Accelerating diagnosis of Parkinson’s disease through risk prediction
| Autor: | Brett K. Beaulieu-Jones, William Yuan, Bruno Leroy, Lee L. Rubin, Tanya Fischer, Nathan Palmer, Scott Lipnick, Catherine Coulouvrat, Karen J. Chandross, Caroline Cohen, Anne-Marie Wills, Richard C. Krolewski, Francesca Frau, Sylvie Bozzi, Isaac S. Kohane, Christine Veyrat-Follet, Dinesh Kumar, Meaghan Cogswell, S. Pablo Sardi |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Prediagnostic Neurology Parkinson's disease Predictive medicine Disease Risk Assessment Machine Learning 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Tremor medicine Humans Medical diagnosis RC346-429 Gait Retrospective Studies 030304 developmental biology 0303 health sciences business.industry Parkinson Disease General Medicine medicine.disease Comorbidity Clinical trial Prodromal Parkinson’s disease Neurology. Diseases of the nervous system Neurology (clinical) Gait Analysis business 030217 neurology & neurosurgery Research Article |
| Zdroj: | BMC Neurology, Vol 21, Iss 1, Pp 1-12 (2021) BMC Neurology |
| ISSN: | 1471-2377 |
| Popis: | Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies. |
| Databáze: | OpenAIRE |
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