Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation | Zendy

Michael P. McRae | Zendy; Isaac Dapkins | Zendy; Iman Sharif | Zendy; Judd Anderman | Zendy; David Fenyö | Zendy; Odai Sinokrot | Zendy; Stella K. Kang | Zendy; Nicolaos Christodoulides | Zendy; Deniz Vurmaz | Zendy; Glen W. Simmons | Zendy; Timothy M. Alcorn | Zendy; Marco J Daoura | Zendy; Stu Gisburne | Zendy; David Zar | Zendy; John T. McDevitt | Zendy

Open Access

Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation

Author(s) -

Michael P. McRae,

Isaac Dapkins,

Iman Sharif,

Judd Anderman,

David Fenyö,

Odai Sinokrot,

Stella K. Kang,

Nicolaos Christodoulides,

Deniz Vurmaz,

Glen W. Simmons,

Timothy M. Alcorn,

Marco J Daoura,

Stu Gisburne,

David Zar,

John T. McDevitt

Publication year - 2020

Publication title -

journal of medical internet research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.446

H-Index - 142

eISSN - 1439-4456

pISSN - 1438-8871

DOI - 10.2196/22033

Subject(s) - triage , procalcitonin , medicine , biomarker , tier 2 network , pandemic , clinical decision support system , risk assessment , intensive care unit , severity of illness , health care , covid-19 , disease , receiver operating characteristic , decision support system , emergency medicine , medical emergency , intensive care medicine , computer science , data mining , sepsis , telecommunications , biochemistry , chemistry , computer security , infectious disease (medical specialty) , economics , economic growth

Background The coronavirus disease (COVID-19) pandemic has resulted in significant morbidity and mortality; large numbers of patients require intensive care, which is placing strain on health care systems worldwide. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. Objective The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. Methods Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, nonlaboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts the probability of mortality using biomarker measurements (C-reactive protein, procalcitonin, D-dimer) and age. Both the Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China, comprising 160 and 375 patients, respectively. Results All biomarkers were measured at significantly higher levels in patients who died vs those who were not hospitalized or discharged ( P <.001). The Tier 1 and Tier 2 internal validations had areas under the curve (AUCs) of 0.79 (95% CI 0.74-0.84) and 0.95 (95% CI 0.92-0.98), respectively. The Tier 1 and Tier 2 external validations had AUCs of 0.79 (95% CI 0.74-0.84) and 0.97 (95% CI 0.95-0.99), respectively. Conclusions Our results demonstrate the validity of the clinical decision support system and mobile app, which are now ready to assist health care providers in making evidence-based decisions when managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics and sites, where application of these tools could lead to improvements in patient outcomes and cost containment.

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