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Predicting Post-Stroke Depression Risk in Elderly Patients Based on Machine Learning: A Retrospective Cohort Study
Liangjin Zhang1, Ping Yu1, Liu Yang1
1Department of Neurology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441021, People's Republic of China.
Objective:
To construct a machine learning-based risk prediction model for post-stroke depression (PSD) in elderly stroke patients by integrating neuroimaging and psychosocial variables, and to improve prediction accuracy for individualized prevention.
Methods:
A retrospective cohort study included 691 elderly (≥60 years) stroke patients from Xiangyang Central Hospital (2016-2023). Baseline clinical data, neuroimaging features (eg, lesion volume, CMBs), and psychosocial variables (eg, SSRS, HAMA-14, HAMD-17) were collected. Seven machine learning models were built; performance was evaluated via AUC, accuracy, and DCA. The optimal model was used to develop a nomogram.
Results:
The logistic regression (LR) model outperformed others, with AUC=0.88 in the test set. Seven independent predictors were identified: NIHSS score, lesion volume, CMB number, DWI range, Fazekas grade, SSRS score, and HAMA-14 score. The LR-derived nomogram showed good calibration and discriminated PSD from non-PSD effectively.
Conclusion:
The LR model with 7 predictors is accurate and stable for elderly PSD prediction. Its nomogram aids early high-risk identification, supporting personalized intervention, though single-center limitations require multi-center validation.
