An Algorithm for Classifying Patients Most Likely to Develop Severe Coronavirus Disease 2019 Illness.

ABSTRACT:

Objectives

To develop an algorithm that predicts an individualized risk of severe coronavirus disease 2019 illness (i.e., ICU admission or death) upon testing positive for coronavirus disease 2019.

Design

A retrospective cohort study.

Setting

Cleveland Clinic Health System.

Patients

Those hospitalized with coronavirus disease 2019 between March 8, 2020, and July 13, 2020.

Interventions

A temporal coronavirus disease 2019 test positive cut point of June 1 was used to separate the development from validation cohorts. Fine and Gray competing risk regression modeling was performed.

Measurements and main results

The development set contained 4,520 patients who tested positive for coronavirus disease 2019 between March 8, 2020, and May 31, 2020. The val

SUBMITTER: Kattan MW 

PROVIDER: S-EPMC7746202 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature