A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients.

ABSTRACT:

Background

Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model.

Methods

Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations.

Results

26,410 of 209,300 (12.6%) inpatients developed AKI during admission

SUBMITTER: Hu Y 

PROVIDER: S-EPMC9573390 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature