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Implementation and learning curve in AI-assisted fluid management during abdominal oncologic surgery: a retrospective
Gilda Pasta1, Luciano Frassanito2, Maria Maciariello3
1Department of Anesthesiology, Pain Therapy and Intensive Care, INT IRCCS Fondazione G. Pascale, Naples, Italy. g.pasta@istitutotumori.na.it.
Background:
Intraoperative fluid management during major abdominal oncologic surgery is complex and highly operator-dependent. Assisted Fluid Management (AFM) is an artificial intelligence-based decision support system designed to guide fluid challenges based on real-time Stroke Volume (SV) analysis. However, limited data are available on how AFM is adopted in routine clinical practice and how clinician interaction with the system evolves over time.
Methods:
We conducted a retrospective observational study based on a prospectively maintained institutional database at a high-volume tertiary referral center. Adult patients undergoing major abdominal oncologic surgery with intraoperative AFM monitoring were included. Two consecutive time periods following AFM implementation were compared. Analyses were performed at the fluid-challenge level and focused on patterns of fluid challenge initiation (clinician-initiated vs AFM-suggested), hemodynamic effectiveness (SV response), and bolus characteristics, as markers of system adoption and learning curve. Postoperative clinical outcomes were not assessed.
Results:
Fifty-nine patients were included, accounting for 404 fluid challenges. Over time, clinician-initiated boluses significantly decreased and AFM-suggested fluid challenges increased (p < 0.001). This shift was associated with higher overall effectiveness of fluid challenges and greater SV responses, particularly for AFM-suggested boluses, which showed a significant improvement in effectiveness and ΔSV over time (p < 0.05).
Conclusions:
Progressive integration of AFM into routine anesthetic practice was associated with measurable changes in clinician behavior and improved physiological effectiveness of intraoperative fluid challenges over time, consistent with a learning curve effect. These findings support the role of AI-based decision support systems in promoting more consistent and physiologically targeted fluid management and provide a foundation for future prospective studies evaluating their impact on clinical outcomes.
