Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19

Abstract

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.

Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods

“…We refer to these composite adverse events as the outcome or target throughout the text. These outcomes were designed to closely follow those of a recent analysis of the EDI at Michigan Medicine [11].…”

“…One model that has been assessed in patients with COVID-19 is the Epic Deterioration Index (EDI; Epic Systems Inc) [10,11]. The EDI is a proprietary clinical early warning system that aims to identify patients at an increased risk of deterioration and who may require a higher level of care.…”

“…The EDI is a proprietary clinical early warning system that aims to identify patients at an increased risk of deterioration and who may require a higher level of care. The EDI has the advantage over models built on COVID-19-specific data in that it is not overfit to small data sets, as it was trained on over 130,000 encounters [11,12]. Recent work has suggested it may be capable of stratifying patients with COVID-19 according to their risk of deterioration [11].…”

“…The EDI has the advantage over models built on COVID-19-specific data in that it is not overfit to small data sets, as it was trained on over 130,000 encounters [11,12]. Recent work has suggested it may be capable of stratifying patients with COVID-19 according to their risk of deterioration [11]. The outcomes used in this study were those considered most relevant to the care of patients with COVID-19 including ICU level of care, mechanical ventilation, and death.…”

“…The outcomes used in this study were those considered most relevant to the care of patients with COVID-19 including ICU level of care, mechanical ventilation, and death. Although the EDI was able to successfully isolate groups of patients at very high and very low risk of deterioration, the overall performance as a continuous predictor was moderately low (area under the receiver operating characteristic curve [AUROC] 0.76, 95% CI 0.68-0.84; n=174) [11]. Additionally, much of the detail surrounding the EDI's structure and internal validation has not been shared publicly.…”