclosporine was added, with improvement. Eight weeks after discharge, she developed fever, acute left-upper-quadrant pain, and marked splenomegaly with abscesses seen on CT imaging (Figure 2).

This continues to be a very puzzling case, and it is worth revisiting her clinical course once again. This is a previously healthy 32-year-old woman with multiple hospital presentations for upper-respiratory symptoms, persistent fever, abdominal pain, and painful cutaneous lesions consistent with pathergy; she was found to have granulomatous hilar lesions, progressive splenomegaly, and skin biopsies consistent with neutrophilic dermatosis. Exhaustive infectious and rheumatologic workup was negative, and no evident malignancy was found. Finally, despite multiple courses of antimicrobials, including standard treatments for tularemia (for which she had positive antibodies), her clinical course failed to improve until the addition of systemic anti-inflammatory agents, which resulted in rapid improvement. She then presented 8 weeks later with recurrent fever and splenomegaly. Given the recurrence and the severity of the splenic pathology, a diagnostic splenectomy is advisable for what appears to be visceral PG. In addition, attempting to identify a trigger of her syndrome is important. PG can be associated with inflammatory bowel disease, hematologic disorders (eg, leukemia, myeloma, myelodysplastic syndrome, and myelofibrosis), and autoimmune diseases, especially inflammatory arthritis.¹ Therefore, a diagnostic colonoscopy and bone marrow biopsy should be considered. With no history or examination supporting inflammatory arthritis and a broad, unrevealing workup, her rheumatologic evaluation is sufficient.

The patient underwent splenectomy. Gross description of the spleen was notable for multiple abscesses, consisting on microscopy of large areas of necrosis with islands of dense neutrophil collections (Figure 3). Microscopic examination failed to demonstrate microorganisms on multiple stains, and there was no microscopic or flow cytometric evidence of lymphoma. The final pathologic diagnosis was multiple sterile splenic abscesses with siderosis, which, in the context of her overall syndrome, was consistent with an entity termed aseptic abscess syndrome (AAS). After discharge, she underwent a slow steroid taper and was ultimately maintained on daily low-dose prednisone. Cyclosporine and dapsone were discontinued in favor of infliximab infusions. She underwent additional diagnostic workup, including an unremarkable colonoscopy and a bone marrow biopsy, which showed monoclonal gammopathy of undetermined significance (MGUS) with an insignificant IgA monoclonal gammopathy. All cutaneous lesions healed. Three years after the splenectomy, while still on infliximab and prednisone, she developed a new aseptic lung abscess, which resolved after increasing her prednisone dose. Six years after splenectomy, she developed an aseptic liver abscess, which resolved after again increasing the frequency of her infliximab infusions.

Diagnostic uncertainty is an intrinsic feature of medical practice—in part because patients often present with undifferentiated and evolving symptoms.² When faced with uncertainty, clinicians are well served by prioritizing a thoughtful differential diagnosis, adopting a stepwise management strategy, and engaging in iterative reassessments of the patient. In this case, a 32-year-old, previously healthy woman presented with an array of symptoms, including abdominal pain, fever, leukocytosis, necrotic skin lesions, necrotizing mediastinal lymphadenitis, pathergy, and splenomegaly. Elements of the history, examination, and diagnostic studies supported a differential diagnosis of tularemia, PG, and AAS. Through stepwise management and ongoing reassessment, she was ultimately diagnosed with AAS.

Tularemia was initially an important diagnostic consideration in this patient, given her potential exposure and positive F tularensis serum antibodies. Francisella tularensis is a Gram-negative coccobacillus found in more than 250 species of fish, ticks, birds, and mammals. In humans, an incubation period of 3 to 5 days is typical. Although clinical manifestations vary, they often include fever, headache, and malaise.³ Other findings may include lymphadenopathy with or without ulcerative cutaneous lesions (glandular or ulceroglandular tularemia) and cough, dyspnea, pleuritic chest pain, and hilar adenopathy (pneumonic tularemia). As noted by the discussant, a pneumonic tularemia syndrome could have explained this patient’s fever, respiratory symptoms, and hilar adenopathy; ulceroglandular tularemia might have explained her cutaneous lesions. Since splenomegaly may be seen in tularemia, this finding was also consistent with the diagnosis. Serum antibody testing is supportive of the diagnosis, while culture confirms it. Standard treatment consists of a 10- to 14-day course of streptomycin, and combination therapy with a fluoroquinolone is recommended in severe cases.⁴ In this patient, however, F tularensis was not demonstrated on culture. Furthermore, she did not experience the expected clinical improvement with treatment. Finally, because both IgG and IgM tularemia antibodies may co-occur up to 10 years following infection, her positive F tularensis serum antibodies did not provide evidence of acute infection.⁵

Recognizing inconsistencies in the diagnosis of tularemia, the focus shifted to PG owing to the patient’s neutrophilic cutaneous lesions, negative infectious workup, and pathergy. Pyoderma gangrenosum is a neutrophilic dermatosis—one of a heterogeneous group of skin conditions characterized by perivascular and diffuse neutrophilic infiltrates without an identifiable infectious agent.⁶ It is a chronic, recurrent cutaneous disease with several variants.⁷ The classic presentation includes painful lower-extremity ulcers with violaceous undermined borders and may be associated with pathergy. Guiding principles for the management of PG include controlling inflammation, optimizing wound healing, and minimizing exacerbating factors.¹ As such, treatment mainstays include local and systemic anti-inflammatory agents and wound care. As the discussant highlighted, in this case the inflammatory skin lesions were suggestive of PG. However, other features of the case, notably, splenomegaly, splenic abscesses, and necrotizing mediastinal lymphadenitis, were more consistent with another diagnosis: AAS. Aseptic abscess syndrome is an autoinflammatory disorder defined by deep, noninfectious abscesses that preferentially affect the spleen.⁸ Additional clinical manifestations include weight loss, fever, abdominal pain, and leukocytosis. Lesions may also affect bone, kidney, liver, lung, lymph node, and skin. In one case series, neutrophilic dermatoses were seen in 20% of AAS cases.⁸ In all cases of AAS, extensive infectious workup is unrevealing, and antibiotics are ineffective. The pathophysiology of AAS is unknown.

Similar to PG, the majority of AAS cases are associated with inflammatory bowel disease, especially Crohn disease.⁹ However, AAS also has associations with conditions such as MGUS, rheumatoid arthritis, spondyloarthritis, and relapsing polychondritis. Histologically, early lesions demonstrate a necrotic core of neutrophils, with or without surrounding palisading histiocytes, and giant cells. In older lesions, neutrophils may be absent; fibrous tissue may be present.⁸ Treatment regimens include splenectomy, corticosteroids, colchicine, thalidomide, tumor necrosis factor (TNF) antagonists, and cyclophosphamide. The discussant astutely recommended a splenectomy for this patient, which was both diagnostic and therapeutic. As in this case, relapse is common. Optimal maintenance therapy is yet to be determined.⁹

Given the overlapping clinical manifestations, shared disease associations, and similar responsiveness to immunosuppression, it is unclear whether AAS represents a new disease entity or a variant of known autoinflammatory disorders. Aseptic abscess syndrome is likely part of a spectrum of autoinflammatory disorders with inflammatory bowel diseases, neutrophilic dermatoses, and other similar diseases.⁸ While infectious visceral abscesses remain more common, this case highlights the clinical manifestation of an emerging and likely underrecognized entity.

• Aseptic abscess syndrome should be considered in patients who present with visceral (particularly splenic) abscesses and negative infectious workup.
• Aseptic abscess syndrome is commonly associated with other autoinflammatory disorders; the majority of reported cases are associated with inflammatory bowel disease, especially Crohn disease.
• Up to 20% of AAS cases are associated with neutrophilic dermatoses such as PG.
• The initial treatment for this syndrome is high-dose intravenous glucocorticoids; maintenance treatment regimens include corticosteroids, colchicine, thalidomide, TNF antagonists, and cyclophosphamide.

The authors would thank Dr Bob Pelz and Dr John Townes for their contributions to the case.

This icon represents the patient’s case. Each paragraph that follows represents the discussant’s thoughts.

A 32-year-old, previously healthy woman presented to the emergency department (ED) with 3 days of nasal pain, congestion, and cough. A day prior, she had consulted with her primary care provider by phone and had been prescribed amoxicillin-clavulanate for presumed bacterial sinusitis. She subsequently developed fever (39 oC) and pleuritic, left-upper-quadrant abdominal pain. In the ED, chest radiograph demonstrated right hilar opacification. Laboratory studies and computed tomography (CT) of the abdomen and pelvis did not identify a cause for her pain. Given the pleuritic nature of her left-upper-quadrant pain, CT pulmonary angiography was ordered. The CT revealed “mass-like” right hilar opacification and lymphadenopathy. No pulmonary emboli were identified. Levofloxacin was prescribed for presumed pneumonia, and the patient was discharged home. The following week, mediastinal biopsy was arranged for evaluation of the right hilar abnormality.

This is a young woman presenting with upper respiratory symptoms, abdominal pain, fever, and hilar lymphadenopathy. Upper respiratory symptoms are common and usually indicate an inflammatory response to allergens or infection, though autoimmune disorders may affect the upper airways. Fever and hilar lymphadenopathy likely also signify an inflammatory response. Taken together, these findings can be associated with mycobacterial or fungal infection, malignancy, and, particularly in a young woman, sarcoidosis, which could explain her abdominal pain if her presentation included splenomegaly. At this point she likely has a systemic illness involving at least the upper, and possibly the lower, respiratory tract.

Within days, her symptoms resolved. Mediastinal biopsy of the hilar node revealed scant pus. Pathology demonstrated suppurative granulomata. Gram stain; bacterial, mycobacterial, and fungal cultures; and 16S ribosomal analyses for bacteria and fungi from the biopsy were unrevealing. For unclear reasons, prior to the biopsy, she was given intramuscular Haemophilus influenzae type B and tetanus, diphtheria, and pertussis vaccines. Two weeks later, she presented again with fever and left-upper-quadrant pain as well as painful skin nodules at her biopsy and vaccination sites. She was admitted for further evaluation. Chest CT showed expansion of the mediastinal lesion and splenic enlargement. Biopsy of a skin lesion revealed suppurative granulomatous dermatitis and panniculitis. Repeat blood cultures were negative, though serum β-D-glucan was weakly positive at 173 pg/mL (reference range, <60 pg/mL). Tissue cultures and Gram, acid-fast, Fite, and Warthin-Starry stains from the skin biopsy were negative. She was discharged on fluconazole and then readmitted 2 days later with dyspnea, fever, and leukocytosis.

The young woman’s symptoms resolved, only to recur days later; her granulomatous hilar lesions grew larger, and new cutaneous and splenic findings appeared. The granulomatous lesions prompt consideration of infectious, malignant, and immune-mediated processes. The negative cultures make infection less likely, although the elevated β-D-glucan may suggest fungal infection. By description, the skin lesions are consistent with pathergy, a phenomenon characterized by trauma-provoked cutaneous lesions or ulcers, which is associated with numerous syndromes, including Behçet syndrome, inflammatory bowel disease, and neutrophilic dermatoses such as pyoderma gangrenosum (PG) and Sweet syndrome. In addition to details about her medical history, it is important to seek evidence of oral ulcers or vasculitis, as Behçet syndrome may be associated with cutaneous, visceral, and ophthalmologic vasculitis.

Her medical history included hypertension and active, 10-pack-year cigarette use. During childhood, she had occasional ingrown hairs and folliculitis. She did not take medications prior to this acute illness. Family history was notable for cardiovascular disease. She rarely consumed alcohol and did not use illicit drugs. She lived in a rural town in the mid–Willamette Valley of Oregon and worked as an administrative assistant. She spent time outdoors, including trail running and golfing. A case of tularemia was recently reported in an area near her home. Her only travel outside of Oregon was to Puerto Vallarta, Mexico, 16 years previously. She grew up on a farm and had no known tuberculosis exposure.

Tularemia is an interesting diagnostic consideration and could explain her fever, cutaneous lesions, and hilar adenopathy. It is plausible that she had clinically mild pneumonic tularemia at the outset and that her cutaneous lesions are variants of ulceroglandular tularemia. Positive antibodies for Francisella tularensis would be expected if this were the cause of her illness. The ingrown hairs raise the possibility of a primary immune deficiency syndrome predisposing her to abscesses. However, they seem to have been of trivial significance to her, making an immune deficiency syndrome unlikely.

On readmission, she was afebrile, normotensive, and tachycardic (114 beats/min), with a normal respiratory rate and oxygen saturation. She was not ill appearing. She had noninjected conjunctiva and no oral lesions. Apart from tachycardia, cardiovascular examination was unremarkable. Abdominal examination was notable for mild distension and a palpable, tender spleen. Musculoskeletal and neurologic examinations were normal. Her skin was notable for various sized (8 cm × 4 cm to 10 cm × 15 cm) painful ulcers with violaceous, friable borders—some with fluctuance and purulent drainage—on her right hand, bilateral arms, right axilla, sternum, and legs (Figure 1).

Laboratory studies were notable for normocytic anemia (hemoglobin, 8.9 g/dL; range, 12.0-16.0 g/dL), leukocytosis (white blood cells, 24,900/µL; range, 4500-11,000/µL), thrombocytosis (platelet count, 690,000/µL; range, 150,000-400,000/µL), and elevated inflammatory markers (C-reactive protein, 33 mg/dL; range, <0.5 mg/dL; erythrocyte sedimentation rate, 78 mm/h; range, <20 mm/h). A complete metabolic panel was within normal limits. Repeat blood cultures and β -D-glucan and 16S ribosomal assays were negative. Polymerase chain reaction testing for Bartonella henselae was negative. Urine probes for Neisseria gonorrhoeae and Chlamydia trachomatis were negative. Rapid plasma regain (RPR) was negative. Antibodies to toxoplasmosis, histoplasmosis, blastomycosis, and aspergillosis were unrevealing. A Coccidioides test by immunodiffusion was negative. Serum antigen tests for Cryptococcus and Epstein-Barr virus (EBV) were negative. EBV, HIV, and hepatitis antibody tests were negative. Rheumatologic studies, including antinuclear, anti-double-stranded DNA, anti-Smith, anti–Sjögren syndrome antigens A and B, anticentromere, anti-topoisomerase (anti-Scl-70), anti-histidyl-transfer-RNA-synthetase (anti-Jo-1), and anti-nucleosome (anti-chromatic) antibodies, were unrevealing. Levels of angiotensin-converting enzyme, rheumatoid factor, complement, cytoplasmic, and perinuclear antineutrophil cytoplasmic antibodies were also normal. A neutrophil oxidative burst test was negative. In addition, peripheral flow cytology and serum and urine protein electrophoresis were negative. Chest CT revealed bilateral lower lobe consolidations concerning for necrotizing pneumonia, splenic enlargement, numerous hypodense splenic lesions, and a 1.3-cm right hilar node, which had decreased in size compared with 1 month prior.

In summary, the patient presented with recurrent upper respiratory symptoms, fever, and abdominal pain; expanding granulomatous hilar lesions, splenomegaly, and cutaneous lesions consistent with pathergy; elevated inflammatory markers and leukocytosis; and a possible exposure to F tularensis. She has had extensive negative infectious workups, except for a weakly positive β-D-glucan, and completed several courses of apparently unhelpful antimicrobials. At this point, the most notable findings are her splenomegaly and inflammatory masses suggesting an inflammatory process, which may be autoimmune in nature. Both vasculitis and sarcoidosis remain possibilities, and malignancy is possible. Given her possible exposure to F tularensis, obtaining serum antibodies to F tularensis, in addition to biopsies of the skin lesions, is advisable.

Laboratory studies revealed a positive F tularensis antibody with a titer of 1:320 and an IgM of 7 U/mL and IgG of 30 U/mL. This was repeated, revealing a titer of 1:540 and an IgM and IgG of 5 U/mL and 20 U/mL, respectively. Given the potential exposure history, the clinical syndrome compatible with tularemia, and an otherwise extensive yet unrevealing evaluation, she was treated with a 10-day course of streptomycin. Her fever persisted, and the splenic lesions increased in size and number, prompting addition of moxifloxacin without apparent benefit. Skin biopsies taken from the patient’s arm were notable for nodular, suppurative, neutrophilic infiltrates and histiocytes in the medium and deep dermis without multinucleated histiocytes or evidence of vasculitis. Fungal, mycobacterial, and bacterial stains from the biopsy were negative. The findings were consistent with but not diagnostic of an acute neutrophilic dermatosis.

At this point, the patient has a confirmed exposure to F tularensis; she also has persistent fever, progressive splenomegaly, and new skin biopsies consistent with neutrophilic dermatosis. Despite the F tularensis antibody positivity, her negative cultures and lack of improvement with multiple courses of antimicrobials argue against an infectious etiology. Accordingly, malignancy should be considered but seems less likely given that no laboratory, imaging, or tissue samples support it. This leaves immune-mediated etiologies, especially autoimmune conditions associated with neutrophilic dermatoses, as the most likely explanation of her inflammatory syndrome. Neutrophilic dermatoses include some vasculitides, Sweet syndrome, PG, Behçet syndrome, and other inflammatory entities. She has no evidence of vasculitis on biopsy. Given the evidence of inflammation and the history of pathergy, Behçet syndrome and PG should be seriously considered.

She underwent incision and drainage of the left leg and mediastinal lesions. A follow-up chest CT revealed stable cutaneous and deep tissue lesions and continued splenic enlargement. She was started on prednisone and dapsone for presumed cutaneous and visceral PG. The lesions improved dramatically and, following a month-long hospitalization, she was discharged on dapsone and a slow prednisone taper. Three weeks after discharge, while on dapsone and prednisone, she developed a new skin lesion. Cyclosporine was added, with improvement. Eight weeks after discharge, she developed fever, acute left-upper-quadrant pain, and marked splenomegaly with abscesses seen on CT imaging (Figure 2).

This continues to be a very puzzling case, and it is worth revisiting her clinical course once again. This is a previously healthy 32-year-old woman with multiple hospital presentations for upper-respiratory symptoms, persistent fever, abdominal pain, and painful cutaneous lesions consistent with pathergy; she was found to have granulomatous hilar lesions, progressive splenomegaly, and skin biopsies consistent with neutrophilic dermatosis. Exhaustive infectious and rheumatologic workup was negative, and no evident malignancy was found. Finally, despite multiple courses of antimicrobials, including standard treatments for tularemia (for which she had positive antibodies), her clinical course failed to improve until the addition of systemic anti-inflammatory agents, which resulted in rapid improvement. She then presented 8 weeks later with recurrent fever and splenomegaly. Given the recurrence and the severity of the splenic pathology, a diagnostic splenectomy is advisable for what appears to be visceral PG. In addition, attempting to identify a trigger of her syndrome is important. PG can be associated with inflammatory bowel disease, hematologic disorders (eg, leukemia, myeloma, myelodysplastic syndrome, and myelofibrosis), and autoimmune diseases, especially inflammatory arthritis.¹ Therefore, a diagnostic colonoscopy and bone marrow biopsy should be considered. With no history or examination supporting inflammatory arthritis and a broad, unrevealing workup, her rheumatologic evaluation is sufficient.

The patient underwent splenectomy. Gross description of the spleen was notable for multiple abscesses, consisting on microscopy of large areas of necrosis with islands of dense neutrophil collections (Figure 3). Microscopic examination failed to demonstrate microorganisms on multiple stains, and there was no microscopic or flow cytometric evidence of lymphoma. The final pathologic diagnosis was multiple sterile splenic abscesses with siderosis, which, in the context of her overall syndrome, was consistent with an entity termed aseptic abscess syndrome (AAS). After discharge, she underwent a slow steroid taper and was ultimately maintained on daily low-dose prednisone. Cyclosporine and dapsone were discontinued in favor of infliximab infusions. She underwent additional diagnostic workup, including an unremarkable colonoscopy and a bone marrow biopsy, which showed monoclonal gammopathy of undetermined significance (MGUS) with an insignificant IgA monoclonal gammopathy. All cutaneous lesions healed. Three years after the splenectomy, while still on infliximab and prednisone, she developed a new aseptic lung abscess, which resolved after increasing her prednisone dose. Six years after splenectomy, she developed an aseptic liver abscess, which resolved after again increasing the frequency of her infliximab infusions.

Diagnostic uncertainty is an intrinsic feature of medical practice—in part because patients often present with undifferentiated and evolving symptoms.² When faced with uncertainty, clinicians are well served by prioritizing a thoughtful differential diagnosis, adopting a stepwise management strategy, and engaging in iterative reassessments of the patient. In this case, a 32-year-old, previously healthy woman presented with an array of symptoms, including abdominal pain, fever, leukocytosis, necrotic skin lesions, necrotizing mediastinal lymphadenitis, pathergy, and splenomegaly. Elements of the history, examination, and diagnostic studies supported a differential diagnosis of tularemia, PG, and AAS. Through stepwise management and ongoing reassessment, she was ultimately diagnosed with AAS.

