Fluid Overload Phenotypes in Critical Illness-A Machine Learning Approach.

ABSTRACT:

Background

The detrimental impact of fluid overload (FO) on intensive care unit (ICU) morbidity and mortality is well known. However, research to identify subgroups of patients particularly prone to fluid overload is scarce. The aim of this cohort study was to derive "FO phenotypes" in the critically ill by using machine learning techniques.

Methods

Retrospective single center study including adult intensive care patients with a length of stay of ≥3 days and sufficient data to compute FO. Data was analyzed by multivariable logistic regression, fast and frugal trees (FFT), classification decision trees (DT), and a random forest (RF) model.

Results

Out of 1772 included patients, 387 (21.8%) met the FO definition. The random forest model had the highest area under the cu

SUBMITTER: Messmer AS 

PROVIDER: S-EPMC8780174 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature