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<p>Advanced Parkinson disease has prognostic and therapeutic implications, yet staging tools are qualitative and difficult to operationalize for longitudinal modelling and cross-cohort comparison. We developed a reproducible operationalization that translates the 13-item Diagnostic Criteria for Advanced Parkinson Disease questionnaire into structured variables and generates longitudinal labels capturing certainty of advanced disease. In the Parkinson’s Progression Markers Initiative near-diagnosis cohort (<i>n</i> = 1,302; up to 13 years), we applied this pipeline to characterize label trajectories and face validity over time. As a proof of utility, we used baseline clinical and genetic features to forecast advanced disease at years 7–11, explicitly separating forecasting from contemporaneous staging. Using a binary long-horizon endpoint, the best year-9 model showed an area under the receiver operating characteristic curve of 0.89 (95% CI 0.81–0.97). In an independent real-world cohort with ≥ 11 years follow-up (<i>n</i> = 35), discrimination attenuated (0.55–0.61), consistent with dataset shift and limited event counts.</p>

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A data-driven framework for long-term risk stratification of advanced Parkinson’s disease using PPMI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月5日

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  • Iñigo Gabilondo,
  • Angela Sáenz,
  • Sandra Seijo,
  • Alvaro Ochoa,
  • Unai Zalabarria,
  • Itziar Cuenca,
  • Beatriz Tijero,
  • Tamara Fernández-Valle,
  • Marta Ruiz,
  • Marian Acera,
  • Inês Sousa,
  • Juan Carlos Gómez‑Esteban,
  • Rocio del Pino

摘要

Advanced Parkinson disease has prognostic and therapeutic implications, yet staging tools are qualitative and difficult to operationalize for longitudinal modelling and cross-cohort comparison. We developed a reproducible operationalization that translates the 13-item Diagnostic Criteria for Advanced Parkinson Disease questionnaire into structured variables and generates longitudinal labels capturing certainty of advanced disease. In the Parkinson’s Progression Markers Initiative near-diagnosis cohort (n = 1,302; up to 13 years), we applied this pipeline to characterize label trajectories and face validity over time. As a proof of utility, we used baseline clinical and genetic features to forecast advanced disease at years 7–11, explicitly separating forecasting from contemporaneous staging. Using a binary long-horizon endpoint, the best year-9 model showed an area under the receiver operating characteristic curve of 0.89 (95% CI 0.81–0.97). In an independent real-world cohort with ≥ 11 years follow-up (n = 35), discrimination attenuated (0.55–0.61), consistent with dataset shift and limited event counts.