A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

Abstract

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.

Keywords: anesthesia; artificial intelligence; machine learning; pediatrics; preoperative care.

Figure 1.

Flow chart showing data selection from the original apricot dataset.

Figure 2.

Feature correlation matrix for all input features. Values are normalized between −1 and 1. Negative values indicate anticorrelation, meaning that changes correlate to opposing changes in the other. Positive values indicate that changes in one tend to correlate to similar changes in the other. ASA, American Society of Anesthesiologists.

Figure 3.

Receiver operating characteristic curves for each model. Diagonal gray line indicates a random selection (AUROC 0.5), and the curves above the line represent better performance.

Figure 4.

Average feature ranking for both models in descending order for the day-of-surgery model taken from 5 different perturbations with different random seeds. The black lines show standard deviations for feature scores. ASA, American Society of Anesthesiologists.