Study Results
Results pending
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Basic Information
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Recruitment Status
RECRUITING
Total Enrollment
60 participants
Study Classification
OBSERVATIONAL
Study Start Date
2023-11-11
Study Completion Date
2025-12-31
Brief Summary
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Perioperative hypotension is a risk factor for perioperative complications. Advances in machine learning and artificial intelligence have produced an algorithm that predicts the occurrence of hypotension episodes by analyzing an arterial pressure waveform. This technology has not been validated in thoracic surgical patients undergoing lung resections with the use of one-lung ventilation (OLV). We planned an observational, prospective multi-centre cohort validation study of the Hypotension Prediction Index (HPI) in patients undergoing lung resection procedures with the use of one-lung ventilation and a lung-protective strategy.
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Detailed Description
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The Hypotension Prediction Index (HPI) is a hemodynamic score designed specifically for prediction of the intraoperative hypotension (IOH) episodes. It is based on an algorithm programmed into Edwards Lifesciences HemoSphere monitor clinical platform (Irvine, CA, USA). The HPI is based on a continuous analysis of an arterial pressure waveform. It is processed in addition to the FloTrac algorithm via proprietary Acumen IQ Sensor and uses an artificial intelligence technology. After internal validation, the algorithm was prospectively, externally and clinically validated in general surgical, perioperative patients, cardiovascular surgical patients, and mechanically ventilated COVID-19 ICU patients.
As opposed to conventional monitoring systems, which display physiological parameters in real life, an HPI algorithm detects the earliest changes, multivariate variability and interactions in the physiologic inter-related data on preload, afterload, and contractility to deliver an index predicting an upcoming hypotensive event. Variables used by the patent-protected algorithm to calculate HPI are as follows: heart rate variability (changes in heart rate/changes in MAP); arterial pressure waveform complexity (approximate waveform entropy, sample waveform entropy, frequency domain measure of higher order harmonics); preload parameters (pulse pressure variation PPV, stroke volume variation SVV); contractility parameters (slope of the ascending part of the pressure waveform above time, dP/dt); and afterload parameters (SVR, dynamic arterial elastance Eadyn), but their relative contribution to final index is not revealed.
Final index values of HPI range from 1 to 100, with increasing numbers representing a greater likelihood of an impending hypotensive event. These events are defined as mean arterial pressure (MAP) \<65 mmHg occurring for over one minute. HPI values predict the occurrence of hypotension five to fifteen minutes before the event, with sensitivity and specificity in both time-frames of greater than 80%. In most studies, a value of 85 HPI predicts a hypotensive episode, and this value is arbitrarily preprogrammed into the HemoSphere monitor to alert the clinician and allow proactive responses to minimize or even entirely prevent intraoperative hypotension.
Parameters used and incorporated into the HemoSphere monitor can guide a clinician in the optimal management of IOH. These "secondary screen" variables include the left ventricular contractility parameter (dP/dt), dynamic preload parameter (SVV) and afterload parameter dynamic arterial elastance Eadyn.
Maximal left ventricular (LV) pressure rise (LV dP/dt max) is a classical marker of LV performance and systolic function. It is conventionally defined as the change in pressure in the left ventricular cavity over the isovolumetric contraction period and it originally requires LV catheterization. In clinical practice a surrogate peripheral arterial pressure waveform is used to estimate dP/dt value and to predict the need for inotropic support.
SVV is a dynamic preload parameter and represents the difference in the left ventricular stroke volume secondary to changes in intrathoracic pressure induced by mechanical ventilation.
The dynamic arterial elastance Eadyn represents the proportion of pulse-pressure variation (PPV) to SVV. It can be used to assess vascular tone, which can predict arterial pressure response after volume loading and/or potential response to vasopressor administration.
Both PPV and SVV are considered superior to static indices to predict fluid responsiveness. They are both based on heart-lung interactions and reflect hemodynamic cyclic changes induced by mechanical ventilation in the closed-chest condition. Their values are significantly correlated with the magnitude of VT. The current low-tidal volume intraoperative ventilatory strategy protects the lungs, but at the same time lowers the reliability of dynamic indices, particularly in open-chest conditions. Due to limited changes in intrathoracic pressure during the respiratory cycle in open lung conditions, there is a risk of receiving false negative parameter values. PPV and SVV seem to be inaccurate in predicting fluid responsiveness in an open-chest setting during cardiothoracic surgery.
The HPI was validated in general surgery and ICU cases, but not in thoracic surgery one-sided open chest procedures. These procedures include not only significantly abnormal physiologic conditions (open pleura and one-lung protective ventilation) but also a high incidence of sudden manual surgical interventions. All these factors can significantly influence and compromise the HPI performance.
The aim of this study is to validate the HPI technology in open-chest lung resection procedures with the use of one-lung ventilation. The study group will comprise 60 consecutive adult patients qualified for lung resection procedures under general anesthesia with open-chest and one-lung ventilation.
The patients will be monitored during the operation using standard invasive hemodynamic monitoring with arterial pressure transducer and concomitantly with HemoSphere monitor with the HPI software attached to the Acumen IQ transducer (Edwards LifeSciences, Irvine, CA, USA). The clinicians will be blinded to the output of the HemoSphere monitor. Hemodynamic waveforms and HPI prediction data including hypotensive events (IOH) will be recorded from the time of arterial cannula insertion until leaving the operation room. Recorded data will be divided into seven cohorts, represented by separate time frames:
0\. Pre-induction baseline, supine, spontaneous breathing (if available and arterial cannula inserted pre-induction)
1. Supine, closed-chest anaesthetized, intubated, two-lung ventilation
2. Lateral decubitus, closed chest, two-lung ventilation
3. Lateral decubitus, closed chest, one-lung ventilation (OLV)
4. Lateral decubitus, open chest, one-lung ventilation (OLV)
5. Lateral decubitus, closed chest, two-lung ventilation post-resection
6. Supine, closed-chest, two-lung ventilation
We will estimate the sensitivity (recall) and positive predictive value (precision) of the HPI algorithm and describe the number of false alarms as well as missed events without explicitly referring to specificity or negative predictive value.
Study conduct and reporting will be performed under the STARD guidelines.
As opposed to conventional monitoring systems, which display physiological parameters in real life, an HPI algorithm detects the earliest changes, multivariate variability and interactions in the physiologic inter-related data on preload, afterload, and contractility to deliver an index predicting an upcoming hypotensive event. Variables used by the patent-protected algorithm to calculate HPI are as follows: heart rate variability (changes in heart rate/changes in MAP); arterial pressure waveform complexity (approximate waveform entropy, sample waveform entropy, frequency domain measure of higher order harmonics); preload parameters (pulse pressure variation PPV, stroke volume variation SVV); contractility parameters (slope of the ascending part of the pressure waveform above time, dP/dt); and afterload parameters (SVR, dynamic arterial elastance Eadyn), but their relative contribution to final index is not revealed.
Final index values of HPI range from 1 to 100, with increasing numbers representing a greater likelihood of an impending hypotensive event. These events are defined as mean arterial pressure (MAP) \<65 mmHg occurring for over one minute. HPI values predict the occurrence of hypotension five to fifteen minutes before the event, with sensitivity and specificity in both time-frames of greater than 80%. In most studies, a value of 85 HPI predicts a hypotensive episode, and this value is arbitrarily preprogrammed into the HemoSphere monitor to alert the clinician and allow proactive responses to minimize or even entirely prevent intraoperative hypotension.
Parameters used and incorporated into the HemoSphere monitor can guide a clinician in the optimal management of IOH. These "secondary screen" variables include the left ventricular contractility parameter (dP/dt), dynamic preload parameter (SVV) and afterload parameter dynamic arterial elastance Eadyn.
Maximal left ventricular (LV) pressure rise (LV dP/dt max) is a classical marker of LV performance and systolic function. It is conventionally defined as the change in pressure in the left ventricular cavity over the isovolumetric contraction period and it originally requires LV catheterization. In clinical practice a surrogate peripheral arterial pressure waveform is used to estimate dP/dt value and to predict the need for inotropic support.
SVV is a dynamic preload parameter and represents the difference in the left ventricular stroke volume secondary to changes in intrathoracic pressure induced by mechanical ventilation.
The dynamic arterial elastance Eadyn represents the proportion of pulse-pressure variation (PPV) to SVV. It can be used to assess vascular tone, which can predict arterial pressure response after volume loading and/or potential response to vasopressor administration.
Both PPV and SVV are considered superior to static indices to predict fluid responsiveness. They are both based on heart-lung interactions and reflect hemodynamic cyclic changes induced by mechanical ventilation in the closed-chest condition. Their values are significantly correlated with the magnitude of VT. The current low-tidal volume intraoperative ventilatory strategy protects the lungs, but at the same time lowers the reliability of dynamic indices, particularly in open-chest conditions. Due to limited changes in intrathoracic pressure during the respiratory cycle in open lung conditions, there is a risk of receiving false negative parameter values. PPV and SVV seem to be inaccurate in predicting fluid responsiveness in an open-chest setting during cardiothoracic surgery.
The HPI was validated in general surgery and ICU cases, but not in thoracic surgery one-sided open chest procedures. These procedures include not only significantly abnormal physiologic conditions (open pleura and one-lung protective ventilation) but also a high incidence of sudden manual surgical interventions. All these factors can significantly influence and compromise the HPI performance.
The aim of this study is to validate the HPI technology in open-chest lung resection procedures with the use of one-lung ventilation. The study group will comprise 60 consecutive adult patients qualified for lung resection procedures under general anesthesia with open-chest and one-lung ventilation.
The patients will be monitored during the operation using standard invasive hemodynamic monitoring with arterial pressure transducer and concomitantly with HemoSphere monitor with the HPI software attached to the Acumen IQ transducer (Edwards LifeSciences, Irvine, CA, USA). The clinicians will be blinded to the output of the HemoSphere monitor. Hemodynamic waveforms and HPI prediction data including hypotensive events (IOH) will be recorded from the time of arterial cannula insertion until leaving the operation room. Recorded data will be divided into seven cohorts, represented by separate time frames:
0\. Pre-induction baseline, supine, spontaneous breathing (if available and arterial cannula inserted pre-induction)
1. Supine, closed-chest anaesthetized, intubated, two-lung ventilation
2. Lateral decubitus, closed chest, two-lung ventilation
3. Lateral decubitus, closed chest, one-lung ventilation (OLV)
4. Lateral decubitus, open chest, one-lung ventilation (OLV)
5. Lateral decubitus, closed chest, two-lung ventilation post-resection
6. Supine, closed-chest, two-lung ventilation
We will estimate the sensitivity (recall) and positive predictive value (precision) of the HPI algorithm and describe the number of false alarms as well as missed events without explicitly referring to specificity or negative predictive value.
Study conduct and reporting will be performed under the STARD guidelines.
Conditions
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Intraoperative Hypotension
Study Design
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Observational Model Type
COHORT
Study Time Perspective
PROSPECTIVE
Study Groups
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The arterial pressure and HPI course in 7 time-windows cohorts in one-lung ventilated patients
60 consecutive adult patients qualified for open-chest lung resection procedures under general anesthesia with one-lung ventilation will be monitored during the operation using standard invasive hemodynamic monitoring with arterial pressure transducer and concomitantly with HemoSphere monitor with the HPI software attached to the Acumen IQ transducer (Edwards LifeSciences, Irvine, CA, USA). The clinicians will be blinded to the output of the HemoSphere monitor. Hemodynamic waveforms and HPI prediction data will be recorded from the time of arterial cannula insertion until leaving the operation room. HPI values and intraoperative hemodynamic course including intraoperative hypotensive events (IOH) will be recorded at all stages of the procedure.
HemoSphere monitor with Acumen Hypotension Prediction Index Software
Intervention Type
DEVICE
Two concomitant courses of intraoperative data will be recorded: 1. the arterial waveform and pressure on the standard hemodynamic patient monitor and 2. the data from the HemoSphere monitor with Acumen Hypotension Prediction Index Software
Interventions
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HemoSphere monitor with Acumen Hypotension Prediction Index Software
Two concomitant courses of intraoperative data will be recorded: 1. the arterial waveform and pressure on the standard hemodynamic patient monitor and 2. the data from the HemoSphere monitor with Acumen Hypotension Prediction Index Software
Intervention Type
DEVICE
Eligibility Criteria
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Inclusion Criteria
* American Society of Anesthesiologists (ASA) physical status II to IV;
* Planned invasive blood pressure monitoring during general anesthesia expected to last more than 2 hours and planned overnight hospitalization.
* Procedures: video-assist thoracoscopic (VATS)-lobectomy, open-thoracotomy lobectomy, pneumonectomy.
* Adults over 18 years old.
* Planned invasive blood pressure monitoring during general anesthesia expected to last more than 2 hours and planned overnight hospitalization.
* Procedures: video-assist thoracoscopic (VATS)-lobectomy, open-thoracotomy lobectomy, pneumonectomy.
* Adults over 18 years old.
Exclusion Criteria
* Urgent/emergency procedures.
* Patients with known clinically important intracardiac shunts.
* Moderate to severe valvular disease.
* Preoperative symptomatic arrhythmias including AF.
* Congestive heart failure with LV ejection fraction less than 35%.
* Refusal of participation
* Patients with known clinically important intracardiac shunts.
* Moderate to severe valvular disease.
* Preoperative symptomatic arrhythmias including AF.
* Congestive heart failure with LV ejection fraction less than 35%.
* Refusal of participation
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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John Paul II Hospital, Krakow
OTHER
Sponsor Role
lead
Responsible Party
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Mirosław Ziętkiewicz
Principal Investigator; Head of the Anesthesia and Intensive Care Unit
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
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Mirosław Ziętkiewicz, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
2nd Anesthesiology and Intensive Care Unit, John Paul II Hospital, Prądnicka St. 80, Kraków, Poland
Locations
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Faculty of Medicine, NKUA Attikon University Hospital
Athens, , Greece
Site Status
NOT_YET_RECRUITING
St. John Paul II Hospital in Krakow
Krakow, Małopolska, Poland
Site Status
RECRUITING
Department of Anesthesiology and Intensive Therapy; Department of Pain Research and Treatment, Faculty of Medical Sciences Zabrze
Zabrze, Silesian Voivodeship, Poland
Site Status
RECRUITING
Countries
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Greece
Poland
Central Contacts
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Facility Contacts
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References
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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.
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BACKGROUND
de Keijzer IN, Vos JJ, Scheeren TWL. Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility. J Clin Monit Comput. 2020 Dec;34(6):1135-1138. doi: 10.1007/s10877-020-00465-3. Epub 2020 Jan 23. No abstract available.
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Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259. No abstract available.
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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.
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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.
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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.
Reference Type
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Vistisen ST, Johnson AEW, Scheeren TWL. Predicting vital sign deterioration with artificial intelligence or machine learning. J Clin Monit Comput. 2019 Dec;33(6):949-951. doi: 10.1007/s10877-019-00343-7. Epub 2019 Jun 28. No abstract available.
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BACKGROUND
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.
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BACKGROUND
Vos JJ, Scheeren TWL. Intraoperative hypotension and its prediction. Indian J Anaesth. 2019 Nov;63(11):877-885. doi: 10.4103/ija.IJA_624_19. Epub 2019 Nov 8.
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BACKGROUND
Sharman JE, Qasem AM, Hanekom L, Gill DS, Lim R, Marwick TH. Radial pressure waveform dP/dt max is a poor indicator of left ventricular systolic function. Eur J Clin Invest. 2007 Apr;37(4):276-81. doi: 10.1111/j.1365-2362.2007.01784.x.
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BACKGROUND
de Waal EE, Rex S, Kruitwagen CL, Kalkman CJ, Buhre WF. Dynamic preload indicators fail to predict fluid responsiveness in open-chest conditions. Crit Care Med. 2009 Feb;37(2):510-5. doi: 10.1097/CCM.0b013e3181958bf7.
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BACKGROUND
Piccioni F, Bernasconi F, Tramontano GTA, Langer M. A systematic review of pulse pressure variation and stroke volume variation to predict fluid responsiveness during cardiac and thoracic surgery. J Clin Monit Comput. 2017 Aug;31(4):677-684. doi: 10.1007/s10877-016-9898-5. Epub 2016 Jun 15.
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Fu Q, Duan M, Zhao F, Mi W. Evaluation of stroke volume variation and pulse pressure variation as predictors of fluid responsiveness in patients undergoing protective one-lung ventilation. Drug Discov Ther. 2015 Aug;9(4):296-302. doi: 10.5582/ddt.2015.01046.
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Tugrul M, Camci E, Karadeniz H, Senturk M, Pembeci K, Akpir K. Comparison of volume controlled with pressure controlled ventilation during one-lung anaesthesia. Br J Anaesth. 1997 Sep;79(3):306-10. doi: 10.1093/bja/79.3.306.
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Montes FR, Pardo DF, Charris H, Tellez LJ, Garzon JC, Osorio C. Comparison of two protective lung ventilatory regimes on oxygenation during one-lung ventilation: a randomized controlled trial. J Cardiothorac Surg. 2010 Nov 2;5:99. doi: 10.1186/1749-8090-5-99.
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Kim KN, Kim DW, Jeong MA, Sin YH, Lee SK. Comparison of pressure-controlled ventilation with volume-controlled ventilation during one-lung ventilation: a systematic review and meta-analysis. BMC Anesthesiol. 2016 Aug 31;16(1):72. doi: 10.1186/s12871-016-0238-6.
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Lin F, Pan L, Huang B, Ruan L, Liang R, Qian W, Ge W. Pressure-controlled versus volume-controlled ventilation during one-lung ventilation in elderly patients with poor pulmonary function. Ann Thorac Med. 2014 Oct;9(4):203-8. doi: 10.4103/1817-1737.140125.
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Lohser J. Evidence-based management of one-lung ventilation. Anesthesiol Clin. 2008 Jun;26(2):241-72, v. doi: 10.1016/j.anclin.2008.01.011.
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Tan J, Song Z, Bian Q, Li P, Gu L. Effects of volume-controlled ventilation vs. pressure-controlled ventilation on respiratory function and inflammatory factors in patients undergoing video-assisted thoracoscopic radical resection of pulmonary carcinoma. J Thorac Dis. 2018 Mar;10(3):1483-1489. doi: 10.21037/jtd.2018.03.03.
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Zhang BJ, Tian HT, Li HO, Meng J. The effects of one-lung ventilation mode on lung function in elderly patients undergoing esophageal cancer surgery. Medicine (Baltimore). 2018 Jan;97(1):e9500. doi: 10.1097/MD.0000000000009500.
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Schick V, Dusse F, Eckardt R, Kerkhoff S, Commotio S, Hinkelbein J, Mathes A. Comparison of Volume-Guaranteed or -Targeted, Pressure-Controlled Ventilation with Volume-Controlled Ventilation during Elective Surgery: A Systematic Review and Meta-Analysis. J Clin Med. 2021 Mar 19;10(6):1276. doi: 10.3390/jcm10061276.
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Song SY, Jung JY, Cho MS, Kim JH, Ryu TH, Kim BI. Volume-controlled versus pressure-controlled ventilation-volume guaranteed mode during one-lung ventilation. Korean J Anesthesiol. 2014 Oct;67(4):258-63. doi: 10.4097/kjae.2014.67.4.258. Epub 2014 Oct 27.
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Lin F, Pan L, Qian W, Ge W, Dai H, Liang Y. Comparison of three ventilatory modes during one-lung ventilation in elderly patients. Int J Clin Exp Med. 2015 Jun 15;8(6):9955-60. eCollection 2015.
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Samantaray A, Hemanth N. Comparison of two ventilation modes in post-cardiac surgical patients. Saudi J Anaesth. 2011 Apr;5(2):173-8. doi: 10.4103/1658-354X.82790.
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Reference Type
BACKGROUND
Other Identifiers
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NB.060.1.011.2022
Identifier Type: -
Identifier Source: org_study_id
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