ArticleMovement disorders : official journal of the Movement Disorder Society2021
Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change.
Article in Movement disorders : official journal of the Movement Disorder Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Longitudinal analysis of peripheral blood immune status and prognosis dynamic prediction for advanced colon cancer with first-line chemotherapy.Cancer immunology, immunotherapy : CII · 2026Article
- Machine learning -driven discovery of Parkinsonian-like phenotypes in idiopathic REM sleep behavior disorder.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Deep learning for the joint analysis of item-level longitudinal and survival data.Journal of applied statistics · 2026Article
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
- Dynamic context-aware multi-modal deep learning for longitudinal prediction of Parkinson's disease progression.Scientific reports · 2025Article
- Dynamic Prediction Using Functional Latent Trait Joint Models for Multivariate Longitudinal Outcomes: An Application to Parkinson's Disease.Statistics in medicine · 2025Article
- Predicting progressive vision loss in glaucoma patients using functional principal component analysis and electronic health records.Frontiers in ophthalmology · 2025Article
- Identifying subtypes of longitudinal motor symptom severity trajectories in early Parkinson's disease patients.Frontiers in neurology · 2025Article
- Validation of a 5-Year Prognostic Model for Parkinson's Disease.Movement disorders clinical practice · 2024Article
- Development and internal validation of a prognostic model for loss of balance and falls in mid- to late-stage Parkinson's disease.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- The severity assessment of Parkinson's disease based on plasma inflammatory factors and third ventricle width by transcranial sonography.CNS neuroscience & therapeutics · 2024Article
- Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study.BMC medicine · 2023Article
- Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs.Frontiers in aging neuroscience · 2023Review
- Transcriptome Analysis Reveals a Two-Gene Signature Links to Motor Progression and Alterations of Immune Cells in Parkinson's Disease.Journal of Parkinson's disease · 2023Article
- Genetically-informed prediction of short-term Parkinson's disease progression.NPJ Parkinson's disease · 2022Article
- Multi-predictor modeling for predicting early Parkinson's disease and non-motor symptoms progression.Frontiers in aging neuroscience · 2022Article
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5 authors.
Funding
Abstract
backgroundPredicting Parkinson's disease (PD) progression may enable better adaptive and targeted treatment planning.
objectiveDevelop 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.
methodsPredictive 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.
resultsThe 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.
conclusionIncorporating 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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