Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models.

ABSTRACT:

Objectives

Assess a machine learning method of serially updated mortality risk.

Design

Retrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO).

Setting

Hospitals caring for children in ICUs.

Patients

A total of 27,354 admissions cared for in ICUs from 2009 to 2018.

Interventions

None.

Main outcome

Hospital mortality risk estimates determined at 6-hour time periods during care in the ICU. Models were truncated at 180 hours due to decreased sample size secondary to discharges and deaths.

Measurements and main results

The Criticality Index, based on physiology, therapy, and care intensity, was computed for each admission for each time period and calibrated to hospital mortality risk (Criticality Index-

SUBMITTER: Trujillo Rivera EA 

PROVIDER: S-EPMC9117400 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature