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<p>Predicting outcomes for people with Parkinson’s (PwP) can enable better information provision, personalised treatments, and enhanced trial design. It is unclear what prognostic models are optimal for use. We systematically reviewed previously published prognostic models for PwP, assessed quality, and made recommendations. We searched MEDLINE and EMBASE for studies developing/validating models predicting clinical outcomes in PwP. We assessed risk of bias and applicability using the PROBAST tool. We screened 1024 references and identified 25 studies (41 prognostic models). The most common outcomes were falls (11 studies), dementia (7) and motor complications (4). Most models made short-term predictions (60% ≤2 years). All studies had concerns about bias, e.g., inadequate population details (<i>n</i> = 16), suboptimal methods for missing data (<i>n</i> = 21), and no external validation (<i>n</i> = 22). 13 models had sufficient information to be used in practice. Further development and validation of prognostic models is needed which follows existing guidelines to reduce risk of bias.</p>

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Systematic review of prognostic models in Parkinson’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月29日

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  • Yan Li,
  • Millie McDonald-Webb,
  • David J. McLernon,
  • Carl E. Counsell,
  • Angus D. Macleod

摘要

Predicting outcomes for people with Parkinson’s (PwP) can enable better information provision, personalised treatments, and enhanced trial design. It is unclear what prognostic models are optimal for use. We systematically reviewed previously published prognostic models for PwP, assessed quality, and made recommendations. We searched MEDLINE and EMBASE for studies developing/validating models predicting clinical outcomes in PwP. We assessed risk of bias and applicability using the PROBAST tool. We screened 1024 references and identified 25 studies (41 prognostic models). The most common outcomes were falls (11 studies), dementia (7) and motor complications (4). Most models made short-term predictions (60% ≤2 years). All studies had concerns about bias, e.g., inadequate population details (n = 16), suboptimal methods for missing data (n = 21), and no external validation (n = 22). 13 models had sufficient information to be used in practice. Further development and validation of prognostic models is needed which follows existing guidelines to reduce risk of bias.