Accurate diagnosis and early detection of complex diseases, such as Parkinson’s disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson’s disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts.
In the population from PPMI, our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900–0·946) with high sensitivity (0·834, 95% CI 0·711–0·883) and specificity (0·903, 95% CI 0·824–0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson’s disease, with AUCs of 0·894 (95% CI 0·867–0·921) in the PDBP cohort, 0·998 (0·992–1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896–0·962) in LABS-PD, and 0·939 (0·891–0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson’s disease converted to Parkinson’s disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson’s disease underwent conversion (test of proportions, p=0·003).