Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation
Journal:
arXiv
Published Date:
Jan 25, 2025
Abstract
Background: Chronic kidney disease (CKD), a progressive disease with high
morbidity and mortality, has become a significant global public health problem.
At present, most of the models used for predicting the progression of CKD are
static models. We aim to develop a dynamic kidney failure prediction model
based on deep learning (KFDeep) for CKD patients, utilizing all available data
on common clinical indicators from real-world Electronic Health Records (EHRs)
to provide real-time predictions.
Findings: A retrospective cohort of 4,587 patients from EHRs of Yinzhou,
China, is used as the development dataset (2,752 patients for training, 917
patients for validation) and internal validation dataset (917 patients), while
a prospective cohort of 934 patients from the Peking University First Hospital
CKD cohort (PKUFH cohort) is used as the external validation dataset. The AUROC
of the KFDeep model reaches 0.946 (95\% CI: 0.922-0.970) on the internal
validation dataset and 0.805 (95\% CI: 0.763-0.847) on the external validation
dataset, both surpassing existing models. The KFDeep model demonstrates stable
performance in simulated dynamic scenarios, with the AUROC progressively
increasing over time. Both the calibration curve and decision curve analyses
confirm that the model is unbiased and safe for practical use, while the SHAP
analysis and hidden layer clustering results align with established medical
knowledge.
Interpretation: The KFDeep model built from real-world EHRs enhances the
prediction accuracy of kidney failure without increasing clinical examination
costs and can be easily integrated into existing hospital systems, providing
physicians with a continuously updated decision-support tool due to its dynamic
design.
