A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease
| Autor: | Ira Shoulson, Iya Khalil, Bernard Ravina, Anthony E. Lang, Kenneth Marek, Jason M. Laramie, Boris Hayete, Shirley Eberly, Andrew B. Singleton, Diane Wuest, Karl Runge, Bruce W. Church, Ajay Verma, Caroline M. Tanner, Paul D. McDonagh |
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| Přispěvatelé: | Toft, Mathias |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Male Aging Parkinson's disease Activities of daily living lcsh:Medicine Neurodegenerative Biochemistry Severity of Illness Index Levodopa Machine Learning 0302 clinical medicine Cognition Theoretical Models Medicine and Health Sciences Medicine Prospective Studies Aetiology Prospective cohort study lcsh:Science Cognitive Impairment Multidisciplinary Parkinson's Disease Movement Disorders Cognitive Neurology Simulation and Modeling Montreal Cognitive Assessment Drugs High-Throughput Nucleotide Sequencing Neurodegenerative Diseases Neurochemistry Parkinson Disease Genomics Single Nucleotide Statistical Middle Aged 3. Good health Neurology Neurological Disease Progression Female Neurochemicals Cohort study Research Article Prognostic variable medicine.medical_specialty Computer and Information Sciences General Science & Technology Cognitive Neuroscience Motor Activity Research and Analysis Methods Polymorphism Single Nucleotide 03 medical and health sciences Physical medicine and rehabilitation Rating scale Clinical Research Artificial Intelligence Genome-Wide Association Studies Genetics Humans Computer Simulation Polymorphism Alleles Demography Aged Pharmacology Models Statistical business.industry lcsh:R Neurosciences Biology and Life Sciences Computational Biology Reproducibility of Results Human Genetics Bayes Theorem Models Theoretical medicine.disease Genome Analysis Brain Disorders 030104 developmental biology People and Places Physical therapy Cognitive Science lcsh:Q business Dopaminergics 030217 neurology & neurosurgery 2.4 Surveillance and distribution Neuroscience Follow-Up Studies |
| Zdroj: | PLoS ONE, Vol 12, Iss 6, p e0178982 (2017) PloS one, vol 12, iss 6 PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | Background There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research. Objective To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD. Methods Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III. Results The most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson’s Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study. Conclusions Baseline function near the time of Parkinson’s disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson’s disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies. |
| Databáze: | OpenAIRE |
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