Cited by 219Hemodynamic Monitoring and TherapyHealthcare Technology and Patient MonitoringCardiac, Anesthesia and Surgical Outcomes

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Ability of an arterial waveform analysis-derived hypotension prediction index to predict future hypotensive events in surgical patients

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Davies, Simon James, Vistisen, Simon Tilma, Jian, Zhongping, Hatib, Feras, Scheeren, Thomas W. L.·2020·Anesthesia & Analgesia

DOI 10.1213/ane.0000000000004121

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Abstract

BACKGROUND: Intraoperative hypotension is associated with worse perioperative outcomes for patients undergoing major noncardiac surgery. The Hypotension Prediction Index is a unitless number that is derived from an arterial pressure waveform trace, and as the number increases, the risk of hypotension occurring in the near future increases. We investigated the diagnostic ability of the Hypotension Prediction Index in predicting impending intraoperative hypotension in comparison to other commonly collected perioperative hemodynamic variables. METHODS: This is a 2-center retrospective analysis of patients undergoing major surgery. Data were downloaded and analyzed from the Edwards Lifesciences EV1000 platform. Receiver operating characteristic curves were constructed for the Hypotension Prediction Index and other hemodynamic variables as well as event rates and time to event. RESULTS: Two hundred fifty-five patients undergoing major surgery were included in the analysis yielding 292,025 data points. The Hypotension Prediction Index predicted hypotension with a sensitivity and specificity of 85.8% (95% CI, 85.8%–85.9%) and 85.8% (95% CI, 85.8%–85.9%) 5 minutes before a hypotensive event (area under the curve, 0.926 [95% CI, 0.925–0.926]); 81.7% (95% CI, 81.6%–81.8%) and 81.7% (95% CI, 81.6%–81.8%) 10 minutes before a hypotensive event (area under the curve, 0.895 [95% CI, 0.894–0.895]); and 80.6% (95% CI, 80.5%–80.7%) and 80.6% (95% CI, 80.5%–80.7%) 15 minutes before a hypotensive event (area under the curve, 0.879 [95% CI, 0.879–0.880]). The Hypotension Prediction Index performed superior to all other measured hemodynamic variables including mean arterial pressure and change in mean arterial pressure over a 3-minute window. CONCLUSIONS: The Hypotension Prediction Index provides an accurate real time and continuous prediction of impending intraoperative hypotension before its occurrence and has superior predictive ability than the commonly measured perioperative hemodynamic variables.

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Background
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Lorente, J. V., Ripollés‐Melchor, J., Jiménez, I., BECERRA, A., Mojarro, I., Fernández-Valdés-Bango, P., Fuentes, M. A., Moreno, A., Agudelo, M. E., de la Vega, A. V. P., et al. (2023) · Frontiers in Anesthesiology · doi:10.3389/fanes.2023.1193886 · Cited by 27
Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting
Kapral, L., Dibiasi, C., Jeremic, N., Bartos, S., Behrens, S., Bilir, A., Heitzinger, C., & Kimberger, O. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102797 · Cited by 33
“8,10,11 For instance, Kendale et al. utilised multiple machine learning techniques to binarily predict the occurrence of hypotension (de ﬁ ned as a single MAP value below 55 mmHg) after the induction of anaesthesia.”
Background
The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S)
Gratz, I., Baruch, M. C., Takla, M., Seaman, J. E., Allen, I. E., McEniry, B., & Deal, E. R. (2020) · BMC Anesthesiology · doi:10.1186/s12871-020-01015-9 · Cited by 26
Intraoperative Hypotension–Physiologic Basis and Future Directions
Awad, H., Alcodray, G., Raza, A., Boulos, R., Essandoh, M. K., Bhandary, S. P., & Dalton, R. (2021) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2021.05.057 · Cited by 26
Advanced medical monitoring for the battlefield: A review on clinical applicability of compensatory reserve measurements for early and accurate hemorrhage detection
Convertino, V. A., & Cardin, S. (2022) · The Journal of Trauma: Injury, Infection, and Critical Care · doi:10.1097/ta.0000000000003595 · Cited by 26
Automated systems for perioperative goal-directed hemodynamic therapy
Coeckelenbergh, S., Zaouter, C., Alexander, B. S., Cannesson, M. P., Rinehart, J. B., Duranteau, J., der Linden, P. V., & Joosten, A. (2019) · Journal of Anesthesia · doi:10.1007/s00540-019-02683-9 · Cited by 24
“As an example, hypotension prediction derived from arterial waveform analysis is now a reality [79, 80].”
Background
Beyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis
Depaskouale, M. A. P., Archonta, S. A., Katsaros, D., Paidakakos, N., Dimakopoulou, A. N., & Matsota, P. K. (2024) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-024-01202-w · Cited by 22
“Jacquet-Lagreze et al. [44]. showed that it is more sensible to use LepMAP or a single MAP value rather than ∆MAP when comparisons with the HPI are performed in contrast to what previous validation studies did [7, 46].”
Intraoperative Hypotension Prediction—A Proactive Perioperative Hemodynamic Management—A Literature Review
Szrama, J., Gradys, A., Bartkowiak, T., Woźniak, A., Kusza, K., & Molnár, Z. (2023) · Medicina · doi:10.3390/medicina59030491 · Cited by 20
“It was demonstrated in a study including 255 patients undergoing major surgery that neither MAP, heart pulse rate, stroke volume, or stroke volume variation are able to predict the occurrence of IH [27].”
BackgroundMethod
The Effect of Intermittent versus Continuous Non-Invasive Blood Pressure Monitoring on the Detection of Intraoperative Hypotension, a Sub-Study
Wijnberge, M., van der Ster, B. J. P., Vlaar, A. P. J., Hollmann, M. W., Geerts, B. F., & Veelo, D. P. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11144083 · Cited by 19
“As intraoperative hypotension occurs frequently and even short durations of intraoperative hypotension may be harmful, wider implementation of continuous monitoring could be of benefit [2,16,17].”
Background
Intraoperative Hypotension Is an Important Modifiable Risk Factor for Major Complications in Spinal Fusion Surgery
Glassman, S. D., Carreon, L. Y., Djurasovic, M., Chappell, D., Saasouh, W., Daniels, C. L., Mahoney, C. H., Brown, M. E., & Gum, J. L. (2024) · Spine · doi:10.1097/brs.0000000000005030 · Cited by 19
“8,16,17 Why IOH was most detrimental during the ﬁ rst hour of surgery is not completely clear.”
Influential
VitalDB: fostering collaboration in anaesthesia research
Vistisen, S. T., Pollard, T., Enevoldsen, J., & Scheeren, T. (2021) · British Journal of Anaesthesia · doi:10.1016/j.bja.2021.03.011 · Cited by 22
Self-citation
Hemodynamic and Intestinal Microcirculatory Changes in a Phenylephrine Corrected Porcine Model of Hemorrhage
Davies, S. J., & Mythen, M. G. (2021) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000005388 · Cited by 17
Self-citation
Hypotension prediction index for prevention of intraoperative hypotension in patients undergoing general anesthesia: a randomized controlled trial
Lai, C.-J., Cheng, Y., Han, Y., Hsiao, P., Lin, P., Chiu, C., Lee, J., Tien, Y., & Chien, K. (2024) · Perioperative Medicine · doi:10.1186/s13741-024-00414-7 · Cited by 17
“The HPI early warning system generates a visible and audible alarm when the HPI value exceeds 85 (Hatib et al.2018), indicating an 85% likelihood that a hypotensive event will occur within the next 15 min (Davies et al.2020).”
BackgroundMethod
Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting
Gathright, R., Mejia, I., Gonzalez, J. M., Torres, S. I. H., Berard, D., & Snider, E. J. (2024) · Sensors · doi:10.3390/s24248204 · Cited by 18
The hypotension prediction index versus mean arterial pressure in predicting intraoperative hypotension
Rellum, S. R., Noteboom, S. H., van der Ster, B. J. P., Schuurmans, J., Kho, E., Vlaar, A. P. J., Schenk, J., & Veelo, D. P. (2025) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000002150 · Cited by 18
Perioperative goal-directed therapy with artificial intelligence to reduce the incidence of intraoperative hypotension and renal failure in patients undergoing lung surgery: A pilot study
Habicher, M., Denn, S. M., Schneck, E., Akbari, A. A., Schmidt, G., Markmann, M., Alkoudmani, I., Koch, C., & Sander, M. (2025) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2025.111777 · Cited by 16
Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review
Mann, J., Lyons, M., O'Rourke, J., & Davies, S. J. (2025) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2025.111782 · Cited by 16
Self-citation
Hypotension Prediction Index Software to Prevent Intraoperative Hypotension during Major Non-Cardiac Surgery: Protocol for a European Multicenter Prospective Observational Registry (EU-HYPROTECT)
García, M. I. M., García-López, D., Gayat, É., Sander, M., Bramlage, P., Cerutti, E., Davies, S. J., Donati, A., Draisci, G., Frey, U. H., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11195585 · Cited by 16
“Validation studies in surgical patients suggest that the HPI may predict hypotension 15 min before the event with a sensitivity and specificity of more than 80% [14,26].”
BackgroundSelf-citation
Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility
de Keijzer, I. N., Vos, J. J., & Scheeren, T. (2020) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-020-00465-3 · Cited by 16
Self-citation
Perioperative hypotension 2021: a contrarian view
Kappen, T. H., & Beattie, W. S. (2021) · British Journal of Anaesthesia · doi:10.1016/j.bja.2021.03.015 · Cited by 16
Prediction and Prevention of Intraoperative Hypotension with the Hypotension Prediction Index: A Narrative Review
Sidiropoulou, T., Tsoumpa, M., Griva, P., Galarioti, V., & Matsota, P. K. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11195551 · Cited by 15
“None of the other hemodynamic parameters showed any prediction ability for IOH [14].”
BackgroundMethodInfluential
Preoperative Identification of Patient-Dependent Blood Pressure Targets Associated With Low Risk of Intraoperative Hypotension During Noncardiac Surgery
Schnetz, M. P., Danks, D. J., & Mahajan, A. (2022) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000006238 · Cited by 15
A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury
Feld, S. I., Hippe, D. S., Miljacic, L., Polissar, N. L., Newman, S.-F., Nair, B. G., & Vavilala, M. S. (2021) · Journal of Neurosurgical Anesthesiology · doi:10.1097/ana.0000000000000819 · Cited by 14
“SaO2 (%) 100 (99-100) 100 (99-100) 100 (99-100) Respiratory rate (breaths/min) 12 (10-14) 12 (10-14) 12 (10-14) Tidal volume (mL) 543 (477-606) 546 (476-605) 536 (478-609) Positive end-expiratory pressure (cmH2O) 4 (3-5) 4 (3-5) 4 (3-5) Peak inspiratory pressure (cmH2O) 23 (19-26) 23 (19-26) 23 (19-26) Temperature (°C…”
Hypotension Prediction Index and Incidence of Perioperative Hypotension: A Single-Center Propensity-Score-Matched Analysis
Runge, J., Graw, J., Grundmann, C. D., Komanek, T., Wischermann, J. M., & Frey, U. H. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12175479 · Cited by 14
“machine learning system and ranges from 0 to 100; thus, it is able to predict hypotension 5 min in advance with 92% sensitivity and specificity when the index exceeds a value of 85 [9,10].”
Background
Intraoperative hypotension prediction in cardiac and noncardiac procedures: is HPI truly worthwhile? A systematic review and meta-analysis
Shirmohamadi, E., Dolama, R. H., Mohammadzadeh, N., Ebrahimi, N., & Ghasemloo, N. (2025) · BMC Anesthesiology · doi:10.1186/s12871-025-03250-4 · Cited by 13
“Table 1 presents a summary of the key characteristics of the included studies [6, 7, 15, 21–39].”
Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU
Sughimoto, K., Levman, J. E. D., Baig, F., Berger, D., Oshima, Y., Kurosawa, H., Aoki, K., Seino, Y., Ueda, T., Liu, H., et al. (2022) · Cardiology in the Young · doi:10.1017/s1047951122000932 · Cited by 13
Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations
Forte, J. C., Mungroop, H. E., de Geus, F., van der Grinten, M. L., Bouma, H. R., Pettilä, V., Scheeren, T., Nijsten, M. W., Mariani, M. A., van der Horst, I. C. C., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-82403-0 · Cited by 12
Self-citation
On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology
Davoud, S. C., & Kovacheva, V. (2023) · Current anesthesiology reports · doi:10.1007/s40140-023-00558-0 · Cited by 13
Effect of the machine learning-derived Hypotension Prediction Index (HPI) combined with diagnostic guidance versus standard care on depth and duration of intraoperative and postoperative hypotension in elective cardiac surgery patients: HYPE-2 – study protocol of a randomised clinical trial
Rellum, S. R., Schuurmans, J., Schenk, J., van der Ster, B. J. P., van der Ven, W. H., Geerts, B. F., Hollmann, M. W., Cherpanath, T. G. V., Lagrand, W. K., Wynandts, P. R., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-061832 · Cited by 14
A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine
Grogan, K. L., Goldsmith, M. P., Masino, A. J., Nelson, O. K., Tsui, F. R., & Allan F. Simpao, M. (2019) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2019.06.009 · Cited by 12
Ability of an Arterial Waveform Analysis–Derived Hypotension Prediction Index to Predict Future Hypotensive Events in Surgical Patients: Erratum
(2023) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000006674 · Cited by 12
Evaluation of pre-induction dynamic arterial elastance as an adjustable predictor of post-induction hypotension: A prospective observational study
Oh, E. J., Min, J. J., Kwon, E., Choi, E. A., & Lee, J. (2023) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2023.111092 · Cited by 11
Hypotension prediction index for the prevention of hypotension during surgery and critical care: A narrative review
Dong, S., Wang, Q., Wang, S., Zhou, C., & Wang, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107995 · Cited by 11
Physiology of Human Hemorrhage and Compensation
Convertino, V. A., Koons, N. J., & Suresh, M. R. (2021) · Comprehensive physiology · doi:10.1002/j.2040-4603.2021.tb00162.x · Cited by 12
Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-technical Perspective
Ghosh, P., Posner, K. L., Hyland, S. L., Cleve, W. V., Bristow, M., Long, D. R., Palla, K., Nair, B. G., Fong, C. T., Pauldine, R. W., et al. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3589953 · Cited by 11
Impact of clinicians’ behavior, an educational intervention with mandated blood pressure and the hypotension prediction index software on intraoperative hypotension: a mixed methods study
de Keijzer, I. N., Vos, J. J., Yates, D., Reynolds, C., Moore, S. G., Lawton, R., Scheeren, T., & Davies, S. J. (2023) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-023-01097-z · Cited by 11
Self-citation
A protocol based on hypotension probability indicator vs. standard care to prevent intraoperative hypotension during supratentorial brain surgery: a prospective randomized pilot trial
Pouska, J., Kletečka, J., Zatloukal, J., Červený, V., & Beneš, J. (2023) · Minerva Anestesiologica · doi:10.23736/s0375-9393.23.17197-5 · Cited by 10
Decreased cardio-respiratory information transfer is associated with deterioration and a poor prognosis in critically ill patients with sepsis
Morandotti, C., Wikner, M., Li, Q., Ito, E., Oyelade, T., Tan, C., Chen, P.-Y., Cawthorn, A., Lilaonitkul, W., & Mani, A. R. (2024) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00642.2024 · Cited by 11
Intraoperative hypotension during robotic-assisted radical prostatectomy
Cotoia, A., Discenza, A., Rauseo, M., Matella, M., Caggianelli, G., Ciaramelletti, R., Mirabella, L., & Cinnella, G. (2025) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000002211 · Cited by 10
Invasive Hemodynamic Monitoring in Acute Heart Failure and Cardiogenic Shock
Baldetti, L., Cosenza, M., Galdieri, C., Gallone, G., Ricchetti, G., Gaspardone, C., Peveri, B., Gramegna, M., Cianfanelli, L., Calvo, F., et al. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm27034 · Cited by 10
“For example, the proprietary Edwards Lifesciences Hypotension Prediction Index (HPI) leverages on a dedicated arterial waveform sensor (Flotrac IQ) and a monitor (HemoSphere) to warn the clinician 5–15 minutes prior to hypotensive events [39].”
Automated Blood Pressure Control
Rinehart, J. B., Lee, S. B., Saugel, B., & Joosten, A. (2020) · Seminars in Respiratory and Critical Care Medicine · doi:10.1055/s-0040-1713083 · Cited by 10
The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients
Cherifa, M., Interian, Y., Blet, A., Resche‐Rigon, M., & Pirracchio, R. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102118 · Cited by 15
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
Szrama, J., Gradys, A., Bartkowiak, T., Woźniak, A., Nowak, Z., Zwoliński, K., Lohani, A., Jawień, N., Smuszkiewicz, P., & Kusza, K. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14020174 · Cited by 10
“This contradicts the main conclusion of the original paper, which claimed that HPI was better than MAP in predicting episodes of MAP 65 mmHg [37].”
BackgroundResultInfluential
The artificial intelligence evidence-based medicine pyramid
Bellini, V., Coccolini, F., Forfori, F., & Bignami, E. G. (2023) · World Journal of Critical Care Medicine · doi:10.5492/wjccm.v12.i2.89 · Cited by 9
Artificial intelligence and perioperative medicine
Bignami, E. G., Cozzani, F., RIO, P. D., & Bellini, V. (2020) · Minerva Anestesiologica · doi:10.23736/s0375-9393.20.14999-x · Cited by 12
Perioperative Risk Stratification and Modification
Matthews, L., Levett, D. Z. H., & Grocott, M. P. W. (2022) · Anesthesiology Clinics · doi:10.1016/j.anclin.2022.03.001 · Cited by 9
AcumenTM hypotension prediction index guidance for prevention and treatment of hypotension in noncardiac surgery: a prospective, single-arm, multicenter trial
Bao, X., Kumar, S. S., Shah, N. J., Penning, D. H., Weinstein, M. L., Malhotra, G., Rose, S., Drover, D. R., Pennington, M. W., Domino, K. B., et al. (2024) · Perioperative Medicine · doi:10.1186/s13741-024-00369-9 · Cited by 8
“The algorithm performance is validated in several small and mostly single-center studies (Hatib et al., 2018; Davies et al., 2020; Frassanito et al., 2022a; Frassanito et al., 2022b; Maheshwari et al., 2021; Ranucci et al., 2019; Shin et al., 2021; van der Ven et al., 2022; Wijnberge et al., 2021b).”
Method

Authors: Davies, S. J., Vistisen, S. T., Jian, Z., Hatib, F., & Scheeren, T. W. L.
Affiliations: National Health Service (GB)
Aarhus University (DK)
Edwards Lifesciences (United States) (US)
University Medical Center Groningen (NL)
Corresponding author: Davies, Simon James
ORCID: Davies, Simon James
Vistisen, Simon Tilma
Hatib, Feras
Scheeren, Thomas W. L.
Venue: Anesthesia & Analgesia
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2020-02
Volume: 130
Issue: 2
Pages: 352-359
ISSN: 0003-2999 (ISSN-L)
DOI: 10.1213/ane.0000000000004121
OpenAlex: W2928352011
Cited by: 219
References: 23
Citations by year: 2026: 26 · 2025: 29 · 2024: 42 · 2023: 33 · 2022: 28 · 2021: 30 · 2020: 24
FWCI: 21.44
Type: journal article
Language: en
Update notices: correction (2023-09-01)
Open access: green
hdl.handle.net
Licence: other-oa
Locations: Anesthesia & Analgesia (publishedVersion)
PubMed (publishedVersion)
Scopus (Elsevier) (submittedVersion)
University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
Funders: Edwards Lifesciences
Topics: Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
Keywords: Hypotension Prediction Index, intraoperative hypotension, arterial waveform analysis, non-cardiac surgery, hemodynamic monitoring, hypotension prediction, mean arterial pressure, perioperative outcomes, sensitivity and specificity, time-to-event prediction
MeSH terms: Aged, Aged, 80 and over, Female, Forecasting, Humans, Hypotension, Intraoperative Complications, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Retrospective Studies, Monitoring, Intraoperative, Arterial Pressure

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Ability of an arterial waveform analysis-derived hypotension prediction index to predict future hypotensive events in surgical patients

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