Interpretable Machine Learning for Cross-Cohort Prediction of Motor Fluctuations in Parkinson's Disease

Background: Motor fluctuations are a common complication in later stages of Parkinson's disease (PD) and significantly affect patients' quality of life. Robustly identifying risk and protective factors for this complication across distinct cohorts could lead to improved disease management. Objectives: The goal was to identify key prognostic factors for motor fluctuations in PD by using machine learning and exploring their associations in the context of the prior literature. Methods: We applied interpretable machine learning techniques for time-to-event analysis and prediction of motor fluctuations within 4 years in three longitudinal PD cohorts. Prognostic models were cross-validated to identify robust predictors, and the performance, stability, calibration, and utility for clinical decision-making were assessed. Results: Cross-validation analyses suggest the effectiveness of the models in identifying significant baseline predictors. Movement Disorder Society-Unified Parkinson's Disease Rating Scale parts I and II, freezing of gait, axial symptoms, rigidity, and pathogenic GBA and LRRK2 variants were positively correlated with motor fluctuations. Conversely, motor fluctuations were inversely associated with tremors and late age of onset of PD. Cross-cohort data integration provides more stable predictions, reducing cohort-specific bias and enhancing robustness. Decision curve and calibration analysis confirms the models' practical utility and alignment of predictions with observed outcomes. Conclusions: Interpretable machine learning models can effectively predict motor fluctuations in PD from baseline clinical data. Cross-cohort data integration increases the stability of selected predictors. Calibration and decision curve analyses confirm the model's reliability and utility for practical clinical applications.