Calibration plot of the prediction in the holdout validation cohort.

GBM, Gradient-boosting model; LSTM, Long short-term memory.</p

Baseline characteristics in the dataset.

BackgroundReducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia.MethodsThis retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital. Intraoperative hypoxemia was defined as oxygen saturation ResultsIn total, 1,540 (11.73%) patients with intraoperative hypoxemia out of 13,130 patients’ records with 2,367 episodes were included for developing the model dataset. After model development, 200 (13.25%) of the 1,510 patients’ records with 289 episodes were used for holdout validation. Among the models developed, the GBM had the highest AUROC of 0.904 (95% confidence interval [CI] 0.902 to 0.906), which was significantly higher than that of the LSTM (0.843, 95% CI 0.840 to 0.846 P P P P ConclusionsMachine learning models can be used to predict upcoming intraoperative hypoxemia in real-time based on the biosignals acquired by patient monitors, which can be useful for clinicians for prediction and proactive treatment of hypoxemia in an intraoperative setting.</div

Ability of models in predicting intraoperative hypoxemia in pediatric patients.

Ability of models in predicting intraoperative hypoxemia in pediatric patients.</p

Post hoc analysis: Statistical significance of comparison of models using different combinations of waveforms.

P-values were calculated with DeLong’s method and corrected using Bonferroni’s method. (DOCX)</p

Flow chart presenting patient selection and data analysis.

PIP, Peak Inspiratory Pressure; EtCO2 End tidal CO2; FiO2, Fraction of inspired oxygen.</p

Comparison of area under the receiver operating characteristic curves for the machine learning models for predicting intraoperative hypoxemia in pediatric patients in (A) test dataset (B) holdout validation dataset.

GBM, Gradient-boosting model; LSTM, Long short-term memory; AUROC, Area under the receiver operating characteristic curve.</p

Simplified plot of the real-time prediction of intraoperative hypoxemia.

Simplified plot of the real-time prediction of intraoperative hypoxemia.</p

Model performance metrics in post hoc analysis.

AUROC, Area under the Receiver Operating Characteristic Curve; AUPRC, Area under the Precision-Recall Curve.</p

Area under the Receiver-operating Characteristic Curve, Area under the Precision-Recall Curve, Sensitivity, and Specificity of our model in predicting intraoperative hypotension.

Value (95% confidence interval); AUROC, area under the receiver operating characteristic; AUPRC, area under the precision-recall curve; ABP, arterial blood pressure; ECG, electrocardiogram; EEG, electroencephalogram.</p

The model performance on consecutively sampled waveforms with 1-minute interval over the entire surgical procedures in 100 randomly selected cases.

(DOCX)</p