ArticleCNS neuroscience & therapeutics2026
Predicting Long-Term Depression Progression in Parkinson's Disease: A Machine-Learning Survival Analysis and Risk Score.
Article in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Depression and Mood Changes in People with Parkinson's Disease over Time: A 5-Year Follow-Up Study.Journal of personalized medicine · 2026Article
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Authors and funding
15 authors.
Funding
Abstract
backgroundDepression 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.
methodsWe 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.
resultsOf 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.
conclusionsAn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.