Rationale: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the coronavirus disease (COVID-19) pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Objectives: To independently evaluate the EDI in hospitalized patients with COVID-19 overall and in disproportionately affected subgroups. Methods: We studied adult patients admitted with COVID-19 to units other than the intensive care unit at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of intensive care unit–level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. The median age of the cohort was 64 (interquartile range, 53–75) with 168 (43%) Black patients and 169 (43%) women. The area under the receiver-operating characteristic curve of the EDI was 0.79 (95% confidence interval, 0.74–0.84). EDI predictions did not differ by race or sex. When exploring clinically relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a sensitivity of 39% and a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusions: We found the EDI identifies small subsets of high-risk and low-risk patients with COVID-19 with good discrimination, although its clinical use as an early warning system is limited by low sensitivity. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among patients with COVID-19.

Introduction:The coronavirus disease 2019 pandemic is straining the capacity of U.S. healthcare systems. Accurately identifying subgroups of hospitalized COVID-19 patients at high-and low-risk for complications would assist in directing resources.Objective: To validate the Epic Deterioration Index (EDI), a predictive model implemented in over 100 U.S. hospitals that has been recently promoted for use in COVID-19 patients. Methods:We studied adult patients admitted with COVID-19 to non-ICU level care at a large academic medical center from March 9 through April 7, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite adverse outcome of ICU-level care, mechanical ventilation, or death during the hospitalization. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI (range 0-100) to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. We evaluated model discrimination and calibration using both raw EDI scores and their slopes.Results: Among 174 COVID-19 patients meeting inclusion criteria, 61 (35%) experienced the composite outcome. Area under the receiver-operating-characteristic curve (AUC) of the EDI was 0.76 (95% CI 0.68-0.84). Patients who met or exceeded an EDI of 64.8 made up 17% of the study cohort and had an 80% probability of experiencing the outcome during their hospitalization with a median lead time of 28 hours from when the threshold was first exceeded to the outcome. Employing the EDI slope lowered the AUCs to 0.68 (95% CI 0.60-0.77) and 0.67 (95% CI 0.59-0.75) for slopes calculated over 4 and 8 hours, respectively. In a subset of 109 patients hospitalized for at least 48 hours and who had not experienced the composite outcome, 14 (13%) patients who never exceeded an EDI of 37.9 had a 93% probability of not experiencing the outcome throughout the rest of their hospitalization, suggesting low risk.

Objective In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and reproducible preprocessing techniques for EHR data. Thus, we developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted from the EHR. Materials and Methods Largely data-driven, FIDDLE systematically transforms structured EHR data into feature vectors, limiting the number of decisions a user must make while incorporating good practices from the literature. To demonstrate its utility and flexibility, we conducted a proof-of-concept experiment in which we applied FIDDLE to 2 publicly available EHR data sets collected from intensive care units: MIMIC-III and the eICU Collaborative Research Database. We trained different ML models to predict 3 clinically important outcomes: in-hospital mortality, acute respiratory failure, and shock. We evaluated models using the area under the receiver operating characteristics curve (AUROC), and compared it to several baselines. Results Across tasks, FIDDLE extracted 2,528 to 7,403 features from MIMIC-III and eICU, respectively. On all tasks, FIDDLE-based models achieved good discriminative performance, with AUROCs of 0.757–0.886, comparable to the performance of MIMIC-Extract, a preprocessing pipeline designed specifically for MIMIC-III. Furthermore, our results showed that FIDDLE is generalizable across different prediction times, ML algorithms, and data sets, while being relatively robust to different settings of user-defined arguments. Conclusions FIDDLE, an open-source preprocessing pipeline, facilitates applying ML to structured EHR data. By accelerating and standardizing labor-intensive preprocessing, FIDDLE can help stimulate progress in building clinically useful ML tools for EHR data.

PURPOSE Acute graft-versus-host disease (aGVHD) remains a significant complication of allogeneic hematopoietic cell transplantation (HCT) and limits its broader application. The ability to predict grade II to IV aGVHD could potentially mitigate morbidity and mortality. To date, researchers have focused on using snapshots of a patient (eg, biomarkers at a single time point) to predict aGVHD onset. We hypothesized that longitudinal data collected and stored in electronic health records (EHRs) could distinguish patients at high risk of developing aGVHD from those at low risk. PATIENTS AND METHODS The study included a cohort of 324 patients undergoing allogeneic HCT at the University of Michigan C.S. Mott Children’s Hospital during 2014 to 2017. Using EHR data, specifically vital sign measurements collected within the first 10 days of transplantation, we built a predictive model using penalized logistic regression for identifying patients at risk for grade II to IV aGVHD. We compared the proposed model with a baseline model trained only on patient and donor characteristics collected at the time of transplantation and performed an analysis of the importance of different input features. RESULTS The proposed model outperformed the baseline model, with an area under the receiver operating characteristic curve of 0.659 versus 0.512 ( P = .019). The feature importance analysis showed that the learned model relied most on temperature and systolic blood pressure, and temporal trends (eg, increasing or decreasing) were more important than the average values. CONCLUSION Leveraging readily available clinical data from EHRs, we developed a machine-learning model for aGVHD prediction in patients undergoing HCT. Continuous monitoring of vital signs, such as temperature, could potentially help clinicians more accurately identify patients at high risk for aGVHD.

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