@article{eb17ef59a80e4202ad539b3044807c20,
title = "Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring",
abstract = "COVID-19 has challenged health systems to learn how to learn. This paper describes the context, methods and challenges for learning to improve COVID-19 care at one academic health center. Challenges to learning include: (1) choosing a right clinical target; (2) designing methods for accurate predictions by borrowing strength from prior patients{\textquoteright} experiences; (3) communicating the methodology to clinicians so they understand and trust it; (4) communicating the predictions to the patient at the moment of clinical decision; and (5) continuously evaluating and revising the methods so they adapt to changing patients and clinical demands. To illustrate these challenges, this paper contrasts two statistical modeling approaches—prospective longitudinal models in common use and retrospective analogues complementary in the COVID-19 context—for predicting future biomarker trajectories and major clinical events. The methods are applied to and validated on a cohort of 1678 patients who were hospitalized with COVID-19 during the early months of the pandemic.",
keywords = "Decision support, Inverse regression, Longitudinal data analysis, Prediction, Statistical graphics",
author = "Zitong Wang and Bowring, {Mary Grace} and Antony Rosen and Brian Garibaldi and Scott Zeger and Akihiko Nishimura",
note = "Funding Information: This work was supported by funding from John Hopkins inHealth, the Johns Hopkins Precision Medicine initiative through JH-CROWN, and the coronavirus disease 2019 (COVID-19) Administrative Supplement for the US Department of Health and Human Services (HHS) Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response. Funding Information: This work was supported by funding from John Hopkins in Health, the Johns Hopkins Precision Medicine initiative through JH-CROWN, and the coronavirus disease 2019 (COVID-19) Administrative Supplement for the US Department of Health and Human Services (HHS) Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response Funding Information: The COVID-19 PMAP Registry was funded by Johns Hopkins Medicine through the Precision Medicine Program. The studies were also supported through the generosity of the collective community of donors to the Johns Hopkins University School of Medicine for COVID-19 research. Funding Information: Dr. Zeger was partially supported by NIH Grants P30AR070254, 5UL1TR003098 and 1U54CA260492 and by the Scleroderma Research Foundation Grant IPN 21054327. Dr. Garibaldi is a member of the FDA Pulmonary-Asthma Drug Advisory Committee; is a consultant for Janssen Research and Development, LLC; has received speaker fees and served on an advisory panel for Gilead; has received speaker fees from Atea. Ms. Bowring is supported by NIH grant T32GM136577. Ms. Wang is partially supported by the Patrick C. Walsh Prostate Cancer Research Fund. Publisher Copyright: {\textcopyright} Institute of Mathematical Statistics, 2022",
year = "2022",
month = may,
doi = "10.1214/22-STS861",
language = "English (US)",
volume = "37",
pages = "251--265",
journal = "Statistical Science",
issn = "0883-4237",
publisher = "Institute of Mathematical Statistics",
number = "2",
}