Identification of the risk factors in perioperative respiratory adverse events in children under general anesthesia and the development of a predictive model

Abstract

Background: This study explored the risk factors of perioperative respiratory adverse events in children under 12 years old undergoing general anesthesia surgery. A prediction model was constructed according to the related risk factors to provide a basis for timely clinical intervention and decision-making.Methods: Children under 12 years old who underwent general anesthesia in our hospital between January 2016 and December 2020 were included in this study. The clinical data, including age, gender, weight, American Society of Anesthesiologists (ASA) grade classification, operation season, preoperative hospital stay, anesthesia time, and postoperative pain score, were collated. Continuous variables were converted to categorical variables. Logistic regression analysis was used to screen independent risk factors and a nomogram was constructed to predict the probability of adverse events. Fitting curves and receiver operating characteristic (ROC) curves were utilized to verify the model.Results: Logistic regression analyses demonstrated that age [odds ratio (OR) =1.32, 95% confidence interval (CI): 1.08 to 1.49], body weight (OR =1.49, 95% CI: 1.21 to 1.84), anesthesia time (OR =1.61, 95% CI: 1.32 to 1.78), and surgery season (OR =1.12, 95% CI: 1.07 to 1.39) were independent risk factors for respiratory adverse events in children undergoing general anesthesia (P<0.05). The risk of respiratoryrelated adverse events increased in children with grade II ASA classification compared to children with grade I ASA classification (P<0.05). Similarly, the risk of respiratory adverse events increased in children with level III pain scores compared to children with level I pain scores (P<0.05). The calibration curve showed that the predicted curve was consistent with the actual curve. The area under the ROC curve (AUC) was 0.707, indicating that model showed great predictive ability.Conclusions: Age, weight, anesthesia time, operation season, ASA grade, and pain score were identified as independent risk factors for respiratory adverse events in children undergoing general anesthesia. Using the above risk factors, a nomogram was established to predict the risk of respiratory system-related adverse events. The predicted results were highly consistent with the actual risk, and the false positive rate was within a reasonable range.

Predictors of perioperative respiratory adverse events among children with upper respiratory tract infection undergoing pediatric ambulatory ilioinguinal surgery: a prospective observational research

“…We included patients with the same age range and the same anesthetic technique for the same type of surgery. This is why these factors, considered predictors of PRAEs in a previous study, 20 were comparable between the two groups in our study. The identification of the risk factors for PRAEs is very interesting in pediatric anesthesia.…”

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

“…The method of screening predictor variables was not as good as LASSO. 45 The limitations in the present study include: (1) this was a single-center prospective study, which may introduce bias. The results of this study need to be verified by data from other centers (2) the risk factors of PRAEs are complex and there are far more factors to be investigated and used to predict the prognosis of PRAE.…”

Ad-hoc preoperative management and respiratory events in pediatric anesthesia during the first COVID-19 lockdown–an observational cohort study

“…We realize the limitations of an observational retrospective study, one of them being different potential modifiers of respiratory risk during pediatric surgery like snoring or medication-related factors. Also, the incidence of respiratory adverse events is subject to seasonal variations [ 31 ], notably characterized by peaks of viral upper respiratory tract infections in children and adolescents during the winter. Last but not least, the limited case number we overlook within this ‘window of opportunity’ for study in early 2020 cannot definitively establish the true incidence of severe respiratory events if an ad-hoc strategy of preoperative management were to become the standard, also considering that the surgical case load of ASA I/II pediatric patients during this period in 2020 was down by 45.9% from the median case load of the period-matched previous three years.…”