一种用于预测快速眼动睡眠行为障碍可能性的预测列线图的开发与验证

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

作者信息

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

机构信息

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.

出版信息

Front Neurol. 2022 Jun 29;13:903721. doi: 10.3389/fneur.2022.903721. eCollection 2022.

DOI:10.3389/fneur.2022.903721

PMID:35847229

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9277017/

Abstract

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 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; = 0.011], smoking (OR, 3.24; 95% CI, 1.84-5.81; < 0.001), physical activity (≥4 times/week) (OR, 0.23; 95% CI, 0.12-0.42; < 0.001), exposure to pesticides (OR, 3.73; 95%CI, 2.08-6.65; < 0.001), constipation (OR, 6.25; 95% CI, 3.58-11.07; < 0.001), depression (OR, 3.66; 95% CI, 1.96-6.75; < 0.001), and daytime somnolence (OR, 3.28; 95% CI, 1.65-6.38; = 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.

摘要

目的

在中国北京的社区人群中开发并验证一种用于特发性快速眼动（REM）睡眠行为障碍（RBD）的预测列线图。

方法

基于经过验证的香港RBD问卷（RBDQ-HK），我们在北京两个社区的1030名社区居民中识别出78名可能患有RBD（pRBD）的个体。应用最小绝对收缩和选择算子（LASSO）回归来识别候选特征并开发列线图。使用自助重抽样进行内部验证。使用受试者操作特征（ROC）曲线的曲线下面积（AUC）评估列线图的辨别力，并通过校准曲线评估预测准确性。进行决策曲线分析（DCA）以评估模型的临床价值。

结果

从31个潜在预测因素中，7个变量被确定为独立预测因素并纳入列线图：帕金森病（PD）或痴呆家族史[比值比（OR），4.59；95%置信区间（CI），1.35 - 14.45；P = 0.011]，吸烟（OR，3.24；95%CI，1.84 - 5.81；P < 0.001），体力活动（≥4次/周）（OR，0.23；95%CI，0.12 - 0.42；P < 0.001），接触农药（OR，3.73；95%CI，2.08 - 6.65；P < 0.001），便秘（OR，6.25；95%CI，3.58 - 11.07；P < 0.001），抑郁（OR，3.66；95%CI，1.96 - 6.75；P < 0.001），以及日间嗜睡（OR，3.28；95%CI，1.65 - 6.38；P = 0.001）。该列线图显示出良好的辨别力，原始AUC为0.885（95%CI，0.845 - 0.925），而经过1000次自助抽样的偏差校正一致性指数（C指数）为0.876。校准曲线和DCA表明列线图具有较高的准确性和临床实用性。