Development and External Validation of a Multidimensional Deep Learning Model to Dynamically Predict Kidney Outcomes in IgA Nephropathy

作者：Tingyu Chen, Tiange Chen, Wenjie Xu, Shaoshan Liang, Feng Xu, Dandan Liang, Xiang Li, Caihong Zeng, Guotong Xie, Zhihong Liu · 发表于：Clinical Journal of the American Society of Nephrology · 年份：2024 · DOI：10.2215/cjn.0000000000000471 · 被引用次数：12 · 研究领域：Renal Diseases and Glomerulopathies、Chronic Kidney Disease and Diabetes

Key Points A dynamic model predicts IgA nephropathy prognosis based on deep learning. Longitudinal clinical data and deep learning improve predictive accuracy and interpretability in GN. Background Accurately predicting kidney outcomes in IgA nephropathy is crucial for clinical decision making. Insufficient use of longitudinal data in previous studies has limited the accuracy and interpretability of prediction models for failing to reflect the chronic nature of IgA nephropathy. The aim of this study was to establish a multivariable dynamic deep learning model using comprehensive longitudinal data for the prediction of kidney outcomes in IgA nephropathy. Methods In this retrospective cohort study of 2056 patients with IgA nephropathy from 18 kidney centers, a total of 28,317 data points were collected by the sliding window method. Among them, 15,462 windows in a single center were randomly assigned to training (80%) and validation (20%) sets and 8797 windows in 18 kidney centers were assigned to an independent test set. Interpretable multivariable long short-term memory, a deep learning model, was implemented to predict kidney outcomes (kidney failure or 50% decline in kidney function) based on time-invariant variables measured at biopsy and time-variant variables measured during follow-up. Risk performance was evaluated using the Kaplan–Meier analysis and C-statistic. Trajectory analysis was performed to assess the various trends of clinical variables during follow-up. Result...