# Evaluation of the therapeutic effect of adaptive deep brain stimulation on motor symptoms and sleep disturbances in Parkinson’s disease and construction of a response prediction model

## TL;DR

Adaptive deep brain stimulation improves motor and sleep symptoms in Parkinson’s patients more than conventional treatments, with a predictive model helping guide clinical decisions.

## Contribution

A novel response prediction model for adaptive deep brain stimulation efficacy in Parkinson’s disease is developed and validated.

## Key findings

- Adaptive deep brain stimulation significantly improved motor and sleep symptoms compared to conventional treatment.
- Age, BMI, disease duration, and blood markers negatively influenced treatment response.
- The predictive model showed good performance in forecasting symptom improvement.

## Abstract

Parkinson’s disease patients often experience symptoms such as motor impairments and sleep disturbances. This study aims to evaluate the efficacy of adaptive deep brain stimulation therapy in improving motor symptoms and sleep disorders in patients with Parkinson’s disease.

This retrospective cohort study included 280 patients with Parkinson’s disease. Baseline data were analyzed to assess changes in motor symptoms and sleep disorders before and after treatment. Factors influencing treatment efficacy were explored using univariate and multivariate logistic regression analyses, based on which a response prediction model was constructed. A generalized linear mixed model was then employed to examine interactions between the response model and other variables.

After treatment, the Unified Parkinson’s Disease Rating Scale Part II (UPDRS II), Unified Parkinson’s Disease Rating Scale Part III (UPDRS III), Parkinson’s Disease Sleep Scale (PDSS), Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale (ESS) scores in the observation group (adaptive deep brain stimulation, aDBS) were significantly lower than those in the control group, indicating better motor and non-motor symptom control. In contrast, the Mini-Mental State Examination (MMSE) score was significantly higher in the observation group, suggesting improved cognitive function. Age, body mass index (BMI), disease duration, Hoehn and Yahr stage, smoking history, and baseline neutrophil-to-lymphocyte ratio (NLR) were negatively associated with symptom improvement. In contrast, adaptive deep brain stimulation (aDBS) treatment showed a significant positive association with symptom improvement. The predictive model constructed based on blood biomarkers, demographic factors, and treatment response demonstrated good predictive performance for clinical improvement. Furthermore, generalized linear mixed model (GLMM) analysis revealed that the response model exerted an antagonistic effect on BMI and high-density lipoprotein (HDL) levels, and a synergistic effect on the platelet-to-lymphocyte ratio (PLR).

The effectiveness of adaptive deep brain stimulation (aDBS) therapy in improving motor symptoms, sleep disorders, and quality of life in patients with Parkinson’s disease is superior to that of conventional treatment. Factors such as patient age, body mass index (BMI), disease duration, Hoehn-Yahr stage, and baseline blood marker levels can influence the efficacy of aDBS. The constructed response model effectively predicts symptom improvement and offers valuable guidance for clinical treatment decisions.