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

ABSTRACT:

Purpose

To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries.

Materials and methods

Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an open-source registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers.

Results

A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both record

SUBMITTER: Park I 

PROVIDER: S-EPMC11865874 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature