Zhang Qin, Shen Fangming, Wei Qingfeng, Liu He, Li Bo, Zhang Qian, Zhang Yueying
Xuzhou Medical University, Xuzhou City, Jiangsu Province, People's Republic of China.
Department of Anesthesiology, The Affiliated Huzhou Hospital, Zhejiang University School of Medicine; Huzhou Central Hospital, Huzhou City, Zhejiang Province, People's Republic of China.
Risk Manag Healthc Policy. 2022 Jan 6;15:1-12. doi: 10.2147/RMHP.S347401. eCollection 2022.
The aim of this study was to explore the associated risk factors of perioperative respiratory adverse events (PRAEs) in children undergoing airway surgery and establish and validate a nomogram prediction model for PRAEs.
This study involved 709 children undergoing airway surgery between November 2020 and July 2021, aged ≤18 years in the affiliated hospital of Xuzhou Medical University. They were divided into training (70%; n = 496) and validation (30%; n = 213) cohorts. The least absolute shrinkage and selection operator (LASSO) was used to develop a risk nomogram model. Concordance index values, calibration plot, decision curve analysis, and the area under the curve (AUC) were examined.
PRAEs were found in 226 of 496 patients (45.6%) and 88 of 213 patients (41.3%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PRAEs were age, obesity, degree of upper respiratory tract infection, premedication, and passive smoking. The risk nomogram model showed good discrimination power, and the AUC generated to predict survival in the training cohort was 0.760 (95% confidence interval, 0.695-0.875). In the validation cohort, the AUC of survival predictions was 0.802 (95% confidence interval, 0.797-0.895). Calibration plots and decision curve analysis showed good model performance in both datasets. The sensitivity and specificity of the risk nomogram model were calculated, and the result showed the sensitivity of 69.5% and 64.8% and specificity of 73.3% and 81.6% for the training and validation cohorts, respectively.
The present study showed the proposed nomogram achieved an optimal prediction of PRAEs in patients undergoing airway surgery, which can provide a certain reference value for predicting the high-risk population of perioperative respiratory adverse events and can lead to reasonable preventive and treatment measures.
本研究旨在探讨气道手术患儿围手术期呼吸不良事件（PRAEs）的相关危险因素，并建立和验证PRAEs的列线图预测模型。
本研究纳入了2020年11月至2021年7月间在徐州医科大学附属医院接受气道手术的709例年龄≤18岁的儿童。他们被分为训练组（70%；n = 496）和验证组（30%；n = 213）。采用最小绝对收缩和选择算子（LASSO）构建风险列线图模型。对一致性指数值、校准图、决策曲线分析和曲线下面积（AUC）进行了检验。
训练组496例患者中有226例（45.6%）发生PRAEs，验证组213例患者中有88例（41.3%）发生PRAEs。与PRAEs相关的围手术期危险因素包括年龄、肥胖、上呼吸道感染程度、术前用药和被动吸烟。风险列线图模型显示出良好的区分能力，训练组预测生存的AUC为0.760（95%置信区间，0.695 - 0.875）。在验证组中，生存预测的AUC为0.802（95%置信区间，0.797 - 0.895）。校准图和决策曲线分析表明该模型在两个数据集中均具有良好的性能。计算了风险列线图模型的敏感性和特异性，结果显示训练组和验证组的敏感性分别为69.5%和64.8%，特异性分别为73.3%和81.6%。
本研究表明所提出的列线图对气道手术患者的PRAEs实现了最佳预测，可为预测围手术期呼吸不良事件的高危人群提供一定的参考价值，并可据此采取合理的预防和治疗措施。