Tularemia was initially an important diagnostic consideration in this patient, given her potential exposure and positive F tularensis serum antibodies. Francisella tularensis is a Gram-negative coccobacillus found in more than 250 species of fish, ticks, birds, and mammals. In humans, an incubation period of 3 to 5 days is typical. Although clinical manifestations vary, they often include fever, headache, and malaise.³ Other findings may include lymphadenopathy with or without ulcerative cutaneous lesions (glandular or ulceroglandular tularemia) and cough, dyspnea, pleuritic chest pain, and hilar adenopathy (pneumonic tularemia). As noted by the discussant, a pneumonic tularemia syndrome could have explained this patient’s fever, respiratory symptoms, and hilar adenopathy; ulceroglandular tularemia might have explained her cutaneous lesions. Since splenomegaly may be seen in tularemia, this finding was also consistent with the diagnosis. Serum antibody testing is supportive of the diagnosis, while culture confirms it. Standard treatment consists of a 10- to 14-day course of streptomycin, and combination therapy with a fluoroquinolone is recommended in severe cases.⁴ In this patient, however, F tularensis was not demonstrated on culture. Furthermore, she did not experience the expected clinical improvement with treatment. Finally, because both IgG and IgM tularemia antibodies may co-occur up to 10 years following infection, her positive F tularensis serum antibodies did not provide evidence of acute infection.⁵

Recognizing inconsistencies in the diagnosis of tularemia, the focus shifted to PG owing to the patient’s neutrophilic cutaneous lesions, negative infectious workup, and pathergy. Pyoderma gangrenosum is a neutrophilic dermatosis—one of a heterogeneous group of skin conditions characterized by perivascular and diffuse neutrophilic infiltrates without an identifiable infectious agent.⁶ It is a chronic, recurrent cutaneous disease with several variants.⁷ The classic presentation includes painful lower-extremity ulcers with violaceous undermined borders and may be associated with pathergy. Guiding principles for the management of PG include controlling inflammation, optimizing wound healing, and minimizing exacerbating factors.¹ As such, treatment mainstays include local and systemic anti-inflammatory agents and wound care. As the discussant highlighted, in this case the inflammatory skin lesions were suggestive of PG. However, other features of the case, notably, splenomegaly, splenic abscesses, and necrotizing mediastinal lymphadenitis, were more consistent with another diagnosis: AAS. Aseptic abscess syndrome is an autoinflammatory disorder defined by deep, noninfectious abscesses that preferentially affect the spleen.⁸ Additional clinical manifestations include weight loss, fever, abdominal pain, and leukocytosis. Lesions may also affect bone, kidney, liver, lung, lymph node, and skin. In one case series, neutrophilic dermatoses were seen in 20% of AAS cases.⁸ In all cases of AAS, extensive infectious workup is unrevealing, and antibiotics are ineffective. The pathophysiology of AAS is unknown.

Similar to PG, the majority of AAS cases are associated with inflammatory bowel disease, especially Crohn disease.⁹ However, AAS also has associations with conditions such as MGUS, rheumatoid arthritis, spondyloarthritis, and relapsing polychondritis. Histologically, early lesions demonstrate a necrotic core of neutrophils, with or without surrounding palisading histiocytes, and giant cells. In older lesions, neutrophils may be absent; fibrous tissue may be present.⁸ Treatment regimens include splenectomy, corticosteroids, colchicine, thalidomide, tumor necrosis factor (TNF) antagonists, and cyclophosphamide. The discussant astutely recommended a splenectomy for this patient, which was both diagnostic and therapeutic. As in this case, relapse is common. Optimal maintenance therapy is yet to be determined.⁹

Given the overlapping clinical manifestations, shared disease associations, and similar responsiveness to immunosuppression, it is unclear whether AAS represents a new disease entity or a variant of known autoinflammatory disorders. Aseptic abscess syndrome is likely part of a spectrum of autoinflammatory disorders with inflammatory bowel diseases, neutrophilic dermatoses, and other similar diseases.⁸ While infectious visceral abscesses remain more common, this case highlights the clinical manifestation of an emerging and likely underrecognized entity.

• Aseptic abscess syndrome should be considered in patients who present with visceral (particularly splenic) abscesses and negative infectious workup.
• Aseptic abscess syndrome is commonly associated with other autoinflammatory disorders; the majority of reported cases are associated with inflammatory bowel disease, especially Crohn disease.
• Up to 20% of AAS cases are associated with neutrophilic dermatoses such as PG.
• The initial treatment for this syndrome is high-dose intravenous glucocorticoids; maintenance treatment regimens include corticosteroids, colchicine, thalidomide, TNF antagonists, and cyclophosphamide.

The authors would thank Dr Bob Pelz and Dr John Townes for their contributions to the case.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

The hospitalist enters admission orders for an 80-year-old woman with hypertension, coronary artery disease, and heart failure with reduced ejection fraction who presented to the emergency department with weight gain, lower extremity edema, and dyspnea on exertion. She has an elevated jugular venous pressure, crackles on pulmonary exam, and bilateral pitting edema with warm extremities. Labs show a sodium of 140 mmol/L and creatinine of 1.4 mg/dL. After ordering intravenous furosemide for management of acute decompensated heart failure (ADHF), the hospitalist arrives at the nutrition section of the CHF Admission Order Set and reflexively picks an option for a fluid-restricted diet.

Patients with ADHF, the leading cause of hospitalization for patients older than 65 years,¹ may present with signs and symptoms of volume overload: shortness of breath, lower-extremity swelling, and end-organ dysfunction. Before the 1980s, treatment of ADHF relied on loop diuretics, bedrest, and fluid restriction to minimize congestive symptoms.² Clinicians based this practice on early theories framing heart failure as primarily an issue of salt and water retention that could be counterbalanced by sodium and fluid restriction.²

Today, hospitalists understand heart failure with reduced ejection fraction (HFrEF) as a heterogenous disease with a shared pathophysiology in which reduced cardiac output, elevated systemic venous pressures, and/or shunting of blood away from the kidneys may all lead to decreased renal perfusion. These phenomena trigger the activation of the renin-angiotensin-aldosterone system (RAAS), leading to sodium and water retention and fluid redistribution.² As part of the modern day treatment regimen, providers continue to place patients on fluid-restricted diets. Guidelines support this practice.^3,4

Since most of the existing literature on the topic of fluid restriction in ADHF relates to HFrEF (left ventricular ejection fraction [LVEF] <40%), as opposed to heart failure with a preserved ejection fraction (HFpEF, LVEF ≥50%), this review will focus on HFrEF patients. Limited existing data support extrapolating these arguments to HFpEF patients as well.⁵

Longstanding conventional wisdom and data extrapolation from the chronic heart failure population has undergirded the practice of fluid restriction for ADHF. Current iterations of the American and European heart failure guidelines recommend fluid restriction of 1.5 to 2.0 L/day in severe ADHF as a management strategy.^3,4 The American guidelines recommend considering restricting fluid intake to 2 L/day for most hospitalized ADHF patients without hyponatremia or diuretic resistance. The guidelines base the recommendation on clinical experience and data from a single randomized trial evaluating the effects of sodium restriction on heart failure outcomes in outpatients recently admitted for ADHF.^4,6 This trial randomly assigned 232 patients with compensated HFrEF to either a normal or low-sodium diet plus oral furosemide. Researchers instructed both groups to adhere to a 1000 mL/day fluid restriction. The authors found a high incidence of readmissions for worsening congestive heart failure among a cohort of patients (n = 54) with a normal sodium diet who were excluded from randomization due to inability to adhere to the prescribed fluid restriction.⁶ Notably, this study did not evaluate patients receiving treatment for ADHF and was not designed to investigate the role of fluid restriction for the treatment of ADHF.

A subsequent study by the same investigators looked more deliberately, although not singularly, at outpatient fluid restriction. This study randomly assigned 410 patients with compensated HFrEF into eight groups by fluid intake (1 L vs 2 L), salt intake (80 mmol vs 120 mmol), and furosemide dose (125 mg twice daily vs 250 mg twice daily). At 180 days, the group receiving the fluid-restricted diet with higher sodium intake and higher diuretic dose had the lowest risk of hospital readmission.⁷Results from these studies of the chronic, compensated heart failure population, in conjunction with longstanding conventional wisdom, have influenced the management of patients hospitalized with ADHF.

From a pathophysiologic perspective, fluid restriction in ADHF may counterproductively lead to RAAS activation.⁸ Congestion develops when arterial underfilling leads to RAAS activation, triggering sodium and water retention.² Furthermore, RAAS activation, as measured by plasma levels of renin, angiotensin II, and aldosterone, correlates with prognosis and mortality in chronic HFrEF.⁹ Analyses from one of the largest databases of biomarkers from ADHF suggest that RAAS is further upregulated during decongestive therapy.¹⁰ While researchers have not studied the effects of fluid restriction on RAAS activation in ADHF patients, extrapolating from these data one may question whether fluid restriction in ADHF patients may further drive RAAS activation. Further activation may contribute to adverse incident outcomes such as worsening renal function.

The most relevant and compelling evidence against fluid restriction to date comes from Travers et al,¹¹ who conducted the first randomized controlled trial examining fluid restriction in ADHF patients. Their small study compared restricted (1 L fluid restriction) vs liberal (free fluid) intake in hospitalized patients with ADHF and demonstrated no difference in duration or daily dose of intravenous diuretics, time to symptomatic improvement, total daily fluid output, or average hospitalization weight loss between the two arms. Furthermore, researchers withdrew more patients in the fluid-restricted arm due to a sustained rise in serum creatinine, suggesting potential harm of this intervention.¹¹ The sample size (N = 67) and fluid-intake difference of only 400 mL between the two groups limited the study results.

In a subsequent randomized controlled trial, Aliti et al¹² examined the clinical outcomes of even more aggressive fluid restriction (800 mL/day) and sodium restriction (800 mg/day) versus liberal intake (at least 2.5 L fluid/day and approximately 3-5 g sodium/day) in hospitalized patients with ADHF (N = 75). While this study evaluated both fluid and sodium restriction, it produced relevant results. The study demonstrated no significant difference in weight loss, use of diuretics, or rehospitalization between the study arms.¹² At 30-day follow-up, researchers found that patients in the intervention group had more congestion and an increased likelihood of having a B-type natriuretic peptide (BNP) level greater than 700 pg/mL. In the subset of all patients with an elevated BNP level greater than 700 pg/mL at the end of the study, patients in the intervention group had a significantly higher rate of readmission (7 out of 22) compared with controls (1 of 20). Moreover, the fluid-restricted group had 50% higher perceived thirst values compared to the control group.¹² The sensation of thirst not only reduces quality of life, but, given that angiotensin II stimulates thirst, it may reflect RAAS activation.¹³ For these reasons, clinicians should consider this side effect seriously, especially when the literature lacks evidence of the benefits from fluid restriction.

Fluid-restrict patients who have chronic hyponatremia (Na <135 mmol/L) due to end-stage HFrEF in select circumstances. Hyponatremia develops in heart failure primarily because of the body’s inability to excrete free water due to non-osmotic arginine vasopressin secretion.⁴ Other processes contribute to hyponatremia, including increased free water intake due to angiotensin II stimulating thirst and decreased glomerular filtration rate limiting the kidney’s ability to excrete free water. Since hyponatremia in heart failure primarily occurs due to derangements of free water regulation, limiting free water intake may help; the American College of Cardiology/American Heart Association and European heart failure guidelines explicitly recommend this strategy for patients with stage D heart failure.^3,4 However, no available randomized data support this practice, and observational data suggest that fluid restriction has limited impact on hyponatremia in ADHF.¹⁴ Guidelines also suggest employing fluid restriction in patients with diuretic resistance as an adjunctive therapy.

Twenty-nine percent of patients with ADHF have comorbid chronic kidney disease (CKD).¹⁵ Providers often prescribe patients with advanced CKD salt- and fluid-restrictive diets due to more limited abilities in sodium and free water excretion. However, no studies have examined the effects of fluid restriction alone without salt restriction in the CKD/ADHF population.

