Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation

TL;DR

This study developed and validated a deep learning-based dynamic model using real-world EHR data to predict kidney failure in real-time, outperforming static models and aiding clinical decision-making.

Contribution

The paper introduces KFDeep, a novel dynamic prediction model leveraging longitudinal clinical data for real-time kidney failure risk assessment, validated across multiple cohorts.

Findings

High AUROC of 0.9311 in internal validation

External validation AUROC of 0.8141

Model deployed in clinical settings for real-time prediction

Abstract

Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. Most existing models are static and fail to capture temporal trends in disease progression, limiting their ability to inform timely interventions. We address this gap by developing a dynamic model that leverages common longitudinal clinical indicators from real-world Electronic Health Records (EHRs) for real-time kidney failure prediction. Findings: A retrospective cohort of 4,587 patients from Yinzhou, China, was used for model development (2,752 patients for training, 917 patients for validation) and internal validation (918 patients), while external validation was conducted on a prospective PKUFH cohort (934 patients). The model demonstrated competitive performance across datasets, with an AUROC of 0.9311 (95%CI, 0.8873-0.9749) in the internal validation cohort and 0.8141 (95%CI, 0.7728-0.8554) in the external validation cohort, alongside progressively improving dynamic predictions, good calibration, and clinically consistent interpretability. KFDeep has been deployed on an open-access website and in primary care settings. Interpretation: The KFDeep model enables dynamic prediction of kidney failure without increasing clinical examination costs. It has been integrated into existing hospital systems, providing physicians with a continuously updated decision-support tool in routine care.