Nomogram to Predict Cognitive State Improvement after Deep Brain Stimulation for Parkinson's Disease.

Abstract

PURPOSE:

Parkinson's disease (PD) is a common neurodegenerative disease, for which cognitive impairment is a non-motor symptom (NMS). Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for PD. This study established a nomogram to predict cognitive improvement rate after STN-DBS in PD patients.

METHODS:

We retrospectively analyzed 103 PD patients who underwent STN-DBS. Patients were followed up to measure improvement in MoCA scores one year after surgery. Univariate and multivariate logistic regression analyses were used to identify factors affecting improvement in cognitive status. A nomogram was developed to predict this factor. The discrimination and fitting performance were evaluated by receiver operating characteristics (ROC) analysis, calibration diagram, and decision curve analysis (DCA).

RESULTS:

Among 103 patients, the mean improvement rate of the MoCA score was 37.3% and the median improvement rate was 27.3%, of which 64% improved cognition, 27% worsened cognition, and 8.7% remained unchanged. Logistic multivariate regression analysis showed that years of education, UPDRSIII drug use, MoCA Preop, and MMSE Preop scores were independent factors affecting the cognitive improvement rate. A nomogram model was subsequently developed. The C-index of the nomogram was 0.98 (95%CI, 0.97-1.00), and the area under the ROC was 0.98 (95%CI 0.97-1.00). The calibration plot and DCA demonstrated the goodness-of-fit between nomogram predictions and actual observations.

CONCLUSION:

Our nomogram could effectively predict the possibility of achieving good cognitive improvement one year after STN-DBS in patients with PD. This model has value in judging the expected cognitive improvement of patients with PD undergoing STN-DBS.

• Authors: Bowen Chang, Chen Ni, Weiwen Zhang, Jiaming Mei, Chi Xiong, Peng Chen, Manli Jiang, Chaoshi Niu
• Journal: Brain sciences (Brain Sci) Vol. 12 Issue 6 (Jun 09 2022) ISSN: 2076-3425 [Print] Switzerland
• PMID: 35741644 (Publication Type: Journal Article)