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Pilakouta Depaskouale MA, Archonta SA, Katsaros DM, Paidakakos NA, Dimakopoulou AN, Matsota PK. Beyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis. J Clin Monit Comput 2024:10.1007/s10877-024-01202-w. [PMID: 39048785 DOI: 10.1007/s10877-024-01202-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
PURPOSE Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. METHODS A systematic search was performed in Pubmed and Scopus to retrieve articles published from January 2018 to February 2024 regarding the impact of the HPI software on reducing IOH in adult patients undergoing non-cardio/thoracic surgery. Excluded were case series, case reports, meta-analyses, systematic reviews, and studies using non-invasive arterial waveform analysis. The risk of bias was assessed by the Cochrane risk-of-bias tool (RoB 2) and the Risk Of Bias In Non-randomised Studies (ROBINS-I). A meta-analysis was undertaken solely for outcomes where sufficient data were available from the included studies. RESULTS 9 RCTs and 5 cohort studies were retrieved. The overall median differences between the HPI-guided and the control groups were - 0.21 (95% CI:-0.33, -0.09) - p < 0.001 for the Time-Weighted Average (TWA) of Mean Arterial Pressure (MAP) < 65mmHg, -3.71 (95% CI= -6.67, -0.74)-p = 0.014 for the incidence of hypotensive episodes per patient, and - 10.11 (95% CI= -15.82, -4.40)-p = 0.001 for the duration of hypotension. Notably a large amount of heterogeneity was detected among the studies. CONCLUSIONS While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.
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Affiliation(s)
- Myrto A Pilakouta Depaskouale
- 2nd Department of Anesthesiology, School of Medicine, National and Kapodistrian University of Athens, "Attikon" Hospital, 1 Rimini Street, Athens, 12462, Greece.
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece.
| | - Stela A Archonta
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Dimitrios M Katsaros
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Nikolaos A Paidakakos
- Department of Neurosurgery, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Antonia N Dimakopoulou
- Department of Anesthesiology, Athens General Hospital "Georgios Gennimatas", 154 Mesogion Avenue, Athens, 11527, Greece
| | - Paraskevi K Matsota
- 2nd Department of Anesthesiology, School of Medicine, National and Kapodistrian University of Athens, "Attikon" Hospital, 1 Rimini Street, Athens, 12462, Greece
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Davies SJ, Sessler DI, Jian Z, Fleming NW, Mythen M, Maheshwari K, Veelo DP, Vlaar AP, Settels J, Scheeren T, van der Ster B, Sander M, Cannesson M, Hatib F. Comparison of differences in cohort (forwards) and case control (backwards) methodological approaches for validation of the Hypotension Prediction Index. Anesthesiology 2024:139987. [PMID: 38557791 DOI: 10.1097/aln.0000000000004989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiological changes that may lead to hypotension. The original validation used a case control (backwards) analysis that has been suggested to be biased. We therefore conducted a cohort (forwards) analysis and compared this to the original validation technique. METHODS We conducted a retrospective analysis of data from previously reported studies. All data were analysed identically with 2 different methodologies and receiver operating characteristic curves (ROC) constructed. Both backwards and forwards analyses were performed to examine differences in area under the ROC for HPI and other haemodynamic variables to predict a MAP < 65mmHg for at least 1 minute 5, 10 and 15 minutes in advance. RESULTS Two thousand and twenty-two patients were included in the analysis, yielding 4,152,124 measurements taken at 20 second intervals. The area-under-the-curve for the index predicting hypotension analysed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947-0.964) vs 0.923 (95% CI, 0.912-0.933) 5 minutes in advance, 0.933 (95% CI, 0.924-0.942) vs 0.923 (95% CI, 0.911-0.933) 10 minutes in advance , and 0.929 (95% CI, 0.918-0.938) vs. 0.926 (95% CI, 0.914-0.937) 15 minutes in advance. No other variable had an area-under-the-curve > 0.7 except for MAP. Area-under-the-curve using forward analysis for MAP predicting hypotension 5, 10, and 15 minutes in advance was 0.932 (95% CI, 0.920-0.940), 0.929 (95% CI, 0.918-0.938), and 0.932 (95% CI, 0.921-0.940). The R 2 for the variation in the index due to MAP was 0.77. CONCLUSION Using an updated methodology, we found the utility of the HPI index to predict future hypotensive events is high, with an area under the receiver-operating-characteristics curve similar to that of the original validation method.
