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Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation

Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation

For example, while Mc Rae et al’s two-tiered model [6] that was trained on 701 patients in New York City to predict mortality was based on actual age, C-reactive protein (CRP), procalcitonin, and D-dimer, Yan et al’s model [2] that was trained on 485 patients from Wuhan selected lactate dehydrogenase (LDH), lymphocyte count, and CRP as the most predictive for mortality. Variations in selected features differed greatly, even when trained to predict similar outcomes on data from patients of the same city.

Joshua M Wang, Wenke Liu, Xiaoshan Chen, Michael P McRae, John T McDevitt, David Fenyö

J Med Internet Res 2021;23(7):e29514

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Article has an altmetric score of 1

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

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

Two-sided tests were considered statistically significant for P We externally validated the Tier 1 Outpatient Model using data from a study of 160 hospitalized patients with COVID-19 from Zhongnan Hospital of Wuhan University. Only patients with complete information (age, systolic blood pressure, gender, diabetes, and cardiovascular comorbidities) were included. The model performance was documented in terms of AUC, sensitivity, specificity, PPV, and NPV.

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

J Med Internet Res 2020;22(8):e22033

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Article has an altmetric score of 2