Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery.

ABSTRACT:

Background

Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population.

Methods

Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), a

SUBMITTER: Yan Y 

PROVIDER: S-EPMC10028074 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature