Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

ABSTRACT:

Rationale

The COVID-19 pandemic induces considerable strain on intensive care unit resources.

Objectives

We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic.

Methods

We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry.

Measurements

The RISC-19-ICU Investigators for Switzerland collected data

SUBMITTER: Henzi A 

PROVIDER: S-EPMC7894868 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature