Intraoperative hypotension and its prediction.

Area of Science:

• Anesthesiology
• Medical Informatics
• Cardiovascular Physiology

Background:

• Intraoperative hypotension (IOH) is common during general anesthesia and linked to adverse patient outcomes.
• Harm from IOH begins below a mean arterial pressure of 65 mmHg, with increased risk correlating to duration and magnitude.
• Proactive prediction and treatment of IOH are crucial for improving patient outcomes.

Purpose of the Study:

• To review the current state of machine learning (ML) for predicting IOH.
• To highlight the clinical application of ML algorithms in hemodynamic monitoring and therapy guidance.

Main Methods:

• Review of ML algorithms utilizing high-fidelity arterial pressure waveform data.
• Analysis of algorithms capable of identifying subtle patterns predictive of IOH.
• Focus on the hypotension prediction index (HPI) and its secondary variables.

Main Results:

• ML algorithms can detect unseen traits in arterial pressure waveforms associated with IOH development.
• The hypotension prediction index (HPI) is a clinically available tool for predicting impending hypotension.
• HPI provides secondary variables to assess preload, contractility, and afterload, aiding in identifying the cause of hypotension.

Conclusions:

• Machine learning offers a promising approach for the prediction and management of intraoperative hypotension.
• Automated algorithms like HPI can enhance hemodynamic monitoring and guide therapeutic interventions.
• Further integration of ML in clinical practice may lead to reduced adverse events associated with IOH.