Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease

• DOI: 10.1038/s41531-022-00409-5
• Publication Date: 2022-11-07T07:02:42Z
• Authors: Joshua Harvey; Rick A. Reijnders; Rachel Cavill; Annelien Duits; Sebastian Köhler; Lars Eijssen; Bart P. F. Rutten; Gemma Shireby; Ali Torkamani; Byron Creese; Albert F. G. Leen...; Katie Lunnon; Ehsan Pishva
• Keywords: 0301 basic medicine; Parkinson's disease; Radboudumc 17: Women's cancers RIHS: Radboud Institute for Health Sciences; 610; predictive markers; Radboud University Medical Center; Article; 3. Good health; 03 medical and health sciences; Medical Psychology - Radboud University Medical Center; 0302 clinical medicine; 616; Neurology. Diseases of the nervous system; RC346-429

AbstractCognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.