Development and Validation of a Risk Nomogram Model for Perioperative Respiratory Adverse Events in Children Undergoing Airway Surgery: An Observational Prospective Cohort Study

Abstract

Qin Zhang,1,* Fangming Shen,1,* Qingfeng Wei,1,* He Liu,2 Bo Li,1 Qian Zhang,3 Yueying Zhang3 1Xuzhou Medical University, Xuzhou City, Jiangsu Province, People’s Republic of China; 2Department of Anesthesiology, The Affiliated Huzhou Hospital, Zhejiang University School of Medicine; Huzhou Central Hospital, Huzhou City, Zhejiang Province, People’s Republic of China; 3Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yueying ZhangDepartment of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, People’s Republic of ChinaTel [phone]Email [email protected]: 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.Patients and Methods: 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.Results: 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.Conclusion: 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.Keywords: perioperative, adverse events, children, LASSO, nomogram

Keywords

• perioperative
• adverse events
• children
• lasso
• nomogram