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’ 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. We emphasize graphical tools to promote physician learning and inform clinical decision making.