In the present day of evidence-based pharmacologic therapies, research indicates that fluid-restriction does not help and potentially may harm. Instead, treat hospitalized HFrEF patients with ADHF with modern, evidence-based pharmacologic therapies and allow the patients to drink when thirsty.

• Treat patients with ADHF and reduced ejection fraction with evidence-based neurohormonal blockade and initiate loop diuretics to alleviate congestion.
• Allow patients with ADHF and reduced ejection fraction to drink when thirsty in the absence of hyponatremia.
• Consider initiating fluid restriction in patients with ADHF and concurrent hyponatremia and/or diuretic resistance. There is little evidence to guide setting specific limits on fluid intake.

CONCLUSION

The hospitalist starts the patient admitted for ADHF on an intravenous loop diuretic, continues her home beta blocker and angiotensin-converting enzyme inhibitor, and does not impose any fluid restriction. Her symptoms of congestion resolve, and she is discharged.

Hospitalists often treat patients with ADHF and reduced ejection fraction with fluid restriction. However, limited evidence supports this practice as part of the management of ADHF. Fluid restriction may have unintended adverse effects of increasing thirst and worsening renal function and quality of life.

What do you do? Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

The hospitalist enters admission orders for an 80-year-old woman with hypertension, coronary artery disease, and heart failure with reduced ejection fraction who presented to the emergency department with weight gain, lower extremity edema, and dyspnea on exertion. She has an elevated jugular venous pressure, crackles on pulmonary exam, and bilateral pitting edema with warm extremities. Labs show a sodium of 140 mmol/L and creatinine of 1.4 mg/dL. After ordering intravenous furosemide for management of acute decompensated heart failure (ADHF), the hospitalist arrives at the nutrition section of the CHF Admission Order Set and reflexively picks an option for a fluid-restricted diet.

Patients with ADHF, the leading cause of hospitalization for patients older than 65 years,¹ may present with signs and symptoms of volume overload: shortness of breath, lower-extremity swelling, and end-organ dysfunction. Before the 1980s, treatment of ADHF relied on loop diuretics, bedrest, and fluid restriction to minimize congestive symptoms.² Clinicians based this practice on early theories framing heart failure as primarily an issue of salt and water retention that could be counterbalanced by sodium and fluid restriction.²

Today, hospitalists understand heart failure with reduced ejection fraction (HFrEF) as a heterogenous disease with a shared pathophysiology in which reduced cardiac output, elevated systemic venous pressures, and/or shunting of blood away from the kidneys may all lead to decreased renal perfusion. These phenomena trigger the activation of the renin-angiotensin-aldosterone system (RAAS), leading to sodium and water retention and fluid redistribution.² As part of the modern day treatment regimen, providers continue to place patients on fluid-restricted diets. Guidelines support this practice.^3,4

Since most of the existing literature on the topic of fluid restriction in ADHF relates to HFrEF (left ventricular ejection fraction [LVEF] <40%), as opposed to heart failure with a preserved ejection fraction (HFpEF, LVEF ≥50%), this review will focus on HFrEF patients. Limited existing data support extrapolating these arguments to HFpEF patients as well.⁵

Longstanding conventional wisdom and data extrapolation from the chronic heart failure population has undergirded the practice of fluid restriction for ADHF. Current iterations of the American and European heart failure guidelines recommend fluid restriction of 1.5 to 2.0 L/day in severe ADHF as a management strategy.^3,4 The American guidelines recommend considering restricting fluid intake to 2 L/day for most hospitalized ADHF patients without hyponatremia or diuretic resistance. The guidelines base the recommendation on clinical experience and data from a single randomized trial evaluating the effects of sodium restriction on heart failure outcomes in outpatients recently admitted for ADHF.^4,6 This trial randomly assigned 232 patients with compensated HFrEF to either a normal or low-sodium diet plus oral furosemide. Researchers instructed both groups to adhere to a 1000 mL/day fluid restriction. The authors found a high incidence of readmissions for worsening congestive heart failure among a cohort of patients (n = 54) with a normal sodium diet who were excluded from randomization due to inability to adhere to the prescribed fluid restriction.⁶ Notably, this study did not evaluate patients receiving treatment for ADHF and was not designed to investigate the role of fluid restriction for the treatment of ADHF.

A subsequent study by the same investigators looked more deliberately, although not singularly, at outpatient fluid restriction. This study randomly assigned 410 patients with compensated HFrEF into eight groups by fluid intake (1 L vs 2 L), salt intake (80 mmol vs 120 mmol), and furosemide dose (125 mg twice daily vs 250 mg twice daily). At 180 days, the group receiving the fluid-restricted diet with higher sodium intake and higher diuretic dose had the lowest risk of hospital readmission.⁷Results from these studies of the chronic, compensated heart failure population, in conjunction with longstanding conventional wisdom, have influenced the management of patients hospitalized with ADHF.

From a pathophysiologic perspective, fluid restriction in ADHF may counterproductively lead to RAAS activation.⁸ Congestion develops when arterial underfilling leads to RAAS activation, triggering sodium and water retention.² Furthermore, RAAS activation, as measured by plasma levels of renin, angiotensin II, and aldosterone, correlates with prognosis and mortality in chronic HFrEF.⁹ Analyses from one of the largest databases of biomarkers from ADHF suggest that RAAS is further upregulated during decongestive therapy.¹⁰ While researchers have not studied the effects of fluid restriction on RAAS activation in ADHF patients, extrapolating from these data one may question whether fluid restriction in ADHF patients may further drive RAAS activation. Further activation may contribute to adverse incident outcomes such as worsening renal function.

The most relevant and compelling evidence against fluid restriction to date comes from Travers et al,¹¹ who conducted the first randomized controlled trial examining fluid restriction in ADHF patients. Their small study compared restricted (1 L fluid restriction) vs liberal (free fluid) intake in hospitalized patients with ADHF and demonstrated no difference in duration or daily dose of intravenous diuretics, time to symptomatic improvement, total daily fluid output, or average hospitalization weight loss between the two arms. Furthermore, researchers withdrew more patients in the fluid-restricted arm due to a sustained rise in serum creatinine, suggesting potential harm of this intervention.¹¹ The sample size (N = 67) and fluid-intake difference of only 400 mL between the two groups limited the study results.

In a subsequent randomized controlled trial, Aliti et al¹² examined the clinical outcomes of even more aggressive fluid restriction (800 mL/day) and sodium restriction (800 mg/day) versus liberal intake (at least 2.5 L fluid/day and approximately 3-5 g sodium/day) in hospitalized patients with ADHF (N = 75). While this study evaluated both fluid and sodium restriction, it produced relevant results. The study demonstrated no significant difference in weight loss, use of diuretics, or rehospitalization between the study arms.¹² At 30-day follow-up, researchers found that patients in the intervention group had more congestion and an increased likelihood of having a B-type natriuretic peptide (BNP) level greater than 700 pg/mL. In the subset of all patients with an elevated BNP level greater than 700 pg/mL at the end of the study, patients in the intervention group had a significantly higher rate of readmission (7 out of 22) compared with controls (1 of 20). Moreover, the fluid-restricted group had 50% higher perceived thirst values compared to the control group.¹² The sensation of thirst not only reduces quality of life, but, given that angiotensin II stimulates thirst, it may reflect RAAS activation.¹³ For these reasons, clinicians should consider this side effect seriously, especially when the literature lacks evidence of the benefits from fluid restriction.

Fluid-restrict patients who have chronic hyponatremia (Na <135 mmol/L) due to end-stage HFrEF in select circumstances. Hyponatremia develops in heart failure primarily because of the body’s inability to excrete free water due to non-osmotic arginine vasopressin secretion.⁴ Other processes contribute to hyponatremia, including increased free water intake due to angiotensin II stimulating thirst and decreased glomerular filtration rate limiting the kidney’s ability to excrete free water. Since hyponatremia in heart failure primarily occurs due to derangements of free water regulation, limiting free water intake may help; the American College of Cardiology/American Heart Association and European heart failure guidelines explicitly recommend this strategy for patients with stage D heart failure.^3,4 However, no available randomized data support this practice, and observational data suggest that fluid restriction has limited impact on hyponatremia in ADHF.¹⁴ Guidelines also suggest employing fluid restriction in patients with diuretic resistance as an adjunctive therapy.

Twenty-nine percent of patients with ADHF have comorbid chronic kidney disease (CKD).¹⁵ Providers often prescribe patients with advanced CKD salt- and fluid-restrictive diets due to more limited abilities in sodium and free water excretion. However, no studies have examined the effects of fluid restriction alone without salt restriction in the CKD/ADHF population.

In the present day of evidence-based pharmacologic therapies, research indicates that fluid-restriction does not help and potentially may harm. Instead, treat hospitalized HFrEF patients with ADHF with modern, evidence-based pharmacologic therapies and allow the patients to drink when thirsty.

• Treat patients with ADHF and reduced ejection fraction with evidence-based neurohormonal blockade and initiate loop diuretics to alleviate congestion.
• Allow patients with ADHF and reduced ejection fraction to drink when thirsty in the absence of hyponatremia.
• Consider initiating fluid restriction in patients with ADHF and concurrent hyponatremia and/or diuretic resistance. There is little evidence to guide setting specific limits on fluid intake.

CONCLUSION

The hospitalist starts the patient admitted for ADHF on an intravenous loop diuretic, continues her home beta blocker and angiotensin-converting enzyme inhibitor, and does not impose any fluid restriction. Her symptoms of congestion resolve, and she is discharged.

Hospitalists often treat patients with ADHF and reduced ejection fraction with fluid restriction. However, limited evidence supports this practice as part of the management of ADHF. Fluid restriction may have unintended adverse effects of increasing thirst and worsening renal function and quality of life.

What do you do? Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, 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 clear-cut 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.

The hospitalist enters admission orders for an 80-year-old woman with hypertension, coronary artery disease, and heart failure with reduced ejection fraction who presented to the emergency department with weight gain, lower extremity edema, and dyspnea on exertion. She has an elevated jugular venous pressure, crackles on pulmonary exam, and bilateral pitting edema with warm extremities. Labs show a sodium of 140 mmol/L and creatinine of 1.4 mg/dL. After ordering intravenous furosemide for management of acute decompensated heart failure (ADHF), the hospitalist arrives at the nutrition section of the CHF Admission Order Set and reflexively picks an option for a fluid-restricted diet.

Patients with ADHF, the leading cause of hospitalization for patients older than 65 years,¹ may present with signs and symptoms of volume overload: shortness of breath, lower-extremity swelling, and end-organ dysfunction. Before the 1980s, treatment of ADHF relied on loop diuretics, bedrest, and fluid restriction to minimize congestive symptoms.² Clinicians based this practice on early theories framing heart failure as primarily an issue of salt and water retention that could be counterbalanced by sodium and fluid restriction.²

Today, hospitalists understand heart failure with reduced ejection fraction (HFrEF) as a heterogenous disease with a shared pathophysiology in which reduced cardiac output, elevated systemic venous pressures, and/or shunting of blood away from the kidneys may all lead to decreased renal perfusion. These phenomena trigger the activation of the renin-angiotensin-aldosterone system (RAAS), leading to sodium and water retention and fluid redistribution.² As part of the modern day treatment regimen, providers continue to place patients on fluid-restricted diets. Guidelines support this practice.^3,4

Since most of the existing literature on the topic of fluid restriction in ADHF relates to HFrEF (left ventricular ejection fraction [LVEF] <40%), as opposed to heart failure with a preserved ejection fraction (HFpEF, LVEF ≥50%), this review will focus on HFrEF patients. Limited existing data support extrapolating these arguments to HFpEF patients as well.⁵

Longstanding conventional wisdom and data extrapolation from the chronic heart failure population has undergirded the practice of fluid restriction for ADHF. Current iterations of the American and European heart failure guidelines recommend fluid restriction of 1.5 to 2.0 L/day in severe ADHF as a management strategy.^3,4 The American guidelines recommend considering restricting fluid intake to 2 L/day for most hospitalized ADHF patients without hyponatremia or diuretic resistance. The guidelines base the recommendation on clinical experience and data from a single randomized trial evaluating the effects of sodium restriction on heart failure outcomes in outpatients recently admitted for ADHF.^4,6 This trial randomly assigned 232 patients with compensated HFrEF to either a normal or low-sodium diet plus oral furosemide. Researchers instructed both groups to adhere to a 1000 mL/day fluid restriction. The authors found a high incidence of readmissions for worsening congestive heart failure among a cohort of patients (n = 54) with a normal sodium diet who were excluded from randomization due to inability to adhere to the prescribed fluid restriction.⁶ Notably, this study did not evaluate patients receiving treatment for ADHF and was not designed to investigate the role of fluid restriction for the treatment of ADHF.

A subsequent study by the same investigators looked more deliberately, although not singularly, at outpatient fluid restriction. This study randomly assigned 410 patients with compensated HFrEF into eight groups by fluid intake (1 L vs 2 L), salt intake (80 mmol vs 120 mmol), and furosemide dose (125 mg twice daily vs 250 mg twice daily). At 180 days, the group receiving the fluid-restricted diet with higher sodium intake and higher diuretic dose had the lowest risk of hospital readmission.⁷Results from these studies of the chronic, compensated heart failure population, in conjunction with longstanding conventional wisdom, have influenced the management of patients hospitalized with ADHF.

From a pathophysiologic perspective, fluid restriction in ADHF may counterproductively lead to RAAS activation.⁸ Congestion develops when arterial underfilling leads to RAAS activation, triggering sodium and water retention.² Furthermore, RAAS activation, as measured by plasma levels of renin, angiotensin II, and aldosterone, correlates with prognosis and mortality in chronic HFrEF.⁹ Analyses from one of the largest databases of biomarkers from ADHF suggest that RAAS is further upregulated during decongestive therapy.¹⁰ While researchers have not studied the effects of fluid restriction on RAAS activation in ADHF patients, extrapolating from these data one may question whether fluid restriction in ADHF patients may further drive RAAS activation. Further activation may contribute to adverse incident outcomes such as worsening renal function.

The most relevant and compelling evidence against fluid restriction to date comes from Travers et al,¹¹ who conducted the first randomized controlled trial examining fluid restriction in ADHF patients. Their small study compared restricted (1 L fluid restriction) vs liberal (free fluid) intake in hospitalized patients with ADHF and demonstrated no difference in duration or daily dose of intravenous diuretics, time to symptomatic improvement, total daily fluid output, or average hospitalization weight loss between the two arms. Furthermore, researchers withdrew more patients in the fluid-restricted arm due to a sustained rise in serum creatinine, suggesting potential harm of this intervention.¹¹ The sample size (N = 67) and fluid-intake difference of only 400 mL between the two groups limited the study results.

In a subsequent randomized controlled trial, Aliti et al¹² examined the clinical outcomes of even more aggressive fluid restriction (800 mL/day) and sodium restriction (800 mg/day) versus liberal intake (at least 2.5 L fluid/day and approximately 3-5 g sodium/day) in hospitalized patients with ADHF (N = 75). While this study evaluated both fluid and sodium restriction, it produced relevant results. The study demonstrated no significant difference in weight loss, use of diuretics, or rehospitalization between the study arms.¹² At 30-day follow-up, researchers found that patients in the intervention group had more congestion and an increased likelihood of having a B-type natriuretic peptide (BNP) level greater than 700 pg/mL. In the subset of all patients with an elevated BNP level greater than 700 pg/mL at the end of the study, patients in the intervention group had a significantly higher rate of readmission (7 out of 22) compared with controls (1 of 20). Moreover, the fluid-restricted group had 50% higher perceived thirst values compared to the control group.¹² The sensation of thirst not only reduces quality of life, but, given that angiotensin II stimulates thirst, it may reflect RAAS activation.¹³ For these reasons, clinicians should consider this side effect seriously, especially when the literature lacks evidence of the benefits from fluid restriction.

Fluid-restrict patients who have chronic hyponatremia (Na <135 mmol/L) due to end-stage HFrEF in select circumstances. Hyponatremia develops in heart failure primarily because of the body’s inability to excrete free water due to non-osmotic arginine vasopressin secretion.⁴ Other processes contribute to hyponatremia, including increased free water intake due to angiotensin II stimulating thirst and decreased glomerular filtration rate limiting the kidney’s ability to excrete free water. Since hyponatremia in heart failure primarily occurs due to derangements of free water regulation, limiting free water intake may help; the American College of Cardiology/American Heart Association and European heart failure guidelines explicitly recommend this strategy for patients with stage D heart failure.^3,4 However, no available randomized data support this practice, and observational data suggest that fluid restriction has limited impact on hyponatremia in ADHF.¹⁴ Guidelines also suggest employing fluid restriction in patients with diuretic resistance as an adjunctive therapy.

Twenty-nine percent of patients with ADHF have comorbid chronic kidney disease (CKD).¹⁵ Providers often prescribe patients with advanced CKD salt- and fluid-restrictive diets due to more limited abilities in sodium and free water excretion. However, no studies have examined the effects of fluid restriction alone without salt restriction in the CKD/ADHF population.

In the present day of evidence-based pharmacologic therapies, research indicates that fluid-restriction does not help and potentially may harm. Instead, treat hospitalized HFrEF patients with ADHF with modern, evidence-based pharmacologic therapies and allow the patients to drink when thirsty.

• Treat patients with ADHF and reduced ejection fraction with evidence-based neurohormonal blockade and initiate loop diuretics to alleviate congestion.
• Allow patients with ADHF and reduced ejection fraction to drink when thirsty in the absence of hyponatremia.
• Consider initiating fluid restriction in patients with ADHF and concurrent hyponatremia and/or diuretic resistance. There is little evidence to guide setting specific limits on fluid intake.

CONCLUSION

The hospitalist starts the patient admitted for ADHF on an intravenous loop diuretic, continues her home beta blocker and angiotensin-converting enzyme inhibitor, and does not impose any fluid restriction. Her symptoms of congestion resolve, and she is discharged.

Hospitalists often treat patients with ADHF and reduced ejection fraction with fluid restriction. However, limited evidence supports this practice as part of the management of ADHF. Fluid restriction may have unintended adverse effects of increasing thirst and worsening renal function and quality of life.

What do you do? Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Let us know what you do in your practice and propose ideas for other “Things We Do for No Reason” topics. Please join in the conversation online at Twitter (#TWDFNR)/Facebook and don’t forget to “Like It” on Facebook or retweet it on Twitter.

Healthcare payment model reform has increased pressure on healthcare systems and hospitalists to improve efficiency and reduce the cost of care. These pressures on the healthcare system have been exacerbated by a global pandemic and an aging patient population straining hospital capacity and resources. Hospital capacity constraints may contribute to hospital crowding and can compromise patient outcomes.¹ Increasing hospital capacity also contributes to an increase in hospitalist census. This increase in census is accompanied by proportional increases in hospitalist burnout, cost of care, and prolonged length of stay (LOS).² Managing LOS reduces “waste” (or non–value-added inpatient days) and can improve outcomes and efficiency within the hospital system.

The benefits for LOS reduction when patients are managed by hospitalists compared with primary care practitioners are well described and are associated with decreases in average LOS and cost.^3-5 The shorter LOS with hospitalist care is most pronounced in older patients with more complex disease processes, which has temporal importance. The Department of Health and Human Services expects the number of American adults aged >65 years to approach 72 million (20% of the US population) by 2030. Hospitalists are positioned to drive evidence-based care pathways and improve the quality of patient care in this growing patient population. We examine the reasons for managing LOS, summarize factors that contribute to an increased LOS (“waste”), and propose a list of evidence-based value drivers for LOS reduction (Table).^2,6-17 Our experience utilizing this approach within Cleveland Clinic Florida following implementation of many of these evidence-based strategies to reduce non–value-added hospital days is also described in the Appendix Figure.

Barriers to sustainable LOS-reduction strategies have evolved, in part, since the introduction of the Medicare Prospective Payment System, which moved hospital Medicare payments to a predetermined fixed rate for each diagnosis-related group. This led to financial pressures on healthcare systems to identify methods to reduce cost and, in turn, contributed to an increase in postacute facility utilization, with alternative payment models developing in parallel.^18,19 These changes along with disaggregated payments between hospitals and postacute facilities have created a formidable challenge to LOS and cost-reduction plans.¹⁹

The usual “why” for reducing LOS includes improving constraints on hospital capacity, strains on resources, and deleterious outcomes. In our experience, an evidence-based approach to LOS management should focus on: (1) reduction in patient hospital days through decreased care variation; (2) stabilizing hospitalist workloads; (3) minimizing the fragmentation inherent to the hospitalist care delivery model; and (4) developing service lines to manage patients hospitalized in an observation status and for those patients undergoing procedures deemed medically complex. The literature is mixed on the impact of LOS reductions on other clinical end points, such as readmissions or mortality, with the preponderance indicating no deleterious impact.^20-22 Managing LOS using an evidence-based approach that addresses the variability of individual patients is essential to the LOS strategies employed. These strategies should focus on process improvements to drive LOS reduction and utilize metrics under the individual hospitalist control to support their contribution to the hospitalist groups’ overall LOS.²³

Intrinsic factors such as hospitalist staffing fragmentation, high rounding census, failing to prioritize patients ready to be discharged, variability in practice, number of consultants  per patient, and hospitalist behaviors contribute to increased LOS.^2,6,8 A first precept to management of LOS at the group level is to recognize all hospitalist services are not created equal, and “lumping” hospitalists into a single efficiency metric would not yield actionable information.

The literature is rife with examples of the significant variation in practice styles among hospitalists. A large study including more than 1000 hospitalists identified practice variation as the strongest predictor of variations in mean LOS.⁷ While Goodwin et al⁷ identified significant variation among hospitalists’ LOS and the discharge destination of patients, much of the variation could be attributable to the hospitals where they practice. These findings ostensibly highlight the importance of LOS strategies being developed collaboratively among hospitalist groups and the healthcare systems they serve. Similar variation exists among hospitalists on teaching services versus nonteaching services. Our experience parallels that of other studies with regard to teaching services that have found that hospitalists on teaching services often have additional responsibilities and are less able to gain the efficiency of nonresident hospitalists services.³ The impact of teaching services on hospitalist efficiencies is an important component when setting expectations at the hospitalist group level for providers on academic services.

Workload and staffing models for hospitalists have a significant impact on hospitalist efficiency and LOS management. As workload increased, Elliot and colleagues² identified a proportional increase in LOS. For occupancies of 75% to 85%, LOS increased exponentially above a daily relative value unit of approximately 25 and a census value of approximately 15. The magnitude of this difference in LOS and cost across the range of hospitalist workloads was $262, with an average increase in LOS of 2 days for every unit increase in census. Higher workloads contributed to inferior discussion of treatment options with patients; delays in discharges; delays in placing discharge orders; and unnecessary testing, procedures, and consults.¹⁴ To mitigate inefficiency and adverse impacts of higher workloads, hospitalist groups should develop mechanisms to absorb surges in census and unanticipated changes to staffing maintaining the workload within a range appropriate to the patient population.

Decreasing fragmentation, when multiple hospitalists care for the patient during hospitalization, is a necessary component of any LOS-reduction strategy. Studies of pneumonia and heart failure have demonstrated that a 10% increase in hosptialist fragmentation is associated with significant increases in LOS.²⁴ Schedules with hospitalists on 7-day rotating rounding blocks have the intuitive advantage of improving care continuity for patients compared with schedules with a shorter number of consecutive rounding days, resulting in fewer hospitalists caring for each patient and decreased “fragmentation.” Additional value drivers for LOS reduction strategies for hospitalists are listed in the Table.

The 2018 State of Hospital Medicine Report highlighted that, among patients discharged by hospitalist groups, 80.8% were inpatient and 19.2% were outpatient. With nearly one in five patients discharged in observation status, it behooves hospitalist programs to work to effectively manage these patients. Indeed, hospitalist-run observation units have been shown to decrease LOS significantly without an increase in return rates to the emergency department or hospital compared with patients managed prior to the introduction of a dedicated observation unit.⁹

Although an in-depth discussion is beyond the scope of the present article, it is worth noting the value of hospitalist comanagement (HCoM) strategies. The impact of HCoM teams is demonstrated by reductions in LOS and cost of care resulting from decreases in medical complications, number of consultants per patient, and a decrease in 30-day readmsissions.¹² The Society of Hospital Medicine Perioperative Care Work Group has outlined a collaborative framework for hospitalists and healthcare systems to draw from.¹⁵

Within the Cleveland Clinic Indian River Hospital (CCIRH) medicine department, many of the aforementioned strategies and tactics were standardized among hospitalist providers. Hospitalists at CCIRH are scheduled on 7-day rotating blocks to reduce fragmentation. In 2019, we targeted a range of 15 to 18 patient contacts per rounding hospitalist per day and utilized a back-up call system to stabilize the hospitalist census. The hospitalist service lines are enhanced through HCoM services with patients cohorted on dedicated HCoM teams. The follow-up to discharge ratio is used to provide feedback at the provider level as both a management and assessment tool.²³ The rounding and admitting teams are dedicated to their responsibility (with the occasional exception necessitating the rounding team assist with admissions when the volumes are high). Direct admissions and transfers from outside hospitals are managed by a dedicated hospital medicine “quarterback” to minimize disruption of the admitting and rounding teams. Barriers to discharge are identified at the time of admission by care management and aggressively managed. Prolonged LOS reports are generated daily and disseminated to care managers and physician leadership. In January 2019, the average LOS for inpatients at CCIRH was 4.4 days. In December 2019, the average LOS for the calendar year to-date at CCIRH was 3.9 days (Appendix Figure).

The value proposition for managing LOS should be viewed in the context of the total cost of care over an extended period of time and not viewed in isolation. Readmission rates serve as a counterbalance to LOS-reduction strategies and contribute to higher costs of care when increased. The 30-day readmission rate for this cohort over this same time period was down slightly compared with the previous year to 12.1%. In addition, observation patients at CCIRH are managed in a closed, geographically cohorted unit, staffed by dedicated advanced-practice providers and physicians dedicated to observation medicine. Over this same time period, more than 5500 patients were managed in the observation unit. These patients had an average LOS of 19.2 hours, with approximately four out of every five patients being discharged to home from an observation status.

The impact of COVID-19 and higher hospital volumes are best visualized in the Appendix Figure. Increases in LOS were observed during periods of COVID-19–related “surges” in hospital volume. These reversals in LOS trends during periods of high occupancy echo earlier findings by Elliot et al² showing that external factors that are not directly under the control of the hospitalist drive LOS and must be considered when developing LOS reduction strategies.

The shift toward value-based payment models provides a strong tailwind for healthcare systems to manage LOS. Hospitalists are well positioned to drive LOS-reduction strategies for the healthcare systems they serve and provide value by driving both quality and efficiency. A complete realization of the value proposition of hospitalist programs in driving LOS-reduction initiatives requires the healthcare systems they serve to provide these teams with the appropriate resources and tools.

Healthcare payment model reform has increased pressure on healthcare systems and hospitalists to improve efficiency and reduce the cost of care. These pressures on the healthcare system have been exacerbated by a global pandemic and an aging patient population straining hospital capacity and resources. Hospital capacity constraints may contribute to hospital crowding and can compromise patient outcomes.¹ Increasing hospital capacity also contributes to an increase in hospitalist census. This increase in census is accompanied by proportional increases in hospitalist burnout, cost of care, and prolonged length of stay (LOS).² Managing LOS reduces “waste” (or non–value-added inpatient days) and can improve outcomes and efficiency within the hospital system.

The benefits for LOS reduction when patients are managed by hospitalists compared with primary care practitioners are well described and are associated with decreases in average LOS and cost.^3-5 The shorter LOS with hospitalist care is most pronounced in older patients with more complex disease processes, which has temporal importance. The Department of Health and Human Services expects the number of American adults aged >65 years to approach 72 million (20% of the US population) by 2030. Hospitalists are positioned to drive evidence-based care pathways and improve the quality of patient care in this growing patient population. We examine the reasons for managing LOS, summarize factors that contribute to an increased LOS (“waste”), and propose a list of evidence-based value drivers for LOS reduction (Table).^2,6-17 Our experience utilizing this approach within Cleveland Clinic Florida following implementation of many of these evidence-based strategies to reduce non–value-added hospital days is also described in the Appendix Figure.

Barriers to sustainable LOS-reduction strategies have evolved, in part, since the introduction of the Medicare Prospective Payment System, which moved hospital Medicare payments to a predetermined fixed rate for each diagnosis-related group. This led to financial pressures on healthcare systems to identify methods to reduce cost and, in turn, contributed to an increase in postacute facility utilization, with alternative payment models developing in parallel.^18,19 These changes along with disaggregated payments between hospitals and postacute facilities have created a formidable challenge to LOS and cost-reduction plans.¹⁹

The usual “why” for reducing LOS includes improving constraints on hospital capacity, strains on resources, and deleterious outcomes. In our experience, an evidence-based approach to LOS management should focus on: (1) reduction in patient hospital days through decreased care variation; (2) stabilizing hospitalist workloads; (3) minimizing the fragmentation inherent to the hospitalist care delivery model; and (4) developing service lines to manage patients hospitalized in an observation status and for those patients undergoing procedures deemed medically complex. The literature is mixed on the impact of LOS reductions on other clinical end points, such as readmissions or mortality, with the preponderance indicating no deleterious impact.^20-22 Managing LOS using an evidence-based approach that addresses the variability of individual patients is essential to the LOS strategies employed. These strategies should focus on process improvements to drive LOS reduction and utilize metrics under the individual hospitalist control to support their contribution to the hospitalist groups’ overall LOS.²³

Intrinsic factors such as hospitalist staffing fragmentation, high rounding census, failing to prioritize patients ready to be discharged, variability in practice, number of consultants  per patient, and hospitalist behaviors contribute to increased LOS.^2,6,8 A first precept to management of LOS at the group level is to recognize all hospitalist services are not created equal, and “lumping” hospitalists into a single efficiency metric would not yield actionable information.

The literature is rife with examples of the significant variation in practice styles among hospitalists. A large study including more than 1000 hospitalists identified practice variation as the strongest predictor of variations in mean LOS.⁷ While Goodwin et al⁷ identified significant variation among hospitalists’ LOS and the discharge destination of patients, much of the variation could be attributable to the hospitals where they practice. These findings ostensibly highlight the importance of LOS strategies being developed collaboratively among hospitalist groups and the healthcare systems they serve. Similar variation exists among hospitalists on teaching services versus nonteaching services. Our experience parallels that of other studies with regard to teaching services that have found that hospitalists on teaching services often have additional responsibilities and are less able to gain the efficiency of nonresident hospitalists services.³ The impact of teaching services on hospitalist efficiencies is an important component when setting expectations at the hospitalist group level for providers on academic services.

Workload and staffing models for hospitalists have a significant impact on hospitalist efficiency and LOS management. As workload increased, Elliot and colleagues² identified a proportional increase in LOS. For occupancies of 75% to 85%, LOS increased exponentially above a daily relative value unit of approximately 25 and a census value of approximately 15. The magnitude of this difference in LOS and cost across the range of hospitalist workloads was $262, with an average increase in LOS of 2 days for every unit increase in census. Higher workloads contributed to inferior discussion of treatment options with patients; delays in discharges; delays in placing discharge orders; and unnecessary testing, procedures, and consults.¹⁴ To mitigate inefficiency and adverse impacts of higher workloads, hospitalist groups should develop mechanisms to absorb surges in census and unanticipated changes to staffing maintaining the workload within a range appropriate to the patient population.

Decreasing fragmentation, when multiple hospitalists care for the patient during hospitalization, is a necessary component of any LOS-reduction strategy. Studies of pneumonia and heart failure have demonstrated that a 10% increase in hosptialist fragmentation is associated with significant increases in LOS.²⁴ Schedules with hospitalists on 7-day rotating rounding blocks have the intuitive advantage of improving care continuity for patients compared with schedules with a shorter number of consecutive rounding days, resulting in fewer hospitalists caring for each patient and decreased “fragmentation.” Additional value drivers for LOS reduction strategies for hospitalists are listed in the Table.

