Key result
Machine learning rules out severe pediatric perioperative critical events with ~95% negative predictive value.
Why the study?
Prediction of pediatric anesthesia risk currently relies on the ASA-PS score despite reported inconsistencies, while serious perioperative complications still occur even in low-risk children.
Can machine learning models predict which pediatric patients are at low risk for severe perioperative adverse events?
Observational (n=30,325)
Yes
Can machine learning models predict which pediatric patients are at low risk for severe perioperative adverse events?
Machine learning algorithms can successfully identify pediatric patients at low risk for critical perioperative events, potentially guiding safe allocation to ambulatory or non-tertiary surgical centers.
May support low-risk pediatric triage; leaves open prospective validation before practice change.
BACKGROUND: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method. AIMS: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day. METHODS: Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA-PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA-PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications. RESULTS: Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6-0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day-of-surgery phase. CONCLUSIONS: This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.
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Gray et al. (2023) conducted an observational in Pediatric anesthesia (ASA-PS I-III) (n=30,325). Machine learning risk stratification models was evaluated on Severe perioperative critical events (respiratory, cardiac, allergic, or neurological complications requiring immediate intervention). Gradient boosting machine learning models identified pediatric patients in ASA-PS class I-III at low risk for severe perioperative critical events with negative predictive values >95% and AUROCs up to 0.722.
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