Accelerating diagnosis of Parkinson's disease through risk prediction.
BMC Neurol
; 21(1): 201, 2021 May 18.
Article
en En
| MEDLINE
| ID: mdl-34006233
ABSTRACT
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Enfermedad de Parkinson
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Año:
2021
Tipo del documento:
Article