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Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change. | LitMetric

Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change.

Mov Disord

Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

Published: December 2021

AI Article Synopsis

  • - The study aims to create a predictive model to track the progression of Parkinson's disease (PD) from early stages (Hoehn and Yahr stage 1 or 2) to a more advanced stage (3), using easily measurable clinical data over time.
  • - By leveraging methods like joint modeling and multivariate functional principal component analysis, researchers developed this model using data from the Parkinson's Progression Marker Initiative (PPMI) and validated it with another study (LABS-PD), showing improved predictive accuracy compared to baseline-only models.
  • - The resulting prognostic index categorizes PD patients into low, mid, and high-risk groups for progression, facilitating better discussions between clinicians and patients about disease outlook and enabling more personalized treatment strategies based

Article Abstract

Background: Predicting Parkinson's disease (PD) progression may enable better adaptive and targeted treatment planning.

Objective: Develop a prognostic model using multiple, easily acquired longitudinal measures to predict temporal clinical progression from Hoehn and Yahr (H&Y) stage 1 or 2 to stage 3 in early PD.

Methods: Predictive longitudinal measures of PD progression were identified by the joint modeling method. Measures were extracted by multivariate functional principal component analysis methods and used as covariates in Cox proportional hazards models. The optimal model was developed from the Parkinson's Progression Marker Initiative (PPMI) data set and confirmed with external validation from the Longitudinal and Biomarker Study in PD (LABS-PD) study.

Results: The proposed prognostic model with longitudinal information of selected clinical measures showed significant advantages in predicting PD temporal progression in comparison to a model with only baseline information (iAUC = 0.812 vs. 0.743). The modeling results allowed the development of a prognostic index for categorizing PD patients into low, mid, and high risk of progression to HY 3 that is offered to facilitate physician-patient discussion on prognosis.

Conclusion: Incorporating longitudinal information of multiple clinical measures significantly enhances predictive performance of prognostic models. Furthermore, the proposed prognostic index enables clinicians to classify patients into different risk groups, which could be adaptively updated as new longitudinal information becomes available. Modeling of this type allows clinicians to utilize observational data sets that inform on disease natural history and specifically, for precision medicine, allows the insertion of a patient's clinical data to calculate prognostic estimates at the individual case level. © 2021 International Parkinson and Movement Disorder Society.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8688189PMC
http://dx.doi.org/10.1002/mds.28730DOI Listing

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