Halil Onder, Zehra Yavuz, Selçuk Comoglu
MANAGE-PD identified substantially more potential DAT candidates than were recommended for DAT in routine clinical practice. Disease duration emerged as an important determinant of clinician recommendation, suggesting that real-world treatment decisions incorporate factors beyond current algorithm-based criteria.
INTRODUCTION: The MANAGE-PD tool was developed to identify patients with Parkinson's disease (PD) who may require treatment optimization or consideration of device-aided therapies (DAT). However, real-world studies have suggested substantial discrepancies between MANAGE-PD classifications and routine clinical decision-making.
METHODS: Consecutive PD patients attending a tertiary movement disorders clinic were evaluated using the MANAGE-PD tool. Clinician treatment decisions and DAT recommendations were assessed independently. Clinical factors associated with clinician DAT recommendation were analyzed.
RESULTS: A total of 252 patients were included. According to MANAGE-PD, 68 patients (27.0%) were classified as Category 3 (potential DAT candidates), whereas only 12 patients (4.8%) received a clinician recommendation for DAT. Among Category 3 patients, only 10 (14.7%) were recommended for DAT. Patients receiving a clinician recommendation had significantly longer disease duration, higher levodopa equivalent daily dose, and were more likely to experience OFF periods exceeding 2 h per day and troublesome dyskinesia. In exploratory multivariable analyses, disease duration (OR 2.29 per 5 years, 95% CI 1.34-3.91) and OFF periods exceeding 2 h per day (OR 6.80, 95% CI 1.67-27.75) remained independently associated with clinician recommendation. Within the MANAGE-PD Category 3 subgroup, disease duration was the only factor associated with clinician DAT recommendation.
CONCLUSIONS: MANAGE-PD identified substantially more potential DAT candidates than were recommended for DAT in routine clinical practice. Disease duration emerged as an important determinant of clinician recommendation, suggesting that real-world treatment decisions incorporate factors beyond current algorithm-based criteria.