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Tools that predict long\u2010term dPD progression are lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed de novo, drug\u2010na\u00efve Parkinson's disease (PD) participants in the Parkinson's Progression Markers Initiative (PPMI; 2011\u20132024). The primary outcome was depressive progression, defined as a sustained worsening in Geriatric Depression Scale\u201015 (GDS\u201015) category over 12 months. Candidate predictors included demographic, motor, and non\u2010motor variables at both total and sub\u2010item 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\u2010index). Shapley Additive exPlanations were applied to identify key predictors and construct an integer\u2010based risk score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 1819 eligible participants, 496 met inclusion criteria (median age 62\u2009years [IQR: 55\u201369]; 61.3% male); 94 (19.0%) progressed over a median 6 year follow\u2010up. RSF achieved the best discrimination (test\u2010set C\u2010index 0.744). Key predictors included age, baseline GDS\u201015; SCOPA\u2013AUT subscores (thermoregulatory, gastrointestinal, cardiovascular); cognition (BJLOT, SDMT); impulse control disorder (QUIP\u2013CS score), and MDS\u2013UPDRS I (sleep problems night, pain and other sensations). The SHAP\u2010derived score stratified patients into low (progression 7.3%), moderate (14.7%), and high\u2010risk (36.5%) groups with clear Kaplan\u2013Meier separation (log\u2010rank\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Time\u2010dependent AUCs were 0.721, 0.770, 0.794, 0.792, and 0.812 at 2, 4, 6, 8, and 10\u2009years.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>An explainable survival model and integer\u2010based risk score using routinely collected measures predicted long\u2010term dPD progression and enabled pragmatic risk stratification to support early, personalized management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cns.70845","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T09:34:12Z","timestamp":1774690452000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Long\u2010Term Depression Progression in Parkinson's Disease: A Machine\u2010Learning Survival Analysis and Risk 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