Determination of the motor status of patients with advanced Parkinson's disease under levodopa-carbidopa intestinal gel using a machine learning model.

Abstract

Despite the careful selection of candidate patients, the levodopa-carbidopa intestinal gel (LCIG) treatment of advanced Parkinson's disease (PD) remains challenging due to a complex interplay between motor and non-motor symptoms. We developed a random forest (RF) model to determine the postoperative motor outcome of patients with advanced PD at 2years under the LCIG therapy by using motor and non-motor data from a Greek multicenter, observational registry (ForHealth S.A.). This was a prospective 24-month, observational study of 59 patients with advanced PD under LCIG treatment from September 2019 to September 2021. Motor status was assessed with the Unified Parkinson's Disease Rating Scale (UPDRS) parts III and IV. Non-motor symptoms (NMS) were assessed by the Non-Motor Symptoms Questionnaire (NMSQ) and the Geriatric Depression Scale (GDS). We demonstrated that the proper combination of motor and non-motor measures significantly determines the motor outcome (UPDRS-III year 2: 23.57 ± 14.22 p < 0.001), reducing the RMSE (root-mean-square-error) from 3.487279 to 3.066292, suggesting that the optimized model performed well. Based on the "IncNodePurity," the major determinant factors of UPDRS-III (year 2) were, in descending order: UPDRS-III (year 0), disease duration, NMSQ (year 2), age, NMSQ (year 0), time off (hours) (year 2), time dyskinesia (year 0), quality of life (year 2) after the LCIG implementation. The novelty of this model is the possibility to determine the motor outcome after two years of LCIG. This model could be also useful for not specialized Parkinson's neurologists, to improve patient counseling, expectation management, and patient satisfaction with LCIG therapy.