Background: Perioperative respiratory adverse events (PRAEs) are the most common cause of serious adverse events in children receiving anesthesia. Our primary aim of this study was to develop and validate a risk prediction tool for the occurrence of PRAE from the onset of anesthesia induction until discharge from the postanesthesia care unit in children younger than 18 years undergoing elective ambulatory anesthesia for surgery and radiology. The incidence of PRAE was studied.
Methods: We analyzed data from 19,059 patients from our department's quality improvement database. The predictor variables were age, sex, ASA physical status, morbid obesity, preexisting pulmonary disorder, preexisting neurologic disorder, and location of ambulatory anesthesia (surgery or radiology). Composite PRAE was defined as the presence of any 1 of the following events: intraoperative bronchospasm, intraoperative laryngospasm, postoperative apnea, postoperative laryngospasm, postoperative bronchospasm, or postoperative prolonged oxygen requirement. Development and validation of the risk prediction tool for PRAE were performed using a split sampling technique to split the database into 2 independent cohorts based on the year when the patient received ambulatory anesthesia for surgery and radiology using logistic regression. A risk score was developed based on the regression coefficients from the validation tool. The performance of the risk prediction tool was assessed by using tests of discrimination and calibration.
Results: The overall incidence of composite PRAE was 2.8%. The derivation cohort included 8904 patients, and the validation cohort included 10,155 patients. The risk of PRAE was 3.9% in the development cohort and 1.8% in the validation cohort. Age ≤ 3 years (versus >3 years), ASA physical status II or III (versus ASA physical status I), morbid obesity, preexisting pulmonary disorder, and surgery (versus radiology) significantly predicted the occurrence of PRAE in a multivariable logistic regression model. A risk score in the range of 0 to 3 was assigned to each significant variable in the logistic regression model, and final score for all risk factors ranged from 0 to 11. A cutoff score of 4 was derived from a receiver operating characteristic curve to determine the high-risk category. The model C-statistic and the corresponding SE for the derivation and validation cohort was 0.64 ± 0.01 and 0.63 ± 0.02, respectively. Sensitivity and SE of the risk prediction tool to identify children at risk for PRAE was 77.6 ± 0.02 in the derivation cohort and 76.2 ± 0.03 in the validation cohort.
Conclusions: The risk tool developed and validated from our study cohort identified 5 risk factors: age ≤ 3 years (versus >3 years), ASA physical status II and III (versus ASA physical status I), morbid obesity, preexisting pulmonary disorder, and surgery (versus radiology) for PRAE. This tool can be used to provide an individual risk score for each patient to predict the risk of PRAE in the preoperative period.