Predicting Post-Induction Hypotension in Diverse Surgical Populations: A Multiclass Classification Universal Model Using Machine Learning Techniques

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: XGBoost model achieved superior performance over other machine learning techniques

• Primary Outcome: Post-induction hypotension occurrence and severity using multiclass classification

• Nnt Or Sensitivity: Accuracy 0.755, F1-score 0.664, multiclass model outperformed binary classification

• Confidence Interval: Not reported

Clinical Application

Requires electronic medical record integration and real-time data processing capabilities Relevant to Australian tertiary hospitals with comprehensive EMR systems. Would require validation in Australian populations and integration with existing perioperative protocols. TGA approval may be needed for clinical decision support software. Adult patients undergoing general anaesthesia for non-cardiac surgery in tertiary care settings

Abstract

PURPOSE: Our study aims to develop a machine learning model that not only predicts the occurrence of post-induction hypotension (PIH) but also assesses its severity, addressing a broader patient population than previous studies which mostly focused on a single population. MATERIALS AND METHODS: In our study, we extracted data from 71473 patients aged 18 years and older who underwent general anesthesia for non-cardiac surgery at a tertiary care hospital, using the electronic medical record system for modeling. We used patient demographics, baseline and pre-induction blood pressure, preoperative laboratory tests, surgical details, and anesthetics data, focusing on predicting PIH. The severity of hypotension was assessed by integrating and calculating the integral value of hypotensive periods. We employed several machine learning techniques and evaluated their performance using accuracy, precision, F1-scores, and macro-averaged area under the curve. Additionally, SHapley Additive exPlanation values were used to determine the key factors influencing the predictions. RESULTS: A multiclass classification model, which assesses hypotension severity through the area of hypotension, surpassed the binary model with an F1-score of 0.664. Among various machine learning techniques, the eXtreme Gradient Boosting (XGBoost) model exhibited superior prediction performance, achieving an accuracy of 0.755 and an F1-score of 0.664. Models using preoperative blood pressure and demographic data outperformed those using other datasets. CONCLUSION: We found that using the XGBoost ensemble machine learning technique aids in predicting PIH before surgery, and introducing a multiclass classification model that indicates the severity of hypotension to clinicians leads to an overall enhancement in prediction performance, thereby increasing its clinical utility for real-world applications.

References

1. 1.Lee, S. W., Lee, D., & Kim, S. H. (2026). Predicting Post-Induction Hypotension in Diverse Surgical Populations: A Multiclass Classification Universal Model Using Machine Learning Techniques. Yonsei Medical Journal. https://doi.org/10.5555/3295222.3295230