Deep learning algorithms for predicting renal replacement therapy initiation in CKD patients: a retrospective cohort study.

ABSTRACT:

Background

Chronic kidney disease (CKD) requires accurate prediction of renal replacement therapy (RRT) initiation risk. This study developed deep learning algorithms (DLAs) to predict RRT risk in CKD patients by incorporating medical history and prescriptions in addition to biochemical investigations.

Methods

A multi-centre retrospective cohort study was conducted in three major hospitals in Hong Kong. CKD patients with an eGFR < 30ml/min/1.73m² were included. DLAs of various structures were created and trained using patient data. Using a test set, the DLAs' predictive performance was compared to Kidney Failure Risk Equation (KFRE).

Results

DLAs outperformed KFRE in predicting RRT initiation risk (CNN + LSTM + ANN layers ROC-AUC = 0.90; CNN ROC-AUC = 0.91;

SUBMITTER: Leung KC 

PROVIDER: S-EPMC10938811 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature