Factors and machine learning models for predicting successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury: a retrospective cohort study based on MIMIC-IV database.

ABSTRACT:

Background

For critically ill patients with acute kidney injury (AKI), there remains controversy regarding the predictive factors affecting the discontinuation of continuous renal replacement therapy (CRRT). This study aims to explore factors associated with successful CRRT discontinuation in AKI patients and to develop predictive models for successful discontinuation.

Methods

We conducted a retrospective study on adult patients with AKI who received CRRT, sourced from the Medical Information Mart for Intensive Care (MIMIC-IV) database. Successful discontinuation of CRRT was defined as no CRRT requirement within 72 h after stopping CRRT. Predictive factors for successful discontinuation of CRRT were analyzed. Additionally, we utilized machine learning algorithms to develop p

SUBMITTER: Sheng S 

PROVIDER: S-EPMC11558904 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature