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

NCT ID: NCT06871150

Last Updated: 2025-03-11

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 150 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-04-30
• Study Completion Date: 2025-10-31

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.

Detailed Description

ASSISTED FLUID MANAGEMENT TECHNOLOGY SOFTWARE The Assisted Fluid Management (AFM) technology, developed by Edwards Lifesciences, is an advanced software designed to assist clinicians in optimizing fluid administration during non-cardiac surgeries. Using arterial pressure waveform analysis, the software evaluates in real-time the patient's response to administered fluids and provides personalized recommendations to achieve specific stroke volume variation targets. Thanks to an algorithm that learns from prior data and the patient's current conditions, the system can predict the effectiveness of a fluid bolus and suggest whether and when to administer it, leaving the clinician with final decision-making control. This combination of automated analysis and clinical flexibility makes it a potentially valuable tool for improving intraoperative fluid management and reducing the complexity of therapeutic decisions.

Will be a randomized controlled trial including patients undergoing major oncological surgery in tertiary care hospitals, with fluid management aligned with the GDHT philosophy.

The analysis will compare three groups:

• FLOTRAC Group: Patients managed using the FLOTRAC technology for intraoperative fluid monitoring and management.
• HPI Group: Patients managed using the Hypotension Prediction Index (HPI) technology.
• HPI-AFM Group: Patients managed using both HPI and AFM technologies for intraoperative fluid management.

The study will be multicentric, involving tertiary care hospitals. This will allow us to collect a sufficiently large and representative sample to ensure statistical validity and generalizability of the results.

Inclusion Criteria:

• Age ≥ 18 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.

Exclusion Criteria:

• 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.

Data will be collected in three phases:

• Preoperative: basic demographic and clinical data.
• Intraoperative: hemodynamic parameters and fluid management.
• Postoperative: complications, length of stay, and clinical outcomes.

Data will be analyzed using parametric and non-parametric tests based on the distribution. A multivariate regression model will be used to control for confounding factors.

The study will be conducted in compliance with GDPR regulations. An informed consent will be required from all participants.

Conditions

Hemodynamic

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

FLOTRAC group

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

Group Type: ACTIVE_COMPARATOR

Flotrac sensor

Intervention Type: 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

HPI group

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

Group Type: ACTIVE_COMPARATOR

HPI

Intervention Type: 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-AFM group

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

Group Type: ACTIVE_COMPARATOR

HPI-AFM

Intervention Type: 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.

Interventions

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

Intervention Type: 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.

Intervention Type: 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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• 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.

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

ASST Sette Laghi

OTHER

Sponsor Role: lead

Responsible Party

Luca Guzzetti

MD

Responsibility Role: PRINCIPAL_INVESTIGATOR

Central Contacts

Luca Guzzetti Luca Guzzetti, MD

Role: CONTACT

luca.guzzetti@asst-settelaghi.it

0039 0332393447

Other Identifiers

24/2025

Identifier Type: -

Identifier Source: org_study_id