Machine Learning-based Early Detection of Intraoperative Anaphylaxis Among Patients with Hypotension Using Real-World
Haoran Su1,2,3, Liang You1,4, Bailin Jiang5
1National Institute of Health Data Science, Peking University, Beijing, China.
Background:
Intraoperative anaphylaxis remains a rare yet fatal condition that has been a challenge due to its unpredictability. The unique characteristics of physiological parameter changes that precede or occur during the early stages of anaphylaxis may enable timely identification. We aimed to develop machine learning methods for the early detection of intraoperative anaphylaxis among patients with hypotension using real-world time-series physiological data.
Methods:
Physiological data, extracted from the EMRs at a 10-second sampling frequency, of patients undergoing surgeries at Peking University People's Hospital (January 1, 2011 - January 1, 2023) were analyzed. Three datasets (Datasets +1Min, +2Min, and +3Min) were constructed, each spanning 10 min before to 1, 2, and 3 min after hypotension, consisting of positive groups (intraoperative anaphylactic patients with hypotension) and negative groups (intraoperative non-anaphylactic patients with hypotension). Random forests, extreme gradient boosting, and categorical boosting (CatBoost) were employed to construct detection models. The model with the best performance was identified with a five-fold cross-validation.
Results:
Datasets +1Min, +2Min, and +3Min contained 49, 48, and 44 positive samples and 980, 960, and 880 negative samples, respectively. CatBoost models performed best on both Datasets +3Min and +2Min, achieving on Dataset +3Min an area under the receiver operator characteristic curve (AUROC) of 0.851 (± 0.082), an area under the precision-recall curve (AUPRC) of 0.431 (± 0.094), a sensitivity of 0.861 (± 0.210), and a specificity of 0.783 (± 0.164); corresponding values on Dataset +2Min were 0.823 (± 0.145), 0.365 (± 0.097), 0.711 (± 0.183), and 0.940 (± 0.065).
Conclusions:
This study suggests the feasibility of early detection of intraoperative anaphylaxis among patients with hypotension using physiological time-series data. The models could alert clinicians to suspected anaphylaxis just 2-3 min after hypotension, enabling early detection of intraoperative anaphylaxis.