A Precision Medicine Model Estimating Off-Medication Motor State in Parkinson`s Disease
A Precision Medicine Model Estimating Off-Medication Motor State in Parkinson`s Disease
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Original abstract
Background: On-medication motor assessment in Parkinson's Disease confounds treatment response with the disease's motor state. To properly quantify an off-medication state, we predicted the patient's underlying motor severity and characterized their response to treatment through individualized trajectories without relying on medication washout.<br><br>Methods: A retrospective, longitudinal analysis was conducted on participants enrolled in the Parkinson's Progression Markers Initiative. We developed and internally validated a linear mixed-effects model using a subject-wise nested cross-validation framework. The primary outcome was MDS-UPDRS III in the off-medication state, approximated by levodopa equivalent daily dose, disease duration, presence of dyskinesia, and MDS-UPDRS III while on medication, as primary predictors. Principal component analysis was applied to model residuals to characterize treatment response patterns across off-medication predictions.<br><br>Findings: The model predicted off-medication MDS-UPDRS III within a 4-point margin. Up to 12% of observations exhibit a limited treatment response, indicating potential under-recognized treatment resistance or dosage ceiling effects. In support of the treatment response patterns, principal component analysis uncovered clinically relevant differences between patterns, including statistically significant MDS-UPDRS III subitems, activities of daily living, and affective state scores between limited and excessive treatment response profiles. Thereby supporting the clinical utility in providing an individualized method of characterizing treatment response.<br><br>Interpretation: By quantifying the underlying disease severity in the off-medication state, we can enhance the characterization of treatment efficacy and support improved treatment decisions in conventional clinical practice. This capability enables the detection of limited treatment responses and highlights potential underrecognized effects in individuals with more severe diseases, which can lead to undertreatment or dosage limitations. Furthermore, assessing medication response is achievable using routinely collected clinical measures.