Hypotension Predictive Index Effect on Intraoperative Hypotension During Pancreatic Surgery.

Perioperative Complication Clinical Trial

— EHPI-Pan  

Official title:

The Effect of Hypotension Predictive Index on Intraoperative Hypotension Events and Duration During Pancreatic Surgery. A Retrospective Pre-post Observational Study.

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06097052
• Other study ID #: EHPI-Pan-15/23
• Status: Completed
• Start date: May 23, 2023
• Est. completion date: January 31, 2024

Study information

• Verified date: March 2024
• Source: Istituto Clinico Humanitas
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Intraoperative hypotension (IOH) is a sudden clinical phenomenon that occurs frequently during general anesthesia. Prevention of IOH has been linked to reduced postoperative organ damage and decreased incidence of perioperative complications. Oncological patients with reduced preoperative physiological reserves may be especially vulnerable to IOH deleterious effects, especially when exposed to prolonged surgical time increase, as it is the case for patients undergoing pancreatic surgery. The investigators aim to study introduction of a new technology able to predict hypotensive events (Hypotension Predictive Index, HPI Acumen™) in terms of its effects on IOH occurrence and burden in patients undergoing pancreatic surgery. The investigators will enroll patients before and after the introduction of HPI monitoring. Further, differences in postoperative outcomes and perioperative complications between before and after populations will be investigated.

Description:

Intraoperative hypotension (IOH) is a relatively common event during surgery, occurring suddenly and often without any warning sign. Predicting IOH would help clinicians to set up a rapid response, reducing the number of hypotensive events and overall time of hypotension. Historically the management of IOH has been reactive, despite the introduction of advanced arterial waveform analysis systems providing a number of cardiovascular parameters (arterial pressure, cardiac output, dynamic indices) which allow advanced intraoperative hemodynamic monitoring (FloTrac™, Edwards Lifesciences Corp., Irvine, USA). This changed when continuous blood pressure wave static and dynamic features extraction combined with Machine-Learning approach proved informative in identifying specific signatures associated with impending exhaustion of compensatory mechanisms. By fitting these features in a mathematical model it became available an easy-to-interpret Hypotension Predictive Index (Acumen™ HPI - Edwards Lifesciences, Irvine, CA), which can predict IOH up to 15 minutes prior to the clinical event. The HPI is expressed in a range from 0 to 100, the higher the number, the higher the probability and the shorter the time of occurrence of the hypotensive event. Therefore, combining arterial waveform analysis and HPI may provide timely prediction and goal-directed treatment plans based on the specific mechanism underlying IOH, such as relative hypovolemia, temporary vasoplegia or decreased inotropism. Several studies have shown that IOH has a great impact on postoperative outcomes and tight control of blood pressure may be important especially in patients undergoing major surgery. These patients are often frail and bear greater risks of developing organ dysfunction, mainly renal insufficiency and myocardial injury. Futier et al. reported a 25% risk reduction of postoperative organ dysfunction when IOH is properly treated during surgery. Further, prospective studies showed that the use of HPI both decreased the total time of hypotension in non-cardiac surgery and reduced post-operative complications and hospital length-of-stay. These studies addressed application of HPI in mixed cohorts of the population (orthopedic surgery, neurosurgery, general and vascular surgery, ENT surgery), but the duration and type of surgery are certainly two major factors affecting the occurrence and the severity of IOH. Among non-cardiac operations, major pancreatic surgery is generally complex and long-lasting. In our center roughly 180 patients undergo major pancreatic surgery every year and in our experience these patients are often debilitated and have reduced physiological reserve at the time of surgery. In addition, post-operative complications including bleeding, sepsis, bilio-digestive fistulas, acute kidney injury are common in this setting. For these reasons, the investigators believe that patients undergoing major pancreatic surgery represent an excellent model to evaluate the efficacy of HPI compared to the FloTrac™ alone as they can benefit from advanced hemodynamic monitoring and IOH prediction tools. The aim of this study is to assess whether patients undergoing pancreatic surgery benefit from early prediction and goal-directed treatment of IOH using the HPI software compared to a goal-directed hemodynamic approach using the FloTrac™ monitoring system. The investigators plan to study the occurrence of IOH with and without the use of HPI in comparison with a FloTrac™ monitoring system. Furthermore, the study aims to evaluate if HPI software inclusion in an intraoperative hemodynamic management protocol leads to improved intraoperative cardiovascular stability and better clinical outcomes. Finally, the investigators want to explore whether the improved clinical experience using HPI predictive model correlates with the degree of IOH by analyzing data from patients managed only with this technology over time. STUDY DESIGN AND POPULATION This is a retrospective observational before-and-after study planned to analyze data from about 60 elective patients undergoing major open pancreatic surgery. Only patients that already signed an informed consent including perioperative data utilization for previous ongoing clinical studies will be considered for the analysis. Continuous blood pressure waveform after the insertion of an arterial line (radial artery was the preferred choice) was monitored in all patients after anesthesia induction. Until March 2022 patients were monitored using the FloTracTM waveform analysis alone, while from April 2022 to present, patients' hemodynamics were managed using the HPI software. The investigators will compare two cohorts (Flotrac group vs. HPI group) to evaluate the impact of the latter technology on IOH. Mean arterial pressure will be defined as the main variable describing the perfusion pressure, while the cardiac index will be considered as an index of oxygen delivery (assuming hemoglobin and pulmonary gas-exchange in normal ranges). PRIMARY OUTCOME The primary outcome will be a significant difference in the time-weighted average (TWA) of MAP < 65mmHg between the two groups. TWA calculation will be made according to the following formula: TWA = (depth of hypotension (mmHg) below a MAP of 65 mmHg × time in minutes spent below a MAP of 65 mmHg) / (total duration of surgery expressed in minutes). SAMPLE SIZE CALCULATION & STATISTICAL ANALYSIS In line with the published literature, the investigators hypothesized that the HPI software-based strategy could reduce the mean TWA-MAP < 65mmHg by approximately 50%. For sample size calculation, the investigators considered 1 mmHg as the reference value of TWA-MAP < 65mmHg for patients managed with the FlotracTM system (unpublished raw data extrapolated from a cohort of 10 patients). To detect a significant difference in TWA-MAP <65 mmHg between the HPI and the Flotrac group of 0.5 mmHg with a standard deviation of 0.4 mmHg, the study requires a sample size of 14 cases for each group to achieve a 90% power and 5% significance level (two-tailed). The sample size calculation was performed with Statulator statistical calculator. Due to the observational nature of the study, the protocol plans to enroll more patients if available. Descriptive statistics of baseline demographic and clinical data will be carried out as follows: continuous variables will be expressed as mean ± SD or median and IQR depending on the respective normality of distribution. Categorical variables will be expressed as absolute value and percentage. Statistical differences in primary and secondary outcomes will be analyzed using unpaired Student's t-test or Mann-Whitney test, depending on the respective normality of distribution. Chi-square or Fisher exact tests will be used for the comparison of discrete variables. A multivariate analysis will be conducted utilizing regression models. The P critical value will be set at 0.05. Statistical analysis will be performed with Stata and SPSS softwares. Graphs will be drawn with Prism Graphpad software.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 48
• Est. completion date: January 31, 2024
• Est. primary completion date: December 1, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Subjects aged 18 years or older.
• Subjects scheduled for major open pancreatic surgery.
• Subjects monitored with FloTrac or HPI invasive arterial blood pressure monitoring.
• Total surgical time > 360min.

Exclusion Criteria:

• Subjects aged 90 years or older
• Subjects classified as ASA IV/V/VI
• Subjects undergoing urgent or emergency surgery

Related Conditions & MeSH terms

• Hypotension
• Hypotension During Surgery
• Perioperative Complication

Intervention

Device:
• Hypotension Predictive Index
Introduction of Hypotension Predictive index and transition from a reactive to a proactive approach of intraoperative hypotension treatment.

Locations

Country	Name	City	State
Italy	Istituto Clinico Humanitas	Rozzano	Lombardia

Sponsors (1)

Lead Sponsor	Collaborator
Istituto Clinico Humanitas

Country where clinical trial is conducted

Italy, 

References & Publications (11)

Augustin T, Burstein MD, Schneider EB, Morris-Stiff G, Wey J, Chalikonda S, Walsh RM. Frailty predicts risk of life-threatening complications and mortality after pancreatic resections. Surgery. 2016 Oct;160(4):987-996. doi: 10.1016/j.surg.2016.07.010. Epub 2016 Aug 18. — View Citation

Frassanito L, Giuri PP, Vassalli F, Piersanti A, Longo A, Zanfini BA, Catarci S, Fagotti A, Scambia G, Draisci G. Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery. J Clin Monit Comput. 2022 Oct;36(5):1325-1332. doi: 10.1007/s10877-021-00763-4. Epub 2021 Oct 7. — View Citation

