Evaluation of the PREDIGT Score in Discriminating Parkinson Disease from Neurological Health
Li, J.; Mestre, T. A.; Mollenhauer, B.; Frasier, M.; Tomlinson, J. J.; Trenkwalder, C.; Ramsay, T.; Manuel, D.; Schlossmacher, M. G.
Show abstract
BackgroundWe previously created the PREDIGT Score as an algorithm to predict the incidence of Parkinson disease. The model rests on a hypothesis-driven formula, [PR=(E+D+I)xGxT], that uses numerical values for five categories known to modulate Parkinsons risk (PR): environmental exposure (E); DNA variants (D); evidence of gene-environment interactions (I); gender (G); and time (T). Notably, the formula does not rely on motor examination results. MethodsTo evaluate the PREDIGT Score, we tested it in two established case-control cohorts: De Novo Parkinson Study (DeNoPa) and Parkinsons Progression Marker Initiative (PPMI). Using baseline data from 589 patients and 309 controls enrolled in the DeNoPa and PPMI cohorts, we evaluated the PREDIGT Scores discriminative performance in distinguishing Parkinsons patients from healthy controls by area-under-the-curve (AUC) analyses. FindingsWhen examining cohorts separately and using all available variables in each cohort to calculate the PREDIGT Score, AUCs were 0.83 (95% CI 0.77-0.89) for DeNoPa and 0.87 (95% CI 0.84-0.9) for PPMI, respectively, in distinguishing Parkinson disease patients from healthy individuals. When combining DeNoPa and PPMI data sets by using eleven variables that had been collected in both cohorts, the PREDIGT Score discriminated patients from controls with an AUC of 0.84 (95% CI 0.81-0.87). The mean score of Parkinson disease patients was significantly higher than that of control individuals at 108.48 (+52.08) and 47.33 (+34.1), respectively (p < 0.0001). InterpretationOur results demonstrate a robust performance of the original PREDIGT Score in distinguishing patients diagnosed with Parkinson disease from neurologically healthy subjects without reliance on motor examination data. In future efforts, the predictive performance of the algorithm will be studied in longitudinal cohorts of at-risk persons. FundingParkinson Canada, Michael J. Fox Foundation, Department of Medicine (The Ottawa Hospital), Uttra & Subash Bhargava Family, Paracelsus-Elena-Klinik Kassel, Parkinson Fond Deutschland, and Deutsche Parkinson Vereinigung.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Development of Parkinson’s disease and its relationship with incidentally-discovered white matter disease and covert brain infarction in a real world cohort 95%
- The age at onset of LRRK2 p.Gly2019Ser Parkinson's disease across ancestries and countries of origin 95%
- Plasma MIA, CRP, and albumin predict cognitive decline in Parkinson’s Disease 95%
Similar papers in this journal
- MAPT allele and haplotype frequencies in Nigerian Africans: population distribution and association with Parkinson’s disease risk and age at onset 96%
- Prodromal Progressive Supranuclear Palsy – insights from the UK Biobank 96%
- impaired bed mobility in prediagnostic and de novo Parkinson’s disease 96%
Similar papers in this journal
- Amplification parameters of the alpha-synuclein seed amplification assay on CSF predict the clinical subtype of Parkinson's Disease at 10-year follow-up 97%
- Parkinson’s Progression Markers Initiative: A Milestone-Based Strategy to Monitor PD Progression 96%
- Prospective role of PAK6 and 14-3-3 gamma as biomarkers for Parkinson's disease 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.