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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT06347211
Other study ID # Attikon Hospital
Secondary ID
Status Recruiting
Phase N/A
First received
Last updated
Start date January 10, 2024
Est. completion date January 10, 2026

Study information

Verified date April 2024
Source Attikon Hospital
Contact Orestis Milionis, MD
Phone +306948333359
Email orestismilionis@gmail.com
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

The study will investigate whether the use of Goal Directed Hemodynamic Therapy implemented with the HPI algorithm using a treatment algorithm will reduce the incidence of hypotension and improve treatment of hypotension.


Description:

The perioperative period is characterized by hemodynamic instability. Intraoperative hypotension (IOH) can be caused by anesthesia drugs, surgical manipulations, hypovolemia or inhibition of the sympathetic nervous system and impairment of baroreflex regulatory mechanisms. In a retrospective analysis performed at the Cleveland Clinic, the risk for acute kidney injury(AKI) and myocardial injury (MI) increased when mean arterial pressure (MAP) was less than 55 mmHg. Further, even short durations of intraoperative hypotension were associated with AKI and MI. Salmasi and coll analyzed whether associations based on relative thresholds were stronger than those based on absolute thresholds regarding blood pressure. They found that there were no clinically important interactions between preoperative blood pressures and the relationship between hypotension and MI or AKI at intraoperative mean arterial blood pressures less than 65 mmHg. Absolute and relative thresholds had comparable ability to discriminate patients with MI or AKI from those without it. The authors concluded that anesthetic management can thus be based on intraoperative pressures without regard to preoperative pressure. In a retrospective cohort study Sun and coll conclude that an increased risk of postoperative stage I AKI occurs when intraoperative MAP was less than 60 mmHg for more than 20 min and less than 55 mmHg for more than 10 min. Hence it is fundamental for the management of any hemodynamically unstable patient the rapid assessment of the factors that determine the cardiovascular collapse, followed by prompt treatment and, ultimately, reversal of the responsible process. Recently a Hypotension Probability Indicator (HPI) algorithm has been developed from Edwards Lifesciences using continuous invasively-measured arterial waveforms to predict hypotension with high accuracy minutes before blood pressure actually decreases. The HPI algorithm can be integrated with a goal-directed hemodynamic treatment (GDHT) to achieve hemodynamic optimization by increasing global blood flow and prevent organ failure. The HPI index, combined with a hypotension management protocol, has shown efficacy in reducing hypotension during surgical procedures. Its effectiveness has been demonstrated in ICU patients with Covid-19. Studies in cardiac surgery cases have been conducted, with ongoing research in cardiac surgery patients (HYPE2 and HPI Care Trial). Maintaining stable arterial pressure and avoiding intraoperative hypotension are crucial during TAVI or MitraClip procedures, achieved through monitored anesthesia care (MAC) or general anesthesia. Based on recent publications and Pinsky's work, a hypotension management protocol integrating GDHT with the HPI algorithm has been developed for TAVI or MitraClip patients.


Recruitment information / eligibility

Status Recruiting
Enrollment 100
Est. completion date January 10, 2026
Est. primary completion date January 10, 2026
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Age >18 years - Will be monitored intraoperatively for >1 hour or will receive general anesthesia for >1 hour - Will undergo intraoperative monitoring with invasive arterial pressure measurement - Mean arterial pressure (MAP) target will be = 65 mmHg intraoperatively - Will sign written informed consent preoperatively - Will undergo TAVI or Mitral Clip under general anesthesia or monitored anesthesia care (MAC) and sedation - American Society of Anesthesiologists (ASA) Physical Status = 4 Exclusion Criteria: - Target for MAP different from 65 mmHg - Severe hypotension preoperatively MAP <65 mmHg - Severe heart failure (e.g., Left Ventricular Ejection Fraction<20%) - Patients needing or will need mechanical circulatory support postoperatively (e.g., intra-aortic pump) - Urgent surgery - Patients with severe pulmonary hypertension (preexisting or detected intraoperatively) - Patients with hemodynamic instability requiring extracorporeal circulation support

Study Design


Related Conditions & MeSH terms


Intervention

Device:
Acumen IQ sensor with Hemosphere monitor incorporating the HPI algorithm
Hemosphere monitoring and requires the use of a AcumenIQ sensor connected to an arterial line (Edwards Lifesciences Corp., Irvine, CA, USA). The sensor has a splitter which enables the splitting of the arterial blood pressure signal to facilitate a blood pressure signal on both the anesthesia machine monitor (standard care) and the HemoSphere monitor (study). In the intervention arm we asked the anesthesiologist and anesthesia nurse to use the study treatment flowchart. If the HPI alarm goes off, which entails both a sound and a flickering light, we ask the anesthesiologist to act upon this alarm immediately. Use of the study treatment flowchart ensures that the anesthesiologist has to think about the underlying cause. The HemoSphere with HPI software has a second screen with variables that provide information about the underlying cause of the predicted hypotension.

Locations

Country Name City State
Greece Attikon Hospital Athens

Sponsors (1)

Lead Sponsor Collaborator
Attikon Hospital

Country where clinical trial is conducted

Greece, 

References & Publications (15)

Davies SJ, Vistisen ST, Jian Z, Hatib F, Scheeren TWL. Ability of an Arterial Waveform Analysis-Derived Hypotension Prediction Index to Predict Future Hypotensive Events in Surgical Patients. Anesth Analg. 2020 Feb;130(2):352-359. doi: 10.1213/ANE.0000000000004121. Erratum In: Anesth Analg. 2023 Sep 1;137(3):e33. — 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

Luo D, Wan X, Liu J, Tong T. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat Methods Med Res. 2018 Jun;27(6):1785-1805. doi: 10.1177/0962280216669183. Epub 2016 Sep 27. — View Citation

Maheshwari K, Shimada T, Yang D, Khanna S, Cywinski JB, Irefin SA, Ayad S, Turan A, Ruetzler K, Qiu Y, Saha P, Mascha EJ, Sessler DI. Hypotension Prediction Index for Prevention of Hypotension during Moderate- to High-risk Noncardiac Surgery. Anesthesiology. 2020 Dec 1;133(6):1214-1222. doi: 10.1097/ALN.0000000000003557. — View Citation

Murabito P, Astuto M, Sanfilippo F, La Via L, Vasile F, Basile F, Cappellani A, Longhitano L, Distefano A, Li Volti G. Proactive Management of Intraoperative Hypotension Reduces Biomarkers of Organ Injury and Oxidative Stress during Elective Non-Cardiac Surgery: A Pilot Randomized Controlled Trial. J Clin Med. 2022 Jan 13;11(2):392. doi: 10.3390/jcm11020392. — View Citation

Pinsky, M.R. (2005). Protocolized Cardiovascular Management Based on Ventricular-arterial Coupling. In: Pinsky, M.R., Payen, D. (eds) Functional Hemodynamic Monitoring. Update in Intensive Care and Emergency Medicine, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26900-2_28

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

Shin B, Maler SA, Reddy K, Fleming NW. Use of the Hypotension Prediction Index During Cardiac Surgery. J Cardiothorac Vasc Anesth. 2021 Jun;35(6):1769-1775. doi: 10.1053/j.jvca.2020.12.025. Epub 2020 Dec 21. — View Citation

Sun LY, Wijeysundera DN, Tait GA, Beattie WS. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015 Sep;123(3):515-23. doi: 10.1097/ALN.0000000000000765. — View Citation

Tsoumpa M, Kyttari A, Matiatou S, Tzoufi M, Griva P, Pikoulis E, Riga M, Matsota P, Sidiropoulou T. The Use of the Hypotension Prediction Index Integrated in an Algorithm of Goal Directed Hemodynamic Treatment during Moderate and High-Risk Surgery. J Clin Med. 2021 Dec 15;10(24):5884. doi: 10.3390/jcm10245884. — View Citation

van der Ven WH, Terwindt LE, Risvanoglu N, Ie ELK, Wijnberge M, Veelo DP, Geerts BF, Vlaar APJ, van der Ster BJP. Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study. J Clin Monit Comput. 2022 Oct;36(5):1397-1405. doi: 10.1007/s10877-021-00778-x. Epub 2021 Nov 13. — View Citation

Walsh M, Devereaux PJ, Garg AX, Kurz A, Turan A, Rodseth RN, Cywinski J, Thabane L, Sessler DI. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013 Sep;119(3):507-15. doi: 10.1097/ALN.0b013e3182a10e26. — View Citation

Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014 Dec 19;14:135. doi: 10.1186/1471-2288-14-135. — 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 15 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary TWA hypotension (measured with Acumen IQ sensor) Time weighted average spent in hypotension (TWA), defined as MAP<65mmHg for =1min Intraoperatively,10 min after induction of anesthesia or commencement of sedation
Secondary Incidence of hypotension Incidence of hypotension, defined as MAP<65mmHg for =1min Intraoperatively,10 min after induction of anesthesia or commencement of sedation
Secondary Time spent in hypotension Time spent in hypotension, in minutes, defined as MAP<65mmHg for =1min Intraoperatively,10 min after induction of anesthesia or commencement of sedation
Secondary Treatment choice (drugs/fluids) Medication used to prevent/treat hypotension. A study member is present at the OR to make notes Intraoperatively,15 min after induction of anesthesia or commencement of sedation
Secondary Diagnostic guidance protocol deviations Diagnostic guidance protocol deviations, a study member is present at the OR to make notes Intraoperatively,15 min after induction of anesthesia or commencement of sedation
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