CNS neuroscience & therapeutics · 2026
Predicting Long-Term Depression Progression in Parkinson's Disease: A Machine-Learning Survival Analysis and Risk Score.
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Liu Defu, Qi Chong, Huang Jiansong, Xie Hutao, Zhuang Yutong, Zhang Quan, Zhao Xuemin, Hu Tianqi, Qin Guofan, Lu Yue, Zhang Xin, Li Sizhe, Yang Mingyu, Zhang Jianguo, Jiang Yin
Аннотация
BACKGROUND: Depression in Parkinson's disease (dPD) is common and heterogeneous, impairs quality of life, and may accelerate disease progression. Tools that predict long-term dPD progression are lacking.
METHODS: We retrospectively analyzed de novo, drug-naïve Parkinson's disease (PD) participants in the Parkinson's Progression Markers Initiative (PPMI; 2011-2024). The primary outcome was depressive progression, defined as a sustained worsening in Geriatric Depression Scale-15 (GDS-15) category over 12 months. Candidate predictors included demographic, motor, and non-motor variables at both total and sub-item levels. Four survival machine learning models, Random Survival Forests (RSF), Extreme Gradient Boosting, Support Vector Survival Machines, and Gradient Boosting Survival Analysis, were evaluated using concordance index (C-index). Shapley Additive exPlanations were applied to identify key predictors and construct an integer-based risk score.
RESULTS: Of 1819 eligible participants, 496 met inclusion criteria (median age 62 years [IQR: 55-69]; 61.3% male); 94 (19.0%) progressed over a median 6 year follow-up. RSF achieved the best discrimination (test-set C-index 0.744). Key predictors included age, baseline GDS-15; SCOPA-AUT subscores (thermoregulatory, gastrointestinal, cardiovascular); cognition (BJLOT, SDMT); impulse control disorder (QUIP-CS score), and MDS-UPDRS I (sleep problems night, pain and other sensations). The SHAP-derived score stratified patients into low (progression 7.3%), moderate (14.7%), and high-risk (36.5%) groups with clear Kaplan-Meier separation (log-rank p < 0.001). Time-dependent AUCs were 0.721, 0.770, 0.794, 0.792, and 0.812 at 2, 4, 6, 8, and 10 years.
CONCLUSIONS: An explainable survival model and integer-based risk score using routinely collected measures predicted long-term dPD progression and enabled pragmatic risk stratification to support early, personalized management.
METHODS: We retrospectively analyzed de novo, drug-naïve Parkinson's disease (PD) participants in the Parkinson's Progression Markers Initiative (PPMI; 2011-2024). The primary outcome was depressive progression, defined as a sustained worsening in Geriatric Depression Scale-15 (GDS-15) category over 12 months. Candidate predictors included demographic, motor, and non-motor variables at both total and sub-item levels. Four survival machine learning models, Random Survival Forests (RSF), Extreme Gradient Boosting, Support Vector Survival Machines, and Gradient Boosting Survival Analysis, were evaluated using concordance index (C-index). Shapley Additive exPlanations were applied to identify key predictors and construct an integer-based risk score.
RESULTS: Of 1819 eligible participants, 496 met inclusion criteria (median age 62 years [IQR: 55-69]; 61.3% male); 94 (19.0%) progressed over a median 6 year follow-up. RSF achieved the best discrimination (test-set C-index 0.744). Key predictors included age, baseline GDS-15; SCOPA-AUT subscores (thermoregulatory, gastrointestinal, cardiovascular); cognition (BJLOT, SDMT); impulse control disorder (QUIP-CS score), and MDS-UPDRS I (sleep problems night, pain and other sensations). The SHAP-derived score stratified patients into low (progression 7.3%), moderate (14.7%), and high-risk (36.5%) groups with clear Kaplan-Meier separation (log-rank p < 0.001). Time-dependent AUCs were 0.721, 0.770, 0.794, 0.792, and 0.812 at 2, 4, 6, 8, and 10 years.
CONCLUSIONS: An explainable survival model and integer-based risk score using routinely collected measures predicted long-term dPD progression and enabled pragmatic risk stratification to support early, personalized management.