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.

Endocan: A biomarker for endothelial dysfunction and inflammation, linking maternal obesity and pediatric obesity in a cohort of preterm neonates

OBJECTIVES

Numerous animal and epidemiologic studies have demonstrated a positive association between maternal obesity in pregnancy and obesity in offspring. The biologic mechanisms of this association remain under investigation. One proposed mechanism includes fetoplacental endothelial dysfunction secondary to inflammation. Endocan is a relatively new biomarker for endothelial dysfunction and inflammation. Our objectives were to examine (1) the association between maternal obesity and neonatal serum endocan at birth, and (2) the association between neonatal serum endocan at birth and pediatric obesity at 24-36 months of age.

STUDY DESIGN

This was a secondary analysis of a prospective cohort of neonates born < 33 weeks gestation. Serum endocan was collected within 48 hours of birth. Serum endocan levels were compared in neonates born to obese mothers vs. those born to non-obese mothers. BMI data were retrospectively collected from cohort neonates between 24 and 36 months of age.

RESULTS

The analysis included 120 mother/neonate dyads. Neonates born to obese mothers had higher median serum endocan at birth compared to neonates born to non-obese mothers (299 ng/L [205-586] vs. 251 ng/L [164-339], p = 0.045). In a linear regression modeled on neonatal serum endocan level, maternal obesity had a statistically significant positive association (p = 0.021). Higher mean serum endocan level at birth was associated with pediatric obesity between 24 and 36 months (obese vs. non-obese offspring; 574 ng/L (222) vs. 321 ng/L (166), p = 0.005).

CONCLUSIONS

In our cohort of preterm neonates, elevated serum endocan at birth was associated with both maternal obesity and downstream pediatric obesity. More research is needed to understand intergenerational transmission of obesity. A large focus has been on epigenetic modification. Endothelial dysfunction and inflammation may play important roles in these pathways. Effective biomarkers, including endocan, may also serve as intermediate outcomes in future pregnancy research.

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.

High Prevalence of Adverse Social Determinants of Health in Dialysis Access Creation Patients in a Safety-Net Setting

BACKGROUND

Patients receiving dialysis access surgery are often exposed to adverse social determinants of health (SDH) that negatively impact their care. Our goal was to characterize these factors experienced by our arteriovenous dialysis access patients and identify differences in health outcomes based on their SDH.

METHODS

We performed a retrospective cohort study of all patients who underwent dialysis access creation (2017-2021) and were screened for SDH at a clinical visit (using THRIVE survey) implemented at an urban, safety-net hospital institution within 1 year of access creation. Demographics, procedural details, early postoperative outcomes, survey responses, and referral to our hospital's preventive food pantry were recorded. Univariable analysis and multivariable analyses were performed to assess for associations with key health outcomes.

RESULTS

There were 190 patients who responded to the survey within 1 year of their operation. At least 1 adverse SDH was identified in 42 (22%) patients. Normalized to number of respondents for each question, adverse SDH identified were difficulty obtaining transportation to medical appointments (18%), food insecurity (16%), difficulty affording utilities (13%), difficulty affording medication (12%), unemployed and seeking employment (9%), unstable housing (7%), difficulty caring for family/friends (6%), and desiring more education (5%). There were 71 (37%) patients who received food pantry referrals. Mean age was 60 years and 38% of patients were female and 64% were Black. More than half of patients (57%) had a tunneled dialysis catheter (TDC) at the time of access creation. Dialysis accesses created were brachiocephalic (39%), brachiobasilic (25%), radiocephalic fistulas (16%), and arteriovenous grafts (14%). Thirty-day emergency department (ED) visits, 30-day readmissions, and 90-day mortality occurred in 23%, 21%, and 2%, respectively. On univariable and multivariable analyses, any adverse SDH determined on survey and food pantry referral were not associated with preoperative dialysis through TDCs, receiving nonautogenous dialysis access, 30-day ED visits and readmissions, or 90-day mortality.

CONCLUSION

Nearly a quarter of dialysis access surgery patients at a safety-net hospital experienced adverse SDH and more than one-third received a food pantry referral. Most common difficulties experienced include difficulty obtaining transportation to medical appointments, food insecurity, and difficulty paying for utilities and medication. Although there were no differences in postoperative outcomes, the high prevalence of these adverse SDH warrants prioritization of resources in this population to ensure healthy equity and further investigation into their effects on health outcomes.

Dementia risk analysis using temporal event modeling on a large real-world dataset

The objective of the study is to identify healthcare events leading to a diagnosis of dementia from a large real-world dataset. This study uses a data-driven approach to identify temporally ordered pairs and trajectories of healthcare codes in the electronic health record (EHR). This allows for discovery of novel temporal risk factors leading to an outcome of interest that may otherwise be unobvious. We identified several known (Down syndrome RR = 116.1, thiamine deficiency RR = 76.1, and Parkinson's disease RR = 41.1) and unknown (Brief psychotic disorder RR = 68.6, Toxic effect of metals RR = 40.4, and Schizoaffective disorders RR = 40.0) factors for a specific dementia diagnosis. The associations with the greatest risk for any dementia diagnosis were found to be primarily related to mental health (Brief psychotic disorder RR = 266.5, Dissociative and conversion disorders RR = 169.8), or neurologic conditions or procedures (Dystonia RR = 121.9, Lumbar Puncture RR = 119.0). Trajectory and clustering analysis identified factors related to cerebrovascular disorders, as well as diagnoses which increase the risk of toxic imbalances. The results of this study have the ability to provide valuable insights into potential patient progression towards dementia and improve recognition of patients at risk for developing dementia.

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.

Providing Remote Aid During a Humanitarian Crisis

Humanitarian crises create opportunities for both in-person and remote aid. Durable, complex, and team-based care may leverage a telemedicine approach for comprehensive support within a conflict zone. Barriers and enablers are detailed, as is the need for mission expansion due to initial program success. Adapting a telemedicine program initially designed for critical care during the severe acute respiratory syndrome coronavirus 2 pandemic offers a solution to data transfer and data analysis issues. Staffing efforts and grouped elements of patient care detail the kinds of remote aid that are achievable. A multiprofessional team-based approach (clinical, administrative, nongovernmental organization, government) can provide comprehensive consultation addressing surgical planning, critical care management, infection and infection control management, and patient transfer for complex care. Operational and network security create parallel concerns relevant to avoid geolocation and network intrusion during consultation. Deliberate approaches to address cultural differences that influence relational dynamics are also essential for mission success.

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.

Experiences and Perceptions of Telehealth Visits in Diabetes Care During and After the COVID-19 Pandemic Among Adults With Type 2 Diabetes and Their Providers: Qualitative Study

BACKGROUND

Since the COVID-19 pandemic, telehealth has been widely adopted in outpatient settings in the United States. Although telehealth visits are publicly accepted in different settings, little is known about the situation after the wide adoption of telehealth from the perspectives of adults with type 2 diabetes mellitus (T2D) and their providers.

OBJECTIVE

This study aims to identify barriers and facilitators of maintaining continuity of care using telehealth for patients with T2D in a diabetes specialty clinic.

METHODS

As the second phase of a multimethod study to understand missed appointments among adults with T2D, we conducted semistructured, individual, in-depth phone or Zoom interviews with 23 adults with T2D (14/23, 61% women; mean age 55.1, SD 14.4, range 35-77 years) and 10 providers from diabetes clinics in a tertiary academic medical center in Maryland. Interviews were audio-recorded, transcribed, and analyzed using thematic content analysis by the research team.

RESULTS

Adults with T2D and their providers generally reported positive experiences with telehealth visits for diabetes care with some technical challenges resulting in the need for in-person visits. We identified the following 3 themes: (1) "perceived benefits of telehealth visits," such as convenience, time and financial efficiencies, and independence from caregivers, benefits shared by both patients and providers; (2) "perceived technological challenges of telehealth visits," such as disparities in digital health literacy, frustration caused by unstable internet connection, and difficulty sharing glucose data, challenges shared by both patients and providers; and (3) "impact of telehealth visits on the quality of diabetes care," including lack of diabetes quality measures and needs and preferences for in-person visits, shared mainly from providers' perspectives with some patient input.

CONCLUSIONS

Telehealth is generally received positively in diabetes care with some persistent challenges that might compromise the quality of diabetes care. Telehealth technology and glucose data platforms must incorporate user experience and user-centered design to optimize telehealth use in diabetes care. Clinical practices need to consider new workflows for telehealth visits to facilitate easier follow-up scheduling and lab completion. Future research to investigate the ideal balance between in-person and telehealth visits in diabetes care is warranted to enhance the quality of diabetes care and to optimize diabetes outcomes. Policy flexibilities should also be considered to broaden access to diabetes care for all patients with T2D.

Critical interactions between race and the highly granular area deprivation index in liver transplant evaluation

Neighborhood socioeconomic deprivation may have important implications on disparities in liver transplant (LT) evaluation. In this retrospective cohort study, we constructed a novel dataset by linking individual patient-level data with the highly granular Area Deprivation Index (ADI), which is advantageous over other neighborhood measures due to: specificity of Census Block-Group (versus Census Tract, Zip code), scoring, and robust variables. Our cohort included 1377 adults referred to our center for LT evaluation 8/1/2016-12/31/2019. Using modified Poisson regression, we tested for effect measure modification of the association between neighborhood socioeconomic status (nSES) and LT evaluation outcomes (listing, initiating evaluation, and death) by race and ethnicity. Compared to patients with high nSES, those with low nSES were at higher risk of not being listed (aRR = 1.14; 95%CI 1.05-1.22; p < .001), of not initiating evaluation post-referral (aRR = 1.20; 95%CI 1.01-1.42; p = .03) and of dying without initiating evaluation (aRR = 1.55; 95%CI 1.09-2.2; p = .01). While White patients with low nSES had similar rates of listing compared to White patients with high nSES (aRR = 1.06; 95%CI .96-1.17; p = .25), Underrepresented patients from neighborhoods with low nSES incurred 31% higher risk of not being listed compared to Underrepresented patients from neighborhoods with high nSES (aRR = 1.31; 95%CI 1.12-1.5; p < .001). Interventions addressing neighborhood deprivation may not only benefit patients with low nSES but may address racial and ethnic inequities.

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.

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/.

Juno's Close Encounter With Ganymede-An Overview

The Juno spacecraft has been in orbit around Jupiter since 2016. Two flybys of Ganymede were executed in 2021, opportunities realized by evolution of Juno's polar orbit over the intervening 5 years. The geometry of the close flyby just prior to the 34th perijove pass by Jupiter brought the spacecraft inside Ganymede's unique magnetosphere. Juno's payload, designed to study Jupiter's magnetosphere, had ample dynamic range to study Ganymede's magnetosphere. The Juno radio system was used both for gravity measurements and for study of Ganymede's ionosphere. Remote sensing of Ganymede returned new results on geology, surface composition, and thermal properties of the surface and subsurface.

Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions

Racial and ethnic disparities persist in access to the liver transplantation (LT) waiting list; however, there is limited knowledge about underlying system-level factors that may be responsible for these disparities. Given the complex nature of LT candidate evaluation, a human factors and systems engineering approach may provide insights. We recruited participants from the LT teams (coordinators, advanced practice providers, physicians, social workers, dieticians, pharmacists, leadership) at two major LT centers. From December 2020 to July 2021, we performed ethnographic observations (participant-patient appointments, committee meetings) and semistructured interviews (N = 54 interviews, 49 observation hours). Based on findings from this multicenter, multimethod qualitative study combined with the Systems Engineering Initiative for Patient Safety 2.0 (a human factors and systems engineering model for health care), we created a conceptual framework describing how transplant work system characteristics and other external factors may improve equity in the LT evaluation process. Participant perceptions about listing disparities described external factors (e.g., structural racism, ambiguous national guidelines, national quality metrics) that permeate the LT evaluation process. Mechanisms identified included minimal transplant team diversity, implicit bias, and interpersonal racism. A lack of resources was a common theme, such as social workers, transportation assistance, non-English-language materials, and time (e.g., more time for education for patients with health literacy concerns). Because of the minimal data collection or center feedback about disparities, participants felt uncomfortable with and unadaptable to unwanted outcomes, which perpetuate disparities. We proposed transplant center-level solutions (i.e., including but not limited to training of staff on health equity) to modifiable barriers in the clinical work system that could help patient navigation, reduce disparities, and improve access to care. Our findings call for an urgent need for transplant centers, national societies, and policy makers to focus efforts on improving equity (tailored, patient-centered resources) using the science of human factors and systems engineering.

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.

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

Methods

Results

Conclusion

Improving antimicrobial prescribing for upper respiratory infections in the emergency department: Implementation of peer comparison with behavioral feedback

OBJECTIVE

To reduce inappropriate antibiotic prescribing for acute respiratory infections (ARIs) by employing peer comparison with behavioral feedback in the emergency department (ED).

DESIGN

A controlled before-and-after study.

SETTING

The study was conducted in 5 adult EDs at teaching and community hospitals in a health system.

PATIENTS

Adults presenting to the ED with a respiratory condition diagnosis code. Hospitalized patients and those with a diagnosis code for a non-respiratory condition for which antibiotics are or may be warranted were excluded.

INTERVENTIONS

After a baseline period from January 2016 to March 2018, 3 EDs implemented a feedback intervention with peer comparison between April 2018 and December 2019 for attending physicians. Also, 2 EDs in the health system served as controls. Using interrupted time series analysis, the inappropriate ARI prescribing rate was calculated as the proportion of antibiotic-inappropriate ARI encounters with a prescription. Prescribing rates were also evaluated for all ARIs. Attending physicians at intervention sites received biannual e-mails with their inappropriate prescribing rate and had access to a dashboard that was updated daily showing their performance relative to their peers.

RESULTS

Among 28,544 ARI encounters, the inappropriate prescribing rate remained stable at the control EDs between the 2 periods (23.0% and 23.8%). At the intervention sites, the inappropriate prescribing rate decreased significantly from 22.0% to 15.2%. Between periods, the overall ARI prescribing rate was 38.1% and 40.6% in the control group and 35.9% and 30.6% in the intervention group.

CONCLUSIONS

Behavioral feedback with peer comparison can be implemented effectively in the ED to reduce inappropriate prescribing for ARIs.

Pediatric Hand Transplantation: A Decision Analysis

Background: The first successful bilateral pediatric hand transplant was performed in 2015. Previous hand transplant decision analysis models have focused on the adult population. This model principally aimed to determine whether adverse outcomes associated with immunosuppression outweigh the benefits of performing bilateral hand transplant surgery in a pediatric candidate. The model also conceptualized the valuation of losing years of life and sought to determine the impact of that valuation on the surgical decision. Methods: A decision model compared undergoing bilateral hand transplant surgery with using prosthetics for an 8-year-old patient. The outcome measure used was quality adjusted life years (QALYs), and sensitivity analysis was performed on the immunosuppressive risks associated with the surgical decision, as well as the perceived valuation of aversion to life years lost. Results: The decision to perform surgery was marginally optimal compared to the prosthetic decision (50.11 QALY vs. 47.95 QALY). A Monte Carlo simulation revealed that this difference may be too marginal to detect an optimal decision (50.14 ± 8.28 QALY vs. 47.95 ± 2.12 QALY). Sensitivity analysis identified decision thresholds related to immunosuppression risks (P = 29% vs. P = 33% modeled), and a trend of increasing risk as a patient is more averse to losing life years. Conclusions: The marginally optimal treatment strategy currently is bilateral hand transplant, compared to prosthetics for pediatric patients. Key determinants of the future optimal strategy will be whether immunosuppressive regimens become safer, with a reduced risk of losing life years due to immunosuppressive complications, and whether prosthetics become more acceptable and enable higher functioning.

Improving Readiness to Manage Intimate Partner Violence in Family Medicine Clinics by Collaboration With a Community Organization

Background and Objectives

Primary care clinicians are in a unique position to address intimate partner violence (IPV) in routine clinical practice. The purpose of this study was to improve clinician readiness to identify and manage IPV in four family medicine residency practice sites on the west side of Chicago by partnering with a local domestic violence organization.

Methods

Practice sites included three federally qualified health centers and one hospital-based office. Eligible clinicians included resident and faculty physicians, nurse practitioners, and certified nurse midwives. We assessed readiness using the validated Physician Readiness to Manage Intimate Partner Violence Survey (PREMIS). We used initial survey results (n=53, 73%) to develop a targeted clinician educational intervention by a community organization. We administered the PREMIS tool postintervention at 1 and 6 months, measuring perceived and actual knowledge, preparedness, and practice issues. We performed comparison statistics to assess aggregate change.

Results

PREMIS response rates were n=53 (72%), n=32 (47%), and n=36 (49%), for preintervention, 1, and 6 months postintervention, respectively. Mean clinician preparedness score improved significantly at 1 and 6 months (P<.001, P<.009). Mean self-perceived knowledge score improved significantly at 1 month (P<.001) and trended toward improvement at 6 months (P=.07). Actual knowledge trended toward improvement at 1 month (P=.07) and after 6 months (P=.05). Mean practice issues scores did not improve significantly.

Conclusions

Participation in a 45-minute targeted educational intervention improved clinician readiness to manage IPV. Collaborating with a community partner builds a relationship for further referrals and advocacy for patients.

Development of an Electronic Algorithm to Identify in Real Time Adults Hospitalized With Suspected Community-Acquired Pneumonia

Background

Community-acquired pneumonia (CAP) is a major driver of hospital antibiotic use. Efficient methods to identify patients treated for CAP in real time using the electronic health record (EHR) are needed. Automated identification of these patients could facilitate systematic tracking, intervention, and feedback on CAP-specific metrics such as appropriate antibiotic choice and duration.

Methods

Using retrospective data, we identified suspected CAP cases by searching for patients who received CAP antibiotics AND had an admitting International Classification of Diseases, Tenth Revision (ICD-10) code for pneumonia OR chest imaging within 24 hours OR bacterial urinary antigen testing within 48 hours of admission (denominator query). We subsequently explored different structured and natural language processing (NLP)–derived data from the EHR to identify CAP cases. We evaluated combinations of these electronic variables through receiver operating characteristic (ROC) curves to assess which best identified CAP cases compared to cases identified by manual chart review. Exclusion criteria were age <18 years, absolute neutrophil count <500 cells/mm³, and admission to an oncology unit.

Results

Compared to the gold standard of chart review, the area under the ROC curve to detect CAP was 0.63 (95% confidence interval [CI], .55–.72; P < .01) using structured data (ie, laboratory and vital signs) and 0.83 (95% CI, .77–.90; P < .01) when NLP-derived data from radiographic reports were included. The sensitivity and specificity of the latter model were 80% and 81%, respectively.

Conclusions

Creating an electronic tool that effectively identifies CAP cases in real time is possible, but its accuracy is dependent on NLP-derived radiographic data.

OP0039 ALPN-303, AN ENHANCED, POTENT DUAL BAFF/APRIL ANTAGONIST ENGINEERED BY DIRECTED EVOLUTION FOR THE TREATMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) AND OTHER B CELL-RELATED AUTOIMMUNE DISEASES

Background:

BAFF and APRIL are TNF superfamily members that form homo- and heteromultimers that bind TACI and BCMA on B cells; BAFF also binds BAFF-R. BAFF and APRIL support B cell development, differentiation, and survival, particularly for plasmablasts and plasma cells, and play critical roles in the pathogenesis of B cell-related autoimmune diseases. In nonclinical models, inhibition of either BAFF or APRIL alone mediates relatively modest effects, whereas their co-neutralization dramatically reduces B cell function, including antibody production. Fc fusions of wild-type (WT) TACI (e.g. atacicept and telitacicept) target both BAFF and APRIL and have demonstrated promising clinical potential in e.g. systemic lupus erythematosus (SLE) and IgA nephropathy but have not yet clearly exhibited long-term and/or complete disease remissions.

Objectives:

To generate a dual BAFF/APRIL antagonist with inhibitory activity superior to WT TACI and BCMA and with the potential to improve clinical outcomes in B cell-mediated diseases.

Methods:

Our directed evolution platform was used to identify a potent variant TNFR domain (vTD) of TACI that exhibits significantly enhanced affinity for BAFF and APRIL as compared to WT TACI; this TACI vTD domain was fused to a human IgG Fc to generate the therapeutic candidate ALPN-303. ALPN-303 was evaluated for functional activity in: 1) human lymphocyte assays, 2) the NOD.Aec1Aec2 spontaneous model of Sjogren’s syndrome (SjS), 3) the bm12-induced mouse model of lupus, 4) the (NZB/NZW)F1 spontaneous model of lupus, and 5) preclinical rodent and cynomolgus monkey pharmacokinetic/pharmacodynamic studies.

Results:

ALPN-303 inhibited BAFF- and APRIL-mediated signaling in vitro in human lymphocyte assays, with significantly lower IC50 values than WT TACI-Fc and belimumab comparators. In all mouse models evaluated, administration of ALPN-303 rapidly and significantly reduced key lymphocyte subsets including plasma cells, germinal center B cells, and follicular T helper cells. ALPN-303 significantly reduced autoantibodies and sialadenitis in the spontaneous SjS model, inhibited glomerular IgG deposition in the bm12-induced model of lupus, and potently suppressed anti-dsDNA autoAbs, blood urea nitrogen levels, proteinuria, sialadenitis, kidney lesions, and renal immune complex deposition in the NZB/W lupus model. As compared to WT TACI-Fc, ALPN-303 exhibited higher serum exposure and significantly and persistently decreased titers of serum IgM, IgG, and IgA antibodies in mice and cynomolgus monkeys (Figure 1).

Figure 1.

ALPN-303 induces more potent suppression, as compared to WT TACI-Fc, of serum immunoglobulins following a single 9 mg/kg IV infusion (on Day 0; arrows) in female cynomolgus monkeys.

Conclusion:

ALPN-303 is a potent BAFF/APRIL antagonist derived from our directed evolution platform that consistently demonstrates encouraging immunomodulatory activity and efficacy in vitro and in vivo, superior in preclinical studies to anti-BAFF antibody and WT TACI-Fc. This novel Fc fusion molecule demonstrates favorable preliminary developability characteristics, including higher serum exposures and more potent immunosuppressive activities, which may enable lower clinical doses and/or longer dosing intervals than WT TACI-Fc therapeutics. ALPN-303 may thus be an attractive development candidate for the treatment of multiple autoimmune and inflammatory diseases, particularly B cell-related diseases such as SLE, SjS, and other connective tissue diseases. Preclinical development is underway to enable the initiation of clinical trials later this year.

Disclosure of Interests:

Stacey R. Dillon Shareholder of: Alpine Immune Sciences, Bristol Myers Squibb, Employee of: Alpine Immune Sciences, Bristol Myers Squibb, Lawrence S. Evans Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Katherine E. Lewis Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Jing Yang Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Mark W. Rixon Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Joe Kuijper Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Dan Demonte Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Janhavi Bhandari Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Steve Levin Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Kayla Kleist Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Sherri Mudri Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Susan Bort Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Daniel Ardourel Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Michelle A. Seaberg Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Rachel Wang Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Chelsea Gudgeon Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Russell Sanderson Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Martin F. Wolfson Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Jan Hillson Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences, Stanford L. Peng Shareholder of: Alpine Immune Sciences, Employee of: Alpine Immune Sciences

Responding to a Pandemic: COVID-19 Projects in the Malone Center

As the scope and scale of the COVID-19 pandemic became clear in early March of 2020, the faculty of the Malone Center engaged in several projects aimed at addressing both immediate and long-term implications of COVID-19. In this article, we briefly outline the processes that we engaged in to identify areas of need, the projects that emerged, and the results of those projects. As we write, some of these projects have reached a natural termination point, whereas others continue. We identify some of the factors that led to projects that moved to implementation, as well as factors that led projects to fail to progress or to be abandoned.

Factors associated with missed appointments by adults with type 2 diabetes mellitus: a systematic review

Keeping regular medical appointments is a key indicator of patient engagement in diabetes care. Nevertheless, a significant proportion of adults with type 2 diabetes mellitus (T2DM) miss their regular medical appointments. In order to prevent and delay diabetes-related complications, it is essential to understand the factors associated with missed appointments among adults with T2DM. We synthesized evidence concerning factors associated with missed appointments among adults with T2DM. Using five electronic databases, including PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, PsycINFO and Web of Science, a systematic literature search was done to identify studies that describe factors related to missed appointments by adults with T2DM. A total of 18 articles met the inclusion criteria. The majority of studies included in this review were cohort studies using medical records. While more than half of the studies were of high quality, the operational definitions of missed appointments varied greatly across studies. Factors associated with missed appointments were categorized as patient characteristics, healthcare system and provider factors and interpersonal factors with inconsistent findings. Patient characteristics was the most commonly addressed category, followed by health system and provider factors. Only three studies addressed interpersonal factors, two of which were qualitative. An increasing number of people live with one or more chronic conditions which require more careful attention to patient-centered care and support. Future research is warranted to address interpersonal factors from patient perspectives to better understand the underlying causes of missed appointments among adults with T2DM.

169. Development of a Real Time Electronic Algorithm to Identify Hospitalized Patients with Community-Acquired Pneumonia

Background

Syndrome-based antibiotic stewardship can be limited by difficulty in finding cases for evaluation. We developed an electronic extraction algorithm to prospectively identify CAP patients.

Methods

We included non-oncology patients ≥18 years old admitted to The Johns Hopkins Hospital from 12/2018 to 3/2019 who 1) received common CAP antibiotics for ≥48 hours after admission and 2) had a bacterial urinary antigen and chest imaging ordered within 48 hours of admission that was not for assessment of endotracheal tube or central line placement. Charts of patients meeting these criteria were reviewed by 2 authors to identify true cases of CAP based on IDSA guidelines. Cases identified in 12/2018 (n=111) were used to explore potential indicators of CAP, and cases identified 1–3/2019 (n=173) were used to evaluate combinations of indicators that could identify patients treated for CAP who did have CAP (true CAP) and did not have CAP (false CAP). This cohort was divided into a training and a validation set (2/3 and 1/3, respectively). Potential indicators included vitals signs, laboratory data and free text extracted via natural language processing (NLP). Predictive performance of composite indicators for true CAP were assessed using receiver-operating characteristics (ROC) curves. The Hosmer-Lemeshow goodness fit test was used to test model fit and the Akaike Information Criteria was used to determine model selection.

Results

True CAP was observed in 41% (71/173) of cases and 14 potential individual indicators were identified (Table). These were combined to make 45 potential composite indicators. ROC curves for selected composite indicators are shown in the Figure. Models without use of NLP-derived variables had poor discriminative ability. The best model included fever, hypoxemia, leukocytosis, and “consolidation” on imaging with a sensitivity and positive predictive value 78.7% and specificity and negative predictive value of 85.7%.

Table. Indicators evaluated to identify patients with CAP

Figure. ROC curves for composite indicators

Conclusion

Patients with CAP can be identified using electronic data but use of NLP-derived radiographic criteria is required. These data can be linked with data on antibiotic use and duration to develop reports for clinicians regarding appropriate CAP diagnosis and treatment.

Disclosures

All Authors: No reported disclosures

Targeted rapid testing for SARS-CoV-2 in the emergency department is associated with large reductions in uninfected patient exposure time

Opportunity exists to decrease healthcare-related exposure to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), preserve infection control resources, and increase care capacity by reducing the time to diagnosis of coronavirus disease 2019 (COVID-19). A retrospective cohort analysis was undertaken to measure the effect of targeted rapid molecular testing for SARS-CoV-2 on these outcomes. In comparison with standard platform testing, rapid testing was associated with a 65.6% reduction (12.6 h) in the median time to removal from the isolation cohort for patients with negative diagnostic results. This translated to an increase in COVID-19 treatment capacity of 3028 bed-hours and 7500 fewer patient interactions that required the use of personal protective equipment per week.

Artificial Intelligence-Based Clinical Decision Support for COVID-19-Where Art Thou?

The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. Although artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date. In this perspective piece, the opportunities and requirements for AI-based clinical decision support systems are identified and challenges that impact "AI readiness" for rapidly emergent healthcare challenges are highlighted.

THU0222 B CELL MODULATORY VARIANT TNF RECEPTOR DOMAINS (VTDS) IDENTIFIED BY DIRECTED EVOLUTION TO INHIBIT BAFF AND APRIL, ALONE OR COMBINED WITH VARIANT IG DOMAINS (VIGD™) THAT INHIBIT T CELL COSTIMULATION, FOR THE TREATMENT OF SEVERE AUTOIMMUNE AND/OR INFLAMMATORY DISEASE

Background:

BAFF and APRIL are TNF superfamily members that bind both TACI and BCMA on B cells; BAFF also binds BAFF-R. Together, BAFF and APRIL support B cell development, differentiation, and survival. Their co-neutralization dramatically reduces B cell function, including antibody production, whereas inhibition of either BAFF or APRIL alone mediates relatively modest effects.

Objectives:

While CTLA-4-based therapeutics that block T cell costimulation provide safe and moderately effective T cell inhibition in many disease settings, and while B cell targeting therapies have demonstrated promising therapeutic potential, we postulate that improved, combined BAFF and APRIL inhibition, either alone or coupled with inhibition of T cell costimulation, will provide more effective and durable relief from severe B cell-related autoimmune diseases like SLE.

Methods:

We used our directed evolution platform to identify variant domains of the TNF family receptors TACI or BCMA that exhibit enhanced affinity for BAFF and APRIL as compared to their wild-type (WT) counterparts. These variant TACI or BCMA domains (vTD), alone or together with platform-derived CTLA-4 domains (vIgD), were fused to a modified human IgG1 Fc lacking effector function, yielding a panel of immunomodulatory molecules: TACI vTD-Fc, BCMA vTD-Fc, TACI vTD/CTLA-4 vIgD-Fc, & BCMA vTD/CTLA-4 vIgD-Fc. All were evaluated for functional activity: 1)in vitroin primary human B cell & MLR assays and in a Jurkat/NF-kB reporter cell line expressing TACI, and 2)in vivoin standard immunization models, and in the bm12-induced and NZB/NZW spontaneous mouse models of lupus.

Results:

The novel engineered TACI vTD-Fc or BCMA vTD-Fc fusion proteins significantly inhibited BAFF- and APRIL-mediated signalingin vitroin TACI+Jurkat cells. TACI (or BCMA) vTD/CTLA-4 vIgD-Fc proteins also attenuated T cell activation in primary human lymphocyte assays. When administered to mice, these molecules rapidly and potently reduced key B and T cell subsets, including plasma cells, follicular T helper cells, germinal center cells, & memory T cells. Treatment with TACI vTD-Fc or TACI vTD/CTLA-4 vIgD-Fc proteins also significantly reduced titers of antigen-specific antibodies in immunized mice more so than abatacept or WT TACI-Fc, and potently suppressed anti-dsDNA autoantibodies, blood urea nitrogen levels, proteinuria, and renal immune complex deposition in the bm12 & NZB/W lupus models.

Conclusion:

Directed evolution of TNFR and IgSF domains has successfully facilitated the development of Fc fusion proteins containing TACI or BCMA vTDs, with or without fusion to CTLA-4 vIgDs. These novel immunomodulators consistently demonstrate potent immunosuppressive activity and efficacyin vitroandin vivo, appearing superior to existing and/or approved immunomodulators like belimumab, abatacept, or atacicept. Such biologics may therefore be attractive candidates for the treatment of serious autoimmune diseases, particularly B cell-related diseases such as SLE, Sjogren’s syndrome, etc.

Disclosure of Interests: :

Stacey R. Dillon Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Lawrence S. Evans Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Mark W. Rixon Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Joe Kuijper Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Dan Demonte Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Katherine E. Lewis Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Steve Levin Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Kayla Kleist Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Sherri Mudri Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Susan Bort Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Janhavi Bhandari Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Fariha Ahmed-Qadri Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Jing Yang Shareholder of: Alpine Immune Sciences, Inc., Employee of: Alpine Immune Sciences, Inc., Michelle A. Seaberg Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Rachel Wang Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Russell Sanderson Shareholder of: Alpine Immune Sciences, Inc., Employee of: Alpine Immune Sciences, Inc., Martin F. Wolfson Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc., Jan Hillson Shareholder of: Alpine Immune Sciences, Inc., Employee of: Alpine Immune Sciences, Inc., Stanford L. Peng Shareholder of: Alpine Immune Sciences, Inc., Employee of: CMO and President of Alpine Immune Sciences, Inc., Kristine M. Swiderek Shareholder of: Shareholder of Alpine Immune Sciences, Inc., Employee of: Employee of Alpine Immune Sciences, Inc.

Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome

OBJECTIVES

Rapid risk stratification and timely treatment are critical to favorable outcomes for patients with acute coronary syndrome (ACS). Our objective was to identify patient and system factors that influence time-dependent quality indicators (QIs) for patients with unstable angina/non-ST elevation myocardial infarction (NSTEMI) in the emergency department (ED).

METHODS

A retrospective, cohort study was conducted during a 42-month period of all patients 24 years or older suspected of having ACS as defined by receiving an electrocardiogram and at least 1 cardiac biomarker test. Cox regression was used to model the effects of patient characteristics, ancillary service use, staffing provisions, equipment availability, and ED and hospital crowding on ACS QIs.

RESULTS

Emergency department adherence rates to national standards for electrocardiogram readout time and biomarker turnaround time were 42% and 37%, respectively. Cox regression models revealed that chief complaints without chest pain and the timing of stress testing and medication administration were associated with the most significant delays.

CONCLUSIONS

Patient and system factors both significantly influenced QI times in this cohort with unstable angina/NSTEMI. These results illustrate both the complexity of diagnosing patients with NSTEMI and the competing effects of clinical and system factors on patient flow through the ED.

577. The Role of Healthcare Worker-Mediated Contact Networks in the Transmission of Vancomycin-Resistant Enterococci

Background

Healthcare workers (HCWs) commonly contact multiple patients daily and serve as an important vector for transmission of pathogens such as vancomycin-resistant enterococci (VRE). Characterizing the HCW-patient network is difficult, which limits understanding of the role of HCWs in the horizontal transmission of pathogens. Electronic health records (EHR) present an opportunity to generate HCW-mediated contact networks and evaluate their impact on transmission.

Methods

Retrospective analysis of patients (PT) admitted to a medical intensive care unit and solid-organ transplant unit between July 2016 and June 2017. Clinical and demographic information, including VRE surveillance swab outcomes, were extracted from the hospital EHR system. PT-HCW-PT connections were defined as HCW contacts with a patient within an hour of another patient. Multi-variable logistic regression was used to analyze factors associated with unit-acquired VRE colonization incidence.

Results

A total of 2,336 patients had a recorded interaction with 4,956 unique HCWs. 146 patients were colonized with VRE on unit-admission, and 29 patients had unit-acquired VRE colonization. HCWs had contact with ~2 (range: 1–23) patients a day and ~6 (range: 1–58) contacts with patients per day (Figure 1), though rates varied by HCW-type. Patients were contacted by ~7 different HCWs resulting in ~28 contacts per day, with nurses being the most common (Figure 2). This resulted in approximately 10 PT-HCW-PT connections per day (range: 1–33) to an average of 3 other patients. After adjusting for known VRE acquisition risk factors, HCW connections to other patients with VRE significantly increased the risk of VRE acquisition (odds ratio = 1.32; 95% CI: 1.20–1.44; Table 1).

Conclusion

Understanding how HCWs connect patients can elucidate how pathogens, such as VRE, spread in the hospital. We demonstrated how EHR data can inform how HCWs connect patients to spread HAIs and the impact of those connections on the spread of VRE. Though EHR data have limitations, as certain activities and contacts are not logged into the system, they provide a scalable and generalizable source for understanding how patients are connected and can be utilized to reduce the spread of nosocomial infections.

Disclosures

All authors: No reported disclosures.

Readmission Risk Trajectories for Patients With Heart Failure Using a Dynamic Prediction Approach: Retrospective Study

Background

Patients hospitalized with heart failure suffer the highest rates of 30-day readmission among other clinically defined patient populations in the United States. Investigation into the predictability of 30-day readmissions can lead to clinical decision support tools and targeted interventions that can help care providers to improve individual patient care and reduce readmission risk.

Objective

This study aimed to develop a dynamic readmission risk prediction model that yields daily predictions for patients hospitalized with heart failure toward identifying risk trajectories over time and identifying clinical predictors associated with different patterns in readmission risk trajectories.

Methods

A two-stage predictive modeling approach combining logistic and beta regression was applied to electronic health record data accumulated daily to predict 30-day readmission for 534 hospital encounters of patients with heart failure over 2750 patient days. Unsupervised clustering was performed on predictions to uncover time-dependent trends in readmission risk over the patient’s hospital stay. We used data collected between September 1, 2013, and August 31, 2015, from a community hospital in Maryland (United States) for patients with a primary diagnosis of heart failure. Patients who died during the hospital stay or were transferred to other acute care hospitals or hospice care were excluded.

Results

Readmission occurred in 107 (107/534, 20.0%) encounters. The out-of-sample area under curve for the 2-stage predictive model was 0.73 (SD 0.08). Dynamic clinical predictors capturing laboratory results and vital signs had the highest predictive value compared with demographic, administrative, medical, and procedural data included. Unsupervised clustering identified four risk trajectory groups: decreasing risk (131/534, 24.5% encounters), high risk (113/534, 21.2%), moderate risk (177/534, 33.1%), and low risk (113/534, 21.2%). The decreasing risk group demonstrated change in average probability of readmission from admission (0.69) to discharge (0.30), whereas the high risk (0.75), moderate risk (0.61), and low risk (0.39) groups maintained consistency over the hospital course. A higher level of hemoglobin, larger decrease in potassium and diastolic blood pressure from admission to discharge, and smaller number of past hospitalizations are associated with decreasing readmission risk (P<.001).

Conclusions

Dynamically predicting readmission and quantifying trends over patients’ hospital stay illuminated differing risk trajectory groups. Identifying risk trajectory patterns and distinguishing predictors may shed new light on indicators of readmission and the isolated effects of the index hospitalization.

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

OBJECTIVES

The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes.

MATERIALS AND METHODS

A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared.

RESULTS

Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists.

DISCUSSION

The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay.

CONCLUSIONS

Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

Predicting probability of perirectal colonization with carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) at hospital unit admission

BACKGROUND

Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing organisms (CPOs), remains limited; recent data suggest that existing policies miss many carriers.

OBJECTIVE

Our objective was to measure the prevalence of CRO and CPO perirectal colonization at hospital unit admission and to use machine learning methods to predict probability of CRO and/or CPO carriage.

METHODS

We performed an observational cohort study of all patients admitted to the medical intensive care unit (MICU) or solid organ transplant (SOT) unit at The Johns Hopkins Hospital between July 1, 2016 and July 1, 2017. Admission perirectal swabs were screened for CROs and CPOs. More than 125 variables capturing preadmission clinical and demographic characteristics were collected from the electronic medical record (EMR) system. We developed models to predict colonization probabilities using decision tree learning.

RESULTS

Evaluating 2,878 admission swabs from 2,165 patients, we found that 7.5% and 1.3% of swabs were CRO and CPO positive, respectively. Organism and carbapenemase diversity among CPO isolates was high. Despite including many characteristics commonly associated with CRO/CPO carriage or infection, overall, decision tree models poorly predicted CRO and CPO colonization (C statistics, 0.57 and 0.58, respectively). In subgroup analyses, however, models did accurately identify patients with recent CRO-positive cultures who use proton-pump inhibitors as having a high likelihood of CRO colonization.

CONCLUSIONS

In this inpatient population, CRO carriage was infrequent but was higher than previously published estimates. Despite including many variables associated with CRO/CPO carriage, models poorly predicted colonization status, likely due to significant host and organism heterogeneity.

Linking Electronic Health Record and Trauma Registry Data: Assessing the Value of Probabilistic Linkage

BACKGROUND

Electronic health record (EHR) systems contain large volumes of novel heterogeneous data that can be linked to trauma registry data to enable innovative research not possible with either data source alone.

OBJECTIVE

This article describes an approach for linking electronically extracted EHR data to trauma registry data at the institutional level and assesses the value of probabilistic linkage.

METHODS

Encounter data were independently obtained from the EHR data warehouse (n = 1,632) and the pediatric trauma registry (n = 1,829) at a Level I pediatric trauma center. Deterministic linkage was attempted using nine different combinations of medical record number (MRN), encounter identity (ID) (visit ID), age, gender, and emergency department (ED) arrival date. True matches from the best performing variable combination were used to create a gold standard, which was used to evaluate the performance of each variable combination, and to train a probabilistic algorithm that was separately used to link records unmatched by deterministic linkage and the entire cohort. Additional records that matched probabilistically were investigated via chart review and compared against records that matched deterministically.

RESULTS

Deterministic linkage with exact matching on any three of MRN, encounter ID, age, gender, and ED arrival date gave the best yield of 1,276 true matches while an additional probabilistic linkage step following deterministic linkage yielded 110 true matches. These records contained a significantly higher number of boys compared to records that matched deterministically and etiology was attributable to mismatch between MRNs in the two data sets. Probabilistic linkage of the entire cohort yielded 1,363 true matches.

CONCLUSION

The combination of deterministic and an additional probabilistic method represents a robust approach for linking EHR data to trauma registry data. This approach may be generalizable to studies involving other registries and databases.

Prolonged wait time is associated with increased mortality for Chilean waiting list patients with non-prioritized conditions

BACKGROUND

Most data on mortality and prognostic factors of universal healthcare waiting lists come from North America, Australasia, and Europe, with little information from South America. We aimed to determine the relationship between medical center-specific waiting time and waiting list mortality in Chile.

METHOD

Using data from all new patients listed in medical specialist waitlists for non-prioritized health problems from 2008 to 2015 in three geographically distant regions of Chile, we constructed hierarchical multivariate survival models to predict mortality risk at two years after registration for each medical center. Kendall rank correlation analysis was used to measure the association between medical center-specific mortality hazard ratio and waiting times.

RESULT

There were 987,497 patients waiting for care at 77 medical centers, including 33,546 (3.40%) who died within two years after registration. Male gender (hazard ratio [HR] = 1.17, 95% confidence interval [CI] 1.1-1.24), older age (HR = 2.88, 95% CI 2.72-3.05), urban residence (HR = 1.19, 95% CI 1.09-1.31), tertiary care (HR = 2.2, 95% CI 2.14-2.26), oncology (HR = 3.57, 95% CI 3.4-3.76), and hematology (HR = 1.6, 95% CI 1.49-1.73) were associated with higher risk of mortality at each medical center with large region-to-region variations. There was a statistically significant association between waiting time variability and death (Z = 2.16, P = 0.0308).

CONCLUSION

Patient wait time for non-prioritized health conditions was associated with increased mortality in Chilean hospitals.

Comparing Electron Energetics and UV Brightness in Jupiter's Northern Polar Region During Juno Perijove 5

We compare electron and UV observations mapping to the same location in Jupiter's northern polar region, poleward of the main aurora, during Juno perijove 5. Simultaneous peaks in UV brightness and electron energy flux are identified when observations map to the same location at the same time. The downward energy flux during these simultaneous observations was not sufficient to generate the observed UV brightness; the upward energy flux was. We propose that the primary acceleration region is below Juno's altitude, from which the more intense upward electrons originate. For the complete interval, the UV brightness peaked at ~240 kilorayleigh (kR); the downward and upward energy fluxes peaked at 60 and 700 mW/m2, respectively. Increased downward energy fluxes are associated with increased contributions from tens of keV electrons. These observations provide evidence that bidirectional electron beams with broad energy distributions can produce tens to hundreds of kilorayleigh polar UV emissions.

Comparing Electron Energetics and UV Brightness in Jupiter's Northern Polar Region During Juno Perijove 5

We compare electron and UV observations mapping to the same location in Jupiter's northern polar region, poleward of the main aurora, during Juno perijove 5. Simultaneous peaks in UV brightness and electron energy flux are identified when observations map to the same location at the same time. The downward energy flux during these simultaneous observations was not sufficient to generate the observed UV brightness; the upward energy flux was. We propose that the primary acceleration region is below Juno's altitude, from which the more intense upward electrons originate. For the complete interval, the UV brightness peaked at ~240 kilorayleigh (kR); the downward and upward energy fluxes peaked at 60 and 700 mW/m², respectively. Increased downward energy fluxes are associated with increased contributions from tens of keV electrons. These observations provide evidence that bidirectional electron beams with broad energy distributions can produce tens to hundreds of kilorayleigh polar UV emissions.

Use of Systems Engineering to Design a Hospital Command Center

BACKGROUND

In hospitals and health systems across the country, patient flow bottlenecks delay care delivery-emergency department boarding and operating room exit holds are familiar examples. In other industries, such as oil, gas, and air traffic control, command centers proactively manage flow through complex systems.

METHODS

A systems engineering approach was used to analyze and maximize existing capacity in one health system, which led to the creation of the Judy Reitz Capacity Command Center. This article describes the key elements of this novel health system command center, which include strategic colocation of teams, automated visual displays of real-time data providing a global view, predictive analytics, standard work and rules-based protocols, and a clear chain of command and guiding tenets. Preliminary data are also shared.

RESULTS

With proactive capacity management, subcycle times decreased and allowed the health system's flagship hospital to increase occupancy from 85% to 92% while decreasing patient delays.

CONCLUSION

The command center was built with three primary goals-reducing emergency department boarding, eliminating operating room holds, and facilitating transfers in from outside facilities-but the command center infrastructure has the potential to improve hospital operations in many other areas.

The association between AB blood group and neonatal disease

BACKGROUND

Numerous studies have examined the association between ABO blood groups and adult disease states, but very few have studied the neonatal population. The objective of this study was to determine the relationship between AB blood group and the occurrence of common neonatal disorders such as neutropenia at birth, sepsis, respiratory distress syndrome (RDS), intraventricular hemorrhage (IVH), retinopathy of prematurity (ROP), and patent ductus arteriosus (PDA) compared to all other blood groups.

METHODS

We performed a retrospective review on 3,981 infants born at 22 0/7 to 42 6/7 weeks' gestational age and compared the relative risk of neonatal diseases in infants with AB blood group to that of infants with all other blood groups (A, B, and O).

RESULTS

When compared to all other blood groups, AB infants demonstrated an increased risk for developing negative clinical outcomes. AB blood group was significantly associated with a 14-89% increased risk of neutropenia at birth, sepsis, RDS, and ROP. Risks for IVH and PDA were not significant.

CONCLUSION

We hypothesize that the phenotypic expression of A and B antigens, rather than the antigens themselves, in the AB group may reveal an enhanced susceptibility to injury at the endothelial level resulting in an increased risk for disease development.

Triage Performance in Emergency Medicine: A Systematic Review

STUDY OBJECTIVE

Rapid growth in emergency department (ED) triage literature has been accompanied by diversity in study design, methodology, and outcome assessment. We aim to synthesize existing ED triage literature by using a framework that enables performance comparisons and benchmarking across triage systems, with respect to clinical outcomes and reliability.

METHODS

PubMed, EMBASE, Scopus, and Web of Science were systematically searched for studies of adult ED triage systems through 2016. Studies evaluating triage systems with evidence of widespread adoption (Australian Triage Scale, Canadian Triage and Acuity Scale, Emergency Severity Index, Manchester Triage Scale, and South African Triage Scale) were cataloged and compared for performance in identifying patients at risk for mortality, critical illness and hospitalization, and interrater reliability. This study was performed and reported in adherence to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

RESULTS

A total of 6,160 publications were identified, with 182 meeting eligibility criteria and 50 with sufficient data for inclusion in comparative analysis. The Canadian Triage and Acuity Scale (32 studies), Emergency Severity Index (43), and Manchester Triage Scale (38) were the most frequently studied triage scales, and all demonstrated similar performance. Most studies (6 of 8) reported high sensitivity (>90%) of triage scales for identifying patients with ED mortality as high acuity at triage. However, sensitivity was low (<80%) for identification of patients who had critical illness outcomes and those who died within days of the ED visit or during the index hospitalization. Sensitivity varied by critical illness and was lower for severe sepsis (36% to 74%), pulmonary embolism (54%), and non-ST-segment elevation myocardial infarction (44% to 85%) compared with ST-segment elevation myocardial infarction (56% to 92%) and general outcomes of ICU admission (58% to 100%) and lifesaving intervention (77% to 98%). Some proportion of hospitalized patients (3% to 45%) were triaged to low acuity (level 4 to 5) in all studies. Reliability measures (κ) were variable across evaluations, with only a minority (11 of 42) reporting κ above 0.8.

CONCLUSION

We found that a substantial proportion of ED patients who die postencounter or are critically ill are not designated as high acuity at triage. Opportunity to improve interrater reliability and triage performance in identifying patients at risk of adverse outcome exists.