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Development and Internal Validation of Machine Learning-Based Risk Prediction Models for Depression in Older Adults Undergoing Maintenance Hemodialysis
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DOI：10.2147/cia.s614034.png)
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Abstract
En 中文
Yumeng Zhang,1,2 Mei Yuan,1,3 Minzhu Chen,4 Mei Long,4 Huiqiang Shang,5 Min Zhang,4 Heting Liang,1 Xiaoli Yuan1 1Department of Nursing, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, People’s Republic of China; 2Faculty of Nursing, Zunyi Medical University, Zunyi, Guizhou, People’s Republic of China; 3Hemodialysis Unit, Chishui People’s Hospital, Zunyi, Guizhou, People’s Republic of China; 4Hemodialysis Unit, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, People’s Republic of China; 5Hemodialysis Unit, Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, People’s Republic of China Correspondence: Xiaoli Yuan, Department of Nursing, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, People’s Republic of China, Email 872428494@qq.com Background: The high prevalence of depressive symptoms in older maintenance hemodialysis (MHD) patients necessitates early screening. This study aimed to identify core predictors of depressive symptoms in older MHD patients and develop internally validated risk prediction models. Methods: We recruited 226 eligible older MHD patients from hemodialysis units of the Affiliated Hospital of Zunyi Medical University and its branches. Data collection included general characteristics, the MHD Self-Management Scale, Dialysis Symptom Index (DSI), Short Physical Performance Battery, Mini Nutritional Assessment-Short Form (MNA-SF), 15-item Geriatric Depression Scale (GDS-15), and self-reported sensory function. Key variables were identified using univariate logistic regression, Least Absolute Shrinkage and Selection Operator regression, and backward stepwise multivariable logistic regression. Five machine learning algorithms were trained using 10-fold repeated cross-validation and internally validated via a stratified 7:3 train–test split. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) with 95% bootstrap CIs, calibration metrics (Brier score, calibration intercept, and slope), F1-scores, and decision curve analysis. Shapley Additive Explanations and partial dependence plots were generated for the Naive Bayes model. Results: Among 226 patients, the prevalence of GDS-15-defined depressive symptoms was 54.4%. All models demonstrated comparable discriminative performance (test bootstrap AUC range: 0.851– 0.865), with DeLong’s test confirming no statistically significant pairwise differences (all P > 0.15). Naive Bayes achieved the optimal overall performance with the highest discrimination (AUC = 0.865, 95% CI: 0.772– 0.944) and best calibration (Brier = 0.151; calibration slope = 0.904). Top predictors were vision impairment, hearing loss, DSI, MNA-SF, and self-management. Conclusion: We identified five key predictors and developed internally validated machine learning models for depression risk screening in older MHD patients. The models require external validation before clinical application. Keywords: maintenance hemodialysis, depression, older adults, machine learning, predictive model
Keywords:
maintenance hemodialysis
depression
older adults
machine learning
predictive model
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