A new, noninvasive model significantly outperforms any single classifier in identifying patients with Parkinson’s disease, according to a study published Aug. 11 in Lancet Neurology. The model is able to discriminate patients without evidence of dopaminergic deficit, typical of Parkinson’s disease, from those patients with aetiologically typical disease, which the authors say, could improve detection of preclinical disease. For complex progressive diseases such as Parkinson’s disease, preclinical diagnosis and low error rates in diagnosis are crucial both not only for clinical care, but also for correctly classifying patients participating in clinical trials. "Unlike dopamine transporter positron emission tomography [DAT] scanning, our model is portable and can be administered remotely at a fraction of the cost: our model costs around US$100 per sample versus DAT scanning, which can cost thousands of dollars per patient," write the authors led by Mike Nalls, Ph.D., from the National Institute on Aging (Bethesda, Md.). "This model might be useful as part of a diagnostic path towards more accurate preclinical detection of Parkinson’s disease." The researchers participating in the Parkinson’s Disease Biomarkers Program and Parkinson’s Progression Marker Initiative developed a model for disease classification using data from the Parkinson’s Progression Marker Initiative study (n= 367 patients […]