Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.

ABSTRACT:

Objectives

Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.

Design

The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical

SUBMITTER: Berzuini C 

PROVIDER: S-EPMC7513423 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature