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

NCT06871150 | Recruiting DMC FDA Device

• 1 other identifier: 24/2025
• Study Type: interventional
• Target: 150
• Locations: 1 country
• Sites: 3

Brief 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

Keywords

GHDT, Hypotension prediction, Assisted Fluid Management

Outcome Measures

Primary Outcomes (1)

• significant difference in intraoperative fluid administration volumes across three different protocols of GDHT supported by AI, in patients undergoing major abdominal oncological surgery

  From start surgery to end surgery

Secondary Outcomes (3)

• • Assess the rate of hypotensive episodes in terms of Time-Weighted Average Hypotension (TWAH) across the three groups.

  From start surgery to end surgery

• Postoperative complications and hospital mortality

  From the end of the surgery to 30 days after discharge

• Total hospital stay duration and/or the number of days spent in intensive care

  From the end of the surgery to 30 days after discharge

Study Arms (3)

FLOTRAC group

ACTIVE COMPARATOR

: Patients managed using the FLOTRAC technology for intraoperative fluid monitoring and management.

Device: Flotrac sensor

HPI group

ACTIVE COMPARATOR

Patients managed using the Hypotension Prediction Index (HPI) technology.

Device: HPI

HPI-AFM group

ACTIVE COMPARATOR

.Patients managed using both HPI and AFM technologies for intraoperative fluid management.

Device: HPI-AFM

Interventions

Flotrac sensor DEVICE

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

FLOTRAC group

HPI DEVICE

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.

HPI group

HPI-AFM DEVICE

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.

HPI-AFM group

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)

You may qualify if:

• Age ≥ 65 years.
• ASA physical status II-III-IV.
• Patients undergoing elective major abdominal oncological surgery.
• Revised Cardiac Index Score ≥ 2.
• Plan to perform the procedure with invasive arterial monitoring.
• Expected surgical time greater than 120 minutes.

You may not qualify if:

• Emergency or urgent surgeries.
• Severe chronic renal failure (creatinine clearance \< 30 ml/min).
• Chronic heart failure (NYHA Class IV).
• Pregnant women.
• Contraindications to pulse contour hemodynamic monitoring.
• Liver surgery.
• Patient refusal.

Sponsors & Collaborators

• ASST Sette Laghi: lead
• Humanitas Research Hospital IRCCS, Rozzano-Milan: collaborator
• A.O. Ospedale Papa Giovanni XXIII: collaborator

Study Sites (3)

ASST Papa Giovanni XXIII

Bergamo, Italy, 24127, Italy

RECRUITING

Humanitas Research Hospital

Rozzano, Italy, 20089, Italy

RECRUITING

University Hospital Varese ASST SetteLaghi

Varese, Italy, 21100, Italy

RECRUITING

Central Study Contacts

Luca Guzzetti Luca Guzzetti, MD

CONTACT

0039 0332393447; luca.guzzetti@asst-settelaghi.it

Giovanni Gallone Giovanni Gallone, MD

CONTACT

00390332393447; giovanni.gallone@asst-settelaghi.it

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD

Study Record Dates

• First Submitted: February 25, 2025
• First Posted: March 11, 2025
• Study Start: March 2, 2026
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): October 30, 2026
• Last Updated: March 25, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE

Locations

IT (3)