Three Different GHDT ( Goal Hemodynamic Directed Therapy) Strategies for Intraoperative Fluid Management Optimization During Major Abdominal Surgery: A Randomized Controlled Trial (SMART FLUID)
Surgical Management and Advanced Real Time Technologies for Fluid Optimization in Major Abdominal Surgery: A Randomized Controlled Trial
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.
Study Overview
Status
Conditions
Intervention / Treatment
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.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: Luca Guzzetti Luca Guzzetti, MD
- Phone Number: 0039 0332393447
- Email: luca.guzzetti@asst-settelaghi.it
Study Contact Backup
- Name: Giovanni Gallone Giovanni Gallone, MD
- Phone Number: 00390332393447
- Email: giovanni.gallone@asst-settelaghi.it
Study Locations
-
-
Italy
-
Bergamo, Italy, Italy, 24127
- Recruiting
- ASST Papa Giovanni XXIII
-
Contact:
- Alberto Benigni Alberto Benigni, MD
- Email: abenigni@asst-pg23.it
-
Sub-Investigator:
- Sara Cazzaniga Sara Cazzaniga, MD
-
Rozzano, Italy, Italy, 20089
- Recruiting
- HUMANITAS Research Hospital
-
Contact:
- Fulvio Nisi Fulvio Nisi, MD
- Email: fulvio.nisi@humanitas.it
-
Sub-Investigator:
- Federico Piccioni Federico Piccioni, PhD
-
Sub-Investigator:
- Enrico Giustiniano Enrico Giustiniano, MD
-
Sub-Investigator:
- Maurizio Cecconi Maurizio Cecconi, Ph
-
Varese, Italy, Italy, 21100
- Recruiting
- University Hospital Varese ASST SetteLaghi
-
Contact:
- Luca Guzzetti Luca Guzzetti, MD
- Email: luca.guzzetti@asst-settelaghi.it
-
Sub-Investigator:
- Giovanni Gallone Giovanni Gallone, MD
-
Sub-Investigator:
- Alessandro Bacuzzi Alessandro Bacuzzi, MD
-
Sub-Investigator:
- Gabriele Selmo Gabriele Selmo, MD
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Older Adult
Accepts Healthy Volunteers
Description
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.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Active Comparator: FLOTRAC group
: Patients managed using the FLOTRAC technology for intraoperative fluid monitoring and management.
|
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 |
|
Active Comparator: HPI group
Patients managed using the Hypotension Prediction Index (HPI) technology.
|
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.
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. |
|
Active Comparator: HPI-AFM group
.Patients managed using both HPI and AFM technologies for intraoperative fluid management.
|
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. |
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
significant difference in intraoperative fluid administration volumes across three different protocols of GDHT supported by AI, in patients undergoing major abdominal oncological surgery
Time Frame: From start surgery to end surgery
|
From start surgery to end surgery
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
• Assess the rate of hypotensive episodes in terms of Time-Weighted Average Hypotension (TWAH) across the three groups.
Time Frame: From start surgery to end surgery
|
The TWAH (Time-Weighted Average Hypotension) will be calculated for the three groups.
This metric evaluates the intensity and duration of hypotensive episodes during the perioperative period or continuous monitoring.
It represents the cumulative hypotension burden normalized by the total duration of monitoring or surgery, providing a unified measure of severity.
|
From start surgery to end surgery
|
|
Postoperative complications and hospital mortality
Time Frame: From the end of the surgery to 30 days after discharge
|
Analyze the rate of postoperative complications and hospital mortality across the three groups. Complications will be classified according to the Clavien-Dindo Classification. |
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
Time Frame: From the end of the surgery to 30 days after discharge
|
Evaluate the total hospital stay duration and/or the number of days spent in intensive care across the three groups.
|
From the end of the surgery to 30 days after discharge
|
Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 24/2025
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
- SAP
- ICF
- ANALYTIC_CODE
- CSR
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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