Frontiers in neurology · 2022
Development and Validation of a Predictive Nomogram for Possible REM Sleep Behavior Disorders.
СтатьяНазвание и аннотация: Перевод в очереди
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Lai Hong, Li Xu-Ying, Hu Junya, Li Wei, Xu Fanxi, Zhu Junge, He Raoli, Weng Huidan, Chen Lina, Yu Jiao, Li Xian, Song Yang, Wang Xianling, Wang Zhanjun, Li Wei, Kang Rong, Li Yuling, Xu Junjie, Deng Yuanfei, Ye Qinyong, Wang Chaodong
Аннотация
OBJECTIVES: To develop and validate a predictive nomogram for idiopathic rapid eye movement (REM) sleep behavior disorder (RBD) in a community population in Beijing, China.
METHODS: Based 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.
RESULTS: From 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.
CONCLUSIONS: This study proposed an effective nomogram with potential application in the individualized prediction for pRBD.
METHODS: Based 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.
RESULTS: From 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.
CONCLUSIONS: This study proposed an effective nomogram with potential application in the individualized prediction for pRBD.