Baseline clinical features outperform structural MRI in predicting rapid cognitive and motor decline in Parkinson's Disease

Baseline clinical features outperform structural MRI in predicting rapid cognitive and motor decline in Parkinson's Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Baseline clinical features outperform structural MRI in predicting rapid cognitive and motor decline in Parkinson's Disease Yusen Wu, Jose A. Santiago, Tatjana Rundek, Phuong Nguyen, Yelena Yesha, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9181650/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Cognitive and motor decline affect more than 80% of individuals with Parkinson's disease within a decade of diagnosis, yet their trajectories remain largely unpredictable at the individual level. Identifying reliable early-stage prognostic markers could define disease-modification windows and improve trial enrichment. The objective of this study was to determine whether structural MRI-derived atrophy rates or baseline clinical features provide superior prediction of rapid cognitive and motor decline in Parkinson’s disease, and to formally validate the resulting models and their clinical utility. Classification models were developed to predict rapid decline, defined as a decrease of ≥ 5 points on the MoCA and an increase of ≥ 10 points on the MDS-UPDRS3 from baseline to any timepoint 3–5 years post-baseline. Models were trained using longitudinal MRI-derived regional atrophy rates and baseline clinical features independently. The PDBP cohort served as an independent external validation set, and the University of Miami cohort was used for risk stratification. Model performance was evaluated using AUROC and complementary classification metrics; calibration, performance ceilings, and clinical utility were further assessed using calibration curves and decision curve analysis. Feature analysis was performed to identify clinically informative predictors. Structural MRI alone demonstrated limited prognostic utility for individual-level prediction. In contrast, baseline clinical features yielded substantially stronger discrimination for both outcomes. Notably, cognitive prediction remained robust after removal of baseline MoCA, whereas motor prediction was strongly dependent on baseline UPDRS3. External validation in the PDBP cohort (N = 541) preserved high NPVs (cognitive: 0.908 [0.872–0.940]; motor: 0.863 [0.818–0.901]). Incorporating first-year trajectory slopes improved AUROC by approximately 5%, supporting a single follow-up visit as a practical refinement timepoint. Performance gains rapidly plateaued after inclusion of a small number of high-value features, indicating an early ceiling effect. Decision curve analysis confirmed net clinical benefit across a wide range of threshold probabilities. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 17 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 20 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9181650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619586006,"identity":"cf412d17-f4cd-4ff4-aecb-220d427037ae","order_by":0,"name":"Yusen Wu","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Yusen","middleName":"","lastName":"Wu","suffix":""},{"id":619586007,"identity":"e105103a-569c-4b7c-8cb6-2e5481470854","order_by":1,"name":"Jose A. 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Identifying reliable early-stage prognostic markers could define disease-modification windows and improve trial enrichment. The objective of this study was to determine whether structural MRI-derived atrophy rates or baseline clinical features provide superior prediction of rapid cognitive and motor decline in Parkinson\u0026rsquo;s disease, and to formally validate the resulting models and their clinical utility. Classification models were developed to predict rapid decline, defined as a decrease of \u0026ge;\u0026thinsp;5 points on the MoCA and an increase of \u0026ge;\u0026thinsp;10 points on the MDS-UPDRS3 from baseline to any timepoint 3\u0026ndash;5 years post-baseline. Models were trained using longitudinal MRI-derived regional atrophy rates and baseline clinical features independently. The PDBP cohort served as an independent external validation set, and the University of Miami cohort was used for risk stratification. Model performance was evaluated using AUROC and complementary classification metrics; calibration, performance ceilings, and clinical utility were further assessed using calibration curves and decision curve analysis. Feature analysis was performed to identify clinically informative predictors. Structural MRI alone demonstrated limited prognostic utility for individual-level prediction. In contrast, baseline clinical features yielded substantially stronger discrimination for both outcomes. Notably, cognitive prediction remained robust after removal of baseline MoCA, whereas motor prediction was strongly dependent on baseline UPDRS3. External validation in the PDBP cohort (N\u0026thinsp;=\u0026thinsp;541) preserved high NPVs (cognitive: 0.908 [0.872\u0026ndash;0.940]; motor: 0.863 [0.818\u0026ndash;0.901]). Incorporating first-year trajectory slopes improved AUROC by approximately 5%, supporting a single follow-up visit as a practical refinement timepoint. Performance gains rapidly plateaued after inclusion of a small number of high-value features, indicating an early ceiling effect. 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