COVID-19 has challenged health systems to learn how to learn. This paper describes the context and methods for learning at one academic health center. Longitudinal regression models are used to represent the joint distribution of major clinical events including discharge, ventilation and death as well as multivariate biomarker processes that describe a patient's disease trajectory. We focus on dynamic models in which both the predictors and outcomes vary over time. We contrast prospective longitudinal models in common use with retrospective analogues that are complementary in the COVID-19 context. The methods are described and then applied to a cohort of 1,678 patients with COVID-19 who were hospitalized in the first year of the pandemic. Emphasis is placed on graphical tools that inform clinical decision making.
翻译:COVID-19要求卫生系统学习如何学习。本文描述了一个学术保健中心学习的背景和方法。纵向回归模型用来代表主要临床事件的联合分布,包括排泄、通风和死亡以及描述病人疾病轨迹的多变量生物标志过程。我们侧重于预测器和结果随时间而变化的动态模型。我们将潜在的纵向模型与在COVID-19背景下相互补充的回溯性模拟模式加以对比。这些方法被描述,然后适用于1,678名在大流行病第一年住院的COVID-19病人。我们强调指导临床决策的图形工具。