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Evaluation of risk factors for impulse control disorder in Parkinson's disease in northern China.
INTRODUCTION: Impulse control disorder (ICD) is a common non-motor symptom of Parkinson's disease (PD), but its risk factors are still controversial. This study aimed to determine the prevalence of ICD in northern China and analyze the risk factors associated with ICD, multiple ICDs, and four subtypes.
METHODS: A total of 285 PD patients were enrolled in this study. Each patient was screened using the Questionnaire for Impulse and Compulsive Control Disorders (QUIP). Stepwise regression analysis was performed to identify independent risk factors, and a prediction model was developed.
RESULTS: The prevalence of ICD in the study population was 11.6%. Stepwise regression analysis showed that ICD was associated with disease duration, motor symptoms, dyskinesia, depression, REM sleep behavior disorder (RBD) and cognitive decline; multiple ICDs were related to coffee history, motor symptoms, dyskinesia, depression, apathy and RBD. The prediction model demonstrated good performance with AUC values of 0.93, 0.88, and 0.66 on the balanced train set, balanced test set, and the original imbalanced data set, respectively.
CONCLUSION: The risk factors for PD-ICD are complex and influenced by regional economic and cultural backgrounds. Clarifying these factors and developing predictive models can help to delay or even prevent the development of ICD through early screening and intervention.
METHODS: A total of 285 PD patients were enrolled in this study. Each patient was screened using the Questionnaire for Impulse and Compulsive Control Disorders (QUIP). Stepwise regression analysis was performed to identify independent risk factors, and a prediction model was developed.
RESULTS: The prevalence of ICD in the study population was 11.6%. Stepwise regression analysis showed that ICD was associated with disease duration, motor symptoms, dyskinesia, depression, REM sleep behavior disorder (RBD) and cognitive decline; multiple ICDs were related to coffee history, motor symptoms, dyskinesia, depression, apathy and RBD. The prediction model demonstrated good performance with AUC values of 0.93, 0.88, and 0.66 on the balanced train set, balanced test set, and the original imbalanced data set, respectively.
CONCLUSION: The risk factors for PD-ICD are complex and influenced by regional economic and cultural backgrounds. Clarifying these factors and developing predictive models can help to delay or even prevent the development of ICD through early screening and intervention.
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