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Affiliation(s)
- Simon J Davies
- Department of Anaesthesia, Critical Care and Perioperative Medicine, York and Scarborough Teaching Hospitals NHS foundation Trust, York, UK
- Centre for Health and Population Science, Hull York Medical School, York, UK
| | | | | | | | - Monty Mythen
- Edwards Lifesciences, Irvine, California, USA
- UCL/UCLH National Institute of Health Research Biomedical Research Centre, London, UK
| | | | - Denise P Veelo
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Alexander Pj Vlaar
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jos Settels
- Edwards Lifesciences, Irvine, California, USA
| | - Thomas Scheeren
- Edwards Lifesciences, Irvine, California, USA
- Department of Anesthesiology, University Medical Centre Groningen, The Netherlands
| | - Bjp van der Ster
- Departments of Anaesthesia and Intensive Care, Amsterdam University Medical Center, Amsterdam, The Netherlands
- ErasmusMc, Rotterdam, The Netherlands
| | - Michael Sander
- Department of Anaesthesiology, Intensive Care Medicine and Pain Medicine, University Hospital Giessen, Giessen, Germany
| | - Maxime Cannesson
- Department of Anesthesiology and Perioperative Medicine, UCLA, California, USA
| | - Feras Hatib
- Edwards Lifesciences, Irvine, California, USA
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Szrama J, Gradys A, Bartkowiak T, Woźniak A, Nowak Z, Zwoliński K, Lohani A, Jawień N, Smuszkiewicz P, Kusza K. The Incidence of Perioperative Hypotension in Patients Undergoing Major Abdominal Surgery with the Use of Arterial Waveform Analysis and the Hypotension Prediction Index Hemodynamic Monitoring-A Retrospective Analysis. J Pers Med 2024; 14:174. [PMID: 38392607 PMCID: PMC10889918 DOI: 10.3390/jpm14020174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/24/2024] Open
Abstract
Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and alerts the clinician of an impending hypotension event. The purpose of the study was to compare the frequency of perioperative hypotension in patients undergoing major abdominal surgery with different types of hemodynamic monitoring. The study included 61 patients who were monitored with the arterial pressure-based cardiac output (APCO) technology (FloTrac group) and 62 patients with the Hypotension Prediction Index algorithm (HPI group). Our primary outcome was the time-weighted average (TWA) of hypotension below < 65 mmHg. The median TWA of hypotension in the FloTrac group was 0.31 mmHg versus 0.09 mmHg in the HPI group (p = 0.000009). In the FloTrac group, the average time of hypotension was 27.9 min vs. 8.1 min in the HPI group (p = 0.000023). By applying the HPI algorithm in addition to an arterial waveform analysis alone, we were able to significantly decrease the frequency and duration of perioperative hypotension events in patients who underwent major abdominal surgery.
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Affiliation(s)
- Jakub Szrama
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Agata Gradys
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Tomasz Bartkowiak
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Amadeusz Woźniak
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Zuzanna Nowak
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Krzysztof Zwoliński
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Ashish Lohani
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Natalia Jawień
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Piotr Smuszkiewicz
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| | - Krzysztof Kusza
- Department of Anesthesiology, Intensive Therapy and Pain Management, Poznan University of Medical Sciences, 60-355 Poznan, Poland
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Vistisen ST, Enevoldsen J. CON: The hypotension prediction index is not a validated predictor of hypotension. Eur J Anaesthesiol 2024; 41:118-121. [PMID: 38085015 DOI: 10.1097/eja.0000000000001939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
The Hypotension Prediction Index (HPI) algorithm is a commercial prediction algorithm developed to predict hypotension, a mean arterial pressure (MAP) below 65 mmHg. Although HPI has been investigated in several studies, recent concerns of have been raised regarding HPI's predictive abilities, which may have been overstated. A selection bias may have forced the HPI algorithm to learn almost exclusively from MAP. This CON position paper describes the selection bias further and summarises the scientific status of HPI's predictive abilities, including the meaning of a recent erratum retracting the primary conclusion of a published HPI validation study. We argue that the HPI algorithm needs re-validation or complete re-development to achieve a clinically relevant 'added value' in comparison with the predictive performance of a simple and costless MAP alarm threshold in the range of 70 to 75 mmHg.
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Affiliation(s)
- Simon Tilma Vistisen
- From the Institute of Clinical Medicine, Aarhus University (STV, JE) and Department of Anaesthesiology & Intensive Care, Aarhus University Hospital, Aarhus, Denmark (STV, JE)
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Michard F, Joosten A, Futier E. Intraoperative blood pressure: could less be more? Br J Anaesth 2023; 131:810-812. [PMID: 37778938 DOI: 10.1016/j.bja.2023.09.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 10/03/2023] Open
Abstract
Retrospective observational studies have reported a significant association between intraoperative hypotension and postoperative morbidity. However, association does not imply causation, and whether preventing intraoperative hypotension can improve patient outcome remains to be demonstrated. In this issue of the British Journal of Anaesthesia, D'Amico and colleagues meta-analysed 10 prospective randomised trials comparing low (≤60 mm Hg) and higher mean arterial pressure targets during anaesthesia and surgery. They did not observe an increase in postoperative morbidity and mortality in the low target group. In contrast, they reported a statistically significant (but not clinically relevant) reduction in postoperative cardiac arrhythmia and hospital length of stay when targeting mean arterial pressure ≤60 mm Hg. These findings suggest that during most surgical cases, intraoperative hypotension is a marker of the severity, frailty, or both rather than a mediator of postoperative complications.
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Affiliation(s)
| | - Alexandre Joosten
- Department of Anesthesiology and Intensive Care, Paris-Saclay University, Paul Brousse Hospital, Assistance Publique Hôpitaux de Paris (AP-HP), Villejuif, France
| | - Emmanuel Futier
- Department of Anesthesia and Critical Care, Université Clermont Auvergne, Hopital d'Estaing, Clermont-Ferrand, France
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