Futier E, Lefrant JY, Guinot PG, Godet T, Lorne E, Cuvillon P, Bertran S, Leone M, Pastene B, Piriou V, Molliex S, Albanese J, Julia JM, Tavernier B, Imhoff E, Bazin JE, Constantin JM, Pereira B, Jaber S; INPRESS Study Group. Effect of Individualized vs Standard Blood Pressure Management Strategies on Postoperative Organ Dysfunction Among High-Risk Patients Undergoing Major Surgery: A Randomized Clinical Trial. JAMA. 2017 Oct 10;318(14):1346-1357. doi: 10.1001/jama.2017.14172. — View Citation

Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018 Oct;129(4):663-674. doi: 10.1097/ALN.0000000000002300. — View Citation

Pecorelli N, Capretti G, Sandini M, Damascelli A, Cristel G, De Cobelli F, Gianotti L, Zerbi A, Braga M. Impact of Sarcopenic Obesity on Failure to Rescue from Major Complications Following Pancreaticoduodenectomy for Cancer: Results from a Multicenter Study. Ann Surg Oncol. 2018 Jan;25(1):308-317. doi: 10.1245/s10434-017-6216-5. Epub 2017 Nov 7. — View Citation

Salmasi V, Maheshwari K, Yang D, Mascha EJ, Singh A, Sessler DI, Kurz A. Relationship between Intraoperative Hypotension, Defined by Either Reduction from Baseline or Absolute Thresholds, and Acute Kidney and Myocardial Injury after Noncardiac Surgery: A Retrospective Cohort Analysis. Anesthesiology. 2017 Jan;126(1):47-65. doi: 10.1097/ALN.0000000000001432. — View Citation

Schneck E, Schulte D, Habig L, Ruhrmann S, Edinger F, Markmann M, Habicher M, Rickert M, Koch C, Sander M. Hypotension Prediction Index based protocolized haemodynamic management reduces the incidence and duration of intraoperative hypotension in primary total hip arthroplasty: a single centre feasibility randomised blinded prospective interventional trial. J Clin Monit Comput. 2020 Dec;34(6):1149-1158. doi: 10.1007/s10877-019-00433-6. Epub 2019 Nov 29. — View Citation

Solares GJ, Garcia D, Monge Garcia MI, Crespo C, Rabago JL, Iglesias F, Larraz E, Zubizarreta I, Rabanal JM. Real-world outcomes of the hypotension prediction index in the management of intraoperative hypotension during non-cardiac surgery: a retrospective clinical study. J Clin Monit Comput. 2023 Feb;37(1):211-220. doi: 10.1007/s10877-022-00881-7. Epub 2022 Jun 2. — View Citation

van der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making. Surgery. 2021 Jun;169(6):1300-1303. doi: 10.1016/j.surg.2020.09.041. Epub 2020 Dec 11. — View Citation

Wesselink EM, Kappen TH, Torn HM, Slooter AJC, van Klei WA. Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review. Br J Anaesth. 2018 Oct;121(4):706-721. doi: 10.1016/j.bja.2018.04.036. Epub 2018 Jun 20. — View Citation

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. — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Time-weighted average of Mean Arterial Pressure < 65mmHg	Area under the threshold of MAP 65mmHg normalized for the total duration of surgery in minutes	intraoperative
Secondary	Average intraoperative Mean Arterial Pressure	Mean intraoperative MAP	intraoperative
Secondary	Frequency of hypotensive events	Number of hypotensive events (MAP < 65mmHg for more than 1 min) per hour of surgery	intraoperative
Secondary	Average absolute time spent during Mean Arterial Pressure < 65mmHg	Time spent in hypotension (MAP < 65mmHg for more than 1 min)	intraoperative
Secondary	Time-weighted average of Mean Arterial Pressure < 55mmHg	Area under the threshold of MAP 55mmHg normalized for the total duration of surgery in minutes	intraoperative
Secondary	Time-weighted average of Mean Arterial Pressure > 100mmHg	Area above the threshold of MAP 100mmHg normalized for the total duration of surgery in minutes	intraoperative
Secondary	Frequency of Stroke Volume Index < 35ml/m2 events	Number of SVI < 35ml/m2 events (SVI < 35ml/m2 for more than 1 min) per hour	intraoperative
Secondary	Average absolute time during Stroke Volume Index < 35ml/m2	Time spent in SVI < 35ml/m2 events (SVI < 35ml/m2 for more than 1 min)	intraoperative
Secondary	Average generalized real variability of Mean Arterial Pressure	Sum of the absolute value of all changes across Mean Arterial Pressure measurements divided by total surgical time	intraoperative
Secondary	Postoperative ICU admission frequency	Postoperative ICU admission frequency	Up to 30 days after operation
Secondary	Length of hospital stay	Length of hospital stay	Up to 30 days after operation
Secondary	Postoperative complication rate	Incidence of complications and burden according to Calvin-Dindo classification	Up to 30 days after operation