This multicentre randomized controlled clinical trial evaluates the impact of artificial intelligence-guided intraoperative intravenous fluid therapy on hemodynamic management and postoperative outcomes in adult patients undergoing major abdominal cancer surgery.
Optimal intraoperative fluid therapy is a critical component of anesthetic management during major oncologic surgery. Both inadequate and excessive fluid administration may contribute to hemodynamic instability and postoperative complications. Episodes of intraoperative hypotension have been consistently associated with increased postoperative morbidi ty and mortality, particularly in high-risk surgical populations. Although goal-directed strategies for intraoperative fluid therapy have been proposed, their implementation in routine clinical practice remains heterogeneous and highly operator-dependent.
Advances in artificial intelligence have enabled the development of decision support systems capable of integrating continuous hemodynamic data derived from standard intraoperative monitoring. These systems are designed to assist clinicians by analyzing multiple physiologic variables in real time and providing recommendations for intravenous fluid administration aimed at supporting circulatory stability, while preserving full clinician control over treatment decisions.
In this trial, participants undergoing major abdominal cancer surgery under general anesthesia are randomly assigned to receive either artificial intelligence-guided intravenous fluid therapy or standard intravenous fluid management according to routine clinical practice. Randomization is centralized and stratified by relevant procedural factors. In the intervention group, intravenous fluid administration is supported by an artificial intelligence-based decision support system that continuously analyzes intraoperative hemodynamic data and generates recommendations for fluid challenges. Clinicians are strongly encouraged to follow the system recommendations; however, they retain full responsibility and may accept or override these recommendations based on their clinical judgment. In the control group, intraoperative fluid therapy is managed according to usual clinical practice without artificial intelligence guidance.
Standard perioperative monitoring is applied in both study groups, including continuous invasive arterial blood pressure monitoring. Intraoperative hemodynamic variables, fluid administration, and use of vasoactive medications are recorded prospectively using electronic anesthesia records and monitoring system outputs. Postoperative clinical data are collected during routine inpatient care and scheduled follow-up.
The study focuses on the intraoperative period as a key window during which hemodynamic management may influence postoperative recovery and longer-term outcomes. By evaluating an artificial intelligence-based decision support approach in a randomized multicentre setting, this trial aims to generate evidence on whether technology-assisted intravenous fluid therapy can improve intraoperative management and support better clinical outcomes in patients undergoing major cancer surgery.
