Development and validation of the creatinine clearance predictor machine learning models in critically ill adults.

ABSTRACT:

Background

In critically ill patients, measured creatinine clearance (CrCl) is the most reliable method to evaluate glomerular filtration rate in routine clinical practice and may vary subsequently on a day-to-day basis. We developed and externally validated models to predict CrCl one day ahead and compared them with a reference reflecting current clinical practice.

Methods

A gradient boosting method (GBM) machine-learning algorithm was used to develop the models on data from 2825 patients from the EPaNIC multicenter randomized controlled trial database. We externally validated the models on 9576 patients from the University Hospitals Leuven, included in the M@tric database. Three models were developed: a "Core" model based on demographic, admission diagnosis, and daily labo

SUBMITTER: Huang CY 

PROVIDER: S-EPMC10327364 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature