Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.

Objective: Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, of those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from those who can be safely discharged. Methods: We developed random forest machine learning (ML) models to estimate needs for critical care within 24 h and inpatient care within 72 h in ED patients with influenza. Predictor data were limited to those recorded prior to ED disposition decision: demographics, ED complaint, medical problems, vital signs, supplemental oxygen use, and laboratory results. Our study population was comprised of adults diagnosed with influenza at one of five EDs in our university health system between January 1, 2017 and May 18, 2022; visits were divided into two cohorts to facilitate model development and validation. Prediction performance was assessed by the area under the receiver operating characteristic curve (AUC) and the Brier score. Results: Among 8032 patients with laboratory-confirmed influenza, incidence of critical care needs was 6.3% and incidence of inpatient care needs was 19.6%. The most common reasons for ED visit were symptoms of respiratory tract infection, fever, and shortness of breath. Model AUCs were 0.89 (95% CI 0.86-0.93) for prediction of critical care and 0.90 (95% CI 0.88-0.93) for inpatient care needs; Brier scores were 0.026 and 0.042, respectively. Importantpredictors included shortness of breath, increasing respiratory rate, and a high number of comorbid diseases. Conclusions: ML methods can be used to accurately predict clinical deterioration in ED patients with influenza and have potential to support ED disposition decision-making.

Racial and Socioeconomic Disparities Evident in Inappropriate Antibiotic Prescribing in the Emergency Department.

STUDY OBJECTIVE: Inappropriate antibiotic prescribing for acute respiratory tract infections is a common source of low-value care in the emergency department (ED). Racial and socioeconomic disparities have been noted in episodes of low-value care, particularly in children. We evaluated whether prescribing rates for acute respiratory tract infections when antibiotics would be inappropriate by guidelines differed by race and socioeconomics. METHODS: A retrospective cross-sectional analysis of adult and pediatric patient encounters in the emergency department (ED) between 2015 and 2023 at 5 hospitals for acute respiratory tract infections that did not require antibiotics by guidelines. Multivariable regression was used to calculate the risk ratio between race, ethnicity, and area deprivation index and inappropriate antibiotic prescribing, controlling for patient age, sex, and relevant comorbidities. RESULTS: A total of 147,401 ED encounters (55% pediatric, 45% adult) were included. At arrival, 4% patients identified as Asian, 50% as Black, 5% as Hispanic, and 23% as White. Inappropriate prescribing was noted in 7.6% of overall encounters, 8% for Asian patients, 6% for Black patients, 5% for Hispanic patients, and 12% for White patients. After adjusting for age, sex, comorbidities, and area deprivation index, White patients had a 1.32 (95% confidence interval, 1.26 to 1.38) higher likelihood of receiving a prescription compared with Black patients. Patients residing in areas of greater socioeconomic deprivation, regardless of race and ethnicity, had a 0.74 (95% confidence interval, 0.70 to 0.78) lower likelihood of receiving a prescription. CONCLUSION: Our results suggest that although overall inappropriate prescribing was relatively low, White patients and patients from wealthier areas were more likely to receive an inappropriate antibiotic prescription.

Association of Active Renin Content With Mortality in Critically Ill Patients: A Post hoc Analysis of the Vitamin C, Thiamine, and Steroids in Sepsis (VICTAS) Trial.

OBJECTIVE: Sepsis is a leading cause of mortality. Predicting outcomes is challenging and few biomarkers perform well. Defects in the renin-angiotensin system (RAS) can predict clinical outcomes in sepsis and may outperform traditional biomarkers. We postulated that RAS dysfunction (elevated active renin, angiotensin 1-7 [Ang-(1-7)], and angiotensin-converting enzyme 2 (ACE2) activity with depressed Ang-II and ACE activity) would be associated with mortality in a cohort of septic patients. DESIGN: Post hoc analysis of patients enrolled in the Vitamin C, Thiamine, and Steroids in Sepsis (VICTAS) randomized controlled trial. SETTING: Forty-three hospitals across the United States. PATIENTS: Biorepository samples of 103 patients. INTERVENTIONS: We analyzed day 0 (within 24 hr of respiratory failure, septic shock, or both) and day 3 samples ( n = 103 and 95, respectively) for assessment of the RAS. The association of RAS values with 30-day mortality was determined using Cox proportional hazards regression with multivariable adjustments for age, sex, VICTAS treatment arm, systolic blood pressure, Sequential Organ Failure Assessment Score, and vasopressor use. MEASUREMENTS AND MAIN RESULTS: High baseline active renin values were associated with higher 30-day mortality when dichotomized to the median of 188.7 pg/mL (hazard ratio [HR] = 2.84 [95% CI, 1.10-7.33], p = 0.031) or stratified into quartiles (Q1 = ref, HR Q2 = 2.01 [0.37-11.04], HR Q3 = 3.22 [0.64-16.28], HR Q4 = 5.58 [1.18-26.32], p for linear trend = 0.023). A 1- sd (593.6 pg/mL) increase in renin from day 0 to day 3 was associated with increased mortality (HR = 3.75 [95% CI, 1.94-7.22], p < 0.001), and patients whose renin decreased had improved survival compared with those whose renin increased (HR 0.22 [95% CI, 0.08-0.60], p = 0.003). Ang-(1-7), ACE2 activity, Ang-II and ACE activity did not show this association. Mortality was attenuated in patients with renin over the median on day 0 who received the VICTAS intervention, but not on day 3 ( p interaction 0.020 and 0.137, respectively). There were no additional consistent patterns of mortality on the RAS from the VICTAS intervention. CONCLUSIONS: Baseline serum active renin levels were strongly associated with mortality in critically ill patients with sepsis. Furthermore, a greater relative activation in circulating renin from day 0 to day 3 was associated with a higher risk of death.

Epidemiology and Clinical Outcomes of Community-Acquired Acute Kidney Injury in the Emergency Department: A Multisite Retrospective Cohort Study.

RATIONALE & OBJECTIVE: The prevalence of community-acquired acute kidney injury (CA-AKI) in the United States and its clinical consequences are not well described. Our objective was to describe the epidemiology of CA-AKI and the associated clinical outcomes. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: 178,927 encounters by 139,632 adults at 5 US emergency departments (EDs) between July 1, 2017, and December 31, 2022. PREDICTORS: CA-AKI identified using KDIGO (Kidney Disease: Improving Global Outcomes) serum creatinine (Scr)-based criteria. OUTCOMES: For encounters resulting in hospitalization, the in-hospital trajectory of AKI severity, dialysis initiation, intensive care unit (ICU) admission, and death. For all encounters, occurrence over 180 days of hospitalization, ICU admission, new or progressive chronic kidney disease, dialysis initiation, and death. ANALYTICAL APPROACH: Multivariable logistic regression analysis to test the association between CA-AKI and measured outcomes. RESULTS: For all encounters, 10.4% of patients met the criteria for any stage of AKI on arrival to the ED. 16.6% of patients admitted to the hospital from the ED had CA-AKI on arrival to the ED. The likelihood of AKI recovery was inversely related to CA-AKI stage on arrival to the ED. Among encounters for hospitalized patients, CA-AKI was associated with in-hospital dialysis initiation (OR, 6.2; 95% CI, 5.1-7.5), ICU admission (OR, 1.9; 95% CI, 1.7-2.0), and death (OR, 2.2; 95% CI, 2.0-2.5) compared with patients without CA-AKI. Among all encounters, CA-AKI was associated with new or progressive chronic kidney disease (OR, 6.0; 95% CI, 5.6-6.4), dialysis initiation (OR, 5.1; 95% CI, 4.5-5.7), subsequent hospitalization (OR, 1.1; 95% CI, 1.1-1.2) including ICU admission (OR, 1.2; 95% CI, 1.1-1.4), and death (OR, 1.6; 95% CI, 1.5-1.7) during the subsequent 180 days. LIMITATIONS: Residual confounding. Study implemented at a single university-based health system. Potential selection bias related to exclusion of patients without an available baseline Scr measurement. Potential ascertainment bias related to limited repeat Scr data during follow-up after an ED visit. CONCLUSIONS: CA-AKI is a common and important entity that is associated with serious adverse clinical consequences during the 6-month period after diagnosis. PLAIN-LANGUAGE SUMMARY: Acute kidney injury (AKI) is a condition characterized by a rapid decline in kidney function. There are many causes of AKI, but few studies have examined how often AKI is already present when patients first arrive to an emergency department seeking medical attention for any reason. We analyzed approximately 175,000 visits to Johns Hopkins emergency departments and found that AKI is common on presentation to the emergency department and that patients with AKI have increased risks of hospitalization, intensive care unit admission, development of chronic kidney disease, requirement of dialysis, and death in the first 6 months after diagnosis. AKI is an important condition for health care professionals to recognize and is associated with serious adverse outcomes.

Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.

BACKGROUND: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions. METHODS: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data. RESULTS: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS. CONCLUSIONS: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.

Do prehospital sepsis alerts decrease time to complete CMS sepsis measures?

INTRODUCTION: In an effort to improve sepsis outcomes the Centers for Medicare and Medicaid Services (CMS) established a time sensitive sepsis management bundle as a core quality measure that includes blood culture collection, serum lactate collection, initiation of intravenous fluid administration, and initiation of broad-spectrum antibiotics. Few studies examine the effects of a prehospital sepsis alert protocol on decreasing time to complete CMS sepsis core measures. METHODS: This study was a retrospective cohort study of patients transported via EMS from December 1, 2018 to December 1, 2019 who met the criteria of the Maryland Statewide EMS sepsis protocol and compared outcomes between patients who activated a prehospital sepsis alert and patients who did not activate a prehospital sepsis alert. The Maryland Institute for Emergency Medical Services Systems developed a sepsis protocol that instructs EMS providers to notify the nearest appropriate facility with a sepsis alert if a patient 18 years of age and older is suspected of having an infection and also presents with at least two of the following: temperature >38 °C or <35.5 °C, a heart rate >100 beats per minute, a respiratory rate >25 breaths per minute or end-tidal carbon dioxide less than or equal to 32 mmHg, a systolic blood pressure <90 mmHg, or a point of care lactate reading greater than or equal to 4 mmol/L. RESULTS: Median time to achieve all four studied CMS sepsis core measures was 103 min [IQR 61-153] for patients who received a prehospital sepsis alert and 106.5 min [IQR 75-189] for patients who did not receive a prehospital sepsis alert (p-value 0.105). Median time to completion was shorter for serum lactate collection (28 min. vs 35 min., p-value 0.019), blood culture collection (28 min. vs 38 min., p-value <0.01), and intravenous fluid administration (54 min. vs 61 min., p-value 0.025) but was not significantly different for antibiotic administration (94 min. vs 103 min., p-value 0.12) among patients who triggered a sepsis alert. CONCLUSION: This study questions the effectiveness of prehospital sepsis alert protocols on decreasing time to complete CMS sepsis core measures. Future studies should address if these times can be impacted by having EMS providers independently administer antibiotics.

Monocyte Distribution Width as a Diagnostic Marker for Infection: A Systematic Review and Meta-analysis.

BACKGROUND: Monocyte distribution width (MDW) is an emerging biomarker for infection. It is available easily and quickly as part of the CBC count, which is performed routinely on hospital admission. The increasing availability and promising results of MDW as a biomarker in sepsis has prompted an expansion of its use to other infectious diseases. RESEARCH QUESTION: What is the diagnostic performance of MDW across multiple infectious disease outcomes and care settings? STUDY DESIGN AND METHODS: A systematic review of the diagnostic performance of MDW across multiple infectious disease outcomes was conducted by searching PubMed, Embase, Scopus, and Web of Science through February 4, 2022. Meta-analysis was performed for outcomes with three or more reports identified (sepsis and COVID-19). Diagnostic performance measures were calculated for individual studies with pooled estimates created by linear mixed-effects models. RESULTS: We identified 29 studies meeting inclusion criteria. Most examined sepsis (19 studies) and COVID-19 (six studies). Pooled estimates of diagnostic performance for sepsis differed by reference standard (Second vs Third International Consensus Definitions for Sepsis and Septic Shock criteria) and tube anticoagulant used and ranged from an area under the receiver operating characteristic curve (AUC) of 0.74 to 0.94, with mean sensitivity of 0.69 to 0.79 and mean specificity of 0.57 to 0.86. For COVID-19, the pooled AUC of MDW was 0.76, mean sensitivity was 0.79, and mean specificity was 0.59. INTERPRETATION: MDW exhibited good diagnostic performance for sepsis and COVID-19. Diagnostic thresholds for sepsis should be chosen with consideration of reference standard and tube type used. TRIAL REGISTRY: Prospero; No.: CRD42020210074; URL: https://www.crd.york.ac.uk/prospero/.

Renal outcomes following intravenous contrast administration in patients with acute kidney injury: a multi-site retrospective propensity-adjusted analysis.

PURPOSE: Evidence of an association between intravenous contrast media (CM) and persistent renal dysfunction is lacking for patients with pre-existing acute kidney injury (AKI). This study was designed to determine the association between intravenous CM administration and persistent AKI in patients with pre-existing AKI. METHODS: A retrospective propensity-weighted and entropy-balanced observational cohort analysis of consecutive hospitalized patients ≥ 18 years old meeting Kidney Disease Improving Global Outcomes (KDIGO) creatinine-based criteria for AKI at time of arrival to one of three emergency departments between 7/1/2017 and 6/30/2021 who did or did not receive intravenous CM. Outcomes included persistent AKI at hospital discharge and initiation of dialysis within 180 days of index encounter. RESULTS: Our analysis included 14,449 patient encounters, with 12.8% admitted to the intensive care unit (ICU). CM was administered in 18.4% of all encounters. AKI resolved prior to hospital discharge for 69.1%. No association between intravenous CM administration and persistent AKI was observed after unadjusted multivariable logistic regression modeling (OR 1; 95% CI 0.89-1.11), propensity weighting (OR 0.93; 95% CI 0.83-1.05), and entropy balancing (OR 0.94; 95% CI 0.83-1.05). Sub-group analysis in those admitted to the ICU yielded similar results. Initiation of dialysis within 180 days was observed in 5.4% of the cohort. An association between CM administration and increased risk of dialysis within 180 days was not observed. CONCLUSION: Among patients with pre-existing AKI, contrast administration was not associated with either persistent AKI at hospital discharge or initiation of dialysis within 180 days. Current consensus recommendations for use of intravenous CM in patients with stable renal disease may also be applied to patients with pre-existing AKI.

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay.

INTRODUCTION: Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS). METHODS: Data was collected from the trauma registry at an urban level one adult trauma center and included patients from 1/1/2014 to 3/31/2019. Trauma patients with one or fewer days LOS were excluded. Single layer and deep artificial neural networks were trained to identify patients in the top quartile of LOS and optimized on area under the receiver operator characteristic curve (AUROC). The predictive performance of the model was assessed on a separate test set using binary classification measures of accuracy, precision, and error. RESULTS: 2953 admitted trauma patients with more than one-day LOS were included in this study. They were 70% male, 60% white, and averaged 47 years-old (SD: 21). 28% were penetrating trauma. Median length of stay was 5 days (IQR 3-9). For prediction of prolonged LOS, the deep neural network achieved an AUROC of 0.80 (95% CI: 0.786-0.814) specificity was 0.95, sensitivity was 0.32, with an overall accuracy of 0.79. CONCLUSION: Machine learning can predict, with excellent specificity, trauma patients who will have prolonged length of stay with only physiologic and demographic data available at the time of admission. These patients may benefit from additional resources with respect to disposition planning at the time of admission.

Management of atrial flutter and atrial fibrillation with rapid ventricular response in patients with acute decompensated heart failure: A systematic review.

OBJECTIVE: The objective was to evaluate the comparative effectiveness and safety of pharmacological and nonpharmacological management options for atrial fibrillation/atrial flutter with rapid ventricular response (AFRVR) in patients with acute decompensated heart failure (ADHF) in the acute care setting. METHODS: This study was a systematic review of observational studies or randomized clinical trials (RCT) of adult patients with AFRVR and concomitant ADHF in the emergency department (ED), intensive care unit, or step-down unit. The primary effectiveness outcome was successful rate or rhythm control. Safety outcomes were adverse events, such as symptomatic hypotension and venous thromboembolism. RESULTS: A total of 6577 unique articles were identified. Five studies met inclusion criteria: one RCT in the inpatient setting and four retrospective studies, two in the ED and the other three in the inpatient setting. In the RCT of diltiazem versus placebo, 22 patients (100%) in the treatment group had a therapeutic response compared to 0/15 (0%) in the placebo group, with no significant safety differences between the two groups. For three of the observational studies, data were limited. One observation study showed no difference between metoprolol and diltiazem for successful rate control, but worsening heart failure symptoms occurred more frequently in those receiving diltiazem compared to metoprolol (19 patients [33%] vs. 10 patients [15%], p = 0.019). A single study included electrical cardioversion (one patient exposed with failure to convert to sinus rhythm) as nonpharmacological management. The overall risk of bias for included studies ranged from serious to critical. Missing data and heterogeneity of definitions for effectiveness and safety outcomes precluded the combination of results for quantitative meta-analysis. CONCLUSIONS: High-level evidence to inform clinical decision making regarding effective and safe management of AFRVR in patients with ADHF in the acute care setting is lacking.

Exploring the impact of missingness on racial disparities in predictive performance of a machine learning model for emergency department triage.

Objective: To investigate how missing data in the patient problem list may impact racial disparities in the predictive performance of a machine learning (ML) model for emergency department (ED) triage. Materials and Methods: Racial disparities may exist in the missingness of EHR data (eg, systematic differences in access, testing, and/or treatment) that can impact model predictions across racialized patient groups. We use an ML model that predicts patients' risk for adverse events to produce triage-level recommendations, patterned after a clinical decision support tool deployed at multiple EDs. We compared the model's predictive performance on sets of observed (problem list data at the point of triage) versus manipulated (updated to the more complete problem list at the end of the encounter) test data. These differences were compared between Black and non-Hispanic White patient groups using multiple performance measures relevant to health equity. Results: There were modest, but significant, changes in predictive performance comparing the observed to manipulated models across both Black and non-Hispanic White patient groups; c-statistic improvement ranged between 0.027 and 0.058. The manipulation produced no between-group differences in c-statistic by race. However, there were small between-group differences in other performance measures, with greater change for non-Hispanic White patients. Discussion: Problem list missingness impacted model performance for both patient groups, with marginal differences detected by race. Conclusion: Further exploration is needed to examine how missingness may contribute to racial disparities in clinical model predictions across settings. The novel manipulation method demonstrated may aid future research.

Monocyte distribution width as a pragmatic screen for SARS-CoV-2 or influenza infection.

Monocyte distribution width (MDW) is a novel marker of monocyte activation, which is known to occur in the immune response to viral pathogens. Our objective was to determine the performance of MDW and other leukocyte parameters as screening tests for SARS-CoV-2 and influenza infection. This was a prospective cohort analysis of adult patients who underwent complete blood count (CBC) and SARS-CoV-2 or influenza testing in an Emergency Department (ED) between January 2020 and July 2021. The primary outcome was SARS-CoV-2 or influenza infection. Secondary outcomes were measures of severity of illness including inpatient hospitalization, critical care admission, hospital lengths of stay and mortality. Descriptive statistics and test performance measures were evaluated for monocyte percentage, MDW, white blood cell (WBC) count, and neutrophil to lymphocyte ratio (NLR). 3,425 ED patient visits were included. SARS-CoV-2 testing was performed during 1,922 visits with a positivity rate of 5.4%; influenza testing was performed during 2,090 with a positivity rate of 2.3%. MDW was elevated in patients with SARS-Cov-2 (median 23.0U; IQR 20.5-25.1) or influenza (median 24.1U; IQR 22.0-26.9) infection, as compared to those without (18.9U; IQR 17.4-20.7 and 19.1U; 17.4-21, respectively, P < 0.001). Monocyte percentage, WBC and NLR values were within normal range in patients testing positive for either virus. MDW identified SARS-CoV-2 and influenza positive patients with an area under the curve (AUC) of 0.83 (95% CI 0.79-0.86) and 0.83 (95% CI 0.77-0.88), respectively. At the accepted cut-off value of 20U for MDW, sensitivities were 83.7% (95% CI 76.5-90.8%) for SARS-CoV-2 and 89.6% (95% CI 80.9-98.2%) for influenza, compared to sensitivities below 45% for monocyte percentage, WBC and NLR. MDW negative predictive values were 98.6% (95% CI 98.0-99.3%) and 99.6% (95% CI 99.3-100.0%) respectively for SARS-CoV-2 and influenza. Monocyte Distribution Width (MDW), available as part of a routine complete blood count (CBC) with differential, may be a useful indicator of SARS-CoV-2 or influenza infection.

Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.

Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. ML models were derived in a retrospective cohort of 21,452 ED patients who visited one of five ED study sites and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation; model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. Incidence of critical care needs within 24 h and inpatient care needs within 72 h were 10.7% and 22.5%, respectively and were similar across study periods. ML model performance was excellent under all conditions, with AUC ranging from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after CDS implementation.

Monocyte anisocytosis increases during multisystem inflammatory syndrome in children with cardiovascular complications.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening complication that can develop weeks to months after an initial SARS-CoV-2 infection. A complex, time-consuming laboratory evaluation is currently required to distinguish MIS-C from other illnesses. New assays are urgently needed early in the evaluation process to expedite MIS-C workup and initiate treatment when appropriate. This study aimed to measure the performance of a monocyte anisocytosis index, obtained on routine complete blood count (CBC), to rapidly identify subjects with MIS-C at risk for cardiac complications. METHODS: We measured monocyte anisocytosis, quantified by monocyte distribution width (MDW), in blood samples collected from children who sought medical care in a single medical center from April 2020 to October 2020 (discovery cohort). After identifying an effective MDW threshold associated with MIS-C, we tested the utility of MDW as a tier 1 assay for MIS-C at multiple institutions from October 2020 to October 2021 (validation cohort). The main outcome was the early screening of MIS-C, with a focus on children with MIS-C who displayed cardiac complications. The screening accuracy of MDW was compared to tier 1 routine laboratory tests recommended for evaluating a child for MIS-C. RESULTS: We enrolled 765 children and collected 846 blood samples for analysis. In the discovery cohort, monocyte anisocytosis, quantified as an MDW threshold of 24.0, had 100% sensitivity (95% CI 78-100%) and 80% specificity (95% CI 69-88%) for identifying MIS-C. In the validation cohort, an initial MDW greater than 24.0 maintained a 100% sensitivity (95% CI 80-100%) and monocyte anisocytosis displayed a diagnostic accuracy greater that other clinically available hematologic parameters. Monocyte anisocytosis decreased with disease resolution to values equivalent to those of healthy controls. CONCLUSIONS: Monocyte anisocytosis detected by CBC early in the clinical workup improves the identification of children with MIS-C with cardiac complications, thereby creating opportunities for improving current practice guidelines.

A Patient Outcomes-Driven Feedback Platform for Emergency Medicine Clinicians: Human-Centered Design and Usability Evaluation of Linking Outcomes Of Patients (LOOP).

BACKGROUND: The availability of patient outcomes-based feedback is limited in episodic care environments such as the emergency department. Emergency medicine (EM) clinicians set care trajectories for a majority of hospitalized patients and provide definitive care to an even larger number of those discharged into the community. EM clinicians are often unaware of the short- and long-term health outcomes of patients and how their actions may have contributed. Despite large volumes of patients and data, outcomes-driven learning that targets individual clinician experiences is meager. Integrated electronic health record (EHR) systems provide opportunity, but they do not have readily available functionality intended for outcomes-based learning. OBJECTIVE: This study sought to unlock insights from routinely collected EHR data through the development of an individualizable patient outcomes feedback platform for EM clinicians. Here, we describe the iterative development of this platform, Linking Outcomes Of Patients (LOOP), under a human-centered design framework, including structured feedback obtained from its use. METHODS: This multimodal study consisting of human-centered design studios, surveys (24 physicians), interviews (11 physicians), and a LOOP application usability evaluation (12 EM physicians for ≥30 minutes each) was performed between August 2019 and February 2021. The study spanned 3 phases: (1) conceptual development under a human-centered design framework, (2) LOOP technical platform development, and (3) usability evaluation comparing pre- and post-LOOP feedback gathering practices in the EHR. RESULTS: An initial human-centered design studio and EM clinician surveys revealed common themes of disconnect between EM clinicians and their patients after the encounter. Fundamental postencounter outcomes of death (15/24, 63% respondents identified as useful), escalation of care (20/24, 83%), and return to ED (16/24, 67%) were determined high yield for demonstrating proof-of-concept in our LOOP application. The studio aided the design and development of LOOP, which integrated physicians throughout the design and content iteration. A final LOOP prototype enabled usability evaluation and iterative refinement prior to launch. Usability evaluation compared to status quo (ie, pre-LOOP) feedback gathering practices demonstrated a shift across all outcomes from "not easy" to "very easy" to obtain and from "not confident" to "very confident" in estimating outcomes after using LOOP. On a scale from 0 (unlikely) to 10 (most likely), the users were very likely (9.5) to recommend LOOP to a colleague. CONCLUSIONS: This study demonstrates the potential for human-centered design of a patient outcomes-driven feedback platform for individual EM providers. We have outlined a framework for working alongside clinicians with a multidisciplined team to develop and test a tool that augments their clinical experience and enables closed-loop learning.

Monocyte distribution width as part of a broad pragmatic sepsis screen in the emergency department.

STUDY OBJECTIVE: Enhancement of a routine complete blood count (CBC) for detection of sepsis in the emergency department (ED) has pragmatic utility for early management. This study evaluated the performance of monocyte distribution width (MDW) alone and in combination with other routine CBC parameters as a screen for sepsis and septic shock in ED patients. METHODS: A prospective cohort analysis of adult patients with a CBC collected at an urban ED from January 2020 through July 2021. The performance of MDW, white blood count (WBC) count, and neutrophil-to-lymphocyte-ratio (NLR) to detect sepsis and septic shock (Sepsis-3 Criteria) was evaluated using diagnostic performance measures. RESULTS: The cohort included 7952 ED patients, with 180 meeting criteria for sepsis; 43 with septic shock and 137 without shock. MDW was highest for patients with septic shock (median 24.8 U, interquartile range [IQR] 22.0-28.1) and trended downward for patients with sepsis without shock (23.9 U, IQR 20.2-26.8), infection (20.4 U, IQR 18.2-23.3), then controls (18.6 U, IQR 17.1-20.4). In isolation, MDW detected sepsis and septic shock with an area under the receiver operator characteristic curve (AUC) of 0.80 (95% confidence interval [CI] 0.77-0.84) and 0.85 (95% CI 0.80-0 .91), respectively. Optimal performance was achieved in combination with WBC count and NLR for detection of sepsis (AUC 0.86, 95% CI 0.83-0.89) and septic shock (0.86, 95% CI 0.80-0.92). CONCLUSION: A CBC differential panel that includes MDW demonstrated strong performance characteristics in a broad ED population suggesting pragmatic value as a rapid screen for sepsis and septic shock.