Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation.

ABSTRACT:

Background

Sepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRRT) being a crucial intervention. However, the optimal timing for CRRT initiation remains controversial.

Methods

Using the MIMIC-IV database for model development and the eICU database for external validation, we analyzed patients with S-AKI to compare survival rates between early and late CRRT initiation groups. Propensity score matching was performed to address potential selection bias. Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤10, medium 11-15, high >15) and creatinine levels (low ≤3 mg/dL, medium 3-5 mg/dL, high >5 mg/dL). Multiple machine learning models were developed and evaluated to

SUBMITTER: Zhuang C 

PROVIDER: S-EPMC11794530 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature