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Cited by 111COVID-19 Clinical Research StudiesMachine Learning in HealthcareSepsis Diagnosis and Treatment

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

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Singh, Karandeep, Valley, Thomas S., Tang, Shengpu, Li, Benjamin Y., Kamran, Fahad, Sjoding, Michael W., Wiens, Jenna, Otles, Erkin, Donnelly, John P., Wei, Melissa Y., McBride, Jonathon P., Cao, Jie, Penoza, Carleen, Ayanian, John Z., Nallamothu, Brahmajee K.·2021·Annals of the American Thoracic Society
DOI 10.1513/annalsats.202006-698ocPMC PMC8328366

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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.

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Figure 1. High-risk analysis plot showing the relationship between the Epic Deterioration Index (EDI) threshold and the sensitivity, specificity, PPV, and NPV, with a histogram demonstrating the distribution of maximum EDI per patient. The high-risk area (≥68.8) is shaded in orange. NPV = negative predictive value; PPV = positive predictive value.
Figure 1. High-risk analysis plot showing the relationship between the Epic Deterioration Index (EDI) threshold and the sensitivity, specificity, PPV, and NPV, with a histogram demonstrating the distribution of maximum EDI per patient. The high-risk area (≥68.8) is shaded in orange. NPV = negative predictive value; PPV = positive predictive value.CC BY-NC-ND · PubMed Central
Figure 2. Distribution of alert times based on exceeding the high-risk threshold (Epic Deterioration Index ≥ 68.8), demonstrating (A) all alerts and (B) alerts in the 24 hours before the outcome, with the first alert highlighted in red. Each point represents a hypothetical alert; no actual alerts were generated.
Figure 2. Distribution of alert times based on exceeding the high-risk threshold (Epic Deterioration Index ≥ 68.8), demonstrating (A) all alerts and (B) alerts in the 24 hours before the outcome, with the first alert highlighted in red. Each point represents a hypothetical alert; no actual alerts were generated.CC BY-NC-ND · PubMed Central
Figure 3. Calibration curve comparing deciles of all predicted Epic Deterioration Index (EDI), rescaled to 0 to 1, with the observed risk, with a line demonstrating ideal calibration (solid) and a histogram of predicted EDI.
Figure 3. Calibration curve comparing deciles of all predicted Epic Deterioration Index (EDI), rescaled to 0 to 1, with the observed risk, with a line demonstrating ideal calibration (solid) and a histogram of predicted EDI.CC BY-NC-ND · PubMed Central
Figure 4. Low-risk analysis plot showing the relationship between the Epic Deterioration Index (EDI) score threshold in the first 48 hours and the sensitivity, specificity, PPV, and NPV, with a histogram demonstrating the distribution of maximum EDI per patient. The low-risk area (<37.9) is shaded in green. NPV = negative predictive value; PPV = positive predictive value.
Figure 4. Low-risk analysis plot showing the relationship between the Epic Deterioration Index (EDI) score threshold in the first 48 hours and the sensitivity, specificity, PPV, and NPV, with a histogram demonstrating the distribution of maximum EDI per patient. The low-risk area (<37.9) is shaded in green. NPV = negative predictive value; PPV = positive predictive value.CC BY-NC-ND · PubMed Central
Figure 5. Four example patients, of whom two experienced an adverse outcome, with shaded risk thresholds. Green (Epic Deterioration Index [EDI] < 37.9) represents low risk, yellow represents intermediate risk (EDI ≥ 37.9 to < 68.8), and orange (EDI ≥ 68.8) represents high risk. EDI scores recorded after the primary outcome are shown in the top panels but were not used in the model validation. The blue line represents transfer to an intensive care unit, and the red line represents the onset of mechanical ventilation.
Figure 5. Four example patients, of whom two experienced an adverse outcome, with shaded risk thresholds. Green (Epic Deterioration Index [EDI] < 37.9) represents low risk, yellow represents intermediate risk (EDI ≥ 37.9 to < 68.8), and orange (EDI ≥ 68.8) represents high risk. EDI scores recorded after the primary outcome are shown in the top panels but were not used in the model validation. The blue line represents transfer to an intensive care unit, and the red line represents the onset of mechanical ventilation.CC BY-NC-ND · PubMed Central

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  1. Robin, pROC: display and analyze ROC curves, 10.32614/CRAN.package.pROC · DOI

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Metadata

Authors
Singh, K., Valley, T. S., Tang, S., Li, B. Y., Kamran, F., Sjoding, M. W., Wiens, J., Otles, E., Donnelly, J. P., Wei, M. Y., McBride, J. P., Cao, J., Penoza, C., Ayanian, J. Z., & Nallamothu, B. K.
Affiliations
  • University of Michigan (US)
  • Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan; and
  • Michigan Medicine (US)
Corresponding author
Singh, Karandeep
ORCID
Venue
Annals of the American Thoracic Society
Publisher
Oxford University Press (OUP)
Published
2021-07-01
Volume
18
Issue
7
Pages
1129-1137
ISSN
  • 2325-6621 (ISSN-L)
  • 2329-6933
DOI
10.1513/annalsats.202006-698oc
PMC
PMC8328366
OpenAlex
W3114626119
Cited by
111
References
16
Citations by year
2026: 12 · 2025: 15 · 2024: 16 · 2023: 16 · 2022: 32 · 2021: 16
FWCI
2.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Annals of the American Thoracic Society (publishedVersion, cc-by-nc-nd)
  • medRxiv (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion)
Funders
  • National Institutes of Health
  • Michigan Institute for Data Science, University of Michigan
  • National Institute on Aging (K23AG056638)
  • National Heart, Lung, and Blood Institute (K01HL136687, L30 HL133988, K23HL140165, K12HL138039)
  • National Institute of General Medical Sciences (T32GM007863)
  • U.S. National Library of Medicine (R01LM013325)
Topics
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
Keywords
COVID-19 pandemic, model evaluation, early warning systems, hospitalized patients, intensive care unit admission, mechanical ventilation, in-hospital mortality, racial and sex disparities, model discrimination, model bias, clinical decision support, health equity, Black patients, women, vulnerable populations

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