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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Proactive monitoring | Experimental | 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. |
|
| Goal Directed Fluid Therapy (GDT) | Active Comparator | The patients have been managed following institutional algorithm aimed to optimize stroke volume index and reduce intraoperative hypotension |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Assisted Fluid Management | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| TWA-MAP<65mmHg | We intend to define two hierarchical primary endpoints. 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). The optimized range of SVI is the range between the patient's baseline SVI rate and the SVI obteined after a 250 ml fluid bolus. | Perioperative |
| TIT-SVI | We intend to define two hierarchical primary endpoints. 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). The optimized range of SVI is the range between the patient's baseline SVI rate and the SVI obteined after a 250 ml fluid bolus. | Perioperative |
| Measure | Description | Time Frame |
|---|---|---|
| Vasopressors | Total norepinephrine dose | Perioperative |
| Fluids | Total amount of intraoperative fluids | Perioperative |
| Measure | Description | Time Frame |
|---|---|---|
| AFM effectiveness | The fraction of AFM software-prompted boluses that resulted in the desired increase in SVI | Perioperative |
| PPCs | Incidence of postoperative pulmonary complications |
Inclusion Criteria:
Exclusion Criteria:
Patients affected by ovarian cancer
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Luciano Frassanito, MD | Contact | +393475256158 | luciano.frassanito@policlinicogemelli.it |
| Name | Affiliation | Role |
|---|---|---|
| Luciano Frassanito | Fondazione Policlinico Universitario Agostino Gemelli IRCCS | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| IRCCS Fondazione Policlinico A. Gemelli | Recruiting | Rome | 00167 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29342393 | Result | 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. | |
| 38411761 |
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| ID | Term |
|---|---|
| D010051 | Ovarian Neoplasms |
| D004487 | Edema |
| ID | Term |
|---|---|
| D004701 | Endocrine Gland Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D010049 | Ovarian Diseases |
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Not provided
Intention to treat
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| Goal Directed Fluid Therapy (GDT) | Other | Institutional goal directed therapy protocol used to optimize SVI and reduce intraoperative hypotension |
|
| Up to one month from the day of surgery |
| Hospital costs | Analysis of hospital costs related to the adoption of AFM software in the clinical practice of our hospital | At discharge to home |
| 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. |
| 38270801 | Result | 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. |
| 24886171 | Result | 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. |
| 28719887 | Result | 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. |
| 30218305 | Result | 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. |
| 35262624 | Result | Lim MC, Chang SJ, Park B, Yoo HJ, Yoo CW, Nam BH, Park SY; HIPEC for Ovarian Cancer Collaborators. Survival After Hyperthermic Intraperitoneal Chemotherapy and Primary or Interval Cytoreductive Surgery in Ovarian Cancer: A Randomized Clinical Trial. JAMA Surg. 2022 May 1;157(5):374-383. doi: 10.1001/jamasurg.2022.0143. |
| 37119322 | Result | 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. |
| 28029449 | Result | 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. |
| 32939254 | Result | 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. |
| 24842135 | Result | Pearse RM, Harrison DA, MacDonald N, Gillies MA, Blunt M, Ackland G, Grocott MP, Ahern A, Griggs K, Scott R, Hinds C, Rowan K; OPTIMISE Study Group. Effect of a perioperative, cardiac output-guided hemodynamic therapy algorithm on outcomes following major gastrointestinal surgery: a randomized clinical trial and systematic review. JAMA. 2014 Jun 4;311(21):2181-90. doi: 10.1001/jama.2014.5305. |
| 29779129 | Result | Joosten A, Hafiane R, Pustetto M, Van Obbergh L, Quackels T, Buggenhout A, Vincent JL, Ickx B, Rinehart J. Practical impact of a decision support for goal-directed fluid therapy on protocol adherence: a clinical implementation study in patients undergoing major abdominal surgery. J Clin Monit Comput. 2019 Feb;33(1):15-24. doi: 10.1007/s10877-018-0156-x. Epub 2018 May 19. |
| 33901281 | Result | 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. |
| 32065827 | Result | 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. |
| D000291 |
| Adnexal Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D005833 | Genital Neoplasms, Female |
| D014565 | Urogenital Neoplasms |
| D000091662 | Genital Diseases |
| D004700 | Endocrine System Diseases |
| D006058 | Gonadal Disorders |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |