| 师炎敏,张鹏,王欢,王逸飞,李晨,杨瑞云,肖新广.上气道形态学参数联合临床特征列线图模型诊断儿童阻塞性睡眠呼吸暂停[J].中国医学影像技术,2022,38(12):1812~1816 |
| 上气道形态学参数联合临床特征列线图模型诊断儿童阻塞性睡眠呼吸暂停 |
| Nomogram model based on upper airway morphological parameters combined with clinical characteristics for diagnosing children obstructive sleep apnea |
| 投稿时间：2022-07-13 修订日期：2022-09-29 |
| DOI：10.13929/j.issn.1003-3289.2022.12.011 |
| 中文关键词: 睡眠呼吸暂停,阻塞性 儿童 体层摄影术,X线计算机 列线图 |
| 英文关键词:sleep apnea, obstructive child tomography, X-ray computed nomogram |
| 基金项目:河南省重点研发与推广专项（科技攻关）项目（212102310707）。 |
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| 中文摘要: |
| 目的 观察以上气道形态学参数联合临床特征建立的列线图模型诊断儿童阻塞性睡眠呼吸暂停（OSA）的效能。方法 收集355例接受睡眠监测及鼻咽部CT检查的 ≤ 10岁儿童的影像学及临床资料，按7：3比例将其随机归入训练集（n=248）或验证集（n=107）；其中237例确诊OSA。以训练集中的OSA为结局变量，采用单因素及多因素logistic回归分析筛选OSA影响因素，建立OSA列线图模型；以受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评价该模型诊断儿童OSA的效能。结果 单因素及多因素logistic回归分析显示，上气道最狭窄处左右径、腺样体形态和睡眠打鼾病程是OSA的独立影响因素（P均<0.05）。以上述3个变量构建OSA列线图模型，ROC曲线显示其诊断OSA的曲线下面积（AUC）为0.93[95%CI（0.89，0.96）]。以Bootstrap法行内部验证，校准曲线的平均绝对误差为0.01；于验证集进行外部验证，其AUC为0.85[95%CI（0.78，0.93）]，校准曲线的平均绝对误差为0.02。DCA示训练集和验证集的净收益率均高于无效线。结论 基于上气道形态学参数联合临床特征建立的列线图模型诊断儿童OSA具有较高价值。 |
| 英文摘要: |
| Objective To explore the diagnostic efficacy of nomogram model based on upper airway morphological parameters combined with clinical characteristics for obstructive sleep apnea (OSA) in children. Methods Imaging and clinical data of 355 children aged ≤ 10 years who underwent polysomnography (PSG) and nasopharyngeal CT examination, including 237 cases of OSA, were analyzed. The children were randomly divided into training set (n=248) and the validation set (n=107) at a ratio of 7:3. Taken OSA in training set as the outcome variable, univariate and multivariate logistic regression analysis were used to screen the impact factors of OSA, and a nomogram model of OSA was established, then receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate its diagnostic efficacy of OSA in children. Results Univariate and multivariate logistic regression analysis showed that the left and right diameters of the narrowest area of the upper airway, the shape of adenoids and course of sleep snoring were all independent impact factors for OSA (all P<0.05). The nomogram model of OSA was constructed with the above 3 variables, and ROC curve showed that the area under the curve (AUC) of this model was 0.93 (95%CI [0.89, 0.96]). Bootstrap method was used for internal verification, and the average absolute error of the calibration curve was 0.01. Then the model was applied to the validation set for external validation, and the results showed that its AUC was 0.85 (95%CI[0.78, 0.93]), and the average absolute error of the calibration curve was 0.02. DCA showed that the net returns of training set and validation set were both higher than the invalid lines. Conclusion Nomogram model based on upper airway morphological parameters combined with clinical features had high diagnostic value for OSA in children. |
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