Artificial Intelligence as a Tool to Support Decision-Making in the Management of Intraoperative Hypotension

Abstract

Introduction: Intraoperative hypotension (IOH) is a frequent complication associated with adverse cardiovascular, cerebral, and renal outcomes, with increased mortality. Recent evidence indicates that cumulative exposure time to hypotension is a significant factor, related to both the duration and severity of hypotensive episodes. The Hypotension Prediction Index (HPI) algorithm, with active alerts, showed that hypotension periods can be significantly reduced. Objectives: To describe an artificial intelligence (AI) algorithm for hypotension prediction integrated into real-time anesthesia record-keeping software and Clinical Decision Support Systems (CDSS) that can warn anesthesiologists about the possibility of the onset of hypotension up to 20 minutes in advance. Methods: This prediction tool incorporates four machine learning classifier models developed using the XGBoost, a supervised learning library that works with decision trees with gradient boosting. These models were trained, validated, and tested based on a database of approximately 0.5 million anesthesia records, using real world data. Results: Accuracy ranged from 84.92% to 89.07%, and sensitivity ranged from 82.15% to 90.86%, with the best results found in five-minute predictions and the worst in twenty minute predictions. The algorithm was tested in all types of surgical procedures but only using adult data. Separate analyses were performed on small, medium, and large procedures of different surgical specialties with similar results. Conclusions: The present study demonstrated the use of an AI algorithm integrated into anesthesia record-keeping software, showing a high accuracy for hypotension prediction. This algorithm is available for all types of anesthesia care in adult procedures.