The 2018 State of Hospital Medicine Report highlighted that, among patients discharged by hospitalist groups, 80.8% were inpatient and 19.2% were outpatient. With nearly one in five patients discharged in observation status, it behooves hospitalist programs to work to effectively manage these patients. Indeed, hospitalist-run observation units have been shown to decrease LOS significantly without an increase in return rates to the emergency department or hospital compared with patients managed prior to the introduction of a dedicated observation unit.⁹

Although an in-depth discussion is beyond the scope of the present article, it is worth noting the value of hospitalist comanagement (HCoM) strategies. The impact of HCoM teams is demonstrated by reductions in LOS and cost of care resulting from decreases in medical complications, number of consultants per patient, and a decrease in 30-day readmsissions.¹² The Society of Hospital Medicine Perioperative Care Work Group has outlined a collaborative framework for hospitalists and healthcare systems to draw from.¹⁵

Within the Cleveland Clinic Indian River Hospital (CCIRH) medicine department, many of the aforementioned strategies and tactics were standardized among hospitalist providers. Hospitalists at CCIRH are scheduled on 7-day rotating blocks to reduce fragmentation. In 2019, we targeted a range of 15 to 18 patient contacts per rounding hospitalist per day and utilized a back-up call system to stabilize the hospitalist census. The hospitalist service lines are enhanced through HCoM services with patients cohorted on dedicated HCoM teams. The follow-up to discharge ratio is used to provide feedback at the provider level as both a management and assessment tool.²³ The rounding and admitting teams are dedicated to their responsibility (with the occasional exception necessitating the rounding team assist with admissions when the volumes are high). Direct admissions and transfers from outside hospitals are managed by a dedicated hospital medicine “quarterback” to minimize disruption of the admitting and rounding teams. Barriers to discharge are identified at the time of admission by care management and aggressively managed. Prolonged LOS reports are generated daily and disseminated to care managers and physician leadership. In January 2019, the average LOS for inpatients at CCIRH was 4.4 days. In December 2019, the average LOS for the calendar year to-date at CCIRH was 3.9 days (Appendix Figure).

The value proposition for managing LOS should be viewed in the context of the total cost of care over an extended period of time and not viewed in isolation. Readmission rates serve as a counterbalance to LOS-reduction strategies and contribute to higher costs of care when increased. The 30-day readmission rate for this cohort over this same time period was down slightly compared with the previous year to 12.1%. In addition, observation patients at CCIRH are managed in a closed, geographically cohorted unit, staffed by dedicated advanced-practice providers and physicians dedicated to observation medicine. Over this same time period, more than 5500 patients were managed in the observation unit. These patients had an average LOS of 19.2 hours, with approximately four out of every five patients being discharged to home from an observation status.

The impact of COVID-19 and higher hospital volumes are best visualized in the Appendix Figure. Increases in LOS were observed during periods of COVID-19–related “surges” in hospital volume. These reversals in LOS trends during periods of high occupancy echo earlier findings by Elliot et al² showing that external factors that are not directly under the control of the hospitalist drive LOS and must be considered when developing LOS reduction strategies.

The shift toward value-based payment models provides a strong tailwind for healthcare systems to manage LOS. Hospitalists are well positioned to drive LOS-reduction strategies for the healthcare systems they serve and provide value by driving both quality and efficiency. A complete realization of the value proposition of hospitalist programs in driving LOS-reduction initiatives requires the healthcare systems they serve to provide these teams with the appropriate resources and tools.

Healthcare payment model reform has increased pressure on healthcare systems and hospitalists to improve efficiency and reduce the cost of care. These pressures on the healthcare system have been exacerbated by a global pandemic and an aging patient population straining hospital capacity and resources. Hospital capacity constraints may contribute to hospital crowding and can compromise patient outcomes.¹ Increasing hospital capacity also contributes to an increase in hospitalist census. This increase in census is accompanied by proportional increases in hospitalist burnout, cost of care, and prolonged length of stay (LOS).² Managing LOS reduces “waste” (or non–value-added inpatient days) and can improve outcomes and efficiency within the hospital system.

The benefits for LOS reduction when patients are managed by hospitalists compared with primary care practitioners are well described and are associated with decreases in average LOS and cost.^3-5 The shorter LOS with hospitalist care is most pronounced in older patients with more complex disease processes, which has temporal importance. The Department of Health and Human Services expects the number of American adults aged >65 years to approach 72 million (20% of the US population) by 2030. Hospitalists are positioned to drive evidence-based care pathways and improve the quality of patient care in this growing patient population. We examine the reasons for managing LOS, summarize factors that contribute to an increased LOS (“waste”), and propose a list of evidence-based value drivers for LOS reduction (Table).^2,6-17 Our experience utilizing this approach within Cleveland Clinic Florida following implementation of many of these evidence-based strategies to reduce non–value-added hospital days is also described in the Appendix Figure.

Barriers to sustainable LOS-reduction strategies have evolved, in part, since the introduction of the Medicare Prospective Payment System, which moved hospital Medicare payments to a predetermined fixed rate for each diagnosis-related group. This led to financial pressures on healthcare systems to identify methods to reduce cost and, in turn, contributed to an increase in postacute facility utilization, with alternative payment models developing in parallel.^18,19 These changes along with disaggregated payments between hospitals and postacute facilities have created a formidable challenge to LOS and cost-reduction plans.¹⁹

The usual “why” for reducing LOS includes improving constraints on hospital capacity, strains on resources, and deleterious outcomes. In our experience, an evidence-based approach to LOS management should focus on: (1) reduction in patient hospital days through decreased care variation; (2) stabilizing hospitalist workloads; (3) minimizing the fragmentation inherent to the hospitalist care delivery model; and (4) developing service lines to manage patients hospitalized in an observation status and for those patients undergoing procedures deemed medically complex. The literature is mixed on the impact of LOS reductions on other clinical end points, such as readmissions or mortality, with the preponderance indicating no deleterious impact.^20-22 Managing LOS using an evidence-based approach that addresses the variability of individual patients is essential to the LOS strategies employed. These strategies should focus on process improvements to drive LOS reduction and utilize metrics under the individual hospitalist control to support their contribution to the hospitalist groups’ overall LOS.²³

Intrinsic factors such as hospitalist staffing fragmentation, high rounding census, failing to prioritize patients ready to be discharged, variability in practice, number of consultants  per patient, and hospitalist behaviors contribute to increased LOS.^2,6,8 A first precept to management of LOS at the group level is to recognize all hospitalist services are not created equal, and “lumping” hospitalists into a single efficiency metric would not yield actionable information.

The literature is rife with examples of the significant variation in practice styles among hospitalists. A large study including more than 1000 hospitalists identified practice variation as the strongest predictor of variations in mean LOS.⁷ While Goodwin et al⁷ identified significant variation among hospitalists’ LOS and the discharge destination of patients, much of the variation could be attributable to the hospitals where they practice. These findings ostensibly highlight the importance of LOS strategies being developed collaboratively among hospitalist groups and the healthcare systems they serve. Similar variation exists among hospitalists on teaching services versus nonteaching services. Our experience parallels that of other studies with regard to teaching services that have found that hospitalists on teaching services often have additional responsibilities and are less able to gain the efficiency of nonresident hospitalists services.³ The impact of teaching services on hospitalist efficiencies is an important component when setting expectations at the hospitalist group level for providers on academic services.

Workload and staffing models for hospitalists have a significant impact on hospitalist efficiency and LOS management. As workload increased, Elliot and colleagues² identified a proportional increase in LOS. For occupancies of 75% to 85%, LOS increased exponentially above a daily relative value unit of approximately 25 and a census value of approximately 15. The magnitude of this difference in LOS and cost across the range of hospitalist workloads was $262, with an average increase in LOS of 2 days for every unit increase in census. Higher workloads contributed to inferior discussion of treatment options with patients; delays in discharges; delays in placing discharge orders; and unnecessary testing, procedures, and consults.¹⁴ To mitigate inefficiency and adverse impacts of higher workloads, hospitalist groups should develop mechanisms to absorb surges in census and unanticipated changes to staffing maintaining the workload within a range appropriate to the patient population.

Decreasing fragmentation, when multiple hospitalists care for the patient during hospitalization, is a necessary component of any LOS-reduction strategy. Studies of pneumonia and heart failure have demonstrated that a 10% increase in hosptialist fragmentation is associated with significant increases in LOS.²⁴ Schedules with hospitalists on 7-day rotating rounding blocks have the intuitive advantage of improving care continuity for patients compared with schedules with a shorter number of consecutive rounding days, resulting in fewer hospitalists caring for each patient and decreased “fragmentation.” Additional value drivers for LOS reduction strategies for hospitalists are listed in the Table.

The 2018 State of Hospital Medicine Report highlighted that, among patients discharged by hospitalist groups, 80.8% were inpatient and 19.2% were outpatient. With nearly one in five patients discharged in observation status, it behooves hospitalist programs to work to effectively manage these patients. Indeed, hospitalist-run observation units have been shown to decrease LOS significantly without an increase in return rates to the emergency department or hospital compared with patients managed prior to the introduction of a dedicated observation unit.⁹

Although an in-depth discussion is beyond the scope of the present article, it is worth noting the value of hospitalist comanagement (HCoM) strategies. The impact of HCoM teams is demonstrated by reductions in LOS and cost of care resulting from decreases in medical complications, number of consultants per patient, and a decrease in 30-day readmsissions.¹² The Society of Hospital Medicine Perioperative Care Work Group has outlined a collaborative framework for hospitalists and healthcare systems to draw from.¹⁵

Within the Cleveland Clinic Indian River Hospital (CCIRH) medicine department, many of the aforementioned strategies and tactics were standardized among hospitalist providers. Hospitalists at CCIRH are scheduled on 7-day rotating blocks to reduce fragmentation. In 2019, we targeted a range of 15 to 18 patient contacts per rounding hospitalist per day and utilized a back-up call system to stabilize the hospitalist census. The hospitalist service lines are enhanced through HCoM services with patients cohorted on dedicated HCoM teams. The follow-up to discharge ratio is used to provide feedback at the provider level as both a management and assessment tool.²³ The rounding and admitting teams are dedicated to their responsibility (with the occasional exception necessitating the rounding team assist with admissions when the volumes are high). Direct admissions and transfers from outside hospitals are managed by a dedicated hospital medicine “quarterback” to minimize disruption of the admitting and rounding teams. Barriers to discharge are identified at the time of admission by care management and aggressively managed. Prolonged LOS reports are generated daily and disseminated to care managers and physician leadership. In January 2019, the average LOS for inpatients at CCIRH was 4.4 days. In December 2019, the average LOS for the calendar year to-date at CCIRH was 3.9 days (Appendix Figure).

The value proposition for managing LOS should be viewed in the context of the total cost of care over an extended period of time and not viewed in isolation. Readmission rates serve as a counterbalance to LOS-reduction strategies and contribute to higher costs of care when increased. The 30-day readmission rate for this cohort over this same time period was down slightly compared with the previous year to 12.1%. In addition, observation patients at CCIRH are managed in a closed, geographically cohorted unit, staffed by dedicated advanced-practice providers and physicians dedicated to observation medicine. Over this same time period, more than 5500 patients were managed in the observation unit. These patients had an average LOS of 19.2 hours, with approximately four out of every five patients being discharged to home from an observation status.

The impact of COVID-19 and higher hospital volumes are best visualized in the Appendix Figure. Increases in LOS were observed during periods of COVID-19–related “surges” in hospital volume. These reversals in LOS trends during periods of high occupancy echo earlier findings by Elliot et al² showing that external factors that are not directly under the control of the hospitalist drive LOS and must be considered when developing LOS reduction strategies.

The shift toward value-based payment models provides a strong tailwind for healthcare systems to manage LOS. Hospitalists are well positioned to drive LOS-reduction strategies for the healthcare systems they serve and provide value by driving both quality and efficiency. A complete realization of the value proposition of hospitalist programs in driving LOS-reduction initiatives requires the healthcare systems they serve to provide these teams with the appropriate resources and tools.

