Three Different GHDT ( Goal Hemodynamic Directed Therapy) Strategies for Intraoperative Fluid Management Optimization During Major Abdominal Surgery: A Randomized Controlled Trial

Summary

Major oncological surgery is among the most complex procedures, involving patients with a combination of high-risk factors that can significantly influence immediate postoperative outcomes and quality of life. The intraoperative hemodynamic management of these patients represents a crucial challenge: maintaining cardiovascular stability and fluid balance during the surgery is associated with reduced complications, including acute kidney injury, myocardial ischemia, and sepsis. Literature has shown that intraoperative fluid administration guided by specific algorithms can reduce complications and improve patient outcomes.

In recent years, innovations in artificial intelligence (AI) have profoundly changed how hemodynamic variables are managed during surgery. AI enables real-time clinical data processing and offers the possibility to predict imminent hypotension episodes, allowing the medical team to intervene proactively. An example of such technologies is the Hypotension Prediction Index (HPI), which uses a machine learning algorithm to analyze hemodynamic data and predict the risk of hypotension with up to 80% accuracy, up to 10 minutes before it occurs. Therefore, softwares that integrate fluid administration volumes with parameters derived from pulse contour systems are used currently, enabling an analysis of the efficacy of administration of fluid boluses. For example, the Assisted Fluid Management (AFM) software helps the clinician in choosing the timing of fluid administration, determining its effectiveness in terms of fluid responsiveness. This allows to reduce complications related to improper intraoperative fluid management, such as organ damage, and optimize the use of fluids and vasopressor drugs.

Despite the growing use of AI in surgery, the clinical and economic impact of such technologies is still under study. Advanced intraoperative hemodynamic management tools have been shown to reduce the duration of hypotensive episodes and improve hemodynamic stability. The clinical impact of such monitoring, in terms of complications and length of postoperative stay, could be crucial to recommend their use in high-risk patient cohorts. This aligns with medical literature showing that postoperative complications increase patient-related hospitalization costs. This study aims to explore the utility of combining a Goal-Directed Hemodynamic Therapy (GDHT) protocol with AI software in three different scenarios.

The primary objective of the study is to evaluate if there is a significant difference in intraoperative fluid administration volumes across three different protocols of GDHT supported by AI, in patients undergoing major abdominal oncological surgery.

The study's secondary objectives include:

* Assess the rate of hypotensive episodes in terms of Time-Weighted Average Hypotension (TWAH) across the three groups.
* Analyze the rate of postoperative complications and hospital mortality across the three groups.
* Evaluate the total hospital stay duration and/or the number of days spent in intensive care across the three groups.

The study aims to provide evidence on the clinical efficacy of haemodynamic monitoring technologies currently present in daily practice. The results will allow us to define an optimization of intraoperative haemodynamic management, improving clinical outcomes and optimizing the use of healthcare resources.

Conditions

• Hemodynamic

Interventions

DEVICE

Flotrac sensor

Traditional management of hemodynamic parameters and intraoperative fluids without the aid of predictive tools based on artificial intelligence. Possibility of having advanced hemodynamic analysis tools such as SV (stroke volume), SVV (stroke volume variation), PPV (pulse pressure variation), CO (cardiac output). The anesthetist will decide whether to administer fluids, vasopressors, or other pharmacological interventions to maintain hemodynamic stability basing on clinical hemodynamic parameters derived from pulse contour systems, in accordance with a specific flowchart. Interventions will be applied when blood pressure decreases, or clinical signs of instability are observed

DEVICE

HPI

The Hypotension Prediction Index (HPI) is an advanced arterial waveform analysis algorithm that uses machine learning to predict hypotensive episodes (defined as mean arterial pressure \[MAP\] \< 65 mmHg) five minutes in advance, achieving high sensitivity and specificity. This technology is based on patient demographic data (e.g., age, height, weight) and hemodynamic parameters derived from arterial waveform analysis. * HPI uses demographic information and hemodynamic signals obtained via a radial arterial catheter. * The signals are analyzed using Edwards Lifesciences' Acumen IQ software, which has been further developed to include prediction of hypotensive episodes. The algorithm provides a numerical value (0-100) reflecting the risk of imminent hypotension. An HPI value above 85 signals a high likelihood of hypotension. The system also provides advanced hemodynamic data, including cardiac output, dynamic arterial elastance, dP/dtmax (systolic slope), and stroke volume.

DEVICE

HPI-AFM

HPI: Provides a predictive index (from 0 to 100) based on real-time hemodynamic data, indicating the probability of the patient developing a hypotensive episode (MAP \< 65 mmHg) within the next 10-15 minutes. AFM: In addition to hypotension prediction, continuously monitors parameters such as stroke volume and cardiac output, providing indications for optimal fluid administration. It is programmed to suggest the quantity and the speed of fluid administration based on real-time data and patient conditions. In conjunction with the HPI system, the AFM suggests administering a specific fluid volume to correct the patient's hemodynamic status. The AFM uses a predictive algorithm to calculate the patient's response to fluid administration, enabling anesthetists to dynamically adjust therapy. The AFM system is based on assisted clinical decisions, where anesthetist receive algorithm-based AI recommendations to proactively administer fluids, avoiding the traditional "reactive" approach.

Sponsors & Collaborators

• Humanitas Research Hospital IRCCS, Rozzano-Milan

  collaborator OTHER

• A.O. Ospedale Papa Giovanni XXIII

  collaborator OTHER

• ASST Sette Laghi

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

65 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-03-02

Primary Completion

2026-09-30

Completion

2026-10-30

FDA Device

Yes

Countries

• Italy

Study Locations