Hospital Medicine

The Children’s Hospital of Philadelphia, Philadelphia, PA.

The effects of COVID-19 on children’s health are multifaceted. In comparison to adults, children typically experience far milder physical consequences when infected with the virus. A notable exception is the newly described multisystem inflammatory syndrome associated with COVID-19 (MIS-C), which has proven to be a source of significant morbidity among the children it affects.¹ Nevertheless, even those children not infected with COVID-19 have suffered due to the disease. School closures have deprived children of opportunities for social and academic growth and, in some cases, the provision of food, social services, medication administration, and many different therapies. Social distancing rules have limited play among children, which is crucial to their development and mental health. The impact on children who have lost family members, including parents, is monumental. Amidst all of this observable suffering, however, the pandemic poses a less visible threat to the health of children.

It is well documented that concern about exposure to COVID-19 has led many adults to avoid emergency departments (EDs) around the world. We believe parents may be avoiding ED visits for their children for the same reason. In the United States, ED volumes dropped approximately 50% during spring 2020.² While EDs saw increasing, and at times overwhelming, numbers of patients with COVID-19, the number of patients presenting with other life-threatening medical issues, including heart attacks and strokes, declined.^3,4 Data from the National Center for Health Statistics this past spring revealed nationwide increases in deaths due to nonrespiratory causes such as diabetes, heart disease, and stroke.⁵ ED avoidance and unprecedented lack of access to outpatient care, though with the intent to reduce overall risk, are likely significant contributors to these deaths.

Pediatric patients, especially the most vulnerable, are similarly at risk for deleterious health-related consequences from ED avoidance and from limited access to primary and outpatient specialty care. Data from Europe indicate dramatic drops in pediatric ED (PED) volumes, as well as an increase in the proportion of ED visits leading to hospitalization.^6,7 These studies suggest that when patients do ultimately present to the PED, they may be more seriously ill.

At our institution, we have seen many COVID-19-negative patients whose medical care has been negatively influenced by the pandemic. A few months ago, a 1-month-old infant with an underlying health condition presented to the PED in extremis after weeks of progressively worsening feeding issues. The infant had been closely followed by the primary care provider (PCP) and subspecialty team via phone calls, televisits, and some office visits. Both physicians and parents had tried to resolve the feeding issues within the outpatient context, explicitly hoping to avoid potential exposure of this fragile patient to COVID-19 in the hospital. On eventual presentation to the PED, the infant was profoundly dehydrated, with significant electrolyte derangement and an acute abdomen, requiring admission to the intensive care unit. Ultimately, a new diagnosis of Hirschsprung disease was made, and the infant was hospitalized for several weeks for weight gain.

Later this summer, a school-aged child with a history of poorly controlled type 1 diabetes presented to an affiliated community hospital comatose and with Kussmaul respirations. Prior to the pandemic, a school nurse administered the child’s morning insulin. Since school closed, the patient had been responsible for administering this dose of insulin while the parents worked outside the home. Despite close and frequent communication between the patient’s endocrinology team and the family, the patient’s glucose and ketone levels began to rise. The parent administered repeated boluses of insulin at home in an attempt to avoid the perceived exposure risk associated with an ED visit. On presentation to the PED, the patient was profoundly altered, with a pH of 7.0. When transfer to a tertiary care center was recommended, the patient’s parent expressed persistent concerns about COVID-19 exposure in the larger hospital, although ultimately consent to transfer was given.

A third case from this summer provides an example of a different type of patient affected by COVID-19: the neonate whose birth circumstances were altered due to the virus. A 3-day-old, full-term infant presented to the ED with hypothermia after PCP referral. The parents had considered both home birth and hospital delivery earlier in the pregnancy, ultimately opting for home birth due to concerns about COVID-19 exposure in the hospital. The pregnancy and delivery were uncomplicated. The neonate did not receive the first hepatitis B vaccine, erythromycin eye ointment, or vitamin K after delivery. In the first 3 days of life, the patient had voided once and stooled once per day. The patient’s mother, inexperienced with breastfeeding and without access to a lactation consultant, was unsure about latch or emptying of her breasts. At the first pediatrician visit, the infant was noted to be hypothermic to 35°C, intermittently bradycardic to the 80s, and with diminished arousal. In the PED, a full sepsis work-up was initiated. Though multiple attempts were made by different providers, only a minimal amount of blood could be drawn, presumably due to dehydration. Of note, the neonate received vitamin K subcutaneously prior to lumbar puncture.

Pediatricians across the country have gone to great lengths to protect their patients and to provide high-quality care both inside and outside the office during this unprecedented time. Nevertheless, these 3 cases illustrate the detrimental effects of COVID-19 on the delivery of pediatric health care. The first 2 cases in particular demonstrate the limitations of even close and consistent phone and televisit follow-up. Telehealth has provided a lifeline for patients and families during the pandemic, and, in most cases, has provided an excellent temporary substitution for office visits. There are, however, limitations to care without physical evaluation. Had the children in the first 2 cases been evaluated in person sooner, they may have been referred to a higher level of care more expediently. Likewise, in all 3 cases, parental reservations about exposing their children to COVID-19 through a trip to the hospital, however well-intentioned, likely played a role in the eventual severity of illness with which each child presented to the hospital.

If we are encountering children in the PED with severe illness due to delayed presentation to care, what about the children we aren’t seeing? As COVID-19 cases rise daily in the United States, we must be aware of the possibility of ED avoidance. We propose a multimodal approach to combat this dangerous phenomenon. Inpatient and ED-based pediatricians must maintain clear and open lines of communication with outpatient colleagues so that we can partner in considering which cases warrant prompt ED evaluation, even in the midst of a pandemic. All pediatricians must remind families that our hospitals remain open and ready to treat children safely. We must promote community awareness of the numerous safety precautions we take every day so that patients and families can feel comfortable seeking care at the hospital; the message of ED and hospital safety must be even more robust for caregivers of our particularly vulnerable children. As always, how we communicate with patients and their families matters. Validating and addressing concerns about COVID-19 exposure, while providing reassurance about the safety of our hospitals, could save children’s lives.

Acknowledgment: Thank you to Dr. Cynthia Mollen and Dr. Kathy Shaw for their reviews of the manuscript.

Corresponding author: Regina L. Toto, MD, Department of Pediatrics, The Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104; [email protected].

Financial disclosures: None.

Keywords: coronavirus; pediatric; children; access to care; emergency department.

The Children’s Hospital of Philadelphia, Philadelphia, PA.

The effects of COVID-19 on children’s health are multifaceted. In comparison to adults, children typically experience far milder physical consequences when infected with the virus. A notable exception is the newly described multisystem inflammatory syndrome associated with COVID-19 (MIS-C), which has proven to be a source of significant morbidity among the children it affects.¹ Nevertheless, even those children not infected with COVID-19 have suffered due to the disease. School closures have deprived children of opportunities for social and academic growth and, in some cases, the provision of food, social services, medication administration, and many different therapies. Social distancing rules have limited play among children, which is crucial to their development and mental health. The impact on children who have lost family members, including parents, is monumental. Amidst all of this observable suffering, however, the pandemic poses a less visible threat to the health of children.

It is well documented that concern about exposure to COVID-19 has led many adults to avoid emergency departments (EDs) around the world. We believe parents may be avoiding ED visits for their children for the same reason. In the United States, ED volumes dropped approximately 50% during spring 2020.² While EDs saw increasing, and at times overwhelming, numbers of patients with COVID-19, the number of patients presenting with other life-threatening medical issues, including heart attacks and strokes, declined.^3,4 Data from the National Center for Health Statistics this past spring revealed nationwide increases in deaths due to nonrespiratory causes such as diabetes, heart disease, and stroke.⁵ ED avoidance and unprecedented lack of access to outpatient care, though with the intent to reduce overall risk, are likely significant contributors to these deaths.

Pediatric patients, especially the most vulnerable, are similarly at risk for deleterious health-related consequences from ED avoidance and from limited access to primary and outpatient specialty care. Data from Europe indicate dramatic drops in pediatric ED (PED) volumes, as well as an increase in the proportion of ED visits leading to hospitalization.^6,7 These studies suggest that when patients do ultimately present to the PED, they may be more seriously ill.

At our institution, we have seen many COVID-19-negative patients whose medical care has been negatively influenced by the pandemic. A few months ago, a 1-month-old infant with an underlying health condition presented to the PED in extremis after weeks of progressively worsening feeding issues. The infant had been closely followed by the primary care provider (PCP) and subspecialty team via phone calls, televisits, and some office visits. Both physicians and parents had tried to resolve the feeding issues within the outpatient context, explicitly hoping to avoid potential exposure of this fragile patient to COVID-19 in the hospital. On eventual presentation to the PED, the infant was profoundly dehydrated, with significant electrolyte derangement and an acute abdomen, requiring admission to the intensive care unit. Ultimately, a new diagnosis of Hirschsprung disease was made, and the infant was hospitalized for several weeks for weight gain.

Later this summer, a school-aged child with a history of poorly controlled type 1 diabetes presented to an affiliated community hospital comatose and with Kussmaul respirations. Prior to the pandemic, a school nurse administered the child’s morning insulin. Since school closed, the patient had been responsible for administering this dose of insulin while the parents worked outside the home. Despite close and frequent communication between the patient’s endocrinology team and the family, the patient’s glucose and ketone levels began to rise. The parent administered repeated boluses of insulin at home in an attempt to avoid the perceived exposure risk associated with an ED visit. On presentation to the PED, the patient was profoundly altered, with a pH of 7.0. When transfer to a tertiary care center was recommended, the patient’s parent expressed persistent concerns about COVID-19 exposure in the larger hospital, although ultimately consent to transfer was given.

A third case from this summer provides an example of a different type of patient affected by COVID-19: the neonate whose birth circumstances were altered due to the virus. A 3-day-old, full-term infant presented to the ED with hypothermia after PCP referral. The parents had considered both home birth and hospital delivery earlier in the pregnancy, ultimately opting for home birth due to concerns about COVID-19 exposure in the hospital. The pregnancy and delivery were uncomplicated. The neonate did not receive the first hepatitis B vaccine, erythromycin eye ointment, or vitamin K after delivery. In the first 3 days of life, the patient had voided once and stooled once per day. The patient’s mother, inexperienced with breastfeeding and without access to a lactation consultant, was unsure about latch or emptying of her breasts. At the first pediatrician visit, the infant was noted to be hypothermic to 35°C, intermittently bradycardic to the 80s, and with diminished arousal. In the PED, a full sepsis work-up was initiated. Though multiple attempts were made by different providers, only a minimal amount of blood could be drawn, presumably due to dehydration. Of note, the neonate received vitamin K subcutaneously prior to lumbar puncture.

Pediatricians across the country have gone to great lengths to protect their patients and to provide high-quality care both inside and outside the office during this unprecedented time. Nevertheless, these 3 cases illustrate the detrimental effects of COVID-19 on the delivery of pediatric health care. The first 2 cases in particular demonstrate the limitations of even close and consistent phone and televisit follow-up. Telehealth has provided a lifeline for patients and families during the pandemic, and, in most cases, has provided an excellent temporary substitution for office visits. There are, however, limitations to care without physical evaluation. Had the children in the first 2 cases been evaluated in person sooner, they may have been referred to a higher level of care more expediently. Likewise, in all 3 cases, parental reservations about exposing their children to COVID-19 through a trip to the hospital, however well-intentioned, likely played a role in the eventual severity of illness with which each child presented to the hospital.

If we are encountering children in the PED with severe illness due to delayed presentation to care, what about the children we aren’t seeing? As COVID-19 cases rise daily in the United States, we must be aware of the possibility of ED avoidance. We propose a multimodal approach to combat this dangerous phenomenon. Inpatient and ED-based pediatricians must maintain clear and open lines of communication with outpatient colleagues so that we can partner in considering which cases warrant prompt ED evaluation, even in the midst of a pandemic. All pediatricians must remind families that our hospitals remain open and ready to treat children safely. We must promote community awareness of the numerous safety precautions we take every day so that patients and families can feel comfortable seeking care at the hospital; the message of ED and hospital safety must be even more robust for caregivers of our particularly vulnerable children. As always, how we communicate with patients and their families matters. Validating and addressing concerns about COVID-19 exposure, while providing reassurance about the safety of our hospitals, could save children’s lives.

Acknowledgment: Thank you to Dr. Cynthia Mollen and Dr. Kathy Shaw for their reviews of the manuscript.

Corresponding author: Regina L. Toto, MD, Department of Pediatrics, The Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104; [email protected].

Financial disclosures: None.

Keywords: coronavirus; pediatric; children; access to care; emergency department.

The Children’s Hospital of Philadelphia, Philadelphia, PA.

The effects of COVID-19 on children’s health are multifaceted. In comparison to adults, children typically experience far milder physical consequences when infected with the virus. A notable exception is the newly described multisystem inflammatory syndrome associated with COVID-19 (MIS-C), which has proven to be a source of significant morbidity among the children it affects.¹ Nevertheless, even those children not infected with COVID-19 have suffered due to the disease. School closures have deprived children of opportunities for social and academic growth and, in some cases, the provision of food, social services, medication administration, and many different therapies. Social distancing rules have limited play among children, which is crucial to their development and mental health. The impact on children who have lost family members, including parents, is monumental. Amidst all of this observable suffering, however, the pandemic poses a less visible threat to the health of children.

It is well documented that concern about exposure to COVID-19 has led many adults to avoid emergency departments (EDs) around the world. We believe parents may be avoiding ED visits for their children for the same reason. In the United States, ED volumes dropped approximately 50% during spring 2020.² While EDs saw increasing, and at times overwhelming, numbers of patients with COVID-19, the number of patients presenting with other life-threatening medical issues, including heart attacks and strokes, declined.^3,4 Data from the National Center for Health Statistics this past spring revealed nationwide increases in deaths due to nonrespiratory causes such as diabetes, heart disease, and stroke.⁵ ED avoidance and unprecedented lack of access to outpatient care, though with the intent to reduce overall risk, are likely significant contributors to these deaths.

Pediatric patients, especially the most vulnerable, are similarly at risk for deleterious health-related consequences from ED avoidance and from limited access to primary and outpatient specialty care. Data from Europe indicate dramatic drops in pediatric ED (PED) volumes, as well as an increase in the proportion of ED visits leading to hospitalization.^6,7 These studies suggest that when patients do ultimately present to the PED, they may be more seriously ill.

At our institution, we have seen many COVID-19-negative patients whose medical care has been negatively influenced by the pandemic. A few months ago, a 1-month-old infant with an underlying health condition presented to the PED in extremis after weeks of progressively worsening feeding issues. The infant had been closely followed by the primary care provider (PCP) and subspecialty team via phone calls, televisits, and some office visits. Both physicians and parents had tried to resolve the feeding issues within the outpatient context, explicitly hoping to avoid potential exposure of this fragile patient to COVID-19 in the hospital. On eventual presentation to the PED, the infant was profoundly dehydrated, with significant electrolyte derangement and an acute abdomen, requiring admission to the intensive care unit. Ultimately, a new diagnosis of Hirschsprung disease was made, and the infant was hospitalized for several weeks for weight gain.

Later this summer, a school-aged child with a history of poorly controlled type 1 diabetes presented to an affiliated community hospital comatose and with Kussmaul respirations. Prior to the pandemic, a school nurse administered the child’s morning insulin. Since school closed, the patient had been responsible for administering this dose of insulin while the parents worked outside the home. Despite close and frequent communication between the patient’s endocrinology team and the family, the patient’s glucose and ketone levels began to rise. The parent administered repeated boluses of insulin at home in an attempt to avoid the perceived exposure risk associated with an ED visit. On presentation to the PED, the patient was profoundly altered, with a pH of 7.0. When transfer to a tertiary care center was recommended, the patient’s parent expressed persistent concerns about COVID-19 exposure in the larger hospital, although ultimately consent to transfer was given.

A third case from this summer provides an example of a different type of patient affected by COVID-19: the neonate whose birth circumstances were altered due to the virus. A 3-day-old, full-term infant presented to the ED with hypothermia after PCP referral. The parents had considered both home birth and hospital delivery earlier in the pregnancy, ultimately opting for home birth due to concerns about COVID-19 exposure in the hospital. The pregnancy and delivery were uncomplicated. The neonate did not receive the first hepatitis B vaccine, erythromycin eye ointment, or vitamin K after delivery. In the first 3 days of life, the patient had voided once and stooled once per day. The patient’s mother, inexperienced with breastfeeding and without access to a lactation consultant, was unsure about latch or emptying of her breasts. At the first pediatrician visit, the infant was noted to be hypothermic to 35°C, intermittently bradycardic to the 80s, and with diminished arousal. In the PED, a full sepsis work-up was initiated. Though multiple attempts were made by different providers, only a minimal amount of blood could be drawn, presumably due to dehydration. Of note, the neonate received vitamin K subcutaneously prior to lumbar puncture.

Pediatricians across the country have gone to great lengths to protect their patients and to provide high-quality care both inside and outside the office during this unprecedented time. Nevertheless, these 3 cases illustrate the detrimental effects of COVID-19 on the delivery of pediatric health care. The first 2 cases in particular demonstrate the limitations of even close and consistent phone and televisit follow-up. Telehealth has provided a lifeline for patients and families during the pandemic, and, in most cases, has provided an excellent temporary substitution for office visits. There are, however, limitations to care without physical evaluation. Had the children in the first 2 cases been evaluated in person sooner, they may have been referred to a higher level of care more expediently. Likewise, in all 3 cases, parental reservations about exposing their children to COVID-19 through a trip to the hospital, however well-intentioned, likely played a role in the eventual severity of illness with which each child presented to the hospital.

If we are encountering children in the PED with severe illness due to delayed presentation to care, what about the children we aren’t seeing? As COVID-19 cases rise daily in the United States, we must be aware of the possibility of ED avoidance. We propose a multimodal approach to combat this dangerous phenomenon. Inpatient and ED-based pediatricians must maintain clear and open lines of communication with outpatient colleagues so that we can partner in considering which cases warrant prompt ED evaluation, even in the midst of a pandemic. All pediatricians must remind families that our hospitals remain open and ready to treat children safely. We must promote community awareness of the numerous safety precautions we take every day so that patients and families can feel comfortable seeking care at the hospital; the message of ED and hospital safety must be even more robust for caregivers of our particularly vulnerable children. As always, how we communicate with patients and their families matters. Validating and addressing concerns about COVID-19 exposure, while providing reassurance about the safety of our hospitals, could save children’s lives.

Acknowledgment: Thank you to Dr. Cynthia Mollen and Dr. Kathy Shaw for their reviews of the manuscript.

Corresponding author: Regina L. Toto, MD, Department of Pediatrics, The Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104; [email protected].

Financial disclosures: None.

Keywords: coronavirus; pediatric; children; access to care; emergency department.

Study Overview

Objective. To assess the association between administration of systemic corticosteroids, compared with usual care or placebo, and 28-day all-cause mortality in critically ill patients with coronavirus disease 2019 (COVID-19).

Design. Prospective meta-analysis with data from 7 randomized clinical trials conducted in 12 countries.

Setting and participants. This prospective meta-analysis included randomized clinical trials conducted between February 26, 2020, and June 9, 2020, that examined the clinical efficacy of administration of corticosteroids in hospitalized COVID-19 patients who were critically ill. Trials were systematically identified from ClinicalTrials.gov, the Chinese Clinical Trial Registry, and the EU Clinical Trials Register, using the search terms COVID-19, corticosteroids, and steroids. Additional trials were identified by experts from the WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group. Senior investigators of these identified trials were asked to participate in weekly calls to develop a protocol for the prospective meta-analysis.¹ Subsequently, trials that had randomly assigned critically ill patients to receive corticosteroids versus usual care or placebo were invited to participate in this meta-analysis. Data were pooled from patients recruited to the participating trials through June 9, 2020, and aggregated in overall and in predefined subgroups.

Main outcome measures. The primary outcome was all-cause mortality up to 30 days after randomization. Because 5 of the included trials reported mortality at 28 days after randomization, the primary outcome was reported as 28-day all-cause mortality. The secondary outcome was serious adverse events (SAEs). The authors also gathered data on the demographic and clinical characteristics of patients, the number of patients lost to follow-up, and outcomes according to intervention group, overall, and in subgroups (ie, patients receiving invasive mechanical ventilation or vasoactive medication; age ≤ 60 years or > 60 years [the median across trials]; sex [male or female]; and the duration patients were symptomatic [≤ 7 days or > 7 days]). For each trial, the risk of bias was assessed independently by 4 investigators using the Cochrane Risk of Bias Assessment Tool for the overall effects of corticosteroids on mortality and SAEs and the effect of assignment and allocated interventions. Inconsistency between trial results was evaluated using the I² statistic. The trials were classified according to the corticosteroids used in the intervention group and the dose administered using a priori-defined cutoffs (15 mg/day of dexamethasone, 400 mg/day of hydrocortisone, and 1 mg/kg/day of methylprednisolone). The primary analysis utilized was an inverse variance-weighted fixed-effect meta-analysis of odds ratios (ORs) for overall mortality. Random-effects meta-analyses with Paule-Mandel estimate of heterogeneity were also performed.

Main results. Seven trials (DEXA-COVID 19, CoDEX, RECOVERY, CAPE COVID, COVID STEROID, REMAP-CAP, and Steroids-SARI) were included in the final meta-analysis. The enrolled patients were from Australia, Brazil, Canada, China, Denmark, France, Ireland, the Netherlands, New Zealand, Spain, the United Kingdom, and the United States. The date of final follow-up was July 6, 2020. The corticosteroids groups included dexamethasone at low (6 mg/day orally or intravenously [IV]) and high (20 mg/day IV) doses; low-dose hydrocortisone (200 mg/day IV or 50 mg every 6 hr IV); and high-dose methylprednisolone (40 mg every 12 hr IV). In total, 1703 patients were randomized, with 678 assigned to the corticosteroids group and 1025 to the usual-care or placebo group. The median age of patients was 60 years (interquartile range, 52-68 years), and 29% were women. The larger number of patients in the usual-care/placebo group was a result of the 1:2 randomization (corticosteroids versus usual care or placebo) in the RECOVERY trial, which contributed 59.1% of patients included in this prospective meta-analysis. The majority of patients were receiving invasive mechanical ventilation at randomization (1559 patients). The administration of adjunctive treatments, such as azithromycin or antiviral agents, varied among the trials. The risk of bias was determined as low for 6 of the 7 mortality results.

A total of 222 of 678 patients in the corticosteroids group died, and 425 of 1025 patients in the usual care or placebo group died. The summary OR was 0.66 (95% confidence interval [CI], 0.53-0.82; P < 0.001) based on a fixed-effect meta-analysis, and 0.70 (95% CI, 0.48-1.01; P = 0.053) based on the random-effects meta-analysis, for 28-day all-cause mortality comparing all corticosteroids with usual care or placebo. There was little inconsistency between trial results (I² = 15.6%; P = 0.31). The fixed-effect summary OR for the association with 28-day all-cause mortality was 0.64 (95% CI, 0.50-0.82; P < 0.001) for dexamethasone compared with usual care or placebo (3 trials, 1282 patients, and 527 deaths); the OR was 0.69 (95% CI, 0.43-1.12; P = 0.13) for hydrocortisone (3 trials, 374 patients, and 94 deaths); and the OR was 0.91 (95% CI, 0.29-2.87; P = 0.87) for methylprednisolone (1 trial, 47 patients, and 26 deaths). Moreover, in trials that administered low-dose corticosteroids, the overall fixed-effect OR for 28-day all-cause mortality was 0.61 (95% CI, 0.48-0.78; P < 0.001). In the subgroup analysis, the overall fixed-effect OR was 0.69 (95% CI, 0.55-0.86) in patients who were receiving invasive mechanical ventilation at randomization, and the OR was 0.41 (95% CI, 0.19-0.88) in patients who were not receiving invasive mechanical ventilation at randomization.

Six trials (all except the RECOVERY trial) reported SAEs, with 64 events occurring among 354 patients assigned to the corticosteroids group and 80 SAEs occurring among 342 patients assigned to the usual-care or placebo group. There was no suggestion that the risk of SAEs was higher in patients who were administered corticosteroids.

Conclusion. The administration of systemic corticosteroids was associated with a lower 28-day all-cause mortality in critically ill patients with COVID-19 compared to those who received usual care or placebo.

Commentary

Corticosteroids are anti-inflammatory and vasoconstrictive medications that have long been used in intensive care units for the treatment of acute respiratory distress syndrome and septic shock. However, the therapeutic role of corticosteroids for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was uncertain at the outset of the COVID-19 pandemic due to concerns that this class of medications may cause an impaired immune response in the setting of a life-threatening SARS-CoV-2 infection. Evidence supporting this notion included prior studies showing that corticosteroid therapy was associated with delayed viral clearance of Middle East respiratory syndrome or a higher viral load of SARS-CoV.^2,3 The uncertainty surrounding the therapeutic use of corticosteroids in treating COVID-19 led to a simultaneous global effort to conduct randomized controlled trials to urgently examine this important clinical question. The open-label Randomized Evaluation of COVID-19 Therapy (RECOVERY) trial, conducted in the UK, was the first large-scale randomized clinical trial that reported the clinical benefit of corticosteroids in treating patients hospitalized with COVID-19. Specifically, it showed that low-dose dexamethasone (6 mg/day) administered orally or IV for up to 10 days resulted in a 2.8% absolute reduction in 28-day mortality, with the greatest benefit, an absolute risk reduction of 12.1%, conferred to patients who were receiving invasive mechanical ventilation at the time of randomization.⁴ In response to these findings, the National Institutes of Health COVID-19 Treatment Guidelines Panel recommended the use of dexamethasone in patients with COVID-19 who are on mechanical ventilation or who require supplemental oxygen, and recommended against the use of dexamethasone for those not requiring supplemental oxygen.⁵

The meta-analysis discussed in this commentary, conducted by the WHO REACT Working Group, has replicated initial findings from the RECOVERY trial. This prospective meta-analysis pooled data from 7 randomized controlled trials of corticosteroid therapy in 1703 critically ill patients hospitalized with COVID-19. Similar to findings from the RECOVERY trial, corticosteroids were associated with lower all-cause mortality at 28 days after randomization, and this benefit was observed both in critically ill patients who were receiving mechanical ventilation or supplemental oxygen without mechanical ventilation. Interestingly, while the OR estimates were imprecise, the reduction in mortality rates was similar between patients who were administered dexamethasone and hydrocortisone, which may suggest a general drug class effect. In addition, the mortality benefit of corticosteroids appeared similar for those aged ≤ 60 years and those aged > 60 years, between female and male patients, and those who were symptomatic for ≤ 7 days or > 7 days before randomization. Moreover, the administration of corticosteroids did not appear to increase the risk of SAEs. While more data are needed, results from the RECOVERY trial and this prospective meta-analysis indicate that corticosteroids should be an essential pharmacologic treatment for COVID-19, and suggest its potential role as a standard of care for critically ill patients with COVID-19.

This study has several limitations. First, not all trials systematically identified participated in the meta-analysis. Second, long-term outcomes after hospital discharge were not captured, and thus the effect of corticosteroids on long-term mortality and other adverse outcomes, such as hospital readmission, remain unknown. Third, because children were excluded from study participation, the effect of corticosteroids on pediatric COVID-19 patients is unknown. Fourth, the RECOVERY trial contributed more than 50% of patients in the current analysis, although there was little inconsistency in the effects of corticosteroids on mortality between individual trials. Last, the meta-analysis was unable to establish the optimal dose or duration of corticosteroid intervention in critically ill COVID-19 patients, or determine its efficacy in patients with mild-to-moderate COVID-19, all of which are key clinical questions that will need to be addressed with further clinical investigations.

The development of effective treatments for COVID-19 is critical to mitigating the devastating consequences of SARS-CoV-2 infection. Several recent COVID-19 clinical trials have shown promise in this endeavor. For instance, the Adaptive COVID-19 Treatment Trial (ACCT-1) found that intravenous remdesivir, as compared to placebo, significantly shortened time to recovery in adult patients hospitalized with COVID-19 who had evidence of lower respiratory tract infection.⁶ Moreover, there is some evidence to suggest that convalescent plasma and aerosol inhalation of IFN-κ may have beneficial effects in treating COVID-19.^7,8 Thus, clinical trials designed to investigate combination therapy approaches including corticosteroids, remdesivir, convalescent plasma, and others are urgently needed to help identify interventions that most effectively treat COVID-19.

Applications for Clinical Practice

The use of corticosteroids in critically ill patients with COVID-19 reduces overall mortality. This treatment is inexpensive and available in most care settings, including low-resource regions, and provides hope for better outcomes in the COVID-19 pandemic.

Katerina Oikonomou, MD, PhD
General Hospital of Larissa, Larissa, Greece
Fred Ko, MD, MS

Study Overview

Objective. To assess the association between administration of systemic corticosteroids, compared with usual care or placebo, and 28-day all-cause mortality in critically ill patients with coronavirus disease 2019 (COVID-19).

Design. Prospective meta-analysis with data from 7 randomized clinical trials conducted in 12 countries.

Setting and participants. This prospective meta-analysis included randomized clinical trials conducted between February 26, 2020, and June 9, 2020, that examined the clinical efficacy of administration of corticosteroids in hospitalized COVID-19 patients who were critically ill. Trials were systematically identified from ClinicalTrials.gov, the Chinese Clinical Trial Registry, and the EU Clinical Trials Register, using the search terms COVID-19, corticosteroids, and steroids. Additional trials were identified by experts from the WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group. Senior investigators of these identified trials were asked to participate in weekly calls to develop a protocol for the prospective meta-analysis.¹ Subsequently, trials that had randomly assigned critically ill patients to receive corticosteroids versus usual care or placebo were invited to participate in this meta-analysis. Data were pooled from patients recruited to the participating trials through June 9, 2020, and aggregated in overall and in predefined subgroups.

Main outcome measures. The primary outcome was all-cause mortality up to 30 days after randomization. Because 5 of the included trials reported mortality at 28 days after randomization, the primary outcome was reported as 28-day all-cause mortality. The secondary outcome was serious adverse events (SAEs). The authors also gathered data on the demographic and clinical characteristics of patients, the number of patients lost to follow-up, and outcomes according to intervention group, overall, and in subgroups (ie, patients receiving invasive mechanical ventilation or vasoactive medication; age ≤ 60 years or > 60 years [the median across trials]; sex [male or female]; and the duration patients were symptomatic [≤ 7 days or > 7 days]). For each trial, the risk of bias was assessed independently by 4 investigators using the Cochrane Risk of Bias Assessment Tool for the overall effects of corticosteroids on mortality and SAEs and the effect of assignment and allocated interventions. Inconsistency between trial results was evaluated using the I² statistic. The trials were classified according to the corticosteroids used in the intervention group and the dose administered using a priori-defined cutoffs (15 mg/day of dexamethasone, 400 mg/day of hydrocortisone, and 1 mg/kg/day of methylprednisolone). The primary analysis utilized was an inverse variance-weighted fixed-effect meta-analysis of odds ratios (ORs) for overall mortality. Random-effects meta-analyses with Paule-Mandel estimate of heterogeneity were also performed.

Main results. Seven trials (DEXA-COVID 19, CoDEX, RECOVERY, CAPE COVID, COVID STEROID, REMAP-CAP, and Steroids-SARI) were included in the final meta-analysis. The enrolled patients were from Australia, Brazil, Canada, China, Denmark, France, Ireland, the Netherlands, New Zealand, Spain, the United Kingdom, and the United States. The date of final follow-up was July 6, 2020. The corticosteroids groups included dexamethasone at low (6 mg/day orally or intravenously [IV]) and high (20 mg/day IV) doses; low-dose hydrocortisone (200 mg/day IV or 50 mg every 6 hr IV); and high-dose methylprednisolone (40 mg every 12 hr IV). In total, 1703 patients were randomized, with 678 assigned to the corticosteroids group and 1025 to the usual-care or placebo group. The median age of patients was 60 years (interquartile range, 52-68 years), and 29% were women. The larger number of patients in the usual-care/placebo group was a result of the 1:2 randomization (corticosteroids versus usual care or placebo) in the RECOVERY trial, which contributed 59.1% of patients included in this prospective meta-analysis. The majority of patients were receiving invasive mechanical ventilation at randomization (1559 patients). The administration of adjunctive treatments, such as azithromycin or antiviral agents, varied among the trials. The risk of bias was determined as low for 6 of the 7 mortality results.

A total of 222 of 678 patients in the corticosteroids group died, and 425 of 1025 patients in the usual care or placebo group died. The summary OR was 0.66 (95% confidence interval [CI], 0.53-0.82; P < 0.001) based on a fixed-effect meta-analysis, and 0.70 (95% CI, 0.48-1.01; P = 0.053) based on the random-effects meta-analysis, for 28-day all-cause mortality comparing all corticosteroids with usual care or placebo. There was little inconsistency between trial results (I² = 15.6%; P = 0.31). The fixed-effect summary OR for the association with 28-day all-cause mortality was 0.64 (95% CI, 0.50-0.82; P < 0.001) for dexamethasone compared with usual care or placebo (3 trials, 1282 patients, and 527 deaths); the OR was 0.69 (95% CI, 0.43-1.12; P = 0.13) for hydrocortisone (3 trials, 374 patients, and 94 deaths); and the OR was 0.91 (95% CI, 0.29-2.87; P = 0.87) for methylprednisolone (1 trial, 47 patients, and 26 deaths). Moreover, in trials that administered low-dose corticosteroids, the overall fixed-effect OR for 28-day all-cause mortality was 0.61 (95% CI, 0.48-0.78; P < 0.001). In the subgroup analysis, the overall fixed-effect OR was 0.69 (95% CI, 0.55-0.86) in patients who were receiving invasive mechanical ventilation at randomization, and the OR was 0.41 (95% CI, 0.19-0.88) in patients who were not receiving invasive mechanical ventilation at randomization.

Six trials (all except the RECOVERY trial) reported SAEs, with 64 events occurring among 354 patients assigned to the corticosteroids group and 80 SAEs occurring among 342 patients assigned to the usual-care or placebo group. There was no suggestion that the risk of SAEs was higher in patients who were administered corticosteroids.

Conclusion. The administration of systemic corticosteroids was associated with a lower 28-day all-cause mortality in critically ill patients with COVID-19 compared to those who received usual care or placebo.

Commentary

Corticosteroids are anti-inflammatory and vasoconstrictive medications that have long been used in intensive care units for the treatment of acute respiratory distress syndrome and septic shock. However, the therapeutic role of corticosteroids for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was uncertain at the outset of the COVID-19 pandemic due to concerns that this class of medications may cause an impaired immune response in the setting of a life-threatening SARS-CoV-2 infection. Evidence supporting this notion included prior studies showing that corticosteroid therapy was associated with delayed viral clearance of Middle East respiratory syndrome or a higher viral load of SARS-CoV.^2,3 The uncertainty surrounding the therapeutic use of corticosteroids in treating COVID-19 led to a simultaneous global effort to conduct randomized controlled trials to urgently examine this important clinical question. The open-label Randomized Evaluation of COVID-19 Therapy (RECOVERY) trial, conducted in the UK, was the first large-scale randomized clinical trial that reported the clinical benefit of corticosteroids in treating patients hospitalized with COVID-19. Specifically, it showed that low-dose dexamethasone (6 mg/day) administered orally or IV for up to 10 days resulted in a 2.8% absolute reduction in 28-day mortality, with the greatest benefit, an absolute risk reduction of 12.1%, conferred to patients who were receiving invasive mechanical ventilation at the time of randomization.⁴ In response to these findings, the National Institutes of Health COVID-19 Treatment Guidelines Panel recommended the use of dexamethasone in patients with COVID-19 who are on mechanical ventilation or who require supplemental oxygen, and recommended against the use of dexamethasone for those not requiring supplemental oxygen.⁵

The meta-analysis discussed in this commentary, conducted by the WHO REACT Working Group, has replicated initial findings from the RECOVERY trial. This prospective meta-analysis pooled data from 7 randomized controlled trials of corticosteroid therapy in 1703 critically ill patients hospitalized with COVID-19. Similar to findings from the RECOVERY trial, corticosteroids were associated with lower all-cause mortality at 28 days after randomization, and this benefit was observed both in critically ill patients who were receiving mechanical ventilation or supplemental oxygen without mechanical ventilation. Interestingly, while the OR estimates were imprecise, the reduction in mortality rates was similar between patients who were administered dexamethasone and hydrocortisone, which may suggest a general drug class effect. In addition, the mortality benefit of corticosteroids appeared similar for those aged ≤ 60 years and those aged > 60 years, between female and male patients, and those who were symptomatic for ≤ 7 days or > 7 days before randomization. Moreover, the administration of corticosteroids did not appear to increase the risk of SAEs. While more data are needed, results from the RECOVERY trial and this prospective meta-analysis indicate that corticosteroids should be an essential pharmacologic treatment for COVID-19, and suggest its potential role as a standard of care for critically ill patients with COVID-19.

This study has several limitations. First, not all trials systematically identified participated in the meta-analysis. Second, long-term outcomes after hospital discharge were not captured, and thus the effect of corticosteroids on long-term mortality and other adverse outcomes, such as hospital readmission, remain unknown. Third, because children were excluded from study participation, the effect of corticosteroids on pediatric COVID-19 patients is unknown. Fourth, the RECOVERY trial contributed more than 50% of patients in the current analysis, although there was little inconsistency in the effects of corticosteroids on mortality between individual trials. Last, the meta-analysis was unable to establish the optimal dose or duration of corticosteroid intervention in critically ill COVID-19 patients, or determine its efficacy in patients with mild-to-moderate COVID-19, all of which are key clinical questions that will need to be addressed with further clinical investigations.

The development of effective treatments for COVID-19 is critical to mitigating the devastating consequences of SARS-CoV-2 infection. Several recent COVID-19 clinical trials have shown promise in this endeavor. For instance, the Adaptive COVID-19 Treatment Trial (ACCT-1) found that intravenous remdesivir, as compared to placebo, significantly shortened time to recovery in adult patients hospitalized with COVID-19 who had evidence of lower respiratory tract infection.⁶ Moreover, there is some evidence to suggest that convalescent plasma and aerosol inhalation of IFN-κ may have beneficial effects in treating COVID-19.^7,8 Thus, clinical trials designed to investigate combination therapy approaches including corticosteroids, remdesivir, convalescent plasma, and others are urgently needed to help identify interventions that most effectively treat COVID-19.

Applications for Clinical Practice

The use of corticosteroids in critically ill patients with COVID-19 reduces overall mortality. This treatment is inexpensive and available in most care settings, including low-resource regions, and provides hope for better outcomes in the COVID-19 pandemic.

Katerina Oikonomou, MD, PhD
General Hospital of Larissa, Larissa, Greece
Fred Ko, MD, MS

Study Overview

Objective. To assess the association between administration of systemic corticosteroids, compared with usual care or placebo, and 28-day all-cause mortality in critically ill patients with coronavirus disease 2019 (COVID-19).

Design. Prospective meta-analysis with data from 7 randomized clinical trials conducted in 12 countries.

Setting and participants. This prospective meta-analysis included randomized clinical trials conducted between February 26, 2020, and June 9, 2020, that examined the clinical efficacy of administration of corticosteroids in hospitalized COVID-19 patients who were critically ill. Trials were systematically identified from ClinicalTrials.gov, the Chinese Clinical Trial Registry, and the EU Clinical Trials Register, using the search terms COVID-19, corticosteroids, and steroids. Additional trials were identified by experts from the WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group. Senior investigators of these identified trials were asked to participate in weekly calls to develop a protocol for the prospective meta-analysis.¹ Subsequently, trials that had randomly assigned critically ill patients to receive corticosteroids versus usual care or placebo were invited to participate in this meta-analysis. Data were pooled from patients recruited to the participating trials through June 9, 2020, and aggregated in overall and in predefined subgroups.

Main outcome measures. The primary outcome was all-cause mortality up to 30 days after randomization. Because 5 of the included trials reported mortality at 28 days after randomization, the primary outcome was reported as 28-day all-cause mortality. The secondary outcome was serious adverse events (SAEs). The authors also gathered data on the demographic and clinical characteristics of patients, the number of patients lost to follow-up, and outcomes according to intervention group, overall, and in subgroups (ie, patients receiving invasive mechanical ventilation or vasoactive medication; age ≤ 60 years or > 60 years [the median across trials]; sex [male or female]; and the duration patients were symptomatic [≤ 7 days or > 7 days]). For each trial, the risk of bias was assessed independently by 4 investigators using the Cochrane Risk of Bias Assessment Tool for the overall effects of corticosteroids on mortality and SAEs and the effect of assignment and allocated interventions. Inconsistency between trial results was evaluated using the I² statistic. The trials were classified according to the corticosteroids used in the intervention group and the dose administered using a priori-defined cutoffs (15 mg/day of dexamethasone, 400 mg/day of hydrocortisone, and 1 mg/kg/day of methylprednisolone). The primary analysis utilized was an inverse variance-weighted fixed-effect meta-analysis of odds ratios (ORs) for overall mortality. Random-effects meta-analyses with Paule-Mandel estimate of heterogeneity were also performed.

Main results. Seven trials (DEXA-COVID 19, CoDEX, RECOVERY, CAPE COVID, COVID STEROID, REMAP-CAP, and Steroids-SARI) were included in the final meta-analysis. The enrolled patients were from Australia, Brazil, Canada, China, Denmark, France, Ireland, the Netherlands, New Zealand, Spain, the United Kingdom, and the United States. The date of final follow-up was July 6, 2020. The corticosteroids groups included dexamethasone at low (6 mg/day orally or intravenously [IV]) and high (20 mg/day IV) doses; low-dose hydrocortisone (200 mg/day IV or 50 mg every 6 hr IV); and high-dose methylprednisolone (40 mg every 12 hr IV). In total, 1703 patients were randomized, with 678 assigned to the corticosteroids group and 1025 to the usual-care or placebo group. The median age of patients was 60 years (interquartile range, 52-68 years), and 29% were women. The larger number of patients in the usual-care/placebo group was a result of the 1:2 randomization (corticosteroids versus usual care or placebo) in the RECOVERY trial, which contributed 59.1% of patients included in this prospective meta-analysis. The majority of patients were receiving invasive mechanical ventilation at randomization (1559 patients). The administration of adjunctive treatments, such as azithromycin or antiviral agents, varied among the trials. The risk of bias was determined as low for 6 of the 7 mortality results.

A total of 222 of 678 patients in the corticosteroids group died, and 425 of 1025 patients in the usual care or placebo group died. The summary OR was 0.66 (95% confidence interval [CI], 0.53-0.82; P < 0.001) based on a fixed-effect meta-analysis, and 0.70 (95% CI, 0.48-1.01; P = 0.053) based on the random-effects meta-analysis, for 28-day all-cause mortality comparing all corticosteroids with usual care or placebo. There was little inconsistency between trial results (I² = 15.6%; P = 0.31). The fixed-effect summary OR for the association with 28-day all-cause mortality was 0.64 (95% CI, 0.50-0.82; P < 0.001) for dexamethasone compared with usual care or placebo (3 trials, 1282 patients, and 527 deaths); the OR was 0.69 (95% CI, 0.43-1.12; P = 0.13) for hydrocortisone (3 trials, 374 patients, and 94 deaths); and the OR was 0.91 (95% CI, 0.29-2.87; P = 0.87) for methylprednisolone (1 trial, 47 patients, and 26 deaths). Moreover, in trials that administered low-dose corticosteroids, the overall fixed-effect OR for 28-day all-cause mortality was 0.61 (95% CI, 0.48-0.78; P < 0.001). In the subgroup analysis, the overall fixed-effect OR was 0.69 (95% CI, 0.55-0.86) in patients who were receiving invasive mechanical ventilation at randomization, and the OR was 0.41 (95% CI, 0.19-0.88) in patients who were not receiving invasive mechanical ventilation at randomization.

Six trials (all except the RECOVERY trial) reported SAEs, with 64 events occurring among 354 patients assigned to the corticosteroids group and 80 SAEs occurring among 342 patients assigned to the usual-care or placebo group. There was no suggestion that the risk of SAEs was higher in patients who were administered corticosteroids.

Conclusion. The administration of systemic corticosteroids was associated with a lower 28-day all-cause mortality in critically ill patients with COVID-19 compared to those who received usual care or placebo.

Commentary

Corticosteroids are anti-inflammatory and vasoconstrictive medications that have long been used in intensive care units for the treatment of acute respiratory distress syndrome and septic shock. However, the therapeutic role of corticosteroids for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was uncertain at the outset of the COVID-19 pandemic due to concerns that this class of medications may cause an impaired immune response in the setting of a life-threatening SARS-CoV-2 infection. Evidence supporting this notion included prior studies showing that corticosteroid therapy was associated with delayed viral clearance of Middle East respiratory syndrome or a higher viral load of SARS-CoV.^2,3 The uncertainty surrounding the therapeutic use of corticosteroids in treating COVID-19 led to a simultaneous global effort to conduct randomized controlled trials to urgently examine this important clinical question. The open-label Randomized Evaluation of COVID-19 Therapy (RECOVERY) trial, conducted in the UK, was the first large-scale randomized clinical trial that reported the clinical benefit of corticosteroids in treating patients hospitalized with COVID-19. Specifically, it showed that low-dose dexamethasone (6 mg/day) administered orally or IV for up to 10 days resulted in a 2.8% absolute reduction in 28-day mortality, with the greatest benefit, an absolute risk reduction of 12.1%, conferred to patients who were receiving invasive mechanical ventilation at the time of randomization.⁴ In response to these findings, the National Institutes of Health COVID-19 Treatment Guidelines Panel recommended the use of dexamethasone in patients with COVID-19 who are on mechanical ventilation or who require supplemental oxygen, and recommended against the use of dexamethasone for those not requiring supplemental oxygen.⁵

The meta-analysis discussed in this commentary, conducted by the WHO REACT Working Group, has replicated initial findings from the RECOVERY trial. This prospective meta-analysis pooled data from 7 randomized controlled trials of corticosteroid therapy in 1703 critically ill patients hospitalized with COVID-19. Similar to findings from the RECOVERY trial, corticosteroids were associated with lower all-cause mortality at 28 days after randomization, and this benefit was observed both in critically ill patients who were receiving mechanical ventilation or supplemental oxygen without mechanical ventilation. Interestingly, while the OR estimates were imprecise, the reduction in mortality rates was similar between patients who were administered dexamethasone and hydrocortisone, which may suggest a general drug class effect. In addition, the mortality benefit of corticosteroids appeared similar for those aged ≤ 60 years and those aged > 60 years, between female and male patients, and those who were symptomatic for ≤ 7 days or > 7 days before randomization. Moreover, the administration of corticosteroids did not appear to increase the risk of SAEs. While more data are needed, results from the RECOVERY trial and this prospective meta-analysis indicate that corticosteroids should be an essential pharmacologic treatment for COVID-19, and suggest its potential role as a standard of care for critically ill patients with COVID-19.

This study has several limitations. First, not all trials systematically identified participated in the meta-analysis. Second, long-term outcomes after hospital discharge were not captured, and thus the effect of corticosteroids on long-term mortality and other adverse outcomes, such as hospital readmission, remain unknown. Third, because children were excluded from study participation, the effect of corticosteroids on pediatric COVID-19 patients is unknown. Fourth, the RECOVERY trial contributed more than 50% of patients in the current analysis, although there was little inconsistency in the effects of corticosteroids on mortality between individual trials. Last, the meta-analysis was unable to establish the optimal dose or duration of corticosteroid intervention in critically ill COVID-19 patients, or determine its efficacy in patients with mild-to-moderate COVID-19, all of which are key clinical questions that will need to be addressed with further clinical investigations.

The development of effective treatments for COVID-19 is critical to mitigating the devastating consequences of SARS-CoV-2 infection. Several recent COVID-19 clinical trials have shown promise in this endeavor. For instance, the Adaptive COVID-19 Treatment Trial (ACCT-1) found that intravenous remdesivir, as compared to placebo, significantly shortened time to recovery in adult patients hospitalized with COVID-19 who had evidence of lower respiratory tract infection.⁶ Moreover, there is some evidence to suggest that convalescent plasma and aerosol inhalation of IFN-κ may have beneficial effects in treating COVID-19.^7,8 Thus, clinical trials designed to investigate combination therapy approaches including corticosteroids, remdesivir, convalescent plasma, and others are urgently needed to help identify interventions that most effectively treat COVID-19.

Applications for Clinical Practice

The use of corticosteroids in critically ill patients with COVID-19 reduces overall mortality. This treatment is inexpensive and available in most care settings, including low-resource regions, and provides hope for better outcomes in the COVID-19 pandemic.

Katerina Oikonomou, MD, PhD
General Hospital of Larissa, Larissa, Greece
Fred Ko, MD, MS

Study Overview

Objective. To investigate the potential of Hospital Fit (a smartphone application with an accelerometer) to enhance physical activity levels and functional recovery following orthopedic surgery.

Design. Nonrandomized, quasi-experimental pilot study.

Settings and participants. Patients scheduled for an elective total knee arthroplasty (TKA) or total hip arthroplasty (THA) at the orthopedic ward of Maastricht University Medical Center in Maastricht, the Netherlands, were invited to participate. Patients scheduled for surgery between January 2017 and December 2018 were recruited for the control group at a rate of 1 patient per week (due to a limited number of accelerometers available). After development of Hospital Fit was completed in December 2018 (and sufficient accelerators had become available), patients scheduled for surgery between February 2019 and May 2019 were recruited for the intervention group. The ratio of patients included in the control and intervention group was set at 2:1, respectively.

At preoperative physiotherapy screenings (scheduled 6 weeks before surgery), patients received verbal and written information about the study. Patients were eligible if they met the following inclusion criteria: receiving physiotherapy after elective TKA or THA; able to walk independently 2 weeks prior to surgery, as scored on the Functional Ambulation Categories (FAC > 3); were expected to be discharged to their own home; were aged 18 years and older; and had a sufficient understanding of the Dutch language. Exclusion criteria were: the presence of contraindications to walking or wearing an accelerometer on the upper leg; admission to the intensive care unit; impaired cognition (delirium/dementia), as reported by the attending doctor; a life expectancy of less than 3 months; and previous participation in this study. Patients were contacted on the day of their surgery, and written informed consent was obtained prior to the initiation of any study activities.

Intervention. Once enrolled, all patients followed a standardized clinical care pathway for TKA or THA (see original article for additional details). Postoperative physiotherapy was administered to all participating patients, starting within 4 hours after surgery. The physiotherapy treatment was aimed at increasing physical activity levels and enhancing functional recovery. Control group patients only received physiotherapy (twice daily, 30 minutes per session) and had their physical activity levels monitored with an accelerometer, without receiving feedback, until functional recovery was achieved, as measured with the modified Iowa Level of Assistance Scale (mILAS). Intervention group patients used Hospital Fit in addition to physiotherapy. Hospital Fit consists of a smartphone-based app, connected to a MOX activity monitor via Bluetooth (device contains a tri-axial accelerometer sensor in a small waterproof housing attached to the upper leg). Hospital Fit enables objective activity monitoring, provides patients and their physiotherapists insights and real-time feedback on the number of minutes spent standing and walking per day, and offers a tailored exercise program supported by videos aimed at stimulating self-management.

Measures. The primary outcome measure was the time spent physically active (total number of minutes standing and walking) per day until discharge. Physical activity was monitored 24 hours a day; days with ≥ 20 hours of wear time were considered valid measurement days and were included in the analysis. After the last treatment session, the accelerometer was removed, and the raw tri-axial accelerometer data were uploaded and processed to classify minutes as “active” (standing and walking) or “sedentary” (lying and sitting). The secondary outcome measures were the achievement of functional recovery on postoperative day 1 (POD1). Functional recovery was assessed by the physiotherapist during each treatment session using the mILAS and was reported in the electronic health record. In the intervention group, it was also reported in the app. The achievement of functional recovery on POD1 was defined as having reached a total mILAS-score of 0 on or before POD1, using a dichotomized outcome (0 = mILAS = 0 > POD1; 1 = mILAS = 0 ≤ POD1).

The independent variables measured were: Hospital Fit use (control versus the intervention group), age, sex, body mass index (BMI), type of surgery (TKA or THA), and comorbidities assessed by the American Society of Anesthesiologists (ASA) classification (ASA class ≤ 2 versus ASA class = 3; a higher score indicates being less fit for surgery). The medical and demographic data measured were the type of walking aid used and length of stay, with the day of surgery being defined as day 1.

Analysis. Data analysis was performed according to the intention-to-treat principle. Missing values were not substituted; drop-outs were not replaced. Descriptive statistics were presented as means (SD) or as 95% confidence intervals (CI) for continuous variables. The median and interquartile ranges (IQR) were used to present non-normally distributed data. The frequencies and percentages were used to present categorical variables. A multiple linear regression analysis was performed to determine the association between the time spent physically active per day and Hospital Fit use, corrected for potential confounding factors (age, sex, BMI, ASA class, and type of surgery). A multiple logistic regression analysis was performed additionally to determine the association between the achievement of functional recovery on POD1 and Hospital Fit use, corrected for potential confounding factors. For all statistical analyses, the level of significance was set at P < 0.05. All statistical analyses were performed using SPSS (version 23.0.0.2; IBM Corporation, Armonk, NY).

Main results. Ninety-seven patients were recruited; after excluding 9 patients because of missing data, 88 were included for analysis, with 61 (69%) in the control group and 27 (31%) in the intervention group. A median (IQR) number of 1.00 (0) valid measurement days (≥ 20 hr wear time) was collected. Physical activity data for 84 patients (95%) was available on POD1 (n = 61 control group, n = 23 intervention group). On postoperative day 2 (POD2), the majority of patients were discharged (n = 61, 69%), and data for only 23 patients (26%) were available (n = 17 control, n = 6 intervention). From postoperative day 3 to day 7, data of valid measurement days were available for just 1 patient (intervention group). Due to the large reduction in valid measurement days from POD2 onward, data from these days were not included in the analysis.

Results of the multiple linear regression analysis showed that, corrected for age, patients who used Hospital Fit stood and walked an average of 28.43 minutes (95% CI, 5.55-51.32) more on POD1 than patients who did not use Hospital Fit. Also, the model showed that an increase in age led to a decrease in the number of minutes standing and walking on POD1. The results of the multiple logistic regression analysis also showed that, corrected for ASA class, the odds of achieving functional recovery on POD1 were 3.08 times higher (95% CI, 1.14-8.31) for patients who used Hospital Fit compared to patients who did not use Hospital Fit. Including ASA class in the model shows that a lower ASA class increased the odds ratio for a functional recovery on POD1.

Conclusion. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ physical activity levels and functional recovery during hospitalization following joint replacement surgery.

Commentary

Although the beneficial effects of physical activity during hospitalization after surgery are well documented, patients continue to spend between 92% and 96% of their time lying or sitting.^1-3 Therefore, strategies aimed at increasing the amount of time spent standing and walking are needed. Postoperative physiotherapy aims to enhance physical activity levels and functional recovery of activities of daily living, which are essential to function independently at home.^4-7 Physiotherapists may be able to advise patients more effectively on their physical activity behavior if continuous physical activity monitoring with real-time feedback is implemented in standard care. Although mobile health (mHealth) tools are being used to monitor physical activity in support of outpatient physiotherapy within the orthopedic rehabilitation pathway,^8-10 there is currently no mHealth tool available that offers hospitalized patients and their physiotherapists essential strategies to enhance their physical activity levels and support their recovery process. In addition, because hospitalized patients frequently use walking aids and often have impaired gait, the algorithm of most available activity monitors is not validated for use in this population.

This study, therefore, is an important contribution to the literature, as it describes a preliminary evaluation of a novel mHealth tool—Hospital Fit—consisting of a smartphone application connected to an accelerometer whose algorithm has been validated to differentiate between lying/sitting and standing/walking among hospitalized patients. Briefly, results from this study showed an increase in the time spent standing and walking, as well as higher odds of functional recovery on POD1 from the introduction of Hospital Fit. While guidelines on the recommended amount of physical activity during hospitalization do not yet exist, an average improvement of 28 minutes (39%) standing and walking on POD1 can be considered a clinically relevant contribution to prevent the negative effects of inactivity.

This study has limitations, particularly related to the study design, which is acknowledged by the authors. The current study was a nonrandomized, quasi-experimental pilot study implemented at a single medical center, and therefore, the results have limited generalizability and more importantly, may not only be attributable to the introduction of Hospital Fit. In addition, as there was lag in patient recruitment where patients were initially selected for the control group over the course of 1 year, followed by selection of patients for the intervention group over 4 months (once Hospital Fit was developed), it is possible that awareness on the importance of physical activity during hospitalization increased among patients and health care professionals, which may have resulted in a bias in favor of the intervention group (and thus a potentially slight overestimation of results). Also, as individual functionalities of Hospital Fit were not investigated, relationships between each functionality and physical activity could not be established. As the authors indicated, future research is needed to determine the effectiveness of Hospital Fit (ie, a larger, cluster randomized controlled trial in a population of hospitalized patients with a longer length of stay). This study design would also enable investigation of the effect of individual functionalities of Hospital Fit on physical activity.

Applications for Clinical Practice

mHealth tools have the potential to increase patient awareness, support personalized care, and stimulate self-management. This study highlights the potential for a novel mHealth tool—Hospital Fit—to improve the amount of physical activity and shorten the time to functional recovery in hospitalized patients following orthopedic surgery. Further, mHealth tools like Hospital Fit may have a greater impact when the hospital stay of a patient permits the use of the tool for a longer period of time. More broadly, continuous objective monitoring through mHealth tools may provide patients and their physiotherapists enhanced and more detailed data to support and create more personalized recovery goals and related strategies.

Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To investigate the potential of Hospital Fit (a smartphone application with an accelerometer) to enhance physical activity levels and functional recovery following orthopedic surgery.

Design. Nonrandomized, quasi-experimental pilot study.

Settings and participants. Patients scheduled for an elective total knee arthroplasty (TKA) or total hip arthroplasty (THA) at the orthopedic ward of Maastricht University Medical Center in Maastricht, the Netherlands, were invited to participate. Patients scheduled for surgery between January 2017 and December 2018 were recruited for the control group at a rate of 1 patient per week (due to a limited number of accelerometers available). After development of Hospital Fit was completed in December 2018 (and sufficient accelerators had become available), patients scheduled for surgery between February 2019 and May 2019 were recruited for the intervention group. The ratio of patients included in the control and intervention group was set at 2:1, respectively.

At preoperative physiotherapy screenings (scheduled 6 weeks before surgery), patients received verbal and written information about the study. Patients were eligible if they met the following inclusion criteria: receiving physiotherapy after elective TKA or THA; able to walk independently 2 weeks prior to surgery, as scored on the Functional Ambulation Categories (FAC > 3); were expected to be discharged to their own home; were aged 18 years and older; and had a sufficient understanding of the Dutch language. Exclusion criteria were: the presence of contraindications to walking or wearing an accelerometer on the upper leg; admission to the intensive care unit; impaired cognition (delirium/dementia), as reported by the attending doctor; a life expectancy of less than 3 months; and previous participation in this study. Patients were contacted on the day of their surgery, and written informed consent was obtained prior to the initiation of any study activities.

Intervention. Once enrolled, all patients followed a standardized clinical care pathway for TKA or THA (see original article for additional details). Postoperative physiotherapy was administered to all participating patients, starting within 4 hours after surgery. The physiotherapy treatment was aimed at increasing physical activity levels and enhancing functional recovery. Control group patients only received physiotherapy (twice daily, 30 minutes per session) and had their physical activity levels monitored with an accelerometer, without receiving feedback, until functional recovery was achieved, as measured with the modified Iowa Level of Assistance Scale (mILAS). Intervention group patients used Hospital Fit in addition to physiotherapy. Hospital Fit consists of a smartphone-based app, connected to a MOX activity monitor via Bluetooth (device contains a tri-axial accelerometer sensor in a small waterproof housing attached to the upper leg). Hospital Fit enables objective activity monitoring, provides patients and their physiotherapists insights and real-time feedback on the number of minutes spent standing and walking per day, and offers a tailored exercise program supported by videos aimed at stimulating self-management.

Measures. The primary outcome measure was the time spent physically active (total number of minutes standing and walking) per day until discharge. Physical activity was monitored 24 hours a day; days with ≥ 20 hours of wear time were considered valid measurement days and were included in the analysis. After the last treatment session, the accelerometer was removed, and the raw tri-axial accelerometer data were uploaded and processed to classify minutes as “active” (standing and walking) or “sedentary” (lying and sitting). The secondary outcome measures were the achievement of functional recovery on postoperative day 1 (POD1). Functional recovery was assessed by the physiotherapist during each treatment session using the mILAS and was reported in the electronic health record. In the intervention group, it was also reported in the app. The achievement of functional recovery on POD1 was defined as having reached a total mILAS-score of 0 on or before POD1, using a dichotomized outcome (0 = mILAS = 0 > POD1; 1 = mILAS = 0 ≤ POD1).

The independent variables measured were: Hospital Fit use (control versus the intervention group), age, sex, body mass index (BMI), type of surgery (TKA or THA), and comorbidities assessed by the American Society of Anesthesiologists (ASA) classification (ASA class ≤ 2 versus ASA class = 3; a higher score indicates being less fit for surgery). The medical and demographic data measured were the type of walking aid used and length of stay, with the day of surgery being defined as day 1.

Analysis. Data analysis was performed according to the intention-to-treat principle. Missing values were not substituted; drop-outs were not replaced. Descriptive statistics were presented as means (SD) or as 95% confidence intervals (CI) for continuous variables. The median and interquartile ranges (IQR) were used to present non-normally distributed data. The frequencies and percentages were used to present categorical variables. A multiple linear regression analysis was performed to determine the association between the time spent physically active per day and Hospital Fit use, corrected for potential confounding factors (age, sex, BMI, ASA class, and type of surgery). A multiple logistic regression analysis was performed additionally to determine the association between the achievement of functional recovery on POD1 and Hospital Fit use, corrected for potential confounding factors. For all statistical analyses, the level of significance was set at P < 0.05. All statistical analyses were performed using SPSS (version 23.0.0.2; IBM Corporation, Armonk, NY).

Main results. Ninety-seven patients were recruited; after excluding 9 patients because of missing data, 88 were included for analysis, with 61 (69%) in the control group and 27 (31%) in the intervention group. A median (IQR) number of 1.00 (0) valid measurement days (≥ 20 hr wear time) was collected. Physical activity data for 84 patients (95%) was available on POD1 (n = 61 control group, n = 23 intervention group). On postoperative day 2 (POD2), the majority of patients were discharged (n = 61, 69%), and data for only 23 patients (26%) were available (n = 17 control, n = 6 intervention). From postoperative day 3 to day 7, data of valid measurement days were available for just 1 patient (intervention group). Due to the large reduction in valid measurement days from POD2 onward, data from these days were not included in the analysis.

Results of the multiple linear regression analysis showed that, corrected for age, patients who used Hospital Fit stood and walked an average of 28.43 minutes (95% CI, 5.55-51.32) more on POD1 than patients who did not use Hospital Fit. Also, the model showed that an increase in age led to a decrease in the number of minutes standing and walking on POD1. The results of the multiple logistic regression analysis also showed that, corrected for ASA class, the odds of achieving functional recovery on POD1 were 3.08 times higher (95% CI, 1.14-8.31) for patients who used Hospital Fit compared to patients who did not use Hospital Fit. Including ASA class in the model shows that a lower ASA class increased the odds ratio for a functional recovery on POD1.

Conclusion. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ physical activity levels and functional recovery during hospitalization following joint replacement surgery.

Commentary

Although the beneficial effects of physical activity during hospitalization after surgery are well documented, patients continue to spend between 92% and 96% of their time lying or sitting.^1-3 Therefore, strategies aimed at increasing the amount of time spent standing and walking are needed. Postoperative physiotherapy aims to enhance physical activity levels and functional recovery of activities of daily living, which are essential to function independently at home.^4-7 Physiotherapists may be able to advise patients more effectively on their physical activity behavior if continuous physical activity monitoring with real-time feedback is implemented in standard care. Although mobile health (mHealth) tools are being used to monitor physical activity in support of outpatient physiotherapy within the orthopedic rehabilitation pathway,^8-10 there is currently no mHealth tool available that offers hospitalized patients and their physiotherapists essential strategies to enhance their physical activity levels and support their recovery process. In addition, because hospitalized patients frequently use walking aids and often have impaired gait, the algorithm of most available activity monitors is not validated for use in this population.

This study, therefore, is an important contribution to the literature, as it describes a preliminary evaluation of a novel mHealth tool—Hospital Fit—consisting of a smartphone application connected to an accelerometer whose algorithm has been validated to differentiate between lying/sitting and standing/walking among hospitalized patients. Briefly, results from this study showed an increase in the time spent standing and walking, as well as higher odds of functional recovery on POD1 from the introduction of Hospital Fit. While guidelines on the recommended amount of physical activity during hospitalization do not yet exist, an average improvement of 28 minutes (39%) standing and walking on POD1 can be considered a clinically relevant contribution to prevent the negative effects of inactivity.

This study has limitations, particularly related to the study design, which is acknowledged by the authors. The current study was a nonrandomized, quasi-experimental pilot study implemented at a single medical center, and therefore, the results have limited generalizability and more importantly, may not only be attributable to the introduction of Hospital Fit. In addition, as there was lag in patient recruitment where patients were initially selected for the control group over the course of 1 year, followed by selection of patients for the intervention group over 4 months (once Hospital Fit was developed), it is possible that awareness on the importance of physical activity during hospitalization increased among patients and health care professionals, which may have resulted in a bias in favor of the intervention group (and thus a potentially slight overestimation of results). Also, as individual functionalities of Hospital Fit were not investigated, relationships between each functionality and physical activity could not be established. As the authors indicated, future research is needed to determine the effectiveness of Hospital Fit (ie, a larger, cluster randomized controlled trial in a population of hospitalized patients with a longer length of stay). This study design would also enable investigation of the effect of individual functionalities of Hospital Fit on physical activity.

Applications for Clinical Practice

mHealth tools have the potential to increase patient awareness, support personalized care, and stimulate self-management. This study highlights the potential for a novel mHealth tool—Hospital Fit—to improve the amount of physical activity and shorten the time to functional recovery in hospitalized patients following orthopedic surgery. Further, mHealth tools like Hospital Fit may have a greater impact when the hospital stay of a patient permits the use of the tool for a longer period of time. More broadly, continuous objective monitoring through mHealth tools may provide patients and their physiotherapists enhanced and more detailed data to support and create more personalized recovery goals and related strategies.

Katrina F. Mateo, PhD, MPH

Study Overview

Objective. To investigate the potential of Hospital Fit (a smartphone application with an accelerometer) to enhance physical activity levels and functional recovery following orthopedic surgery.

Design. Nonrandomized, quasi-experimental pilot study.

Settings and participants. Patients scheduled for an elective total knee arthroplasty (TKA) or total hip arthroplasty (THA) at the orthopedic ward of Maastricht University Medical Center in Maastricht, the Netherlands, were invited to participate. Patients scheduled for surgery between January 2017 and December 2018 were recruited for the control group at a rate of 1 patient per week (due to a limited number of accelerometers available). After development of Hospital Fit was completed in December 2018 (and sufficient accelerators had become available), patients scheduled for surgery between February 2019 and May 2019 were recruited for the intervention group. The ratio of patients included in the control and intervention group was set at 2:1, respectively.

At preoperative physiotherapy screenings (scheduled 6 weeks before surgery), patients received verbal and written information about the study. Patients were eligible if they met the following inclusion criteria: receiving physiotherapy after elective TKA or THA; able to walk independently 2 weeks prior to surgery, as scored on the Functional Ambulation Categories (FAC > 3); were expected to be discharged to their own home; were aged 18 years and older; and had a sufficient understanding of the Dutch language. Exclusion criteria were: the presence of contraindications to walking or wearing an accelerometer on the upper leg; admission to the intensive care unit; impaired cognition (delirium/dementia), as reported by the attending doctor; a life expectancy of less than 3 months; and previous participation in this study. Patients were contacted on the day of their surgery, and written informed consent was obtained prior to the initiation of any study activities.

Intervention. Once enrolled, all patients followed a standardized clinical care pathway for TKA or THA (see original article for additional details). Postoperative physiotherapy was administered to all participating patients, starting within 4 hours after surgery. The physiotherapy treatment was aimed at increasing physical activity levels and enhancing functional recovery. Control group patients only received physiotherapy (twice daily, 30 minutes per session) and had their physical activity levels monitored with an accelerometer, without receiving feedback, until functional recovery was achieved, as measured with the modified Iowa Level of Assistance Scale (mILAS). Intervention group patients used Hospital Fit in addition to physiotherapy. Hospital Fit consists of a smartphone-based app, connected to a MOX activity monitor via Bluetooth (device contains a tri-axial accelerometer sensor in a small waterproof housing attached to the upper leg). Hospital Fit enables objective activity monitoring, provides patients and their physiotherapists insights and real-time feedback on the number of minutes spent standing and walking per day, and offers a tailored exercise program supported by videos aimed at stimulating self-management.

Measures. The primary outcome measure was the time spent physically active (total number of minutes standing and walking) per day until discharge. Physical activity was monitored 24 hours a day; days with ≥ 20 hours of wear time were considered valid measurement days and were included in the analysis. After the last treatment session, the accelerometer was removed, and the raw tri-axial accelerometer data were uploaded and processed to classify minutes as “active” (standing and walking) or “sedentary” (lying and sitting). The secondary outcome measures were the achievement of functional recovery on postoperative day 1 (POD1). Functional recovery was assessed by the physiotherapist during each treatment session using the mILAS and was reported in the electronic health record. In the intervention group, it was also reported in the app. The achievement of functional recovery on POD1 was defined as having reached a total mILAS-score of 0 on or before POD1, using a dichotomized outcome (0 = mILAS = 0 > POD1; 1 = mILAS = 0 ≤ POD1).

The independent variables measured were: Hospital Fit use (control versus the intervention group), age, sex, body mass index (BMI), type of surgery (TKA or THA), and comorbidities assessed by the American Society of Anesthesiologists (ASA) classification (ASA class ≤ 2 versus ASA class = 3; a higher score indicates being less fit for surgery). The medical and demographic data measured were the type of walking aid used and length of stay, with the day of surgery being defined as day 1.

Analysis. Data analysis was performed according to the intention-to-treat principle. Missing values were not substituted; drop-outs were not replaced. Descriptive statistics were presented as means (SD) or as 95% confidence intervals (CI) for continuous variables. The median and interquartile ranges (IQR) were used to present non-normally distributed data. The frequencies and percentages were used to present categorical variables. A multiple linear regression analysis was performed to determine the association between the time spent physically active per day and Hospital Fit use, corrected for potential confounding factors (age, sex, BMI, ASA class, and type of surgery). A multiple logistic regression analysis was performed additionally to determine the association between the achievement of functional recovery on POD1 and Hospital Fit use, corrected for potential confounding factors. For all statistical analyses, the level of significance was set at P < 0.05. All statistical analyses were performed using SPSS (version 23.0.0.2; IBM Corporation, Armonk, NY).

Main results. Ninety-seven patients were recruited; after excluding 9 patients because of missing data, 88 were included for analysis, with 61 (69%) in the control group and 27 (31%) in the intervention group. A median (IQR) number of 1.00 (0) valid measurement days (≥ 20 hr wear time) was collected. Physical activity data for 84 patients (95%) was available on POD1 (n = 61 control group, n = 23 intervention group). On postoperative day 2 (POD2), the majority of patients were discharged (n = 61, 69%), and data for only 23 patients (26%) were available (n = 17 control, n = 6 intervention). From postoperative day 3 to day 7, data of valid measurement days were available for just 1 patient (intervention group). Due to the large reduction in valid measurement days from POD2 onward, data from these days were not included in the analysis.

Results of the multiple linear regression analysis showed that, corrected for age, patients who used Hospital Fit stood and walked an average of 28.43 minutes (95% CI, 5.55-51.32) more on POD1 than patients who did not use Hospital Fit. Also, the model showed that an increase in age led to a decrease in the number of minutes standing and walking on POD1. The results of the multiple logistic regression analysis also showed that, corrected for ASA class, the odds of achieving functional recovery on POD1 were 3.08 times higher (95% CI, 1.14-8.31) for patients who used Hospital Fit compared to patients who did not use Hospital Fit. Including ASA class in the model shows that a lower ASA class increased the odds ratio for a functional recovery on POD1.

Conclusion. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ physical activity levels and functional recovery during hospitalization following joint replacement surgery.

Commentary

Although the beneficial effects of physical activity during hospitalization after surgery are well documented, patients continue to spend between 92% and 96% of their time lying or sitting.^1-3 Therefore, strategies aimed at increasing the amount of time spent standing and walking are needed. Postoperative physiotherapy aims to enhance physical activity levels and functional recovery of activities of daily living, which are essential to function independently at home.^4-7 Physiotherapists may be able to advise patients more effectively on their physical activity behavior if continuous physical activity monitoring with real-time feedback is implemented in standard care. Although mobile health (mHealth) tools are being used to monitor physical activity in support of outpatient physiotherapy within the orthopedic rehabilitation pathway,^8-10 there is currently no mHealth tool available that offers hospitalized patients and their physiotherapists essential strategies to enhance their physical activity levels and support their recovery process. In addition, because hospitalized patients frequently use walking aids and often have impaired gait, the algorithm of most available activity monitors is not validated for use in this population.

This study, therefore, is an important contribution to the literature, as it describes a preliminary evaluation of a novel mHealth tool—Hospital Fit—consisting of a smartphone application connected to an accelerometer whose algorithm has been validated to differentiate between lying/sitting and standing/walking among hospitalized patients. Briefly, results from this study showed an increase in the time spent standing and walking, as well as higher odds of functional recovery on POD1 from the introduction of Hospital Fit. While guidelines on the recommended amount of physical activity during hospitalization do not yet exist, an average improvement of 28 minutes (39%) standing and walking on POD1 can be considered a clinically relevant contribution to prevent the negative effects of inactivity.

This study has limitations, particularly related to the study design, which is acknowledged by the authors. The current study was a nonrandomized, quasi-experimental pilot study implemented at a single medical center, and therefore, the results have limited generalizability and more importantly, may not only be attributable to the introduction of Hospital Fit. In addition, as there was lag in patient recruitment where patients were initially selected for the control group over the course of 1 year, followed by selection of patients for the intervention group over 4 months (once Hospital Fit was developed), it is possible that awareness on the importance of physical activity during hospitalization increased among patients and health care professionals, which may have resulted in a bias in favor of the intervention group (and thus a potentially slight overestimation of results). Also, as individual functionalities of Hospital Fit were not investigated, relationships between each functionality and physical activity could not be established. As the authors indicated, future research is needed to determine the effectiveness of Hospital Fit (ie, a larger, cluster randomized controlled trial in a population of hospitalized patients with a longer length of stay). This study design would also enable investigation of the effect of individual functionalities of Hospital Fit on physical activity.

Applications for Clinical Practice

mHealth tools have the potential to increase patient awareness, support personalized care, and stimulate self-management. This study highlights the potential for a novel mHealth tool—Hospital Fit—to improve the amount of physical activity and shorten the time to functional recovery in hospitalized patients following orthopedic surgery. Further, mHealth tools like Hospital Fit may have a greater impact when the hospital stay of a patient permits the use of the tool for a longer period of time. More broadly, continuous objective monitoring through mHealth tools may provide patients and their physiotherapists enhanced and more detailed data to support and create more personalized recovery goals and related strategies.

Katrina F. Mateo, PhD, MPH

Wernicke-Korsakoff syndrome is a cluster of symptoms attributed to a disorder of vitamin B1 (thiamine) deficiency, manifesting as a combined presentation of alcohol-induced Wernicke encephalopathy (WE) and Korsakoff syndrome (KS).¹ While there is consensus on the characteristic presentation and symptoms of WE, there is a lack of agreement on the exact definition of KS. The classic triad describing WE consists of ataxia, ophthalmoplegia, and confusion; however, reports now suggest that a majority of patients exhibit only 1 or 2 of the elements of the triad. KS is often seen as a condition of chronic thiamine deficiency manifesting as memory impairment alongside a cognitive and behavioral decline, with no clear consensus on the sequence of appearance of symptoms. The typical relationship is thought to be a progression of WE to KS if untreated.

From a mental health perspective, WE presents with delirium and confusion whereas KS manifests with irreversible dementia and a cognitive deterioration. Though it is commonly taught that KS-induced memory loss is permanent due to neuronal damage (classically identified as damage to the mammillary bodies - though other structures have been implicated as well), more recent research suggest otherwise.² A review published in 2018, for example, gathered several case reports and case series that suggest significant improvement in memory and cognition attributed to behavioral and pharmacologic interventions, indicating this as an area deserving of further study.³ About 20% of patients diagnosed with WE by autopsy exhibited none of the classical triad symptoms prior to death.⁴ Hence, these conditions are surmised to be significantly underdiagnosed and misdiagnosed.

Though consensus regarding the appropriate treatment regimen is lacking for WE, a common protocol consists of high-dose parenteral thiamine for 4 to 7 days.⁵ This is usually followed by daily oral thiamine repletion until the patient either achieves complete abstinence from alcohol (ideal) or decreases consumption. The goal is to allow thiamine stores to replete and maintain at minimum required body levels moving forward. In this case report, we highlight the utilization of a long-term, unconventional intramuscular (IM) thiamine repletion regimen to ensure maintenance of a patient’s mental status, highlighting discrepancies in our understanding of the mechanisms at play in WE and its treatment.

Case Presentation

A 65-year-old male patient with a more than 3-decade history of daily hard liquor intake, multiple psychiatric hospitalizations for WE, and a prior suicide attempt, presented to the emergency department (ED) with increased frequency of falls, poor oral intake, confabulation, and diminished verbal communication. A chart review revealed memory impairment alongside the diagnoses of schizoaffective disorder and WE, and confusion that was responsive to thiamine administration as well as a history of hypertension, hyperlipidemia, osteoarthritis, and urinary retention secondary to benign prostatic hyperplasia (BPH).

On examination the patient was found to be disoriented with a clouded sensorium. While the history of heavy daily alcohol use was clear in the chart and confirmed by other sources, it appeared unlikely that the patient had been using alcohol in the preceding month due to restricted access in his most recent living environment (a shared apartment with daily nursing assistance). He reported no lightheadedness, dizziness, palpitations, numbness, tingling, or any head trauma. He also negated the presence of active mood symptoms, auditory or visual hallucinations or suicidal ideation (SI)

The patient was admitted to the Internal Medicine Service and received a workup for the causes of delirium, including consideration of normal pressure hydrocephalus (NPH) and other neurologic conditions. Laboratory tests including a comprehensive metabolic panel, thyroid stimulating hormone, urinalysis, urine toxicology screen, and vitamin B12 and folate levels were in normal ranges. Although brain imaging revealed enlarged ventricles, NPH was considered unlikely because of the absence of ophthalmologic abnormalities, like gaze nystagmus, and urinary incontinence; conversely, there was some presence of urinary retention attributed to BPH and required an admission a few months prior. Moreover, magnetic resonance images showed that the ventricles were enlarged slightly out of proportion to the sulci, which can be seen with predominantly central volume loss compared with the pattern typically seen in NPH.

In light of concern for WE and the patient's history, treatment with IV thiamine and IV fluids was initiated and the Liaison Psychiatry Service was consulted for cognitive disability and treatment of his mood. Administration of IV thiamine rapidly restored his sensorium, but he became abruptly disorganized as the IV regimen graduated to an oral thiamine dose of 200 mg 3 times daily. Simultaneously, as medical stabilization was achieved, the patient was transferred to the inpatient psychiatry unit to address the nonresolving cognitive impairment and behavioral disorganization. This specifically involved newly emerging, impulsive, self-harming behaviors like throwing himself on the ground and banging his head on the floor. Such behaviors along with paucity of speech and decreased oral intake, ultimately warranted constant observation, which led to a decrease in self-harming activity. All this behavior was noted even though the patient was adherent to oral administration of thiamine. Throughout this time, the patient underwent several transfers back and forth between the Psychiatry and Internal Medicine services due to ongoing concern for the possibility of delirium or WE. However, the Neurology and Internal Medicine services did not feel that WE would explain the patient’s mental and behavioral status, in part due to his ongoing adherence with daily oral thiamine dosing that was not associated with improvement in mental status.

Recollecting the patient’s improvement with the parenteral thiamine regimen (IV and IM), the psychiatry unit tried a thiamine regimen of 200 mg IM and 100 mg oral 2 times daily. After about 2 weeks on this regimen, the patient subsequently achieved remarkable improvement in his cognitive and behavioral status, with resolution of selfharming behaviors. The patient was noted to be calmer, more linear, and more oriented, though he remained incompletely oriented throughout his hospitalization. As improvement in sensorium was established and the patient’s hospital stay prolonged (Figure), his mood symptoms began manifesting as guilt, low energy, decreased appetite, withdrawal, and passive SI. This was followed by a trial of lithium that was discontinued due to elevated creatine levels. As the patient continued to report depression, a multidrug regimen of divalproex, fluoxetine, and quetiapine was administered, which lead to remarkable improvement.

At this time, it was concluded that the stores of thiamine in the patient’s body may have been replenished, the alcohol intake completely ceased and that he needed to be weaned off of thiamine. The next step taken was reduction of the twice daily 200 mg IM thiamine dose to a once daily regimen, and oral thiamine was put on hold. Over the next 48 hours, the patient became less verbal, more withdrawn, incontinent of urine, and delirious. The twice daily IM 200 mg thiamine was restarted, but this time the patient demonstrated very slow improvement. After 2 weeks, the IM thiamine 200 mg was increased to 3 times daily, and the patient showed marked improvement in recall, mood, and effect.

Several attempts were made to reduce the IM thiamine burden on the patient and/ or transition to an exclusively oral regimen. However, he rapidly decompensated within hours of each attempt to taper the IM dose and required immediate reinstation. On the IM thiamine regimen, he eventually appeared to reach a stable cognitive and affective baseline marked by incomplete orientation but pleasant affect, he reported no mood complaints, behavioral stability, and an ability to comply with care needs and have simple conversations. Some speech content remained disorganized particularly if engaged beyond simple exchanges.

The patient was discharged to a skilled nursing facility after a month of 3 times daily IM administration of thiamine. Within the next 24 hours, the patient returned to the ED with the originally reported symptoms of ataxia, agitation, and confusion. On inquiry, it was revealed that the ordered vials of IM thiamine for injection had not arrived with him at the nursing facility and he had missed 2 doses. The blood laboratory results, scans, and all other parameters were otherwise found to be normal and the patient was adherent to his prescribed antipsychotics and antidepressants. As anticipated, restoration of the IM thiamine regimen revived his baseline within hours. While confusion and delirium resolved completely with treatment, the memory impairments persisted. This patient has been administered a 3 times daily IM dose of 200 mg thiamine for more than 2 years with a stable cognitive clinical picture.

Discussion

According to data from the 2016 National Survey on Drug Use and Health, 16 million individuals in the US aged ≥ 12 years reported heavy alcohol use, which is defined as binge drinking on ≥ 5 days in the past month.6,7 Thiamine deficiency is an alcoholrelated disorder that is frequently encountered in hospital settings. This deficiency can also occur in the context of malabsorption, malnutrition, a prolonged course of vomiting, and bariatric surgery.^8,9

The deficiency in thiamine, which is sometimes known as WE, manifests rarely with all 3 of the classic triad of gait disturbances, abnormal eye movements, and mental status changes, with only 16.5% of patients displaying all of the triad.⁴ Moreover, there may be additional symptoms not listed in this triad, such as memory impairment, bilateral sixth nerve palsy, ptosis, hypotension, and hypothermia.^10.11 This inconsistent presentation makes the diagnosis challenging and therefore requires a higher threshold for suspicion. If undiagnosed and/or untreated, WE can lead to chronic thiamine deficiency causing permanent brain damage in the guise of KS. This further increases the importance of timely diagnosis and treatment.

Our case highlights the utilization of an unconventional thiamine regimen that appeared to be temporally associated with mental status improvement. The patient’s clouded sensorium and confusion could not be attributed to metabolic, encephalopathic, or infectious pathologies due to the absence of supportive laboratory evidence. He responded to IV and IM doses of thiamine, but repeated attempts to taper the IM doses with the objective of transitioning to oral thiamine supplementation were followed by immediate decompensations in mental status. This was atypical of WE as the patient seemed adequately replete with thiamine, and missing a few doses should not be enough to deplete his stores. Thus, reflecting a unique case of thiamine-dependent chronically set WE when even a single missed dose of thiamine adversely affected the patient’s cognitive baseline. Interesting to note is this patient’s memory issue, as evident by clinical examination and dating back at least 5 years as per chart review. This premature amnestic component of his presentation indicates a likely parallel running KS component of his presentation. Conversely, the patient’s long history of alcohol use disorder, prior episodes of WE, and ideal response achieved only on parenteral thiamine repletion further supported the diagnosis of WE and our impression of the scenario.

Even though this patient had prior episodes of WE, there remained diagnostic uncertainty regarding his altered mental status for some time before the nonoral thiamine repletion treatment was implemented. Particularly in this admission, the patient’s mental status frequently waxed and waned and there was the additional confusion of whether a potential psychiatric etiology contributed to some of the elements of his presentation, such as his impulsive self-harm behaviors. This behavior led to recurrent transfers among the Psychiatry Service, Internal Medicine Service, and the ED.

The patient’s presentation did not reflect the classical triad of WE, and while this is consistent with the majority of clinical manifestations, various services were reluctant to attribute his symptoms to WE. Once the threshold of suspicion of thiamine deficiency was lowered and the deficit treated more aggressively, the patient seemed to improve tremendously. Presence of memory problems and confabulation, both of which this patient exhibited, are suggestive of KS and are not expected to recover with treatment, yet for this patient there did seem to be some improvement—though not complete resolution. This is consistent with newer evidence suggesting that some recovery from the deficits seen in KS is possible.³

Once diagnosed, the treatment objective is the replenishment of thiamine stores and optimization of the metabolic scenario of the body to prevent recurrence. For acute WE symptoms, many regimens call for 250 to 500 mg of IV thiamine supplementation 2 to 3 times daily for 3 to 5 days. High dose IV thiamine (≥ 500 mg daily) has been proposed to be efficacious and free of considerable adverse effects.¹² A study conducted at the University of North Carolina described thiamine prescribing practices in a large academic hospital, analyzing data with the objective of assessing outcomes of ordering high-dose IV thiamine (HDIV, ≥ 200 mg IV twice daily) to patients with encephalopathy. ¹³ The researchers concluded that HDIV, even though rarely prescribed, was associated with decreased inpatient mortality in bivariable models. However, in multivariable analyses this decrease was found to be clinically insignificant. Our patient benefitted from both IV and IM delivery.

Ideally, after the initial IV thiamine dose, oral administration of thiamine 250 to 1,000 mg is continued until a reduction, if not abstinence, from alcohol use is achieved.⁵ Many patients are discharged on an oral maintenance dose of thiamine 100 mg. Oral thiamine is poorly absorbed and less effective in both prophylaxis and treatment of newly diagnosed WE; therefore, it is typically used only after IM or IV replenishment. It remains unclear why this patient required IM thiamine multiple times per day to maintain his mental status, and why he would present with selfinjurious behaviors after missing doses. The patient’s response can be attributed to late-onset defects in oral thiamine absorption at the carrier protein level of the brush border and basolateral membranes of his jejunum; however, an invasive procedure like a jejunal biopsy to establish the definitive etiology was neither necessary nor practical once treatment response was observed. ¹⁴ Other possible explanations include rapid thiamine metabolism, poor gastrointestinal absorption and a late-onset deficit in the thiamine diffusion mechanisms, and active transport systems (thiamine utilization depends on active transport in low availability states and passive transport when readily available). The nature of these mechanisms deserves further study. Less data have been reported on the administration and utility of IM thiamine for chronic WE; hence, our case report is one of the first illustrating the role of this method for sustained repletion.

Conclusions

This case presented a clinical dilemma because the conventional treatment regimen for WE didn’t yield the desired outcome until the mode and duration of thiamine administration were adjusted. It illustrates the utility of a sustained intensive thiamine regimen irrespective of sobriety status, as opposed to the traditional regimen of parenteral (primarily IV) thiamine for 3 to 7 days, followed by oral repletion until the patient achieves sustained abstinence. In this patient’s case, access to nursing care postdischarge facilitated his continued adherence to IM thiamine therapy.

The longitudinal time course of this case suggests a relationship between this route of administration and improvement in symptom burden and indicates that this patient may have a long-term need for IM thiamine to maintain his baseline mental status. Of great benefit in such patients would be the availability of a long-acting IM thiamine therapy. Risk of overdose is unlikely due to the water solubility of B group vitamins.

This case report highlights the importance of setting a high clinical suspicion for WE due to its ever-increasing incidence in these times. We also wish to direct researchers to consider other out-of-the-box treatment options in case of failure of the conventional regime. In documenting this patient report, we invite more medical providers to investigate and explore other therapeutic options for WE treatment with the aim of decreasing both morbidity and mortality secondary to the condition.

Wernicke-Korsakoff syndrome is a cluster of symptoms attributed to a disorder of vitamin B1 (thiamine) deficiency, manifesting as a combined presentation of alcohol-induced Wernicke encephalopathy (WE) and Korsakoff syndrome (KS).¹ While there is consensus on the characteristic presentation and symptoms of WE, there is a lack of agreement on the exact definition of KS. The classic triad describing WE consists of ataxia, ophthalmoplegia, and confusion; however, reports now suggest that a majority of patients exhibit only 1 or 2 of the elements of the triad. KS is often seen as a condition of chronic thiamine deficiency manifesting as memory impairment alongside a cognitive and behavioral decline, with no clear consensus on the sequence of appearance of symptoms. The typical relationship is thought to be a progression of WE to KS if untreated.

From a mental health perspective, WE presents with delirium and confusion whereas KS manifests with irreversible dementia and a cognitive deterioration. Though it is commonly taught that KS-induced memory loss is permanent due to neuronal damage (classically identified as damage to the mammillary bodies - though other structures have been implicated as well), more recent research suggest otherwise.² A review published in 2018, for example, gathered several case reports and case series that suggest significant improvement in memory and cognition attributed to behavioral and pharmacologic interventions, indicating this as an area deserving of further study.³ About 20% of patients diagnosed with WE by autopsy exhibited none of the classical triad symptoms prior to death.⁴ Hence, these conditions are surmised to be significantly underdiagnosed and misdiagnosed.

Though consensus regarding the appropriate treatment regimen is lacking for WE, a common protocol consists of high-dose parenteral thiamine for 4 to 7 days.⁵ This is usually followed by daily oral thiamine repletion until the patient either achieves complete abstinence from alcohol (ideal) or decreases consumption. The goal is to allow thiamine stores to replete and maintain at minimum required body levels moving forward. In this case report, we highlight the utilization of a long-term, unconventional intramuscular (IM) thiamine repletion regimen to ensure maintenance of a patient’s mental status, highlighting discrepancies in our understanding of the mechanisms at play in WE and its treatment.

Case Presentation

A 65-year-old male patient with a more than 3-decade history of daily hard liquor intake, multiple psychiatric hospitalizations for WE, and a prior suicide attempt, presented to the emergency department (ED) with increased frequency of falls, poor oral intake, confabulation, and diminished verbal communication. A chart review revealed memory impairment alongside the diagnoses of schizoaffective disorder and WE, and confusion that was responsive to thiamine administration as well as a history of hypertension, hyperlipidemia, osteoarthritis, and urinary retention secondary to benign prostatic hyperplasia (BPH).

On examination the patient was found to be disoriented with a clouded sensorium. While the history of heavy daily alcohol use was clear in the chart and confirmed by other sources, it appeared unlikely that the patient had been using alcohol in the preceding month due to restricted access in his most recent living environment (a shared apartment with daily nursing assistance). He reported no lightheadedness, dizziness, palpitations, numbness, tingling, or any head trauma. He also negated the presence of active mood symptoms, auditory or visual hallucinations or suicidal ideation (SI)

The patient was admitted to the Internal Medicine Service and received a workup for the causes of delirium, including consideration of normal pressure hydrocephalus (NPH) and other neurologic conditions. Laboratory tests including a comprehensive metabolic panel, thyroid stimulating hormone, urinalysis, urine toxicology screen, and vitamin B12 and folate levels were in normal ranges. Although brain imaging revealed enlarged ventricles, NPH was considered unlikely because of the absence of ophthalmologic abnormalities, like gaze nystagmus, and urinary incontinence; conversely, there was some presence of urinary retention attributed to BPH and required an admission a few months prior. Moreover, magnetic resonance images showed that the ventricles were enlarged slightly out of proportion to the sulci, which can be seen with predominantly central volume loss compared with the pattern typically seen in NPH.

In light of concern for WE and the patient's history, treatment with IV thiamine and IV fluids was initiated and the Liaison Psychiatry Service was consulted for cognitive disability and treatment of his mood. Administration of IV thiamine rapidly restored his sensorium, but he became abruptly disorganized as the IV regimen graduated to an oral thiamine dose of 200 mg 3 times daily. Simultaneously, as medical stabilization was achieved, the patient was transferred to the inpatient psychiatry unit to address the nonresolving cognitive impairment and behavioral disorganization. This specifically involved newly emerging, impulsive, self-harming behaviors like throwing himself on the ground and banging his head on the floor. Such behaviors along with paucity of speech and decreased oral intake, ultimately warranted constant observation, which led to a decrease in self-harming activity. All this behavior was noted even though the patient was adherent to oral administration of thiamine. Throughout this time, the patient underwent several transfers back and forth between the Psychiatry and Internal Medicine services due to ongoing concern for the possibility of delirium or WE. However, the Neurology and Internal Medicine services did not feel that WE would explain the patient’s mental and behavioral status, in part due to his ongoing adherence with daily oral thiamine dosing that was not associated with improvement in mental status.

Recollecting the patient’s improvement with the parenteral thiamine regimen (IV and IM), the psychiatry unit tried a thiamine regimen of 200 mg IM and 100 mg oral 2 times daily. After about 2 weeks on this regimen, the patient subsequently achieved remarkable improvement in his cognitive and behavioral status, with resolution of selfharming behaviors. The patient was noted to be calmer, more linear, and more oriented, though he remained incompletely oriented throughout his hospitalization. As improvement in sensorium was established and the patient’s hospital stay prolonged (Figure), his mood symptoms began manifesting as guilt, low energy, decreased appetite, withdrawal, and passive SI. This was followed by a trial of lithium that was discontinued due to elevated creatine levels. As the patient continued to report depression, a multidrug regimen of divalproex, fluoxetine, and quetiapine was administered, which lead to remarkable improvement.

At this time, it was concluded that the stores of thiamine in the patient’s body may have been replenished, the alcohol intake completely ceased and that he needed to be weaned off of thiamine. The next step taken was reduction of the twice daily 200 mg IM thiamine dose to a once daily regimen, and oral thiamine was put on hold. Over the next 48 hours, the patient became less verbal, more withdrawn, incontinent of urine, and delirious. The twice daily IM 200 mg thiamine was restarted, but this time the patient demonstrated very slow improvement. After 2 weeks, the IM thiamine 200 mg was increased to 3 times daily, and the patient showed marked improvement in recall, mood, and effect.

Several attempts were made to reduce the IM thiamine burden on the patient and/ or transition to an exclusively oral regimen. However, he rapidly decompensated within hours of each attempt to taper the IM dose and required immediate reinstation. On the IM thiamine regimen, he eventually appeared to reach a stable cognitive and affective baseline marked by incomplete orientation but pleasant affect, he reported no mood complaints, behavioral stability, and an ability to comply with care needs and have simple conversations. Some speech content remained disorganized particularly if engaged beyond simple exchanges.

The patient was discharged to a skilled nursing facility after a month of 3 times daily IM administration of thiamine. Within the next 24 hours, the patient returned to the ED with the originally reported symptoms of ataxia, agitation, and confusion. On inquiry, it was revealed that the ordered vials of IM thiamine for injection had not arrived with him at the nursing facility and he had missed 2 doses. The blood laboratory results, scans, and all other parameters were otherwise found to be normal and the patient was adherent to his prescribed antipsychotics and antidepressants. As anticipated, restoration of the IM thiamine regimen revived his baseline within hours. While confusion and delirium resolved completely with treatment, the memory impairments persisted. This patient has been administered a 3 times daily IM dose of 200 mg thiamine for more than 2 years with a stable cognitive clinical picture.

Discussion

According to data from the 2016 National Survey on Drug Use and Health, 16 million individuals in the US aged ≥ 12 years reported heavy alcohol use, which is defined as binge drinking on ≥ 5 days in the past month.6,7 Thiamine deficiency is an alcoholrelated disorder that is frequently encountered in hospital settings. This deficiency can also occur in the context of malabsorption, malnutrition, a prolonged course of vomiting, and bariatric surgery.^8,9

The deficiency in thiamine, which is sometimes known as WE, manifests rarely with all 3 of the classic triad of gait disturbances, abnormal eye movements, and mental status changes, with only 16.5% of patients displaying all of the triad.⁴ Moreover, there may be additional symptoms not listed in this triad, such as memory impairment, bilateral sixth nerve palsy, ptosis, hypotension, and hypothermia.^10.11 This inconsistent presentation makes the diagnosis challenging and therefore requires a higher threshold for suspicion. If undiagnosed and/or untreated, WE can lead to chronic thiamine deficiency causing permanent brain damage in the guise of KS. This further increases the importance of timely diagnosis and treatment.

Our case highlights the utilization of an unconventional thiamine regimen that appeared to be temporally associated with mental status improvement. The patient’s clouded sensorium and confusion could not be attributed to metabolic, encephalopathic, or infectious pathologies due to the absence of supportive laboratory evidence. He responded to IV and IM doses of thiamine, but repeated attempts to taper the IM doses with the objective of transitioning to oral thiamine supplementation were followed by immediate decompensations in mental status. This was atypical of WE as the patient seemed adequately replete with thiamine, and missing a few doses should not be enough to deplete his stores. Thus, reflecting a unique case of thiamine-dependent chronically set WE when even a single missed dose of thiamine adversely affected the patient’s cognitive baseline. Interesting to note is this patient’s memory issue, as evident by clinical examination and dating back at least 5 years as per chart review. This premature amnestic component of his presentation indicates a likely parallel running KS component of his presentation. Conversely, the patient’s long history of alcohol use disorder, prior episodes of WE, and ideal response achieved only on parenteral thiamine repletion further supported the diagnosis of WE and our impression of the scenario.

Even though this patient had prior episodes of WE, there remained diagnostic uncertainty regarding his altered mental status for some time before the nonoral thiamine repletion treatment was implemented. Particularly in this admission, the patient’s mental status frequently waxed and waned and there was the additional confusion of whether a potential psychiatric etiology contributed to some of the elements of his presentation, such as his impulsive self-harm behaviors. This behavior led to recurrent transfers among the Psychiatry Service, Internal Medicine Service, and the ED.

The patient’s presentation did not reflect the classical triad of WE, and while this is consistent with the majority of clinical manifestations, various services were reluctant to attribute his symptoms to WE. Once the threshold of suspicion of thiamine deficiency was lowered and the deficit treated more aggressively, the patient seemed to improve tremendously. Presence of memory problems and confabulation, both of which this patient exhibited, are suggestive of KS and are not expected to recover with treatment, yet for this patient there did seem to be some improvement—though not complete resolution. This is consistent with newer evidence suggesting that some recovery from the deficits seen in KS is possible.³

Once diagnosed, the treatment objective is the replenishment of thiamine stores and optimization of the metabolic scenario of the body to prevent recurrence. For acute WE symptoms, many regimens call for 250 to 500 mg of IV thiamine supplementation 2 to 3 times daily for 3 to 5 days. High dose IV thiamine (≥ 500 mg daily) has been proposed to be efficacious and free of considerable adverse effects.¹² A study conducted at the University of North Carolina described thiamine prescribing practices in a large academic hospital, analyzing data with the objective of assessing outcomes of ordering high-dose IV thiamine (HDIV, ≥ 200 mg IV twice daily) to patients with encephalopathy. ¹³ The researchers concluded that HDIV, even though rarely prescribed, was associated with decreased inpatient mortality in bivariable models. However, in multivariable analyses this decrease was found to be clinically insignificant. Our patient benefitted from both IV and IM delivery.

Ideally, after the initial IV thiamine dose, oral administration of thiamine 250 to 1,000 mg is continued until a reduction, if not abstinence, from alcohol use is achieved.⁵ Many patients are discharged on an oral maintenance dose of thiamine 100 mg. Oral thiamine is poorly absorbed and less effective in both prophylaxis and treatment of newly diagnosed WE; therefore, it is typically used only after IM or IV replenishment. It remains unclear why this patient required IM thiamine multiple times per day to maintain his mental status, and why he would present with selfinjurious behaviors after missing doses. The patient’s response can be attributed to late-onset defects in oral thiamine absorption at the carrier protein level of the brush border and basolateral membranes of his jejunum; however, an invasive procedure like a jejunal biopsy to establish the definitive etiology was neither necessary nor practical once treatment response was observed. ¹⁴ Other possible explanations include rapid thiamine metabolism, poor gastrointestinal absorption and a late-onset deficit in the thiamine diffusion mechanisms, and active transport systems (thiamine utilization depends on active transport in low availability states and passive transport when readily available). The nature of these mechanisms deserves further study. Less data have been reported on the administration and utility of IM thiamine for chronic WE; hence, our case report is one of the first illustrating the role of this method for sustained repletion.

Conclusions

This case presented a clinical dilemma because the conventional treatment regimen for WE didn’t yield the desired outcome until the mode and duration of thiamine administration were adjusted. It illustrates the utility of a sustained intensive thiamine regimen irrespective of sobriety status, as opposed to the traditional regimen of parenteral (primarily IV) thiamine for 3 to 7 days, followed by oral repletion until the patient achieves sustained abstinence. In this patient’s case, access to nursing care postdischarge facilitated his continued adherence to IM thiamine therapy.

The longitudinal time course of this case suggests a relationship between this route of administration and improvement in symptom burden and indicates that this patient may have a long-term need for IM thiamine to maintain his baseline mental status. Of great benefit in such patients would be the availability of a long-acting IM thiamine therapy. Risk of overdose is unlikely due to the water solubility of B group vitamins.

This case report highlights the importance of setting a high clinical suspicion for WE due to its ever-increasing incidence in these times. We also wish to direct researchers to consider other out-of-the-box treatment options in case of failure of the conventional regime. In documenting this patient report, we invite more medical providers to investigate and explore other therapeutic options for WE treatment with the aim of decreasing both morbidity and mortality secondary to the condition.

Wernicke-Korsakoff syndrome is a cluster of symptoms attributed to a disorder of vitamin B1 (thiamine) deficiency, manifesting as a combined presentation of alcohol-induced Wernicke encephalopathy (WE) and Korsakoff syndrome (KS).¹ While there is consensus on the characteristic presentation and symptoms of WE, there is a lack of agreement on the exact definition of KS. The classic triad describing WE consists of ataxia, ophthalmoplegia, and confusion; however, reports now suggest that a majority of patients exhibit only 1 or 2 of the elements of the triad. KS is often seen as a condition of chronic thiamine deficiency manifesting as memory impairment alongside a cognitive and behavioral decline, with no clear consensus on the sequence of appearance of symptoms. The typical relationship is thought to be a progression of WE to KS if untreated.

From a mental health perspective, WE presents with delirium and confusion whereas KS manifests with irreversible dementia and a cognitive deterioration. Though it is commonly taught that KS-induced memory loss is permanent due to neuronal damage (classically identified as damage to the mammillary bodies - though other structures have been implicated as well), more recent research suggest otherwise.² A review published in 2018, for example, gathered several case reports and case series that suggest significant improvement in memory and cognition attributed to behavioral and pharmacologic interventions, indicating this as an area deserving of further study.³ About 20% of patients diagnosed with WE by autopsy exhibited none of the classical triad symptoms prior to death.⁴ Hence, these conditions are surmised to be significantly underdiagnosed and misdiagnosed.

Though consensus regarding the appropriate treatment regimen is lacking for WE, a common protocol consists of high-dose parenteral thiamine for 4 to 7 days.⁵ This is usually followed by daily oral thiamine repletion until the patient either achieves complete abstinence from alcohol (ideal) or decreases consumption. The goal is to allow thiamine stores to replete and maintain at minimum required body levels moving forward. In this case report, we highlight the utilization of a long-term, unconventional intramuscular (IM) thiamine repletion regimen to ensure maintenance of a patient’s mental status, highlighting discrepancies in our understanding of the mechanisms at play in WE and its treatment.

Case Presentation

A 65-year-old male patient with a more than 3-decade history of daily hard liquor intake, multiple psychiatric hospitalizations for WE, and a prior suicide attempt, presented to the emergency department (ED) with increased frequency of falls, poor oral intake, confabulation, and diminished verbal communication. A chart review revealed memory impairment alongside the diagnoses of schizoaffective disorder and WE, and confusion that was responsive to thiamine administration as well as a history of hypertension, hyperlipidemia, osteoarthritis, and urinary retention secondary to benign prostatic hyperplasia (BPH).

On examination the patient was found to be disoriented with a clouded sensorium. While the history of heavy daily alcohol use was clear in the chart and confirmed by other sources, it appeared unlikely that the patient had been using alcohol in the preceding month due to restricted access in his most recent living environment (a shared apartment with daily nursing assistance). He reported no lightheadedness, dizziness, palpitations, numbness, tingling, or any head trauma. He also negated the presence of active mood symptoms, auditory or visual hallucinations or suicidal ideation (SI)

The patient was admitted to the Internal Medicine Service and received a workup for the causes of delirium, including consideration of normal pressure hydrocephalus (NPH) and other neurologic conditions. Laboratory tests including a comprehensive metabolic panel, thyroid stimulating hormone, urinalysis, urine toxicology screen, and vitamin B12 and folate levels were in normal ranges. Although brain imaging revealed enlarged ventricles, NPH was considered unlikely because of the absence of ophthalmologic abnormalities, like gaze nystagmus, and urinary incontinence; conversely, there was some presence of urinary retention attributed to BPH and required an admission a few months prior. Moreover, magnetic resonance images showed that the ventricles were enlarged slightly out of proportion to the sulci, which can be seen with predominantly central volume loss compared with the pattern typically seen in NPH.

In light of concern for WE and the patient's history, treatment with IV thiamine and IV fluids was initiated and the Liaison Psychiatry Service was consulted for cognitive disability and treatment of his mood. Administration of IV thiamine rapidly restored his sensorium, but he became abruptly disorganized as the IV regimen graduated to an oral thiamine dose of 200 mg 3 times daily. Simultaneously, as medical stabilization was achieved, the patient was transferred to the inpatient psychiatry unit to address the nonresolving cognitive impairment and behavioral disorganization. This specifically involved newly emerging, impulsive, self-harming behaviors like throwing himself on the ground and banging his head on the floor. Such behaviors along with paucity of speech and decreased oral intake, ultimately warranted constant observation, which led to a decrease in self-harming activity. All this behavior was noted even though the patient was adherent to oral administration of thiamine. Throughout this time, the patient underwent several transfers back and forth between the Psychiatry and Internal Medicine services due to ongoing concern for the possibility of delirium or WE. However, the Neurology and Internal Medicine services did not feel that WE would explain the patient’s mental and behavioral status, in part due to his ongoing adherence with daily oral thiamine dosing that was not associated with improvement in mental status.

Recollecting the patient’s improvement with the parenteral thiamine regimen (IV and IM), the psychiatry unit tried a thiamine regimen of 200 mg IM and 100 mg oral 2 times daily. After about 2 weeks on this regimen, the patient subsequently achieved remarkable improvement in his cognitive and behavioral status, with resolution of selfharming behaviors. The patient was noted to be calmer, more linear, and more oriented, though he remained incompletely oriented throughout his hospitalization. As improvement in sensorium was established and the patient’s hospital stay prolonged (Figure), his mood symptoms began manifesting as guilt, low energy, decreased appetite, withdrawal, and passive SI. This was followed by a trial of lithium that was discontinued due to elevated creatine levels. As the patient continued to report depression, a multidrug regimen of divalproex, fluoxetine, and quetiapine was administered, which lead to remarkable improvement.

At this time, it was concluded that the stores of thiamine in the patient’s body may have been replenished, the alcohol intake completely ceased and that he needed to be weaned off of thiamine. The next step taken was reduction of the twice daily 200 mg IM thiamine dose to a once daily regimen, and oral thiamine was put on hold. Over the next 48 hours, the patient became less verbal, more withdrawn, incontinent of urine, and delirious. The twice daily IM 200 mg thiamine was restarted, but this time the patient demonstrated very slow improvement. After 2 weeks, the IM thiamine 200 mg was increased to 3 times daily, and the patient showed marked improvement in recall, mood, and effect.

Several attempts were made to reduce the IM thiamine burden on the patient and/ or transition to an exclusively oral regimen. However, he rapidly decompensated within hours of each attempt to taper the IM dose and required immediate reinstation. On the IM thiamine regimen, he eventually appeared to reach a stable cognitive and affective baseline marked by incomplete orientation but pleasant affect, he reported no mood complaints, behavioral stability, and an ability to comply with care needs and have simple conversations. Some speech content remained disorganized particularly if engaged beyond simple exchanges.

The patient was discharged to a skilled nursing facility after a month of 3 times daily IM administration of thiamine. Within the next 24 hours, the patient returned to the ED with the originally reported symptoms of ataxia, agitation, and confusion. On inquiry, it was revealed that the ordered vials of IM thiamine for injection had not arrived with him at the nursing facility and he had missed 2 doses. The blood laboratory results, scans, and all other parameters were otherwise found to be normal and the patient was adherent to his prescribed antipsychotics and antidepressants. As anticipated, restoration of the IM thiamine regimen revived his baseline within hours. While confusion and delirium resolved completely with treatment, the memory impairments persisted. This patient has been administered a 3 times daily IM dose of 200 mg thiamine for more than 2 years with a stable cognitive clinical picture.

Discussion

According to data from the 2016 National Survey on Drug Use and Health, 16 million individuals in the US aged ≥ 12 years reported heavy alcohol use, which is defined as binge drinking on ≥ 5 days in the past month.6,7 Thiamine deficiency is an alcoholrelated disorder that is frequently encountered in hospital settings. This deficiency can also occur in the context of malabsorption, malnutrition, a prolonged course of vomiting, and bariatric surgery.^8,9

The deficiency in thiamine, which is sometimes known as WE, manifests rarely with all 3 of the classic triad of gait disturbances, abnormal eye movements, and mental status changes, with only 16.5% of patients displaying all of the triad.⁴ Moreover, there may be additional symptoms not listed in this triad, such as memory impairment, bilateral sixth nerve palsy, ptosis, hypotension, and hypothermia.^10.11 This inconsistent presentation makes the diagnosis challenging and therefore requires a higher threshold for suspicion. If undiagnosed and/or untreated, WE can lead to chronic thiamine deficiency causing permanent brain damage in the guise of KS. This further increases the importance of timely diagnosis and treatment.

Our case highlights the utilization of an unconventional thiamine regimen that appeared to be temporally associated with mental status improvement. The patient’s clouded sensorium and confusion could not be attributed to metabolic, encephalopathic, or infectious pathologies due to the absence of supportive laboratory evidence. He responded to IV and IM doses of thiamine, but repeated attempts to taper the IM doses with the objective of transitioning to oral thiamine supplementation were followed by immediate decompensations in mental status. This was atypical of WE as the patient seemed adequately replete with thiamine, and missing a few doses should not be enough to deplete his stores. Thus, reflecting a unique case of thiamine-dependent chronically set WE when even a single missed dose of thiamine adversely affected the patient’s cognitive baseline. Interesting to note is this patient’s memory issue, as evident by clinical examination and dating back at least 5 years as per chart review. This premature amnestic component of his presentation indicates a likely parallel running KS component of his presentation. Conversely, the patient’s long history of alcohol use disorder, prior episodes of WE, and ideal response achieved only on parenteral thiamine repletion further supported the diagnosis of WE and our impression of the scenario.

Even though this patient had prior episodes of WE, there remained diagnostic uncertainty regarding his altered mental status for some time before the nonoral thiamine repletion treatment was implemented. Particularly in this admission, the patient’s mental status frequently waxed and waned and there was the additional confusion of whether a potential psychiatric etiology contributed to some of the elements of his presentation, such as his impulsive self-harm behaviors. This behavior led to recurrent transfers among the Psychiatry Service, Internal Medicine Service, and the ED.

The patient’s presentation did not reflect the classical triad of WE, and while this is consistent with the majority of clinical manifestations, various services were reluctant to attribute his symptoms to WE. Once the threshold of suspicion of thiamine deficiency was lowered and the deficit treated more aggressively, the patient seemed to improve tremendously. Presence of memory problems and confabulation, both of which this patient exhibited, are suggestive of KS and are not expected to recover with treatment, yet for this patient there did seem to be some improvement—though not complete resolution. This is consistent with newer evidence suggesting that some recovery from the deficits seen in KS is possible.³

Once diagnosed, the treatment objective is the replenishment of thiamine stores and optimization of the metabolic scenario of the body to prevent recurrence. For acute WE symptoms, many regimens call for 250 to 500 mg of IV thiamine supplementation 2 to 3 times daily for 3 to 5 days. High dose IV thiamine (≥ 500 mg daily) has been proposed to be efficacious and free of considerable adverse effects.¹² A study conducted at the University of North Carolina described thiamine prescribing practices in a large academic hospital, analyzing data with the objective of assessing outcomes of ordering high-dose IV thiamine (HDIV, ≥ 200 mg IV twice daily) to patients with encephalopathy. ¹³ The researchers concluded that HDIV, even though rarely prescribed, was associated with decreased inpatient mortality in bivariable models. However, in multivariable analyses this decrease was found to be clinically insignificant. Our patient benefitted from both IV and IM delivery.

Ideally, after the initial IV thiamine dose, oral administration of thiamine 250 to 1,000 mg is continued until a reduction, if not abstinence, from alcohol use is achieved.⁵ Many patients are discharged on an oral maintenance dose of thiamine 100 mg. Oral thiamine is poorly absorbed and less effective in both prophylaxis and treatment of newly diagnosed WE; therefore, it is typically used only after IM or IV replenishment. It remains unclear why this patient required IM thiamine multiple times per day to maintain his mental status, and why he would present with selfinjurious behaviors after missing doses. The patient’s response can be attributed to late-onset defects in oral thiamine absorption at the carrier protein level of the brush border and basolateral membranes of his jejunum; however, an invasive procedure like a jejunal biopsy to establish the definitive etiology was neither necessary nor practical once treatment response was observed. ¹⁴ Other possible explanations include rapid thiamine metabolism, poor gastrointestinal absorption and a late-onset deficit in the thiamine diffusion mechanisms, and active transport systems (thiamine utilization depends on active transport in low availability states and passive transport when readily available). The nature of these mechanisms deserves further study. Less data have been reported on the administration and utility of IM thiamine for chronic WE; hence, our case report is one of the first illustrating the role of this method for sustained repletion.

Conclusions

This case presented a clinical dilemma because the conventional treatment regimen for WE didn’t yield the desired outcome until the mode and duration of thiamine administration were adjusted. It illustrates the utility of a sustained intensive thiamine regimen irrespective of sobriety status, as opposed to the traditional regimen of parenteral (primarily IV) thiamine for 3 to 7 days, followed by oral repletion until the patient achieves sustained abstinence. In this patient’s case, access to nursing care postdischarge facilitated his continued adherence to IM thiamine therapy.

The longitudinal time course of this case suggests a relationship between this route of administration and improvement in symptom burden and indicates that this patient may have a long-term need for IM thiamine to maintain his baseline mental status. Of great benefit in such patients would be the availability of a long-acting IM thiamine therapy. Risk of overdose is unlikely due to the water solubility of B group vitamins.

This case report highlights the importance of setting a high clinical suspicion for WE due to its ever-increasing incidence in these times. We also wish to direct researchers to consider other out-of-the-box treatment options in case of failure of the conventional regime. In documenting this patient report, we invite more medical providers to investigate and explore other therapeutic options for WE treatment with the aim of decreasing both morbidity and mortality secondary to the condition.

Children with medical complexity account for 30% of pediatric hospitalizations and half of all pediatric hospital costs.¹ They frequently experience long lengths of stay (LOS), which are associated with hospital-acquired infections, high costs, and family stress.

In this issue of the Journal of Hospital Medicine, Steuart and colleagues investigate one opportunity to decrease LOS in a subset of children with medical complexity by studying the impact of discharge before patients’ return to their respiratory baseline status.² They examined 632 hospitalizations in children with neurologic impairment who required increased respiratory support for acute respiratory infections. After adjustment for demographic characteristics, clinical complexity, and acute illness severity, there was no difference in the risk of 30-day hospital reutilization (ie, emergency department revisits and readmissions) when comparing the 30% of children discharged before returning to their respiratory baseline with the 70% discharged at baseline (reutilization rates of 32.8% and 31.8%, respectively).

Twenty-six percent required readmission. This rate is four times that reported for children overall, and higher than the rate for children with the top 10 chronic conditions (range, 6%-21%).³ It also exceeds the median 30-day risk-standardized readmission rates for adult conditions targeted by the Centers for Medicare & Medicaid Services (range, 12%-22%).⁴ The high readmission rate demonstrates the vulnerability of this population and their need for support in hospital-to-home transitions.

These results suggest important areas for future research. First, the findings need to be replicated by multicenter studies to better understand their generalizability. Second, we need more information about the respiratory support required at discharge, which was not captured in this study. For example, clinicians and families may be more comfortable with discharge for a patient who needs slightly higher levels of their baseline support rather than a new modality of respiratory support. Third, we need to better understand the home context of patients discharged before return to respiratory baseline. Lack of home nursing, in particular, has been associated with discharge delays and prolonged LOS in this population.

This study prompts reconsideration of discharge criteria for acute respiratory infections, which often include return to respiratory baseline. Discharge before respiratory baseline for healthy children with bronchiolitis who were discharged on home supplemental oxygen has been associated with shorter hospitalizations and lower costs without differences in reutilization.⁵ Steuart and colleagues demonstrate the potential of this approach in children with neurologic impairment. One key question remains: Which children are most appropriate for discharge before return to respiratory baseline? Family engagement in discussions of goals of hospitalization, self-efficacy, and discharge readiness are important.⁶ These conversations provide context that informs discharge decisions. If the patient is stable and both the medical team and family are comfortable with discharge before respiratory baseline, there may be opportunities to engage in shared decision-making around discharge criteria.

The vulnerability of this population, evidenced by their high rates of readmission, reinforces the importance of family engagement, understanding these children’s diverse needs, and further research to identify effective interventions to support safe transitions from hospital to home.

Children with medical complexity account for 30% of pediatric hospitalizations and half of all pediatric hospital costs.¹ They frequently experience long lengths of stay (LOS), which are associated with hospital-acquired infections, high costs, and family stress.

In this issue of the Journal of Hospital Medicine, Steuart and colleagues investigate one opportunity to decrease LOS in a subset of children with medical complexity by studying the impact of discharge before patients’ return to their respiratory baseline status.² They examined 632 hospitalizations in children with neurologic impairment who required increased respiratory support for acute respiratory infections. After adjustment for demographic characteristics, clinical complexity, and acute illness severity, there was no difference in the risk of 30-day hospital reutilization (ie, emergency department revisits and readmissions) when comparing the 30% of children discharged before returning to their respiratory baseline with the 70% discharged at baseline (reutilization rates of 32.8% and 31.8%, respectively).

Twenty-six percent required readmission. This rate is four times that reported for children overall, and higher than the rate for children with the top 10 chronic conditions (range, 6%-21%).³ It also exceeds the median 30-day risk-standardized readmission rates for adult conditions targeted by the Centers for Medicare & Medicaid Services (range, 12%-22%).⁴ The high readmission rate demonstrates the vulnerability of this population and their need for support in hospital-to-home transitions.

These results suggest important areas for future research. First, the findings need to be replicated by multicenter studies to better understand their generalizability. Second, we need more information about the respiratory support required at discharge, which was not captured in this study. For example, clinicians and families may be more comfortable with discharge for a patient who needs slightly higher levels of their baseline support rather than a new modality of respiratory support. Third, we need to better understand the home context of patients discharged before return to respiratory baseline. Lack of home nursing, in particular, has been associated with discharge delays and prolonged LOS in this population.

This study prompts reconsideration of discharge criteria for acute respiratory infections, which often include return to respiratory baseline. Discharge before respiratory baseline for healthy children with bronchiolitis who were discharged on home supplemental oxygen has been associated with shorter hospitalizations and lower costs without differences in reutilization.⁵ Steuart and colleagues demonstrate the potential of this approach in children with neurologic impairment. One key question remains: Which children are most appropriate for discharge before return to respiratory baseline? Family engagement in discussions of goals of hospitalization, self-efficacy, and discharge readiness are important.⁶ These conversations provide context that informs discharge decisions. If the patient is stable and both the medical team and family are comfortable with discharge before respiratory baseline, there may be opportunities to engage in shared decision-making around discharge criteria.

The vulnerability of this population, evidenced by their high rates of readmission, reinforces the importance of family engagement, understanding these children’s diverse needs, and further research to identify effective interventions to support safe transitions from hospital to home.

Children with medical complexity account for 30% of pediatric hospitalizations and half of all pediatric hospital costs.¹ They frequently experience long lengths of stay (LOS), which are associated with hospital-acquired infections, high costs, and family stress.

In this issue of the Journal of Hospital Medicine, Steuart and colleagues investigate one opportunity to decrease LOS in a subset of children with medical complexity by studying the impact of discharge before patients’ return to their respiratory baseline status.² They examined 632 hospitalizations in children with neurologic impairment who required increased respiratory support for acute respiratory infections. After adjustment for demographic characteristics, clinical complexity, and acute illness severity, there was no difference in the risk of 30-day hospital reutilization (ie, emergency department revisits and readmissions) when comparing the 30% of children discharged before returning to their respiratory baseline with the 70% discharged at baseline (reutilization rates of 32.8% and 31.8%, respectively).

Twenty-six percent required readmission. This rate is four times that reported for children overall, and higher than the rate for children with the top 10 chronic conditions (range, 6%-21%).³ It also exceeds the median 30-day risk-standardized readmission rates for adult conditions targeted by the Centers for Medicare & Medicaid Services (range, 12%-22%).⁴ The high readmission rate demonstrates the vulnerability of this population and their need for support in hospital-to-home transitions.

These results suggest important areas for future research. First, the findings need to be replicated by multicenter studies to better understand their generalizability. Second, we need more information about the respiratory support required at discharge, which was not captured in this study. For example, clinicians and families may be more comfortable with discharge for a patient who needs slightly higher levels of their baseline support rather than a new modality of respiratory support. Third, we need to better understand the home context of patients discharged before return to respiratory baseline. Lack of home nursing, in particular, has been associated with discharge delays and prolonged LOS in this population.

This study prompts reconsideration of discharge criteria for acute respiratory infections, which often include return to respiratory baseline. Discharge before respiratory baseline for healthy children with bronchiolitis who were discharged on home supplemental oxygen has been associated with shorter hospitalizations and lower costs without differences in reutilization.⁵ Steuart and colleagues demonstrate the potential of this approach in children with neurologic impairment. One key question remains: Which children are most appropriate for discharge before return to respiratory baseline? Family engagement in discussions of goals of hospitalization, self-efficacy, and discharge readiness are important.⁶ These conversations provide context that informs discharge decisions. If the patient is stable and both the medical team and family are comfortable with discharge before respiratory baseline, there may be opportunities to engage in shared decision-making around discharge criteria.

The vulnerability of this population, evidenced by their high rates of readmission, reinforces the importance of family engagement, understanding these children’s diverse needs, and further research to identify effective interventions to support safe transitions from hospital to home.

Despite concerted national efforts to decrease pediatric readmissions, recent data suggest that preventable and all-cause readmission rates of hospitalized children remain unchanged.¹ Because some readmissions may be caused by inadequate postdischarge follow-up, nurse (RN) home visits offer the prospect of addressing unresolved clinical issues after discharge and ameliorating patient and family concerns that may otherwise prompt re-presentation for acute care. Yet a recent trial of this approach, the Hospital to Home Outcomes (H2O) trial,² found the opposite to be true: participants receiving home nurse visits had higher reutilization rates than did participants in the control group. This raises interesting questions: Is it time to revisit postdischarge outreach as an intervention to reduce pediatric readmissions—and even pediatric readmissions altogether as an outcome metric?

In this issue of the Journal of Hospital Medicine, Riddle et al³ explored the perspectives of key stakeholders to understand the factors driving increased reutilization after postdischarge home visits in the H2O trial and obtained feedback for improving potential interventions. The investigators used a qualitative approach that consisted of telephone interviews with 33 parents who were enrolled in the H2O trial and in-person focus groups with 10 home care RNs involved in the trial, 12 hospital medicine physicians, and 7 primary care physicians (PCPs). Inductive thematic analysis was used to analyze responses to open-ended questions through a rigorous, iterative and multidisciplinary process. Key themes elicited from stakeholders included questions about the clinical appropriateness of reutilization episodes; the influence of insufficiently contextualized “red flag,” or warning sign, instructions given to parents in facilitating reutilization; the potential for hospital-employed home care nurses to inadvertently promote emergency department rather than PCP follow-up; and escalation of care exceeding that expected in a PCP office. Stakeholders suggested the intervention could be improved by enhancing postdischarge communication between home care RNs, hospital medicine physicians, and PCPs; tailoring home visits to specific clinical, patient, and family scenarios; and more clearly framing “red flags.”

We welcome the work of Riddle and colleagues in exposing the elements of home visits that may have led to increased utilization, and their proposed next steps to improve the intervention—enhancing contact with PCP offices and focusing interventions on specific populations—unquestionably have merit. We agree that this may be particularly true in children with medical complexity (a population that was excluded from this study), who have unique discharge needs and account for over half of pediatric readmissions.⁴ However, we suggest that the instinct to refine the design of the study intervention should be weighed against alternative possibilities: that postdischarge interventions are simply not effective in decreasing reutilization or, at the very least, that the findings of the H2O trial should not lead us to invest the resources required to further discern the efficacy of postdischarge interventions.

This counter-intuitive possibility is only compounded by the fact that reutilization rates were not improved in the study group’s H2O II trial, a follow-up study that focused on postdischarge nurse telephone calls as the intervention of interest⁵; and indeed, the results of these two, well-designed negative trials have been previously cited to propose postdischarge nurse contact as a potential target of deimplementation efforts.⁶ In the pediatric population, in which caregivers rather than patients themselves are generally responsible for seeking out care, postdischarge outreach may inevitably escalate concerning findings that will result in reutilization. Instead, perhaps the H2O study findings should prompt a broader exploration for alternative solutions to pediatric readmission reduction. One such solution could build on the finding by Riddle et al that stakeholders perceive ambiguity in whether discharging physicians, or rather PCPs, have ownership of clinical issues after discharge. Rather than asking visiting RNs to triangulate between inpatient and outpatient physicians, developing systems to directly integrate PCPs in the hospital discharge process for select patients—for instance, through leveraging the rapid expansion of telemedicine services during the COVID-19 crisis—may promote shared understanding of a patient’s illness trajectory and follow-up needs.

Importantly, the authors also noted that despite the findings of increased reutilization, parents who received home visits expressed their wishes to receive home visits in the future. While not a central finding of the study, this validates a hypothesis expressed in prior work by the H2O study group: “Hospital quality readmission metrics may not be well aligned with family desires for improved postdischarge transitions.”⁵ Given that efforts to reduce pediatric readmission have been largely unsuccessful and that readmission events are relatively uncommon in the general pediatric population,⁴ the parental wishes resonate with existing calls in the literature to consider looking beyond readmissions reduction in isolation as a quality metric. In contrast to the increasing presence of hospital reimbursement penalties among state Medicaid agencies for readmissions, a shift in focus toward outcome measures that are patient- and family-centered is imperative.^1,7 If home visits are not ultimately a solution to pediatric reutilization reduction, they may nonetheless still enable families to effectively manage the concerns that families endorse following discharge, including medication safety and social hardships.⁸

In summary, Riddle et al not only provided important context for the unexpected outcome of a well-designed randomized clinical trial but also provided a rich source of qualitative data that furthers our understanding of a child’s discharge home from the hospital through the perspective of multiple stakeholders. While the authors offer well-reasoned next steps in narrowing the intervention population of interest and enhancing connections of families with PCP care, it may be time to broadly revisit postdischarge interventions and outcomes to identify new approaches and redefine quality measures for hospital-to-home transitions of children and their families.

Despite concerted national efforts to decrease pediatric readmissions, recent data suggest that preventable and all-cause readmission rates of hospitalized children remain unchanged.¹ Because some readmissions may be caused by inadequate postdischarge follow-up, nurse (RN) home visits offer the prospect of addressing unresolved clinical issues after discharge and ameliorating patient and family concerns that may otherwise prompt re-presentation for acute care. Yet a recent trial of this approach, the Hospital to Home Outcomes (H2O) trial,² found the opposite to be true: participants receiving home nurse visits had higher reutilization rates than did participants in the control group. This raises interesting questions: Is it time to revisit postdischarge outreach as an intervention to reduce pediatric readmissions—and even pediatric readmissions altogether as an outcome metric?

In this issue of the Journal of Hospital Medicine, Riddle et al³ explored the perspectives of key stakeholders to understand the factors driving increased reutilization after postdischarge home visits in the H2O trial and obtained feedback for improving potential interventions. The investigators used a qualitative approach that consisted of telephone interviews with 33 parents who were enrolled in the H2O trial and in-person focus groups with 10 home care RNs involved in the trial, 12 hospital medicine physicians, and 7 primary care physicians (PCPs). Inductive thematic analysis was used to analyze responses to open-ended questions through a rigorous, iterative and multidisciplinary process. Key themes elicited from stakeholders included questions about the clinical appropriateness of reutilization episodes; the influence of insufficiently contextualized “red flag,” or warning sign, instructions given to parents in facilitating reutilization; the potential for hospital-employed home care nurses to inadvertently promote emergency department rather than PCP follow-up; and escalation of care exceeding that expected in a PCP office. Stakeholders suggested the intervention could be improved by enhancing postdischarge communication between home care RNs, hospital medicine physicians, and PCPs; tailoring home visits to specific clinical, patient, and family scenarios; and more clearly framing “red flags.”

We welcome the work of Riddle and colleagues in exposing the elements of home visits that may have led to increased utilization, and their proposed next steps to improve the intervention—enhancing contact with PCP offices and focusing interventions on specific populations—unquestionably have merit. We agree that this may be particularly true in children with medical complexity (a population that was excluded from this study), who have unique discharge needs and account for over half of pediatric readmissions.⁴ However, we suggest that the instinct to refine the design of the study intervention should be weighed against alternative possibilities: that postdischarge interventions are simply not effective in decreasing reutilization or, at the very least, that the findings of the H2O trial should not lead us to invest the resources required to further discern the efficacy of postdischarge interventions.

This counter-intuitive possibility is only compounded by the fact that reutilization rates were not improved in the study group’s H2O II trial, a follow-up study that focused on postdischarge nurse telephone calls as the intervention of interest⁵; and indeed, the results of these two, well-designed negative trials have been previously cited to propose postdischarge nurse contact as a potential target of deimplementation efforts.⁶ In the pediatric population, in which caregivers rather than patients themselves are generally responsible for seeking out care, postdischarge outreach may inevitably escalate concerning findings that will result in reutilization. Instead, perhaps the H2O study findings should prompt a broader exploration for alternative solutions to pediatric readmission reduction. One such solution could build on the finding by Riddle et al that stakeholders perceive ambiguity in whether discharging physicians, or rather PCPs, have ownership of clinical issues after discharge. Rather than asking visiting RNs to triangulate between inpatient and outpatient physicians, developing systems to directly integrate PCPs in the hospital discharge process for select patients—for instance, through leveraging the rapid expansion of telemedicine services during the COVID-19 crisis—may promote shared understanding of a patient’s illness trajectory and follow-up needs.

Importantly, the authors also noted that despite the findings of increased reutilization, parents who received home visits expressed their wishes to receive home visits in the future. While not a central finding of the study, this validates a hypothesis expressed in prior work by the H2O study group: “Hospital quality readmission metrics may not be well aligned with family desires for improved postdischarge transitions.”⁵ Given that efforts to reduce pediatric readmission have been largely unsuccessful and that readmission events are relatively uncommon in the general pediatric population,⁴ the parental wishes resonate with existing calls in the literature to consider looking beyond readmissions reduction in isolation as a quality metric. In contrast to the increasing presence of hospital reimbursement penalties among state Medicaid agencies for readmissions, a shift in focus toward outcome measures that are patient- and family-centered is imperative.^1,7 If home visits are not ultimately a solution to pediatric reutilization reduction, they may nonetheless still enable families to effectively manage the concerns that families endorse following discharge, including medication safety and social hardships.⁸

In summary, Riddle et al not only provided important context for the unexpected outcome of a well-designed randomized clinical trial but also provided a rich source of qualitative data that furthers our understanding of a child’s discharge home from the hospital through the perspective of multiple stakeholders. While the authors offer well-reasoned next steps in narrowing the intervention population of interest and enhancing connections of families with PCP care, it may be time to broadly revisit postdischarge interventions and outcomes to identify new approaches and redefine quality measures for hospital-to-home transitions of children and their families.

Despite concerted national efforts to decrease pediatric readmissions, recent data suggest that preventable and all-cause readmission rates of hospitalized children remain unchanged.¹ Because some readmissions may be caused by inadequate postdischarge follow-up, nurse (RN) home visits offer the prospect of addressing unresolved clinical issues after discharge and ameliorating patient and family concerns that may otherwise prompt re-presentation for acute care. Yet a recent trial of this approach, the Hospital to Home Outcomes (H2O) trial,² found the opposite to be true: participants receiving home nurse visits had higher reutilization rates than did participants in the control group. This raises interesting questions: Is it time to revisit postdischarge outreach as an intervention to reduce pediatric readmissions—and even pediatric readmissions altogether as an outcome metric?

In this issue of the Journal of Hospital Medicine, Riddle et al³ explored the perspectives of key stakeholders to understand the factors driving increased reutilization after postdischarge home visits in the H2O trial and obtained feedback for improving potential interventions. The investigators used a qualitative approach that consisted of telephone interviews with 33 parents who were enrolled in the H2O trial and in-person focus groups with 10 home care RNs involved in the trial, 12 hospital medicine physicians, and 7 primary care physicians (PCPs). Inductive thematic analysis was used to analyze responses to open-ended questions through a rigorous, iterative and multidisciplinary process. Key themes elicited from stakeholders included questions about the clinical appropriateness of reutilization episodes; the influence of insufficiently contextualized “red flag,” or warning sign, instructions given to parents in facilitating reutilization; the potential for hospital-employed home care nurses to inadvertently promote emergency department rather than PCP follow-up; and escalation of care exceeding that expected in a PCP office. Stakeholders suggested the intervention could be improved by enhancing postdischarge communication between home care RNs, hospital medicine physicians, and PCPs; tailoring home visits to specific clinical, patient, and family scenarios; and more clearly framing “red flags.”

We welcome the work of Riddle and colleagues in exposing the elements of home visits that may have led to increased utilization, and their proposed next steps to improve the intervention—enhancing contact with PCP offices and focusing interventions on specific populations—unquestionably have merit. We agree that this may be particularly true in children with medical complexity (a population that was excluded from this study), who have unique discharge needs and account for over half of pediatric readmissions.⁴ However, we suggest that the instinct to refine the design of the study intervention should be weighed against alternative possibilities: that postdischarge interventions are simply not effective in decreasing reutilization or, at the very least, that the findings of the H2O trial should not lead us to invest the resources required to further discern the efficacy of postdischarge interventions.

This counter-intuitive possibility is only compounded by the fact that reutilization rates were not improved in the study group’s H2O II trial, a follow-up study that focused on postdischarge nurse telephone calls as the intervention of interest⁵; and indeed, the results of these two, well-designed negative trials have been previously cited to propose postdischarge nurse contact as a potential target of deimplementation efforts.⁶ In the pediatric population, in which caregivers rather than patients themselves are generally responsible for seeking out care, postdischarge outreach may inevitably escalate concerning findings that will result in reutilization. Instead, perhaps the H2O study findings should prompt a broader exploration for alternative solutions to pediatric readmission reduction. One such solution could build on the finding by Riddle et al that stakeholders perceive ambiguity in whether discharging physicians, or rather PCPs, have ownership of clinical issues after discharge. Rather than asking visiting RNs to triangulate between inpatient and outpatient physicians, developing systems to directly integrate PCPs in the hospital discharge process for select patients—for instance, through leveraging the rapid expansion of telemedicine services during the COVID-19 crisis—may promote shared understanding of a patient’s illness trajectory and follow-up needs.

Importantly, the authors also noted that despite the findings of increased reutilization, parents who received home visits expressed their wishes to receive home visits in the future. While not a central finding of the study, this validates a hypothesis expressed in prior work by the H2O study group: “Hospital quality readmission metrics may not be well aligned with family desires for improved postdischarge transitions.”⁵ Given that efforts to reduce pediatric readmission have been largely unsuccessful and that readmission events are relatively uncommon in the general pediatric population,⁴ the parental wishes resonate with existing calls in the literature to consider looking beyond readmissions reduction in isolation as a quality metric. In contrast to the increasing presence of hospital reimbursement penalties among state Medicaid agencies for readmissions, a shift in focus toward outcome measures that are patient- and family-centered is imperative.^1,7 If home visits are not ultimately a solution to pediatric reutilization reduction, they may nonetheless still enable families to effectively manage the concerns that families endorse following discharge, including medication safety and social hardships.⁸

In summary, Riddle et al not only provided important context for the unexpected outcome of a well-designed randomized clinical trial but also provided a rich source of qualitative data that furthers our understanding of a child’s discharge home from the hospital through the perspective of multiple stakeholders. While the authors offer well-reasoned next steps in narrowing the intervention population of interest and enhancing connections of families with PCP care, it may be time to broadly revisit postdischarge interventions and outcomes to identify new approaches and redefine quality measures for hospital-to-home transitions of children and their families.

“Confidence comes from being prepared.”

—John Wooden

Hospital medicine is a field that requires a constant state of readiness and flexibility. With respect to patient care, constant preparedness is required because conditions change. This necessitates always having a backup plan, or Plan B. For example, your patient with a gastrointestinal (GI) bleed should have two large-bore intravenous (IV) catheters and packed red blood cells (RBCs) typed and crossed. If the patient becomes unstable, the response is not just doing more of the same (IV fluids and proton pump inhibitors); the focus shifts to your Plan B: call GI, transfuse blood, transfer the patient to the intensive care unit.

In contrast to clinical scenarios, there is often a lack of readiness to deal with rapid changes in workflow. Without a plan, efficiency decreases, stress levels rise, and both patients and providers alike suffer the consequences. Patients spending extended periods of time in the Emergency Department (ED) receive less timely services and often don’t benefit from the expertise that they would receive in inpatient units.¹ This is particularly true in an era in which many hospitals are experiencing higher overall volume and surges are more common.

Ideally, readiness should manifest as the ability to adapt to changes at the individual, hospitalist team, and leadership levels. Having a Plan B in the practice of hospital medicine is a focused exercise for anticipating future problems and addressing them prospectively. When thinking about a Plan B, the following are some steps to consider:

1. Identify Triggers. In the earlier example of the GI bleed, our triggers for Plan B would be a change in vitals or a brisk drop in hemoglobin. Regarding hospital workflow, the triggers might include low service or bed capacity or a decreased number of expected discharges for the day. Perhaps a high ED census or increased surgical volume will trigger your plan to handle the surge.

2. Define Your Response. At both an individual and service level, there are steps you might consider in your Plan B. On teaching services, this might mean prioritizing rounding on patients that you’re expecting to discharge so they’re able to leave the hospital sooner. For patients on observation status who are boarding in the ED for extended periods, there might be opportunities to safely discharge them with follow-up or even complete their work-up in the ED. There may be circumstances in which providers should exceed the usual service capacity and conditions in which it is truly unsafe to exceed that limit. If there are resources available to increase staffing, consider how to best utilize them.

3. Engage Broadly and Proactively. It is very difficult to execute a Plan B (or frankly a Plan A) without buy-in from your stakeholders. This starts with the rank and file, those on your team who will actually execute the plan. The leadership of your department or division, the ED, and nursing will also likely need to provide input. If financial resources for flexing up staff are part of your plan, the hospital administration might need to weigh in. It is best to engage stakeholders early on rather than during a crisis.

4. Constant Assessment and Improvement. Going back to our example of our patient with a GI bleed, you’re constantly reevaluating your patient to determine if your Plan B is working. Similarly, you should collect data and reassess the effectiveness of your plan. There are likely opportunities to improve it.

There are no textbook chapters or medical school lectures to prepare hospitalists for these real-world crises. Yet failing to have a Plan B is to surrender a tremendous amount of personal control in the face of chaos, to jeopardize patient care, and to ultimately forgo the opportunity to achieve a level of mastery in a field predicated on readiness.

“Confidence comes from being prepared.”

—John Wooden

Hospital medicine is a field that requires a constant state of readiness and flexibility. With respect to patient care, constant preparedness is required because conditions change. This necessitates always having a backup plan, or Plan B. For example, your patient with a gastrointestinal (GI) bleed should have two large-bore intravenous (IV) catheters and packed red blood cells (RBCs) typed and crossed. If the patient becomes unstable, the response is not just doing more of the same (IV fluids and proton pump inhibitors); the focus shifts to your Plan B: call GI, transfuse blood, transfer the patient to the intensive care unit.

In contrast to clinical scenarios, there is often a lack of readiness to deal with rapid changes in workflow. Without a plan, efficiency decreases, stress levels rise, and both patients and providers alike suffer the consequences. Patients spending extended periods of time in the Emergency Department (ED) receive less timely services and often don’t benefit from the expertise that they would receive in inpatient units.¹ This is particularly true in an era in which many hospitals are experiencing higher overall volume and surges are more common.

Ideally, readiness should manifest as the ability to adapt to changes at the individual, hospitalist team, and leadership levels. Having a Plan B in the practice of hospital medicine is a focused exercise for anticipating future problems and addressing them prospectively. When thinking about a Plan B, the following are some steps to consider:

1. Identify Triggers. In the earlier example of the GI bleed, our triggers for Plan B would be a change in vitals or a brisk drop in hemoglobin. Regarding hospital workflow, the triggers might include low service or bed capacity or a decreased number of expected discharges for the day. Perhaps a high ED census or increased surgical volume will trigger your plan to handle the surge.

2. Define Your Response. At both an individual and service level, there are steps you might consider in your Plan B. On teaching services, this might mean prioritizing rounding on patients that you’re expecting to discharge so they’re able to leave the hospital sooner. For patients on observation status who are boarding in the ED for extended periods, there might be opportunities to safely discharge them with follow-up or even complete their work-up in the ED. There may be circumstances in which providers should exceed the usual service capacity and conditions in which it is truly unsafe to exceed that limit. If there are resources available to increase staffing, consider how to best utilize them.

3. Engage Broadly and Proactively. It is very difficult to execute a Plan B (or frankly a Plan A) without buy-in from your stakeholders. This starts with the rank and file, those on your team who will actually execute the plan. The leadership of your department or division, the ED, and nursing will also likely need to provide input. If financial resources for flexing up staff are part of your plan, the hospital administration might need to weigh in. It is best to engage stakeholders early on rather than during a crisis.

4. Constant Assessment and Improvement. Going back to our example of our patient with a GI bleed, you’re constantly reevaluating your patient to determine if your Plan B is working. Similarly, you should collect data and reassess the effectiveness of your plan. There are likely opportunities to improve it.

There are no textbook chapters or medical school lectures to prepare hospitalists for these real-world crises. Yet failing to have a Plan B is to surrender a tremendous amount of personal control in the face of chaos, to jeopardize patient care, and to ultimately forgo the opportunity to achieve a level of mastery in a field predicated on readiness.

“Confidence comes from being prepared.”

—John Wooden

Hospital medicine is a field that requires a constant state of readiness and flexibility. With respect to patient care, constant preparedness is required because conditions change. This necessitates always having a backup plan, or Plan B. For example, your patient with a gastrointestinal (GI) bleed should have two large-bore intravenous (IV) catheters and packed red blood cells (RBCs) typed and crossed. If the patient becomes unstable, the response is not just doing more of the same (IV fluids and proton pump inhibitors); the focus shifts to your Plan B: call GI, transfuse blood, transfer the patient to the intensive care unit.

In contrast to clinical scenarios, there is often a lack of readiness to deal with rapid changes in workflow. Without a plan, efficiency decreases, stress levels rise, and both patients and providers alike suffer the consequences. Patients spending extended periods of time in the Emergency Department (ED) receive less timely services and often don’t benefit from the expertise that they would receive in inpatient units.¹ This is particularly true in an era in which many hospitals are experiencing higher overall volume and surges are more common.

Ideally, readiness should manifest as the ability to adapt to changes at the individual, hospitalist team, and leadership levels. Having a Plan B in the practice of hospital medicine is a focused exercise for anticipating future problems and addressing them prospectively. When thinking about a Plan B, the following are some steps to consider:

1. Identify Triggers. In the earlier example of the GI bleed, our triggers for Plan B would be a change in vitals or a brisk drop in hemoglobin. Regarding hospital workflow, the triggers might include low service or bed capacity or a decreased number of expected discharges for the day. Perhaps a high ED census or increased surgical volume will trigger your plan to handle the surge.

2. Define Your Response. At both an individual and service level, there are steps you might consider in your Plan B. On teaching services, this might mean prioritizing rounding on patients that you’re expecting to discharge so they’re able to leave the hospital sooner. For patients on observation status who are boarding in the ED for extended periods, there might be opportunities to safely discharge them with follow-up or even complete their work-up in the ED. There may be circumstances in which providers should exceed the usual service capacity and conditions in which it is truly unsafe to exceed that limit. If there are resources available to increase staffing, consider how to best utilize them.

3. Engage Broadly and Proactively. It is very difficult to execute a Plan B (or frankly a Plan A) without buy-in from your stakeholders. This starts with the rank and file, those on your team who will actually execute the plan. The leadership of your department or division, the ED, and nursing will also likely need to provide input. If financial resources for flexing up staff are part of your plan, the hospital administration might need to weigh in. It is best to engage stakeholders early on rather than during a crisis.

4. Constant Assessment and Improvement. Going back to our example of our patient with a GI bleed, you’re constantly reevaluating your patient to determine if your Plan B is working. Similarly, you should collect data and reassess the effectiveness of your plan. There are likely opportunities to improve it.

There are no textbook chapters or medical school lectures to prepare hospitalists for these real-world crises. Yet failing to have a Plan B is to surrender a tremendous amount of personal control in the face of chaos, to jeopardize patient care, and to ultimately forgo the opportunity to achieve a level of mastery in a field predicated on readiness.

Last year we pledged to lead by example and improve representation within the Journal of Hospital Medicine community.¹ By emphasizing diversity, we expand the pool of faculty to whom leadership opportunities are available. A diverse team will put forth a broader range of ideas for consideration, spur greater innovation, and promote diversity in both published content and authorship, ensuring that the spectrum of content we publish reflects and benefits all patients to whom we provide care.

We write to share our progress, first reporting on gender equity. Currently, 45% of the journal leadership team are women, increased from 30% in 2018. In the past year, we also developed processes to collect peer reviewer and author demographic information through our manuscript management system. These processes helped us understand our baseline state.

Prior to developing these processes, we discussed our goals and potential approaches with Society of Hospital Medicine leaders; medical school deans of diversity, equity, and inclusion; department chairs in pediatrics and internal medicine; women, underrepresented minorities, and LGBTQ+ faculty; and trainees. We achieved consensus as a journal leadership team and implemented a new data collection system in July 2019. We focused on first and last authors given the importance of these positions for promotion and tenure. We requested that peer reviewers and authors provide demographic data, including gender (with nonbinary as an option), race, and ethnicity; “prefer not to answer” was a response option for each question. These data were not available during the manuscript decision process. Authors who did not submit information received up to three reminder emails from the Editor-in-Chief encouraging them to provide demographic information and stating the rationale for the request. We did not use gender identifying algorithms (eg, assignment of gender probability based on name) or visit professional websites; our intent was author self-identification.

We categorized Journal of Hospital Medicine article types as research, generally solicited, and generally unsolicited (Table). Among research articles, the proportion of women and men were similar with women accounting for 47% of first authors (vs 47% men) and 33% of last authors (vs 35% men) (Table). However, 27% of last authors left this field blank. Among solicited article types, there was an equal proportion of women and men for first but not for last authors. Among unsolicited article types, a smaller proportion of women accounted for first authors. While the proportion of women and men was equal among last authors, 45% left this field blank.

Collecting author demographics and reporting our data on gender represent an important first step for the journal. In the upcoming year, we will develop strategies to obtain more complete data and report our performance on race, ethnicity, and intersectionality, and continue deliberate efforts to improve equity within all areas of the journal, including reviewer, author, and editorial roles. We are committed to continue sharing our progress.

Last year we pledged to lead by example and improve representation within the Journal of Hospital Medicine community.¹ By emphasizing diversity, we expand the pool of faculty to whom leadership opportunities are available. A diverse team will put forth a broader range of ideas for consideration, spur greater innovation, and promote diversity in both published content and authorship, ensuring that the spectrum of content we publish reflects and benefits all patients to whom we provide care.

We write to share our progress, first reporting on gender equity. Currently, 45% of the journal leadership team are women, increased from 30% in 2018. In the past year, we also developed processes to collect peer reviewer and author demographic information through our manuscript management system. These processes helped us understand our baseline state.

Prior to developing these processes, we discussed our goals and potential approaches with Society of Hospital Medicine leaders; medical school deans of diversity, equity, and inclusion; department chairs in pediatrics and internal medicine; women, underrepresented minorities, and LGBTQ+ faculty; and trainees. We achieved consensus as a journal leadership team and implemented a new data collection system in July 2019. We focused on first and last authors given the importance of these positions for promotion and tenure. We requested that peer reviewers and authors provide demographic data, including gender (with nonbinary as an option), race, and ethnicity; “prefer not to answer” was a response option for each question. These data were not available during the manuscript decision process. Authors who did not submit information received up to three reminder emails from the Editor-in-Chief encouraging them to provide demographic information and stating the rationale for the request. We did not use gender identifying algorithms (eg, assignment of gender probability based on name) or visit professional websites; our intent was author self-identification.

We categorized Journal of Hospital Medicine article types as research, generally solicited, and generally unsolicited (Table). Among research articles, the proportion of women and men were similar with women accounting for 47% of first authors (vs 47% men) and 33% of last authors (vs 35% men) (Table). However, 27% of last authors left this field blank. Among solicited article types, there was an equal proportion of women and men for first but not for last authors. Among unsolicited article types, a smaller proportion of women accounted for first authors. While the proportion of women and men was equal among last authors, 45% left this field blank.

Collecting author demographics and reporting our data on gender represent an important first step for the journal. In the upcoming year, we will develop strategies to obtain more complete data and report our performance on race, ethnicity, and intersectionality, and continue deliberate efforts to improve equity within all areas of the journal, including reviewer, author, and editorial roles. We are committed to continue sharing our progress.

Last year we pledged to lead by example and improve representation within the Journal of Hospital Medicine community.¹ By emphasizing diversity, we expand the pool of faculty to whom leadership opportunities are available. A diverse team will put forth a broader range of ideas for consideration, spur greater innovation, and promote diversity in both published content and authorship, ensuring that the spectrum of content we publish reflects and benefits all patients to whom we provide care.

We write to share our progress, first reporting on gender equity. Currently, 45% of the journal leadership team are women, increased from 30% in 2018. In the past year, we also developed processes to collect peer reviewer and author demographic information through our manuscript management system. These processes helped us understand our baseline state.

Prior to developing these processes, we discussed our goals and potential approaches with Society of Hospital Medicine leaders; medical school deans of diversity, equity, and inclusion; department chairs in pediatrics and internal medicine; women, underrepresented minorities, and LGBTQ+ faculty; and trainees. We achieved consensus as a journal leadership team and implemented a new data collection system in July 2019. We focused on first and last authors given the importance of these positions for promotion and tenure. We requested that peer reviewers and authors provide demographic data, including gender (with nonbinary as an option), race, and ethnicity; “prefer not to answer” was a response option for each question. These data were not available during the manuscript decision process. Authors who did not submit information received up to three reminder emails from the Editor-in-Chief encouraging them to provide demographic information and stating the rationale for the request. We did not use gender identifying algorithms (eg, assignment of gender probability based on name) or visit professional websites; our intent was author self-identification.

We categorized Journal of Hospital Medicine article types as research, generally solicited, and generally unsolicited (Table). Among research articles, the proportion of women and men were similar with women accounting for 47% of first authors (vs 47% men) and 33% of last authors (vs 35% men) (Table). However, 27% of last authors left this field blank. Among solicited article types, there was an equal proportion of women and men for first but not for last authors. Among unsolicited article types, a smaller proportion of women accounted for first authors. While the proportion of women and men was equal among last authors, 45% left this field blank.

Collecting author demographics and reporting our data on gender represent an important first step for the journal. In the upcoming year, we will develop strategies to obtain more complete data and report our performance on race, ethnicity, and intersectionality, and continue deliberate efforts to improve equity within all areas of the journal, including reviewer, author, and editorial roles. We are committed to continue sharing our progress.

Delirium is the most common postsurgical complication for older adults, with incidence of 15%-54%, depending on surgery type.¹ Increasing numbers of older adults are undergoing surgery²; and those who develop delirium experience negative consequences including longer lengths of stay, higher likelihood of institutional discharge, and increased morbidity and mortality.³ The American Geriatrics Society Expert Panel on Postoperative Delirium in Older Adults and the European Society of Anaesthesiology⁴ recommend routine screening for delirium in those at risk.

Ultrabrief screens are designed to rule out delirium quickly and identify a subset of patients who require further testing.⁵ Our group, and others, have previously published ultrabrief screens for the general medicine, nonsurgical population and for patients with dementia.^5,6 The UB-2 is an ultrabrief screen consisting of “Months of the year backward” (MOYB) and “What day of the week is it?”, which has a sensitivity of 93% and specificity of 64% in hospitalized older adults and takes less than 40 seconds to administer.⁵ However, no such screens for delirium have been developed for the group with relatively high cognitive and physical functioning undergoing scheduled major surgery in which delirium may present differently. Thus, the purpose of this study was to develop an ultrabrief screen for postoperative delirium using data from a large study of delirium in cognitively intact, older adults undergoing scheduled major noncardiac surgery.

We performed a secondary data analysis on 560 patients enrolled between June 18, 2010, and August 8, 2013, in the Successful Aging After Elective Surgery (SAGES) study,⁷ an ongoing prospective cohort study of older adults undergoing major elective surgeries (eg, total hip or knee replacement; lumbar, cervical, or sacral laminectomy; lower extremity arterial bypass surgery; open abdominal aortic aneurysm repair; and open or laparoscopic colectomy). Exclusion criteria included evidence of dementia, delirium, prior hospitalization within 3 months, legal blindness, severe deafness, terminal condition, history of schizophrenia or psychosis, and history of alcohol abuse or withdrawal. The Institutional Review Boards of Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, and Hebrew SeniorLife, all in Boston, Massachusetts, approved the study.

SAGES Delirium Assessment and Additional Variables

The presence or absence of delirium was based on daily in-hospital assessments by trained research staff using the Confusion Assessment Method (CAM)⁸ long form. The Delirium Symptom Interview (DSI)⁹ and information related to acute changes in mental status were also included as provided by nursing staff and/or family. Delirium severity was determined using the CAM-S.¹⁰ Participants in The SAGES Study had an initial baseline, presurgical assessment in their homes. Cognitive and physical functioning, depression, comorbidities, laboratory, and self-reported demographic data were collected.

Statistical Analyses

We included CAM delirium data from postoperative days (POD) 1 and 2 for each participant, if available; postoperative day 0 was not included because of potential residual anesthetic effects. We chose these days because most delirium began on POD1 or 2, and patients started being discharged on POD3. We considered all one-, two-, and three-item combinations of the 12 cognitive items of the 3D-CAM¹¹ because of their demonstrated high information content for CAM diagnostic features per Item Response Theory.¹² There were 12 possible one-item screens, 66 two-item screens, and 220 three-item screens. Sensitivity, specificity, and 95% confidence intervals for each were compared with CAM delirium determination. An ideal ultrabrief screen for delirium has high sensitivity with moderate specificity; general guidelines considered based on investigator consensus included screens with a sensitivity higher than 0.90 and specificity greater than 0.70. Because these screens are used to quickly rule out delirium, we also present the percent positive screen among the entire population (whether delirium is present or not). Screens with a positive screen rate of more than 50% are unlikely to be helpful in ruling out delirium quickly in a large enough fraction of the population. We also required that in multiple item screens, no two items should assess the same CAM feature. For instance, we would eliminate a two-item screen with MOYB and four-digit span since both items measure CAM Feature 2 (Inattention). Finally, we evaluated screen performance separately on POD1 and POD2. Switching screens by POD can be confusing, so we chose a single best screen that retained excellent performance over both days. Data analyses used SAS version 9.4 (SAS Institute, Cary, North Carolina).

The dataset included 560 adults who had an average age of 76.6 years (SD = 5.2), were 58% women, and were highly educated (15.0 years; SD = 2.9; Table). Postoperative delirium occurred during one or more days in 134 individuals (24%). A total of 1,100 delirium assessments were used, with 113 that were CAM positive (10.3%). For POD1, we used 551 assessments, 61 of which were positive (11.1%); for POD2, 549 assessments were used, with 51 positive (9.3%). Appendix Tables present the positive screen rates, sensitivities, specificities, and 95% confidence intervals of all 12 one-item screens and the 12 best performing two- and three-item screens in order of decreasing sensitivity.

The best ultrabrief screen from POD1 included the following three items: “Does the patient report feeling confused?”, MOYB, and “Does the patient appear sleepy?”, with a sensitivity of 0.95 (95% CI, 0.87-0.99) and specificity of 0.73 (95% CI, 0.69-0.77). The same combination of items has a sensitivity of 0.88 (95% CI, 0.77-0.96) and a specificity of 0.70 (95% CI, 0.66-0.74) on POD2. When POD1 and POD2 are combined, the sensitivity is 0.92 (95% CI, 0.85-0.96) and specificity is 0.72 (95% CI, 0.69-0.74). We consider this to be our best screen overall.

We identified a three-item screen for delirium after elective surgery consisting of “Does the patient report feeling confused?”, MOYB, and “Does the patient appear sleepy?” In our own prior work, we identified a two-item screen consisting of MOYB and “What is the day of the week?” as the best ultrabrief screen for delirium in general medicine populations (termed the “UB-2”)⁵ and a subsequent screen for patients with delirium superimposed on dementia (DSD) including “What type of place is this?”, Days of the Week Backward, and “Does the patient appear sleepy?”⁶ All three contain a test of attention (a cardinal feature of delirium) and a test of orientation, although the specific test for that varies. Both the surgical and DSD screens include “Does the patient appear sleepy?”, which addresses a reduced level of consciousness. This might be particularly important in the postoperative setting because of residual effects of anesthesia and/or postoperative analgesic medications contributing to delirium. Work done by others confirms our current findings, which is that MOYB is the best single item for most groups. Belleli et al¹³ and Han et al¹⁴ included MOYB as the single attentional item in the 4AT and B-CAM, respectively. The Nu-DESC has been used as a screen in surgical patients; however, it involves only nursing observations and no direct questioning of the patient.¹⁵

The Figure describes how our “best screen” could be integrated into clinical care. One or more “positive” or incorrect responses on these three items constitutes a positive screen that should be further evaluated with the CAM or 3D-CAM. If all three items are correct or negative, this effectively rules out delirium; however, continued periodic screening on a daily (or per shift) basis is indicated. On repeat testing, if any of the previously negative or correct items becomes positive or incorrect, this would be evidence for Acute Change, CAM Feature 1. Finally, it should be noted that, if all three items in our best screen are positive, full CAM criteria for delirium diagnosis are met within the screen itself, and no further testing is required. We envision this process being facilitated by use of an app-based program that generates optimal screening items based on patient and setting characteristics.

There are several limitations that must be noted. First, our three-item screen may not generalize to nonsurgical candidates or those undergoing emergent surgery and should be tested in these groups. Second, the SAGES sample is relatively homogenous with respect to racial and ethnic diversity and was highly educated with little functional impairment and no dementia. Therefore, results may not be generalizable to populations with lower educational attainment and/or preexisting mental and physical disabilities. A third limitation is that screen items were included in the reference standard delirium assessment, leading to a potential bias toward increased sensitivity. Finally, all screens were derived from secondary data analysis and further research will be needed to prospectively validate the results. Despite these limitations, this study has several strengths including the use of a well-characterized surgical population and a rigorous approach to delirium measurement. It is one of the first studies to identify a screening tool targeted to identifying delirium in postoperative older adults.

Future research should prospectively validate our screening tool and test its implementation in a real-world clinical environment. As part of this process, clinicians should document barriers and facilitators to widespread implementation. The goal of such screens is to facilitate early identification of postoperative delirium, which will allow timely intervention to address underlying causes and prevent adverse consequences, thereby improving the outcomes of vulnerable older surgical patients.

Delirium is the most common postsurgical complication for older adults, with incidence of 15%-54%, depending on surgery type.¹ Increasing numbers of older adults are undergoing surgery²; and those who develop delirium experience negative consequences including longer lengths of stay, higher likelihood of institutional discharge, and increased morbidity and mortality.³ The American Geriatrics Society Expert Panel on Postoperative Delirium in Older Adults and the European Society of Anaesthesiology⁴ recommend routine screening for delirium in those at risk.

Ultrabrief screens are designed to rule out delirium quickly and identify a subset of patients who require further testing.⁵ Our group, and others, have previously published ultrabrief screens for the general medicine, nonsurgical population and for patients with dementia.^5,6 The UB-2 is an ultrabrief screen consisting of “Months of the year backward” (MOYB) and “What day of the week is it?”, which has a sensitivity of 93% and specificity of 64% in hospitalized older adults and takes less than 40 seconds to administer.⁵ However, no such screens for delirium have been developed for the group with relatively high cognitive and physical functioning undergoing scheduled major surgery in which delirium may present differently. Thus, the purpose of this study was to develop an ultrabrief screen for postoperative delirium using data from a large study of delirium in cognitively intact, older adults undergoing scheduled major noncardiac surgery.

We performed a secondary data analysis on 560 patients enrolled between June 18, 2010, and August 8, 2013, in the Successful Aging After Elective Surgery (SAGES) study,⁷ an ongoing prospective cohort study of older adults undergoing major elective surgeries (eg, total hip or knee replacement; lumbar, cervical, or sacral laminectomy; lower extremity arterial bypass surgery; open abdominal aortic aneurysm repair; and open or laparoscopic colectomy). Exclusion criteria included evidence of dementia, delirium, prior hospitalization within 3 months, legal blindness, severe deafness, terminal condition, history of schizophrenia or psychosis, and history of alcohol abuse or withdrawal. The Institutional Review Boards of Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, and Hebrew SeniorLife, all in Boston, Massachusetts, approved the study.

SAGES Delirium Assessment and Additional Variables

The presence or absence of delirium was based on daily in-hospital assessments by trained research staff using the Confusion Assessment Method (CAM)⁸ long form. The Delirium Symptom Interview (DSI)⁹ and information related to acute changes in mental status were also included as provided by nursing staff and/or family. Delirium severity was determined using the CAM-S.¹⁰ Participants in The SAGES Study had an initial baseline, presurgical assessment in their homes. Cognitive and physical functioning, depression, comorbidities, laboratory, and self-reported demographic data were collected.

Statistical Analyses

We included CAM delirium data from postoperative days (POD) 1 and 2 for each participant, if available; postoperative day 0 was not included because of potential residual anesthetic effects. We chose these days because most delirium began on POD1 or 2, and patients started being discharged on POD3. We considered all one-, two-, and three-item combinations of the 12 cognitive items of the 3D-CAM¹¹ because of their demonstrated high information content for CAM diagnostic features per Item Response Theory.¹² There were 12 possible one-item screens, 66 two-item screens, and 220 three-item screens. Sensitivity, specificity, and 95% confidence intervals for each were compared with CAM delirium determination. An ideal ultrabrief screen for delirium has high sensitivity with moderate specificity; general guidelines considered based on investigator consensus included screens with a sensitivity higher than 0.90 and specificity greater than 0.70. Because these screens are used to quickly rule out delirium, we also present the percent positive screen among the entire population (whether delirium is present or not). Screens with a positive screen rate of more than 50% are unlikely to be helpful in ruling out delirium quickly in a large enough fraction of the population. We also required that in multiple item screens, no two items should assess the same CAM feature. For instance, we would eliminate a two-item screen with MOYB and four-digit span since both items measure CAM Feature 2 (Inattention). Finally, we evaluated screen performance separately on POD1 and POD2. Switching screens by POD can be confusing, so we chose a single best screen that retained excellent performance over both days. Data analyses used SAS version 9.4 (SAS Institute, Cary, North Carolina).

The dataset included 560 adults who had an average age of 76.6 years (SD = 5.2), were 58% women, and were highly educated (15.0 years; SD = 2.9; Table). Postoperative delirium occurred during one or more days in 134 individuals (24%). A total of 1,100 delirium assessments were used, with 113 that were CAM positive (10.3%). For POD1, we used 551 assessments, 61 of which were positive (11.1%); for POD2, 549 assessments were used, with 51 positive (9.3%). Appendix Tables present the positive screen rates, sensitivities, specificities, and 95% confidence intervals of all 12 one-item screens and the 12 best performing two- and three-item screens in order of decreasing sensitivity.

The best ultrabrief screen from POD1 included the following three items: “Does the patient report feeling confused?”, MOYB, and “Does the patient appear sleepy?”, with a sensitivity of 0.95 (95% CI, 0.87-0.99) and specificity of 0.73 (95% CI, 0.69-0.77). The same combination of items has a sensitivity of 0.88 (95% CI, 0.77-0.96) and a specificity of 0.70 (95% CI, 0.66-0.74) on POD2. When POD1 and POD2 are combined, the sensitivity is 0.92 (95% CI, 0.85-0.96) and specificity is 0.72 (95% CI, 0.69-0.74). We consider this to be our best screen overall.

We identified a three-item screen for delirium after elective surgery consisting of “Does the patient report feeling confused?”, MOYB, and “Does the patient appear sleepy?” In our own prior work, we identified a two-item screen consisting of MOYB and “What is the day of the week?” as the best ultrabrief screen for delirium in general medicine populations (termed the “UB-2”)⁵ and a subsequent screen for patients with delirium superimposed on dementia (DSD) including “What type of place is this?”, Days of the Week Backward, and “Does the patient appear sleepy?”⁶ All three contain a test of attention (a cardinal feature of delirium) and a test of orientation, although the specific test for that varies. Both the surgical and DSD screens include “Does the patient appear sleepy?”, which addresses a reduced level of consciousness. This might be particularly important in the postoperative setting because of residual effects of anesthesia and/or postoperative analgesic medications contributing to delirium. Work done by others confirms our current findings, which is that MOYB is the best single item for most groups. Belleli et al¹³ and Han et al¹⁴ included MOYB as the single attentional item in the 4AT and B-CAM, respectively. The Nu-DESC has been used as a screen in surgical patients; however, it involves only nursing observations and no direct questioning of the patient.¹⁵

The Figure describes how our “best screen” could be integrated into clinical care. One or more “positive” or incorrect responses on these three items constitutes a positive screen that should be further evaluated with the CAM or 3D-CAM. If all three items are correct or negative, this effectively rules out delirium; however, continued periodic screening on a daily (or per shift) basis is indicated. On repeat testing, if any of the previously negative or correct items becomes positive or incorrect, this would be evidence for Acute Change, CAM Feature 1. Finally, it should be noted that, if all three items in our best screen are positive, full CAM criteria for delirium diagnosis are met within the screen itself, and no further testing is required. We envision this process being facilitated by use of an app-based program that generates optimal screening items based on patient and setting characteristics.

There are several limitations that must be noted. First, our three-item screen may not generalize to nonsurgical candidates or those undergoing emergent surgery and should be tested in these groups. Second, the SAGES sample is relatively homogenous with respect to racial and ethnic diversity and was highly educated with little functional impairment and no dementia. Therefore, results may not be generalizable to populations with lower educational attainment and/or preexisting mental and physical disabilities. A third limitation is that screen items were included in the reference standard delirium assessment, leading to a potential bias toward increased sensitivity. Finally, all screens were derived from secondary data analysis and further research will be needed to prospectively validate the results. Despite these limitations, this study has several strengths including the use of a well-characterized surgical population and a rigorous approach to delirium measurement. It is one of the first studies to identify a screening tool targeted to identifying delirium in postoperative older adults.

Future research should prospectively validate our screening tool and test its implementation in a real-world clinical environment. As part of this process, clinicians should document barriers and facilitators to widespread implementation. The goal of such screens is to facilitate early identification of postoperative delirium, which will allow timely intervention to address underlying causes and prevent adverse consequences, thereby improving the outcomes of vulnerable older surgical patients.

Delirium is the most common postsurgical complication for older adults, with incidence of 15%-54%, depending on surgery type.¹ Increasing numbers of older adults are undergoing surgery²; and those who develop delirium experience negative consequences including longer lengths of stay, higher likelihood of institutional discharge, and increased morbidity and mortality.³ The American Geriatrics Society Expert Panel on Postoperative Delirium in Older Adults and the European Society of Anaesthesiology⁴ recommend routine screening for delirium in those at risk.

Ultrabrief screens are designed to rule out delirium quickly and identify a subset of patients who require further testing.⁵ Our group, and others, have previously published ultrabrief screens for the general medicine, nonsurgical population and for patients with dementia.^5,6 The UB-2 is an ultrabrief screen consisting of “Months of the year backward” (MOYB) and “What day of the week is it?”, which has a sensitivity of 93% and specificity of 64% in hospitalized older adults and takes less than 40 seconds to administer.⁵ However, no such screens for delirium have been developed for the group with relatively high cognitive and physical functioning undergoing scheduled major surgery in which delirium may present differently. Thus, the purpose of this study was to develop an ultrabrief screen for postoperative delirium using data from a large study of delirium in cognitively intact, older adults undergoing scheduled major noncardiac surgery.

We performed a secondary data analysis on 560 patients enrolled between June 18, 2010, and August 8, 2013, in the Successful Aging After Elective Surgery (SAGES) study,⁷ an ongoing prospective cohort study of older adults undergoing major elective surgeries (eg, total hip or knee replacement; lumbar, cervical, or sacral laminectomy; lower extremity arterial bypass surgery; open abdominal aortic aneurysm repair; and open or laparoscopic colectomy). Exclusion criteria included evidence of dementia, delirium, prior hospitalization within 3 months, legal blindness, severe deafness, terminal condition, history of schizophrenia or psychosis, and history of alcohol abuse or withdrawal. The Institutional Review Boards of Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, and Hebrew SeniorLife, all in Boston, Massachusetts, approved the study.

SAGES Delirium Assessment and Additional Variables

The presence or absence of delirium was based on daily in-hospital assessments by trained research staff using the Confusion Assessment Method (CAM)⁸ long form. The Delirium Symptom Interview (DSI)⁹ and information related to acute changes in mental status were also included as provided by nursing staff and/or family. Delirium severity was determined using the CAM-S.¹⁰ Participants in The SAGES Study had an initial baseline, presurgical assessment in their homes. Cognitive and physical functioning, depression, comorbidities, laboratory, and self-reported demographic data were collected.

Statistical Analyses

We included CAM delirium data from postoperative days (POD) 1 and 2 for each participant, if available; postoperative day 0 was not included because of potential residual anesthetic effects. We chose these days because most delirium began on POD1 or 2, and patients started being discharged on POD3. We considered all one-, two-, and three-item combinations of the 12 cognitive items of the 3D-CAM¹¹ because of their demonstrated high information content for CAM diagnostic features per Item Response Theory.¹² There were 12 possible one-item screens, 66 two-item screens, and 220 three-item screens. Sensitivity, specificity, and 95% confidence intervals for each were compared with CAM delirium determination. An ideal ultrabrief screen for delirium has high sensitivity with moderate specificity; general guidelines considered based on investigator consensus included screens with a sensitivity higher than 0.90 and specificity greater than 0.70. Because these screens are used to quickly rule out delirium, we also present the percent positive screen among the entire population (whether delirium is present or not). Screens with a positive screen rate of more than 50% are unlikely to be helpful in ruling out delirium quickly in a large enough fraction of the population. We also required that in multiple item screens, no two items should assess the same CAM feature. For instance, we would eliminate a two-item screen with MOYB and four-digit span since both items measure CAM Feature 2 (Inattention). Finally, we evaluated screen performance separately on POD1 and POD2. Switching screens by POD can be confusing, so we chose a single best screen that retained excellent performance over both days. Data analyses used SAS version 9.4 (SAS Institute, Cary, North Carolina).

The dataset included 560 adults who had an average age of 76.6 years (SD = 5.2), were 58% women, and were highly educated (15.0 years; SD = 2.9; Table). Postoperative delirium occurred during one or more days in 134 individuals (24%). A total of 1,100 delirium assessments were used, with 113 that were CAM positive (10.3%). For POD1, we used 551 assessments, 61 of which were positive (11.1%); for POD2, 549 assessments were used, with 51 positive (9.3%). Appendix Tables present the positive screen rates, sensitivities, specificities, and 95% confidence intervals of all 12 one-item screens and the 12 best performing two- and three-item screens in order of decreasing sensitivity.

The best ultrabrief screen from POD1 included the following three items: “Does the patient report feeling confused?”, MOYB, and “Does the patient appear sleepy?”, with a sensitivity of 0.95 (95% CI, 0.87-0.99) and specificity of 0.73 (95% CI, 0.69-0.77). The same combination of items has a sensitivity of 0.88 (95% CI, 0.77-0.96) and a specificity of 0.70 (95% CI, 0.66-0.74) on POD2. When POD1 and POD2 are combined, the sensitivity is 0.92 (95% CI, 0.85-0.96) and specificity is 0.72 (95% CI, 0.69-0.74). We consider this to be our best screen overall.

We identified a three-item screen for delirium after elective surgery consisting of “Does the patient report feeling confused?”, MOYB, and “Does the patient appear sleepy?” In our own prior work, we identified a two-item screen consisting of MOYB and “What is the day of the week?” as the best ultrabrief screen for delirium in general medicine populations (termed the “UB-2”)⁵ and a subsequent screen for patients with delirium superimposed on dementia (DSD) including “What type of place is this?”, Days of the Week Backward, and “Does the patient appear sleepy?”⁶ All three contain a test of attention (a cardinal feature of delirium) and a test of orientation, although the specific test for that varies. Both the surgical and DSD screens include “Does the patient appear sleepy?”, which addresses a reduced level of consciousness. This might be particularly important in the postoperative setting because of residual effects of anesthesia and/or postoperative analgesic medications contributing to delirium. Work done by others confirms our current findings, which is that MOYB is the best single item for most groups. Belleli et al¹³ and Han et al¹⁴ included MOYB as the single attentional item in the 4AT and B-CAM, respectively. The Nu-DESC has been used as a screen in surgical patients; however, it involves only nursing observations and no direct questioning of the patient.¹⁵

The Figure describes how our “best screen” could be integrated into clinical care. One or more “positive” or incorrect responses on these three items constitutes a positive screen that should be further evaluated with the CAM or 3D-CAM. If all three items are correct or negative, this effectively rules out delirium; however, continued periodic screening on a daily (or per shift) basis is indicated. On repeat testing, if any of the previously negative or correct items becomes positive or incorrect, this would be evidence for Acute Change, CAM Feature 1. Finally, it should be noted that, if all three items in our best screen are positive, full CAM criteria for delirium diagnosis are met within the screen itself, and no further testing is required. We envision this process being facilitated by use of an app-based program that generates optimal screening items based on patient and setting characteristics.

There are several limitations that must be noted. First, our three-item screen may not generalize to nonsurgical candidates or those undergoing emergent surgery and should be tested in these groups. Second, the SAGES sample is relatively homogenous with respect to racial and ethnic diversity and was highly educated with little functional impairment and no dementia. Therefore, results may not be generalizable to populations with lower educational attainment and/or preexisting mental and physical disabilities. A third limitation is that screen items were included in the reference standard delirium assessment, leading to a potential bias toward increased sensitivity. Finally, all screens were derived from secondary data analysis and further research will be needed to prospectively validate the results. Despite these limitations, this study has several strengths including the use of a well-characterized surgical population and a rigorous approach to delirium measurement. It is one of the first studies to identify a screening tool targeted to identifying delirium in postoperative older adults.

Future research should prospectively validate our screening tool and test its implementation in a real-world clinical environment. As part of this process, clinicians should document barriers and facilitators to widespread implementation. The goal of such screens is to facilitate early identification of postoperative delirium, which will allow timely intervention to address underlying causes and prevent adverse consequences, thereby improving the outcomes of vulnerable older surgical patients.

Reduction in serious pediatric medical errors has been achieved through sharing of best practices and structured collaboration.¹ However, limited progress has been made in reducing complex, multifactorial events such as unrecognized and undertreated patient deterioration events.² To address this critical gap, interventions to improve clinician situation awareness (SA) have increasingly been applied.³

SA is the ability to recognize and monitor cues regarding what is happening, create a comprehensive picture with available information, and extrapolate whether it indicates adverse developments either immediately or in the near future.⁴ Methods such as care team huddling^5-8 and using standardized patient acuity scoring instruments⁹ increase SA shared across care team roles. Shared SA is the degree to which each team member possesses a common understanding of what is going on. A team is considered to have shared SA when all the individuals agree on both what is happening (accurate perception and comprehension) and what is going to happen in the future (correct projection). Shared SA for high-risk patients in the pediatric intensive care unit (PICU) has not previously been described and may be an opportunity to improve interprofessional team communication for the sickest patients. Shared SA for high-risk patient status is only one aspect of SA, but it facilitates team-based mitigation planning and is an important starting place for understanding opportunities to improve SA. The primary objective of this study was to measure and compare SA among care team roles regarding patients with high-risk status in the PICU.

We conducted a prospective, cross-sectional study from March 2018 to July 2019 examining the individual and shared SA of patient care team trios: the nurse, respiratory therapist (RT), and pediatric resident. The Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC) determined this study to be non–human-subjects research.

Setting

Research was conducted in the 35-bed PICU of CCHMC, a 500-bed academic free-standing quaternary care children’s hospital.

Participants

We conducted independent surveys of the nurse, RT, and pediatric resident (care team trio) caring for each patient regarding the patient’s clinical deterioration risk status. No patients or care team trios were excluded.

Reference Standard

In 2016, a local panel of experts derived clinical criteria to determine high-risk status for PICU patients, the definition of which, as well as other study terms, appears in Table 1. A PICU attending or fellow identifies a patient as “high risk” when these clinical criteria are met. A plan for prevention and mitigation is formulated and documented for high-risk patients by the PICU attending or fellow at two preexisting daily SA huddles. This plan includes prevention measures to take immediately, specific vital sign thresholds for early identification of deterioration, and guidance on which emergency medication order sets should be utilized to expedite treatment in the event of clinical decline. Dissemination of the care team’s plan is the responsibility of the PICU fellow with additional follow-up by the charge nurse to improve reliability. Identification of high-risk status and development of the prevention and mitigation plan, as completed by the PICU fellow or attending, served as the reference standard for this study.

Survey Instrument Development

The locally developed survey tool was modeled after a validated handoff communication instrument.¹⁰ The tool covered the patient’s risk status, which high-risk clinical criteria were met, the presence and content of a mitigation plan, and planned patient interventions (Appendix).

Data Collection

Care team trios were sampled weekly on weekdays during day and night shifts within 4 to 6 hours of the SA huddle by a core group of three research assistants. Care team trios for one group of five to nine patients within a small geographically isolated pod were surveyed each time. The care team trio was surveyed individually regarding the patient’s risk status, the high-risk clinical criteria met, the presence and content of a mitigation plan, and planned patient interventions. The responses were compared for accuracy against the reference standard, which was defined as identification of high-risk patient status and development of the prevention and mitigation plan as completed by the PICU fellow or attending.

Data Analysis

Rates of agreement between the reference standard and individual members of the care team trio were evaluated via a calculation of proportions by care team role. The agreement between each care team trio member and the reference standard was compared with the nurse role performance using chi-square tests. Rates of concordance within the members of the care team trio were calculated via Light’s kappa for determination of high-risk status.¹¹ Assuming a correct assessment of high-risk status of 62%,¹² with a difference between groups of 10%, a sample size of 400 bedside provider trios gives a power of 85% at the P < .05 significance level for a two-sided chi-square test.

Between March 1, 2018, and July 11, 2019, 400 care team trios were surveyed. Seventy-three trios cared for patients designated high risk (Table 2 for N and proportions). Among all surveyed trios, 94% of nurses (reference), 95% of RTs (P = .4), and 87% of residents (P = .002) identified patient’s risk status correctly. Care trio member concordance for high-risk status was moderate agreement as assessed by a kappa of 0.57 (95% CI, 0.25-0.90).

Of the 73 high-risk patients, nurses correctly identified risk status for 82% (reference), RTs 85% (P = .7), and residents 67% (P = .04). For high-risk patients, nurses identified the presence of a mitigation plan for 98% of patients (reference), RTs 90% (P = .06), and residents 88% (P = .03). Among the care team members who correctly identified the presence of a mitigation plan, nurses were able to specify the correct plan for 83% of patients (reference), RTs for 68% (P = .09), and residents for 70% (P = .11; Figure).

When shared SA for high-risk patients was examined more closely, all three care team roles correctly identified the clinical reason for high-risk status for 32% of patients, with only one or two clinicians being correct for 53%. All three care team clinicians were incorrect for 15% of high-risk patients. Among trios with partial accuracy in which two of three care team members correctly identified a patient as high risk, we examined which care-member was most likely to be incorrect. Nurses incorrectly identified risk for 17% of patients (reference), RTs 19% (P = .8), and residents 64% (P < .0001).

Examining 400 care team trios, we found lower individual SA for residents, compared with nurses, regarding high-risk status, the reason for this status, and the presence of a mitigation plan. In all reported measures except for the content of mitigation plans, residents were significantly less correct than the bedside nurses while RTs performed similarly to bedside nurses throughout. In addition, there was only moderate agreement between care team roles, which shows further opportunities for improvement in shared SA. The disparities between care team roles are consistent with studies that suggest certain factors grounded in institutional culture and interpersonal dynamics, such as poor communication, can lead to breakdowns in shared knowledge.^13,14 Communication issues demonstrate differences across care team roles¹⁴ and may provide insight into barriers to individual and shared SA throughout the care team.

In addition, the effects of patient load on SA needs further study. While our PICU nurses are commonly assigned to 1 to 2 patients, RTs care for 7 to 11 patients, and an on-call resident may be covering 15 to 20 patients during a high-census season. The increased patient load cannot serve as an excuse for the knowledge gap regarding high-risk status and mitigation plan, but may provide an opportunity to support residents and other medical providers through the use of clinical decision-­support tools that indicate high-risk status and represent mitigation plans.¹²

This study has multiple limitations. First, while we based our survey tool on a communication assessment tool with prior validity evidence,^10,12 our tool has not been used prior to this study. The adapted tool contained relevant categorizations of patient information, including explicit statement of patient status and planned treatment consistent with study definitions of SA, and has been used in the critical care setting previously.¹¹ The survey tool used to measure SA in this study was locally designed and implemented only within the study unit, which could lead to decreased reliability and generalizability of the results to other units and institutions at large. Second, while the sample size for the primary measure (N = 400) was adequately powered because our baseline SA was higher than estimated, we had insufficient power for some subgroup analyses that can lead to type II errors. Third, care team trios may have been surveyed repeatedly on the same patient without adjustment in the results for repeated measures. However, as we surveyed on average only once a week and alternated areas of the PICU surveyed, it is unlikely that it affected results given that the most lengths of stay within the PICU range from 3 to 4 days. Finally, individual characteristics of patients were not collected for this work, and therefore, no adjustments or further analysis can be made on the effect of the patient characteristic on the care team role SA.

This study is the first to assess differences in individual and shared SA within a PICU by care team role. Efforts to expand on these findings should include investigation into the causes for the disparities in SA among care team roles for individual patients and among the care teams of high-risk and normal-risk patients. Given the association between increased SA and improved patient outcomes,⁴ future efforts should be structured to address care team role–specific gaps in SA because these may advance the quality of care in the pediatric inpatient setting.

Reduction in serious pediatric medical errors has been achieved through sharing of best practices and structured collaboration.¹ However, limited progress has been made in reducing complex, multifactorial events such as unrecognized and undertreated patient deterioration events.² To address this critical gap, interventions to improve clinician situation awareness (SA) have increasingly been applied.³

SA is the ability to recognize and monitor cues regarding what is happening, create a comprehensive picture with available information, and extrapolate whether it indicates adverse developments either immediately or in the near future.⁴ Methods such as care team huddling^5-8 and using standardized patient acuity scoring instruments⁹ increase SA shared across care team roles. Shared SA is the degree to which each team member possesses a common understanding of what is going on. A team is considered to have shared SA when all the individuals agree on both what is happening (accurate perception and comprehension) and what is going to happen in the future (correct projection). Shared SA for high-risk patients in the pediatric intensive care unit (PICU) has not previously been described and may be an opportunity to improve interprofessional team communication for the sickest patients. Shared SA for high-risk patient status is only one aspect of SA, but it facilitates team-based mitigation planning and is an important starting place for understanding opportunities to improve SA. The primary objective of this study was to measure and compare SA among care team roles regarding patients with high-risk status in the PICU.

We conducted a prospective, cross-sectional study from March 2018 to July 2019 examining the individual and shared SA of patient care team trios: the nurse, respiratory therapist (RT), and pediatric resident. The Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC) determined this study to be non–human-subjects research.

Setting

Research was conducted in the 35-bed PICU of CCHMC, a 500-bed academic free-standing quaternary care children’s hospital.

Participants

We conducted independent surveys of the nurse, RT, and pediatric resident (care team trio) caring for each patient regarding the patient’s clinical deterioration risk status. No patients or care team trios were excluded.

Reference Standard

In 2016, a local panel of experts derived clinical criteria to determine high-risk status for PICU patients, the definition of which, as well as other study terms, appears in Table 1. A PICU attending or fellow identifies a patient as “high risk” when these clinical criteria are met. A plan for prevention and mitigation is formulated and documented for high-risk patients by the PICU attending or fellow at two preexisting daily SA huddles. This plan includes prevention measures to take immediately, specific vital sign thresholds for early identification of deterioration, and guidance on which emergency medication order sets should be utilized to expedite treatment in the event of clinical decline. Dissemination of the care team’s plan is the responsibility of the PICU fellow with additional follow-up by the charge nurse to improve reliability. Identification of high-risk status and development of the prevention and mitigation plan, as completed by the PICU fellow or attending, served as the reference standard for this study.

Survey Instrument Development

The locally developed survey tool was modeled after a validated handoff communication instrument.¹⁰ The tool covered the patient’s risk status, which high-risk clinical criteria were met, the presence and content of a mitigation plan, and planned patient interventions (Appendix).

Data Collection

Care team trios were sampled weekly on weekdays during day and night shifts within 4 to 6 hours of the SA huddle by a core group of three research assistants. Care team trios for one group of five to nine patients within a small geographically isolated pod were surveyed each time. The care team trio was surveyed individually regarding the patient’s risk status, the high-risk clinical criteria met, the presence and content of a mitigation plan, and planned patient interventions. The responses were compared for accuracy against the reference standard, which was defined as identification of high-risk patient status and development of the prevention and mitigation plan as completed by the PICU fellow or attending.

Data Analysis

Rates of agreement between the reference standard and individual members of the care team trio were evaluated via a calculation of proportions by care team role. The agreement between each care team trio member and the reference standard was compared with the nurse role performance using chi-square tests. Rates of concordance within the members of the care team trio were calculated via Light’s kappa for determination of high-risk status.¹¹ Assuming a correct assessment of high-risk status of 62%,¹² with a difference between groups of 10%, a sample size of 400 bedside provider trios gives a power of 85% at the P < .05 significance level for a two-sided chi-square test.

Between March 1, 2018, and July 11, 2019, 400 care team trios were surveyed. Seventy-three trios cared for patients designated high risk (Table 2 for N and proportions). Among all surveyed trios, 94% of nurses (reference), 95% of RTs (P = .4), and 87% of residents (P = .002) identified patient’s risk status correctly. Care trio member concordance for high-risk status was moderate agreement as assessed by a kappa of 0.57 (95% CI, 0.25-0.90).

Of the 73 high-risk patients, nurses correctly identified risk status for 82% (reference), RTs 85% (P = .7), and residents 67% (P = .04). For high-risk patients, nurses identified the presence of a mitigation plan for 98% of patients (reference), RTs 90% (P = .06), and residents 88% (P = .03). Among the care team members who correctly identified the presence of a mitigation plan, nurses were able to specify the correct plan for 83% of patients (reference), RTs for 68% (P = .09), and residents for 70% (P = .11; Figure).

When shared SA for high-risk patients was examined more closely, all three care team roles correctly identified the clinical reason for high-risk status for 32% of patients, with only one or two clinicians being correct for 53%. All three care team clinicians were incorrect for 15% of high-risk patients. Among trios with partial accuracy in which two of three care team members correctly identified a patient as high risk, we examined which care-member was most likely to be incorrect. Nurses incorrectly identified risk for 17% of patients (reference), RTs 19% (P = .8), and residents 64% (P < .0001).

Examining 400 care team trios, we found lower individual SA for residents, compared with nurses, regarding high-risk status, the reason for this status, and the presence of a mitigation plan. In all reported measures except for the content of mitigation plans, residents were significantly less correct than the bedside nurses while RTs performed similarly to bedside nurses throughout. In addition, there was only moderate agreement between care team roles, which shows further opportunities for improvement in shared SA. The disparities between care team roles are consistent with studies that suggest certain factors grounded in institutional culture and interpersonal dynamics, such as poor communication, can lead to breakdowns in shared knowledge.^13,14 Communication issues demonstrate differences across care team roles¹⁴ and may provide insight into barriers to individual and shared SA throughout the care team.

In addition, the effects of patient load on SA needs further study. While our PICU nurses are commonly assigned to 1 to 2 patients, RTs care for 7 to 11 patients, and an on-call resident may be covering 15 to 20 patients during a high-census season. The increased patient load cannot serve as an excuse for the knowledge gap regarding high-risk status and mitigation plan, but may provide an opportunity to support residents and other medical providers through the use of clinical decision-­support tools that indicate high-risk status and represent mitigation plans.¹²

This study has multiple limitations. First, while we based our survey tool on a communication assessment tool with prior validity evidence,^10,12 our tool has not been used prior to this study. The adapted tool contained relevant categorizations of patient information, including explicit statement of patient status and planned treatment consistent with study definitions of SA, and has been used in the critical care setting previously.¹¹ The survey tool used to measure SA in this study was locally designed and implemented only within the study unit, which could lead to decreased reliability and generalizability of the results to other units and institutions at large. Second, while the sample size for the primary measure (N = 400) was adequately powered because our baseline SA was higher than estimated, we had insufficient power for some subgroup analyses that can lead to type II errors. Third, care team trios may have been surveyed repeatedly on the same patient without adjustment in the results for repeated measures. However, as we surveyed on average only once a week and alternated areas of the PICU surveyed, it is unlikely that it affected results given that the most lengths of stay within the PICU range from 3 to 4 days. Finally, individual characteristics of patients were not collected for this work, and therefore, no adjustments or further analysis can be made on the effect of the patient characteristic on the care team role SA.

This study is the first to assess differences in individual and shared SA within a PICU by care team role. Efforts to expand on these findings should include investigation into the causes for the disparities in SA among care team roles for individual patients and among the care teams of high-risk and normal-risk patients. Given the association between increased SA and improved patient outcomes,⁴ future efforts should be structured to address care team role–specific gaps in SA because these may advance the quality of care in the pediatric inpatient setting.

Reduction in serious pediatric medical errors has been achieved through sharing of best practices and structured collaboration.¹ However, limited progress has been made in reducing complex, multifactorial events such as unrecognized and undertreated patient deterioration events.² To address this critical gap, interventions to improve clinician situation awareness (SA) have increasingly been applied.³

SA is the ability to recognize and monitor cues regarding what is happening, create a comprehensive picture with available information, and extrapolate whether it indicates adverse developments either immediately or in the near future.⁴ Methods such as care team huddling^5-8 and using standardized patient acuity scoring instruments⁹ increase SA shared across care team roles. Shared SA is the degree to which each team member possesses a common understanding of what is going on. A team is considered to have shared SA when all the individuals agree on both what is happening (accurate perception and comprehension) and what is going to happen in the future (correct projection). Shared SA for high-risk patients in the pediatric intensive care unit (PICU) has not previously been described and may be an opportunity to improve interprofessional team communication for the sickest patients. Shared SA for high-risk patient status is only one aspect of SA, but it facilitates team-based mitigation planning and is an important starting place for understanding opportunities to improve SA. The primary objective of this study was to measure and compare SA among care team roles regarding patients with high-risk status in the PICU.

We conducted a prospective, cross-sectional study from March 2018 to July 2019 examining the individual and shared SA of patient care team trios: the nurse, respiratory therapist (RT), and pediatric resident. The Institutional Review Board at Cincinnati Children’s Hospital Medical Center (CCHMC) determined this study to be non–human-subjects research.

Setting

Research was conducted in the 35-bed PICU of CCHMC, a 500-bed academic free-standing quaternary care children’s hospital.

Participants

We conducted independent surveys of the nurse, RT, and pediatric resident (care team trio) caring for each patient regarding the patient’s clinical deterioration risk status. No patients or care team trios were excluded.

Reference Standard

In 2016, a local panel of experts derived clinical criteria to determine high-risk status for PICU patients, the definition of which, as well as other study terms, appears in Table 1. A PICU attending or fellow identifies a patient as “high risk” when these clinical criteria are met. A plan for prevention and mitigation is formulated and documented for high-risk patients by the PICU attending or fellow at two preexisting daily SA huddles. This plan includes prevention measures to take immediately, specific vital sign thresholds for early identification of deterioration, and guidance on which emergency medication order sets should be utilized to expedite treatment in the event of clinical decline. Dissemination of the care team’s plan is the responsibility of the PICU fellow with additional follow-up by the charge nurse to improve reliability. Identification of high-risk status and development of the prevention and mitigation plan, as completed by the PICU fellow or attending, served as the reference standard for this study.

Survey Instrument Development

The locally developed survey tool was modeled after a validated handoff communication instrument.¹⁰ The tool covered the patient’s risk status, which high-risk clinical criteria were met, the presence and content of a mitigation plan, and planned patient interventions (Appendix).

Data Collection

Care team trios were sampled weekly on weekdays during day and night shifts within 4 to 6 hours of the SA huddle by a core group of three research assistants. Care team trios for one group of five to nine patients within a small geographically isolated pod were surveyed each time. The care team trio was surveyed individually regarding the patient’s risk status, the high-risk clinical criteria met, the presence and content of a mitigation plan, and planned patient interventions. The responses were compared for accuracy against the reference standard, which was defined as identification of high-risk patient status and development of the prevention and mitigation plan as completed by the PICU fellow or attending.

Data Analysis

Rates of agreement between the reference standard and individual members of the care team trio were evaluated via a calculation of proportions by care team role. The agreement between each care team trio member and the reference standard was compared with the nurse role performance using chi-square tests. Rates of concordance within the members of the care team trio were calculated via Light’s kappa for determination of high-risk status.¹¹ Assuming a correct assessment of high-risk status of 62%,¹² with a difference between groups of 10%, a sample size of 400 bedside provider trios gives a power of 85% at the P < .05 significance level for a two-sided chi-square test.

Between March 1, 2018, and July 11, 2019, 400 care team trios were surveyed. Seventy-three trios cared for patients designated high risk (Table 2 for N and proportions). Among all surveyed trios, 94% of nurses (reference), 95% of RTs (P = .4), and 87% of residents (P = .002) identified patient’s risk status correctly. Care trio member concordance for high-risk status was moderate agreement as assessed by a kappa of 0.57 (95% CI, 0.25-0.90).

Of the 73 high-risk patients, nurses correctly identified risk status for 82% (reference), RTs 85% (P = .7), and residents 67% (P = .04). For high-risk patients, nurses identified the presence of a mitigation plan for 98% of patients (reference), RTs 90% (P = .06), and residents 88% (P = .03). Among the care team members who correctly identified the presence of a mitigation plan, nurses were able to specify the correct plan for 83% of patients (reference), RTs for 68% (P = .09), and residents for 70% (P = .11; Figure).

When shared SA for high-risk patients was examined more closely, all three care team roles correctly identified the clinical reason for high-risk status for 32% of patients, with only one or two clinicians being correct for 53%. All three care team clinicians were incorrect for 15% of high-risk patients. Among trios with partial accuracy in which two of three care team members correctly identified a patient as high risk, we examined which care-member was most likely to be incorrect. Nurses incorrectly identified risk for 17% of patients (reference), RTs 19% (P = .8), and residents 64% (P < .0001).

Examining 400 care team trios, we found lower individual SA for residents, compared with nurses, regarding high-risk status, the reason for this status, and the presence of a mitigation plan. In all reported measures except for the content of mitigation plans, residents were significantly less correct than the bedside nurses while RTs performed similarly to bedside nurses throughout. In addition, there was only moderate agreement between care team roles, which shows further opportunities for improvement in shared SA. The disparities between care team roles are consistent with studies that suggest certain factors grounded in institutional culture and interpersonal dynamics, such as poor communication, can lead to breakdowns in shared knowledge.^13,14 Communication issues demonstrate differences across care team roles¹⁴ and may provide insight into barriers to individual and shared SA throughout the care team.

In addition, the effects of patient load on SA needs further study. While our PICU nurses are commonly assigned to 1 to 2 patients, RTs care for 7 to 11 patients, and an on-call resident may be covering 15 to 20 patients during a high-census season. The increased patient load cannot serve as an excuse for the knowledge gap regarding high-risk status and mitigation plan, but may provide an opportunity to support residents and other medical providers through the use of clinical decision-­support tools that indicate high-risk status and represent mitigation plans.¹²

This study has multiple limitations. First, while we based our survey tool on a communication assessment tool with prior validity evidence,^10,12 our tool has not been used prior to this study. The adapted tool contained relevant categorizations of patient information, including explicit statement of patient status and planned treatment consistent with study definitions of SA, and has been used in the critical care setting previously.¹¹ The survey tool used to measure SA in this study was locally designed and implemented only within the study unit, which could lead to decreased reliability and generalizability of the results to other units and institutions at large. Second, while the sample size for the primary measure (N = 400) was adequately powered because our baseline SA was higher than estimated, we had insufficient power for some subgroup analyses that can lead to type II errors. Third, care team trios may have been surveyed repeatedly on the same patient without adjustment in the results for repeated measures. However, as we surveyed on average only once a week and alternated areas of the PICU surveyed, it is unlikely that it affected results given that the most lengths of stay within the PICU range from 3 to 4 days. Finally, individual characteristics of patients were not collected for this work, and therefore, no adjustments or further analysis can be made on the effect of the patient characteristic on the care team role SA.

This study is the first to assess differences in individual and shared SA within a PICU by care team role. Efforts to expand on these findings should include investigation into the causes for the disparities in SA among care team roles for individual patients and among the care teams of high-risk and normal-risk patients. Given the association between increased SA and improved patient outcomes,⁴ future efforts should be structured to address care team role–specific gaps in SA because these may advance the quality of care in the pediatric inpatient setting.