Personalized predictive hemodynamic management for major oncologic surgery: effect of progressive implementation of monitoring of digital medical devices with artificial intelligence-based algorithms.

Journal: Minerva anestesiologica
Published Date: Jun 30, 2025

Abstract

BACKGROUND: Avoiding intraoperative hypotension and maintaining an adequate cardiac output (CO) during surgery is crucial to ensure tissue oxygen delivery to the tissues and avoid excessive fluid administration. Assisted fluid management (AFM) is a "decision-support" system based on artificial intelligence (AI) that helps the clinician to manage fluids during major surgeries. Hypotension Prediction Index (HPI) is a predictive parameter of intraoperative hypotension. The aim of this study was to assess the relative contribution of different levels of technologic assistance (CO monitoring, CO plus HPI, and CO plus HPI and AFM) to the improvement of hemodynamic management, applied to comparable cohorts of non-cardiac surgical patients.

Authors

  • Gilda Pasta
    Department of Anesthesiology, Pain Therapy and Intensive Care INT IRCCS G. Pascale Foundation, Naples, Italy.
  • Luciano Frassanito
    Department of Emergency, Anesthesiologic and Intensive Care Sciences, IRCCS Fondazione Policlinico A. Gemelli, Largo A. Gemelli 8, 00168 Rome, Italy.
  • Mariangela Calabria
    Department of Anesthesiology, Pain Therapy and Intensive Care INT IRCCS G. Pascale Foundation, Naples, Italy.
  • Francesco Vassalli
    Department of Critical Care and Perinatal Medicine, IRCCS Istituto G. Gaslini, 16147 Genoa, Italy.
  • Andrea Belli
    Istituto Nazionale Tumori di Napoli, IRCCS "G. Pascale", Napoli, Italy.
  • Giulia Torricella
    Edwards Lifesciences, Irvine, CA, USA.
  • Arturo Cuomo
    Division of Anesthesia and Pain Medicine, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples 80131, Italy.
  • Francesca Bifulco
    Department of Anesthesiology, Pain Therapy and Intensive Care INT IRCCS G. Pascale Foundation, Naples, Italy.

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