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

Horwitz and Detsky¹ provide readers with a personal, experientially based primer on how healthcare professionals can more effectively engage on Twitter. As experienced physicians, researchers, and active social media users, the authors outline pragmatic and specific recommendations on how to engage misinformation and add value to social media discourse. We applaud the authors for offering best-practice approaches that are valuable to newcomers as well as seasoned social media users. In highlighting that social media is merely a modern tool for engagement and discussion, the authors underscore the time-held idea that only when a tool is used effectively will it yield the desired outcome. As a medical journal that regularly uses social media as a tool for outreach and dissemination, we could not agree more with the authors’ assertion.

Since 2015, the Journal of Hospital Medicine (JHM) has used social media to engage its readership and extend the impact of the work published in its pages. Like Horwitz and Detsky, JHM has developed insights and experience in how medical journals, organizations, institutions, and other academic programs can use social media effectively. Because of our experience in this area, we are often asked how to build a successful and sustainable social media presence. Here, we share five primary lessons on how to use social media as a tool to disseminate, connect, and engage.

As the flagship journal for the field of hospital medicine, we seek to disseminate the ideas and research that will inform health policy, optimize healthcare delivery, and improve patient outcomes while also building and sustaining an online community for professional engagement and growth. Our social media goals provide direction on how to interact, allow us to focus attention on what is important, and motivate our growth in this area. Simply put, we believe that using social media without defined goals would be like sailing a ship without a rudder.

As your organization establishes its goals, it is important to consider with whom you want to connect. Knowing your audience will allow you to better tailor the content you deliver through social media. For instance, we understand that as a journal focused on hospital medicine, our audience consists of busy clinicians, researchers, and medical educators who are trying to efficiently gather the most up-to-date information in our field. Recognizing this, we produce (and make available for download) Visual Abstracts and publish them on Twitter to help our followers assimilate information from new studies quickly and easily.² Moreover, we recognize that our followers are interested in how to use social media in their professional lives and have published several articles in this topic area.^3-5

We have found that having multiple individuals on our social media team has led to greater creativity and thoughtfulness on how we engage our readership. Our teams span generations, clinical experience, institutions, and cultural backgrounds. This intentional approach has allowed for diversity in thoughts and opinions and has helped shape the JHM social media message. Additionally, we have not only formalized editorial roles through the creation of Digital Media Editor positions, but we have also created the JHM Digital Media Fellowship, a training program and development pipeline for those interested in cultivating organization-based social media experiences and skill sets.⁶

Many organizations believe that successful social media outreach means creating an account and posting content when convenient. Experience has taught us that daily postings and regular engagement will build your brand as a regular and reliable source of information for your followers. Additionally, while many academic journals and organizations only occasionally post material and rarely interact with their followers, we have found that engaging and facilitating conversations through our monthly Twitter discussion (#JHMChat) has established a community, created opportunities for professional networking, and further disseminated the work published in JHM.⁷ As an academic journal or organization entering this field, recognize the product for which people follow you and deliver that product on a consistent basis.

It will only be a matter of time before your organization makes a misstep on social media. Instead of hiding, we recommend stepping into that tension and owning the mistake. For example, we recently published an article that contained a culturally offensive term. As a journal, we reflected on our error and took concrete steps to correct it. Further, we shared our thoughts with our followers to ensure transparency.⁸ Moving forward, we have inserted specific stopgaps in our editorial review process to avoid such missteps in the future.

Although every organization will have different goals and reasons for engaging on social media, we believe these central tenets will help optimize the use of this platform. Although we have established specific objectives for our engagement on social media, we believe Horwitz and Detsky¹ put it best when they note that, at the end of the day, our ultimate goal is in “…promoting knowledge and science in a way that helps us all live healthier and happier lives."

Horwitz and Detsky¹ provide readers with a personal, experientially based primer on how healthcare professionals can more effectively engage on Twitter. As experienced physicians, researchers, and active social media users, the authors outline pragmatic and specific recommendations on how to engage misinformation and add value to social media discourse. We applaud the authors for offering best-practice approaches that are valuable to newcomers as well as seasoned social media users. In highlighting that social media is merely a modern tool for engagement and discussion, the authors underscore the time-held idea that only when a tool is used effectively will it yield the desired outcome. As a medical journal that regularly uses social media as a tool for outreach and dissemination, we could not agree more with the authors’ assertion.

Since 2015, the Journal of Hospital Medicine (JHM) has used social media to engage its readership and extend the impact of the work published in its pages. Like Horwitz and Detsky, JHM has developed insights and experience in how medical journals, organizations, institutions, and other academic programs can use social media effectively. Because of our experience in this area, we are often asked how to build a successful and sustainable social media presence. Here, we share five primary lessons on how to use social media as a tool to disseminate, connect, and engage.

As the flagship journal for the field of hospital medicine, we seek to disseminate the ideas and research that will inform health policy, optimize healthcare delivery, and improve patient outcomes while also building and sustaining an online community for professional engagement and growth. Our social media goals provide direction on how to interact, allow us to focus attention on what is important, and motivate our growth in this area. Simply put, we believe that using social media without defined goals would be like sailing a ship without a rudder.

As your organization establishes its goals, it is important to consider with whom you want to connect. Knowing your audience will allow you to better tailor the content you deliver through social media. For instance, we understand that as a journal focused on hospital medicine, our audience consists of busy clinicians, researchers, and medical educators who are trying to efficiently gather the most up-to-date information in our field. Recognizing this, we produce (and make available for download) Visual Abstracts and publish them on Twitter to help our followers assimilate information from new studies quickly and easily.² Moreover, we recognize that our followers are interested in how to use social media in their professional lives and have published several articles in this topic area.^3-5

We have found that having multiple individuals on our social media team has led to greater creativity and thoughtfulness on how we engage our readership. Our teams span generations, clinical experience, institutions, and cultural backgrounds. This intentional approach has allowed for diversity in thoughts and opinions and has helped shape the JHM social media message. Additionally, we have not only formalized editorial roles through the creation of Digital Media Editor positions, but we have also created the JHM Digital Media Fellowship, a training program and development pipeline for those interested in cultivating organization-based social media experiences and skill sets.⁶

Many organizations believe that successful social media outreach means creating an account and posting content when convenient. Experience has taught us that daily postings and regular engagement will build your brand as a regular and reliable source of information for your followers. Additionally, while many academic journals and organizations only occasionally post material and rarely interact with their followers, we have found that engaging and facilitating conversations through our monthly Twitter discussion (#JHMChat) has established a community, created opportunities for professional networking, and further disseminated the work published in JHM.⁷ As an academic journal or organization entering this field, recognize the product for which people follow you and deliver that product on a consistent basis.

It will only be a matter of time before your organization makes a misstep on social media. Instead of hiding, we recommend stepping into that tension and owning the mistake. For example, we recently published an article that contained a culturally offensive term. As a journal, we reflected on our error and took concrete steps to correct it. Further, we shared our thoughts with our followers to ensure transparency.⁸ Moving forward, we have inserted specific stopgaps in our editorial review process to avoid such missteps in the future.

Although every organization will have different goals and reasons for engaging on social media, we believe these central tenets will help optimize the use of this platform. Although we have established specific objectives for our engagement on social media, we believe Horwitz and Detsky¹ put it best when they note that, at the end of the day, our ultimate goal is in “…promoting knowledge and science in a way that helps us all live healthier and happier lives."

Horwitz and Detsky¹ provide readers with a personal, experientially based primer on how healthcare professionals can more effectively engage on Twitter. As experienced physicians, researchers, and active social media users, the authors outline pragmatic and specific recommendations on how to engage misinformation and add value to social media discourse. We applaud the authors for offering best-practice approaches that are valuable to newcomers as well as seasoned social media users. In highlighting that social media is merely a modern tool for engagement and discussion, the authors underscore the time-held idea that only when a tool is used effectively will it yield the desired outcome. As a medical journal that regularly uses social media as a tool for outreach and dissemination, we could not agree more with the authors’ assertion.

Since 2015, the Journal of Hospital Medicine (JHM) has used social media to engage its readership and extend the impact of the work published in its pages. Like Horwitz and Detsky, JHM has developed insights and experience in how medical journals, organizations, institutions, and other academic programs can use social media effectively. Because of our experience in this area, we are often asked how to build a successful and sustainable social media presence. Here, we share five primary lessons on how to use social media as a tool to disseminate, connect, and engage.

As the flagship journal for the field of hospital medicine, we seek to disseminate the ideas and research that will inform health policy, optimize healthcare delivery, and improve patient outcomes while also building and sustaining an online community for professional engagement and growth. Our social media goals provide direction on how to interact, allow us to focus attention on what is important, and motivate our growth in this area. Simply put, we believe that using social media without defined goals would be like sailing a ship without a rudder.

As your organization establishes its goals, it is important to consider with whom you want to connect. Knowing your audience will allow you to better tailor the content you deliver through social media. For instance, we understand that as a journal focused on hospital medicine, our audience consists of busy clinicians, researchers, and medical educators who are trying to efficiently gather the most up-to-date information in our field. Recognizing this, we produce (and make available for download) Visual Abstracts and publish them on Twitter to help our followers assimilate information from new studies quickly and easily.² Moreover, we recognize that our followers are interested in how to use social media in their professional lives and have published several articles in this topic area.^3-5

We have found that having multiple individuals on our social media team has led to greater creativity and thoughtfulness on how we engage our readership. Our teams span generations, clinical experience, institutions, and cultural backgrounds. This intentional approach has allowed for diversity in thoughts and opinions and has helped shape the JHM social media message. Additionally, we have not only formalized editorial roles through the creation of Digital Media Editor positions, but we have also created the JHM Digital Media Fellowship, a training program and development pipeline for those interested in cultivating organization-based social media experiences and skill sets.⁶

Many organizations believe that successful social media outreach means creating an account and posting content when convenient. Experience has taught us that daily postings and regular engagement will build your brand as a regular and reliable source of information for your followers. Additionally, while many academic journals and organizations only occasionally post material and rarely interact with their followers, we have found that engaging and facilitating conversations through our monthly Twitter discussion (#JHMChat) has established a community, created opportunities for professional networking, and further disseminated the work published in JHM.⁷ As an academic journal or organization entering this field, recognize the product for which people follow you and deliver that product on a consistent basis.

It will only be a matter of time before your organization makes a misstep on social media. Instead of hiding, we recommend stepping into that tension and owning the mistake. For example, we recently published an article that contained a culturally offensive term. As a journal, we reflected on our error and took concrete steps to correct it. Further, we shared our thoughts with our followers to ensure transparency.⁸ Moving forward, we have inserted specific stopgaps in our editorial review process to avoid such missteps in the future.

Although every organization will have different goals and reasons for engaging on social media, we believe these central tenets will help optimize the use of this platform. Although we have established specific objectives for our engagement on social media, we believe Horwitz and Detsky¹ put it best when they note that, at the end of the day, our ultimate goal is in “…promoting knowledge and science in a way that helps us all live healthier and happier lives."