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Feasibility of a Machine Learning Classifier for Predicting Post-Induction Hypotension in Non-Cardiac Surgery.

Insun Park^1,2, Jae Hyon Park^3,4, Young Hyun Koo¹

¹Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.

Yonsei Medical Journal

|February 25, 2025

Summary

This summary is machine-generated.

Machine learning models can predict post-induction hypotension (PIH) in non-cardiac surgery patients. Key predictors include diastolic blood pressure and mean arterial pressure during anesthetic induction.

Keywords:

Anesthesia artificial intelligence general general surgery hypotension machine learning

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Area of Science:

Anesthesiology
Medical Informatics
Machine Learning

Background:

Post-induction hypotension (PIH) is a common complication in non-cardiac surgeries.
Predicting PIH is crucial for patient safety and optimizing anesthetic management.

Purpose of the Study:

To develop and evaluate machine learning (ML) classifiers for predicting PIH.
To identify key preoperative and early intraoperative predictors of PIH.

Main Methods:

Utilized data from 3669 non-cardiac surgery cases in the VitalDB database.
Defined PIH as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes post-induction.
Trained and compared six ML algorithms, assessing performance using Area Under the Receiver Operating Characteristic Curve (AUROC).

Main Results:

63.3% of cases experienced PIH.
Random forest and extremely gradient boosting regressors achieved the highest AUROC of 0.772.
Mean diastolic blood pressure, minimum MAP, and minimum diastolic blood pressure during induction were the most significant predictors.

Conclusions:

ML classifiers demonstrate moderate performance in predicting PIH.
These models hold potential for real-time PIH risk assessment during surgery.