Proactive Hemodynamic Management During Cytoreductive Surgery and HIPEC

NCT ID: NCT07070973

Last Updated: 2025-08-11

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

52 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-09-01

Study Completion Date

2025-12-31

Brief Summary

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Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) is a complex surgical procedure carried out through laparotomic approach. After CRS-HIPEC morbidity and mortality rates go up to 20%-40% and 3% respectively, and acute kidney injury and pulmonary effusion/oedema are the most frequent postoperative complications.

Intraoperative hypotension and risk of fluid overload are common. Efficient and accurate control of arterial pressure and cardiac output is a major concern during CRS-HIPEC.

The aim of this study is to perform a pilot study describing a hemodynamic management protocol based on artificial intelligence - derived parameters, that allows to implement the standard goal directed therapy (GDT) protocol in term of amount of IOH and stroke volume (SV) optimization.

Specifically, this study aims to test the hypotheses that a hemodynamic protocol based on HPI-AFM monitoring compared to standard GDT helps clinicians reduce IOH during surgery and improve the time "in-target" range of SV index.

The study cohort will be compared to an historical cohort of 50 patients underwent to CRS-HIPEC between 2022 and 2024, managed with an institutional goal directed therapy protocol.

Detailed Description

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Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) is a complex surgical procedure carried out through laparotomic approach. Intraoperative management represents a challenge for anesthesiologist: hemodynamic instability (due to extremely destructive surgery with fluids shift and blood loss), biochemical abnormalities (worsened by cytotoxic chemotherapic agents peritoneal infusion, with profound metabolic acidosis and electrolytes disturbance), and extreme thermal fluctuations related to the different phases of surgery.

CRS-HIPEC is fraught with significant drawbacks: morbidity and mortality rates go up to 20%-40% and 3% respectively, and acute kidney injury and pulmonary effusion/oedema are the most frequent postoperative complications. More than 70% of the patients are admitted in Intensive Care Unit after the surgery.

Intraoperative hypotension (IOH) during CRS-HIPEC is common, and as it is associated with the potential for harm \[9\]. At the same time, extensive fluid resuscitation in peritoneal cancer patients is associated with a poor postoperative outcome, and avoiding fluid overload is recommended. Optimizing flow and pressure requires repeated measurement of both variables and use of established protocols for vasopressor and fluid administration (the so called "individualized hemodynamic management") has been shown to be associated with decreased postoperative complications compared to routine care. Efficient and accurate control of arterial pressure and cardiac output is a major concern during CRS-HIPEC, because it requires frequent manual adjustments of vasopressor infusion rates and timely fluid administration.

A peculiar aspect of CRS-HIPEC is the delicate phase of intraoperative chemotherapy. During this procedure, hyperthermia and the extensive cytolysis caused by the chemotherapeutic drug lead to multifactorial metabolic acidosis and intense vasodilation: managing fluid therapy and vasoconstrictor drugs is often extremely challenging.

The aim of this study is to perform a pilot study describing a hemodynamic management protocol based on artificial intelligence - derived parameters, that allows to implement the standard goal directed therapy (GDT) protocol in term of amount of IOH and stroke volume (SV) optimization during CRS-HIPEC.

All patients included in the study will have a radial arterial catheter inserted before or immediately after anesthesia induction and connected to Hemosphere. The clinician will follow an algorithm based on Assisted Fluid Management (AFM) for fluid infusion and on Hypotension Prediction Index (HPI) to titrate vasopressors and inotropes. AFM will be set as "medium" during cytoreductive phase and "restrictive" during HIPEC. A maximum volume of fluids per patients will be set at 10 ml/kg/hr.

Specifically, this study aims to test the hypotheses that a hemodynamic protocol based on HPI-AFM monitoring compared to standard GDT helps clinicians reduce IOH during surgery and improve the time "in-target" range of SV index (a value that estimates the time with SVI within target range, characterizing optimization of cardiac function), avoiding a detrimental fluid overload.

Two hierarchical primary endpoints were defined. The first primary endpoint will be the time-weighted average (TWA) - MAP less than 65 mmHg (expressed in mmHg) throughout surgery. The second primary endpoint will be the time spent during surgery in an optimized range of SVI (the "in target" range will be defined at the beginning of hemodynamic monitoring).

Secondary endpoints will be total norepinephrine dose and total amount of fluids.

Exploratory outcomes will be the fraction of AFM software-prompted boluses that resulted in the desired increase in SV, the incidence of post-operative complications (specifically postoperative Pulmonary Complications), ICU and hospital length of stay (LOS) and the analysis of hospital costs related to the adoption of AFM software in the clinical practice of our hospital.

The study cohort will be compared to an historical cohort of 50 patients underwent to CRS-HIPEC between 2022 and 2024, managed with an institutional goal directed therapy protocol.

Conditions

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Ovarian Cancer Hemodynamic Instability Fluid Overload

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

PARALLEL

Intention to treat
Primary Study Purpose

TREATMENT

Blinding Strategy

NONE

Study Groups

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Proactive monitoring

The patients will be managed following an algorithm based on Assisted Fluid Management (AFM) for fluid infusion and on Hypotension Prediction Index (HPI) to titrate vasopressors and inotropes. AFM will be set as "medium" during cytoreductive phase and "restrictive" during HIPEC.

Group Type EXPERIMENTAL

Assisted Fluid Management

Intervention Type DEVICE

Intraoperative fluid infusion will be regulated according to the Assisted Fluid Management (AFM) software and on Hypotension Prediction Index (HPI) will be used to titrate vasopressors and inotropes.

Goal Directed Fluid Therapy (GDT)

The patients have been managed following institutional algorithm aimed to optimize stroke volume index and reduce intraoperative hypotension

Group Type ACTIVE_COMPARATOR

Goal Directed Fluid Therapy (GDT)

Intervention Type OTHER

Institutional goal directed therapy protocol used to optimize SVI and reduce intraoperative hypotension

Interventions

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Assisted Fluid Management

Intraoperative fluid infusion will be regulated according to the Assisted Fluid Management (AFM) software and on Hypotension Prediction Index (HPI) will be used to titrate vasopressors and inotropes.

Intervention Type DEVICE

Goal Directed Fluid Therapy (GDT)

Institutional goal directed therapy protocol used to optimize SVI and reduce intraoperative hypotension

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* age \> 18 years
* patient scheduled for CRS for ovarian cancer

Exclusion Criteria

* patients' refusal
* cardiac arrythmia
* pregnancy.
Minimum Eligible Age

18 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Fondazione Policlinico Universitario Agostino Gemelli IRCCS

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Luciano Frassanito

Role: PRINCIPAL_INVESTIGATOR

Fondazione Policlinico Universitario A. Gemelli, IRCCS

Central Contacts

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Luciano Frassanito, MD

Role: CONTACT

+393475256158

References

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van Driel WJ, Koole SN, Sikorska K, Schagen van Leeuwen JH, Schreuder HWR, Hermans RHM, de Hingh IHJT, van der Velden J, Arts HJ, Massuger LFAG, Aalbers AGJ, Verwaal VJ, Kieffer JM, Van de Vijver KK, van Tinteren H, Aaronson NK, Sonke GS. Hyperthermic Intraperitoneal Chemotherapy in Ovarian Cancer. N Engl J Med. 2018 Jan 18;378(3):230-240. doi: 10.1056/NEJMoa1708618.

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Ghirardi V, Trozzi R, Scanu FR, Giannarelli D, Santullo F, Costantini B, Naldini A, Panico C, Frassanito L, Scambia G, Fagotti A. Expanding the Use of HIPEC in Ovarian Cancer at Time of Interval Debulking Surgery to FIGO Stage IV and After 6 Cycles of Neoadjuvant Chemotherapy: A Prospective Analysis on Perioperative and Oncologic Outcomes. Ann Surg Oncol. 2024 May;31(5):3350-3360. doi: 10.1245/s10434-024-15042-0. Epub 2024 Feb 27.

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Wang JY, Gross M, Urban RR, Jorge S. Intraperitoneal and Hyperthermic Intraperitoneal Chemotherapy for the Treatment of Ovarian Cancer. Curr Treat Options Oncol. 2024 Mar;25(3):313-329. doi: 10.1007/s11864-023-01171-3. Epub 2024 Jan 4.

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Kajdi ME, Beck-Schimmer B, Held U, Kofmehl R, Lehmann K, Ganter MT. Anaesthesia in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy: retrospective analysis of a single centre three-year experience. World J Surg Oncol. 2014 May 1;12:136. doi: 10.1186/1477-7819-12-136.

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Mendonca FT, Guimaraes MM, de Matos SH, Dusi RG. Anesthetic management of Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy (CRS/HIPEC): The importance of hydro-electrolytic and acid-basic control. Int J Surg Case Rep. 2017;38:1-4. doi: 10.1016/j.ijscr.2017.07.011. Epub 2017 Jul 10.

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Esteve-Perez N, Ferrer-Robles A, Gomez-Romero G, Fabian-Gonzalez D, Verd-Rodriguez M, Mora-Fernandez LC, Segura-Sampedro JJ, Tejada-Gavela S, Morales-Soriano R. Goal-directed therapy in cytoreductive surgery with hyperthermic intraperitoneal chemotherapy: a prospective observational study. Clin Transl Oncol. 2019 Apr;21(4):451-458. doi: 10.1007/s12094-018-1944-y. Epub 2018 Sep 14.

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Reference Type RESULT
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Frassanito L, Giuri PP, Vassalli F, Piersanti A, Garcia MIM, Sonnino C, Zanfini BA, Catarci S, Antonelli M, Draisci G. Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial. J Clin Monit Comput. 2023 Aug;37(4):1081-1093. doi: 10.1007/s10877-023-01017-1. Epub 2023 Apr 29.

Reference Type RESULT
PMID: 37119322 (View on PubMed)

Desale MG, Tanner EJ 3rd, Sinno AK, Angarita AA, Fader AN, Stone RL, Levinson KL, Bristow RE, Roche KL. Perioperative fluid status and surgical outcomes in patients undergoing cytoreductive surgery for advanced epithelial ovarian cancer. Gynecol Oncol. 2016 Oct 28:S0090-8258(16)31501-3. doi: 10.1016/j.ygyno.2016.10.027. Online ahead of print.

Reference Type RESULT
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Bossy M, Nyman M, Madhuri TK, Tailor A, Chatterjee J, Butler-Manuel S, Ellis P, Feldheiser A, Creagh-Brown B. The need for post-operative vasopressor infusions after major gynae-oncologic surgery within an ERAS (Enhanced Recovery After Surgery) pathway. Perioper Med (Lond). 2020 Sep 7;9:26. doi: 10.1186/s13741-020-00158-0. eCollection 2020.

Reference Type RESULT
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Maheshwari K, Malhotra G, Bao X, Lahsaei P, Hand WR, Fleming NW, Ramsingh D, Treggiari MM, Sessler DI, Miller TE; Assisted Fluid Management Study Team. Assisted Fluid Management Software Guidance for Intraoperative Fluid Administration. Anesthesiology. 2021 Aug 1;135(2):273-283. doi: 10.1097/ALN.0000000000003790.

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Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, Schenk J, Terwindt LE, Hollmann MW, Vlaar AP, Veelo DP. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020 Mar 17;323(11):1052-1060. doi: 10.1001/jama.2020.0592.

Reference Type RESULT
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Other Identifiers

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7488

Identifier Type: -

Identifier Source: org_study_id

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