TY - JOUR
T1 - Managing COVID-19 with a clinical decision support tool in a community health network
T2 - Algorithm development and validation
AU - McRae, Michael P.
AU - Dapkins, Isaac P.
AU - Sharif, Iman
AU - Anderman, Judd
AU - Fenyo, David
AU - Sinokrot, Odai
AU - Kang, Stella K.
AU - Christodoulides, Nicolaos J.
AU - Vurmaz, Deniz
AU - Simmons, Glennon W.
AU - Alcorn, Timothy M.
AU - Daoura, Marco J.
AU - Gisburne, Stu
AU - Zar, David
AU - McDevitt, John T.
N1 - Funding Information:
A portion of this work was funded by Renaissance Health Service Corporation and Delta Dental of Michigan. The authors thank Zhibing Lu and colleagues of Zhongnan Hospital of Wuhan University for providing the data used for external validation in this study. The authors also thank Ye Yuan, Hui Xu, Shusheng Li, and colleagues of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology for providing open access to the data used for external validation in this study.
Publisher Copyright:
© 2020 Journal of Medical Internet Research. All rights reserved.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - App
KW - Artificial intelligence
KW - Biomarkers
KW - COVID-19
KW - Clinical decision support system
KW - Coronavirus
KW - Disease severity
KW - Family health center
KW - Mobile app
KW - Point of care
KW - Betacoronavirus/pathogenicity
KW - Pandemics
KW - Coronavirus/pathogenicity
KW - Humans
KW - Coronavirus Infections/epidemiology
KW - Male
KW - Decision Support Systems, Clinical/standards
KW - SARS-CoV-2
KW - Pneumonia, Viral/epidemiology
KW - Female
KW - Community Networks/standards
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U2 - 10.2196/22033
DO - 10.2196/22033
M3 - Article
C2 - 32750010
AN - SCOPUS:85090072154
SN - 1439-4456
VL - 22
JO - Journal of Medical Internet Research
JF - Journal of Medical Internet Research
IS - 8
M1 - 22033
ER -