Development and Validation of a Predictive Nomogram for Possible REM Sleep Behavior Disorders

Frontiers in Neurology

Yuanfei Deng¹  Junya Hu²  Jiao Yu³  Qinyong Ye³  Huidan Weng³  Raoli He³  Lina Chen³  Xian Li⁴  Yang Song⁴  Junge Zhu⁴  Hong Lai⁴  Wei Li⁴  Zhanjun Wang⁴  Fanxi Xu⁴  Xianling Wang⁴  Chaodong Wang⁴  Xu-Ying Li⁵  Junjie Xu⁶  Yuling Li⁷  Rong Kang⁷ 

[1] Department of Geriatric Disease, Peking University Shenzhen Hospital, Shenzhen, China;Department of Neurobiology, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital of Capital Medical University, Beijing, China;Department of Neurology, Fujian Key Laboratory of Molecular Neurology, Fujian Medical University Union Hospital, Institute of Neuroscience, Fujian Medical University, Fuzhou, China;Department of Neurology, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital of Capital Medical University, Beijing, China;Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China;The Qinglonghu Community Health Service Center, Beijing, China;The Xinjiekou Community Health Service Center, Beijing, China;

关键词: REM sleep behavior disorder (RBD);    LASSO;    nomogram;    decision curve analysis (DCA);    predictive model;   

DOI  :  10.3389/fneur.2022.903721

来源: DOAJ

【 摘 要 】

ObjectivesTo develop and validate a predictive nomogram for idiopathic rapid eye movement (REM) sleep behavior disorder (RBD) in a community population in Beijing, China.MethodsBased on the validated RBD questionnaire-Hong Kong (RBDQ-HK), we identified 78 individuals with possible RBD (pRBD) in 1,030 community residents from two communities in Beijing. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify candidate features and develop the nomogram. Internal validation was performed using bootstrap resampling. The discrimination of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the predictive accuracy was assessed via a calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical value of the model.ResultsFrom 31 potential predictors, 7 variables were identified as the independent predictive factors and assembled into the nomogram: family history of Parkinson's disease (PD) or dementia [odds ratio (OR), 4.59; 95% confidence interval (CI), 1.35–14.45; p = 0.011], smoking (OR, 3.24; 95% CI, 1.84–5.81; p < 0.001), physical activity (≥4 times/week) (OR, 0.23; 95% CI, 0.12–0.42; p < 0.001), exposure to pesticides (OR, 3.73; 95%CI, 2.08–6.65; p < 0.001), constipation (OR, 6.25; 95% CI, 3.58–11.07; p < 0.001), depression (OR, 3.66; 95% CI, 1.96–6.75; p < 0.001), and daytime somnolence (OR, 3.28; 95% CI, 1.65–6.38; p = 0.001). The nomogram displayed good discrimination, with original AUC of 0.885 (95% CI, 0.845–0.925), while the bias-corrected concordance index (C-index) with 1,000 bootstraps was 0.876. The calibration curve and DCA indicated the high accuracy and clinical usefulness of the nomogram.ConclusionsThis study proposed an effective nomogram with potential application in the individualized prediction for pRBD.

【 授权许可】

Unknown