"Hypotension Probability Indicator" in TAVI/MitraClip for Hypotension Management
NCT ID: NCT06347211
Last Updated: 2025-01-14
Study Results
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Basic Information
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COMPLETED
NA
100 participants
INTERVENTIONAL
2024-01-10
2024-12-10
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
PREVENTION
DOUBLE
Study Groups
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HPI + GDHT treatment
HPI + GDHT using Acumen IQ and Hemosphere monitor with HPI algorithm incorporated following our protocol for hemodynamic treatment (fluids,vasopressors and inotropes).
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.
Control
Conventional treatment with invasive blood pressure monitoring. Administration of fluids and/or vasopressors are guided by standard hemodynamic parameters at the discretion of the attending physician.
No interventions assigned to this group
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* 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
* 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
18 Years
ALL
No
Sponsors
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Attikon Hospital
OTHER
Responsible Party
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Milionis Orestis
Principal Investigator
Principal Investigators
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Tatiana Sidiropoulou, MD,PhD
Role: PRINCIPAL_INVESTIGATOR
Attikon Hospital
Locations
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Attikon Hospital
Athens, Athens, Greece
Countries
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References
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
Other Identifiers
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Attikon Hospital
Identifier Type: -
Identifier Source: org_study_id
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