Levodopa responsiveness in Parkinson's disease: harnessing real-life experience with machine-learning analysis.

Abstract

Responsiveness to levodopa varies greatly among patients with Parkinson's disease (PD). The factors that affect it are ill defined. The aim of the study was to identify factors predictive of long-term response to levodopa. The medical records of 296 patients with PD (mean age of onset, 62.2 ± 9.7years) were screened for demographics, previous treatments, and clinical phenotypes. All patients were assessed with the Unified PD Rating Scale (UPDRS)-III before and 3months after levodopa initiation. Regression and machine-learning analyses were used to determine factors that are associated with levodopa responsiveness and might identify patients who will benefit from treatment. The UPDRS-III score improved by ≥ 30% (good response) in 128 patients (43%). On regression analysis, female gender, young age at onset, and early use of dopamine agonists predicted a good response. Time to initiation of levodopa treatment had no effect on responsiveness except in patients older than 72years, who were less responsive. Machine-learning analysis validated these factors and added several others: symptoms of rigidity and bradykinesia, disease onset in the legs and on the left side, and fewer white vascular ischemic changes, comorbidities, and pre-non-motor symptoms. The main determinants of variations in levodopa responsiveness are gender, age, and clinical phenotype. Early use of dopamine agonists does not hamper levodopa responsiveness. In addition to validating the regression analysis results, machine-learning methods helped to determine the specific clinical phenotype of patients who may benefit from levodopa in terms of comorbidities and pre-motor and non-motor symptoms.