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Predictive models for the incidence of Parkinson’s disease: systematic review and critical appraisal

  • Yancong Chen , Yinyan Gao , Xuemei Sun , Zhenhua Liu , Zixuan Zhang , Lang Qin , Jinlu Song , Huan Wang and Irene X.Y. Wu EMAIL logo
Published/Copyright: July 11, 2022
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Reviews in the Neurosciences
From the journal Reviews in the Neurosciences Volume 34 Issue 1

Abstract

Numerous predictive models for Parkinson’s disease (PD) incidence have been published recently. However, the model performance and methodological quality of those available models are yet needed to be summarized and assessed systematically. In this systematic review, we systematically reviewed the published predictive models for PD incidence and assessed their risk of bias and applicability. Three international databases were searched. Cohort or nested case-control studies that aimed to develop or validate a predictive model for PD incidence were considered eligible. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used for risk of bias and applicability assessment. Ten studies covering 10 predictive models were included. Among them, four studies focused on model development, covering eight models, while the remaining six studies focused on model external validation, covering two models. The discrimination of the eight new development models was generally poor, with only one model reported C index > 0.70. Four out of the six external validation studies showed excellent or outstanding discrimination. All included studies had high risk of bias. Three predictive models (the International Parkinson and Movement Disorder Society [MDS] prodromal PD criteria, the model developed by Karabayir et al. and models validated by Faust et al.) are recommended for clinical application by considering model performance and resource-demanding. In conclusion, the performance and methodological quality of most of the identified predictive models for PD incidence were unsatisfactory. The MDS prodromal PD criteria, model developed by Karabayir et al. and model validated by Faust et al. may be considered for clinical use.

Keywords: Parkinson’s disease; predictive model; systematic review
Corresponding author: Irene X.Y. Wu, Xiangya School of Public Health, Central South University, No. 238, Shang Ma Yuan ling Alley, Kaifu District, Changsha 410078, Hunan, China; and Hunan Provincial Key Laboratory of Clinical Epidemiology, Central South University, Changsha 410078, China, E-mail:

Funding source: The National Key R&D Program of China

Award Identifier / Grant number: No. 2020YFC2008600

Funding source: The Special Funding for the Construction of Innovative Provinces in Hunan

Award Identifier / Grant number: No. 2019SK2141

Funding source: The China Oceanwide Holding Group Project Fund

Award Identifier / Grant number: No. 143010100

Funding source: The High-level Talents Introduction Plan from Central South University

Award Identifier / Grant number: No. 502045003

  1. Research funding: The research was supported by the National Key R&D Program of China (No. 2020YFC2008600), the Special Funding for the Construction of Innovative Provinces in Hunan (No. 2019SK2141), the China Oceanwide Holding Group Project Fund (No. 143010100), and the High-level Talents Introduction Plan from Central South University (No. 502045003).

  2. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Conflict of interest statement: We have no conflict of interest to declare.

  4. Availability of data and material: The datasets analyzed during the current study are publicly available from the corresponding author.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/revneuro-2022-0012).

Received: 2022-02-06
Accepted: 2022-05-26
Published Online: 2022-07-11
Published in Print: 2023-01-27

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/revneuro-2022-0012
Keywords for this article
Parkinson’s disease; predictive model; systematic review

Articles in the same Issue

  1. Frontmatter
  2. The many facets of CD26/dipeptidyl peptidase 4 and its inhibitors in disorders of the CNS – a critical overview
  3. Functional changes in brain oscillations in dementia: a review
  4. The role of microRNA-485 in neurodegenerative diseases
  5. Predictive models for the incidence of Parkinson’s disease: systematic review and critical appraisal
  6. Involvement of nerve growth factor (NGF) in chronic neuropathic pain – a systematic review
  7. Immunosenescence of brain accelerates Alzheimer’s disease progression
  8. Pathophysiological aspects of complex PTSD – a neurobiological account in comparison to classic posttraumatic stress disorder and borderline personality disorder
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