Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study.

ABSTRACT:

Background

Unlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD).

Methods

This retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019.

SUBMITTER: Carmichael H 

PROVIDER: S-EPMC8159260 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature