Objective  To analyze the influencing factors of antiparkinsonian drugs (APD) medication adherence (MA) in patients with Parkinson's disease (PD), and to construct a prediction model for APD-medication non-adherence (MNA).

Methods  Data of PD patients who visited the outpatient department of Zhejiang Provincial People's Hospital from March 2023 to March 2025 were retrospectively analyzed. The general information questionnaire and Morisky Medication Adherence Scale-8 (MMSA-8) were used for investigation. According to MMSA-8, the patients were divided into the MNA group and the MA group. The differences of between the two groups were compared. Stepwise logistic regression (LR) analysis was applied to screen the potential influencing factors of APD-MNA and the APD-MNA prediction model was construct. The predictive efficiency, calibration ability, and clinical benefit ability of the prediction model were evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve.

Results  Stepwise LR analysis showed that age (≥60 years old) [OR=2.803, 95%CI (1.320-5.953)], PD complications (yes) [OR=4.359, 95%CI (1.945-9.770)], APD used types (≥5 types) [OR=32.200, 95%CI (12.134-85.451)], Social Support Scale score (>22 points) [23-44 points: OR=0.184, 95%CI (0.085-0.401); 45-66 points: OR=0.017, 95%CI (0.005-0.056)], and anxiety disorder (yes) [OR=2.467, 95%CI (1.237-4.922)] were independent influencing factors for MNA in PD patients. ROC analysis showed that the AUC (95%CI) of the APD-MNA prediction model was 0.856 (0.816-0.896); the calibration curve indicated that the "predicted MNA probability" was generally consistent with the "actual MNA probability"; the clinical decision curve suggested that the APD-MNA prediction model could provide clinical benefits within a certain threshold range.

Conclusion  The APD-MNA prediction model may provide reference for the clinical identification of high-risk patients with poor medication compliance.

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