The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Ability of an arterial waveform analysis-derived hypotension prediction index to predict future hypotensive events in surgical patients
Open accessAbstract
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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Incidence of Intraoperative Hypotension as a Function of the Chosen Definition
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Hatib, F., Jian, Z., Buddi, S., Lee, C., Settels, J., Sibert, K., Rinehart, J., & Cannesson, M. (2018) · Anesthesiology · doi:10.1097/aln.0000000000002300 · Cited by 614
Self-citationPost-induction hypotension and early intraoperative hypotension associated with general anaesthesia
Südfeld, S., Brechnitz, S., Wagner, J. Y., Reese, P. C., Pinnschmidt, H. O., Reuter, D. A., & Saugel, B. (2017) · British Journal of Anaesthesia · doi:10.1093/bja/aex127 · Cited by 332
Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension
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Shock index in diagnosing early acute hypovolemia
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A Randomized Trial of Continuous Noninvasive Blood Pressure Monitoring During Noncardiac Surgery
Maheshwari, K. K., Khanna, S. K., Bajracharya, G. R., Makarova, N., Riter, Q., Raza, S., Cywiński, J. B., ARGALIOUS, M. Y., Kurz, A. M., & Sessler, D. I. (2018) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000003482 · Cited by 175
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Items 201–219 of 219
Mohammadi, I., Firouzabadi, S. R., Hosseinpour, M., Akhlaghpasand, M., Hajikarimloo, B., Tavanaei, R., Izadi, A., Nejad, S. Z., & Eghbali, F. (2024) · Figshare · doi:10.6084/m9.figshare.26500399
Mohammadi, I., Firouzabadi, S. R., Hosseinpour, M., Akhlaghpasand, M., Hajikarimloo, B., Tavanaei, R., Izadi, A., Nejad, S. Z., & Eghbali, F. (2024) · Figshare · doi:10.6084/m9.figshare.26757760
Upstream Detection of Cardiogenic Shock: Redefining the Window of Opportunity
Jaiswal, A., Etts, K., & Baran, D. A. (2026) · Journal of Cardiac Failure - Intersections · doi:10.1016/j.yjcafi.2026.01.004
Gołdyn, M. J., Lichoń, J. M., Kras, M., Karasiewicz, A., Kuklo, M., Łepecki, D., Karolak, A., & Jakubowska, E. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.1(49).2026.5039
Personalizing anesthetic management in trauma patients using machine learning
James, A., & Moyer, J.-D. (2026) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000001637
Interesse dell'intelligenza artificiale in anestesia-rianimazione
Aries, P., Belot, F., Py, N., Humbert, P., & Dubost, C. (2026) · EMC - Anestesia-Rianimazione · doi:10.1016/s1283-0771(26)51713-4
Martín-Arrabal, A., Peinado, F. M., Arrabal‐Polo, M. Á., Gálvez-Muñoz, A. J., Saz, T., Olvera-García, M. M., Serrano-Atero, M. S., López-Soto, S., & Fernández, M. (2026) · Medical Sciences · doi:10.3390/medsci14020210
“Intraoperative hypotension (IOH) is one of the most frequent manifestations of haemodynamic instability during general anaesthesia [9,10].”
Monard, C., Ferlay, C., Whiting, L., Trächsel, B., & Schneider, A. (2026) · Journal of Intensive Medicine · doi:10.1016/j.jointm.2026.03.002
“While evaluated in surgical settings with promising results, [ 14–16 ] its performance and usefulness in other settings, such as during fluid removal in critically ill patients, where numerous other confounding factors could influence its prediction, remains unknown.”
Getting to the heart of blood pressure control during caesarean section
Pinder, E., & Salahelden, M. (2026) · Anaesthesia · doi:10.1111/anae.70244
“…prediction index involves algorithm-based analysis of features of the arterial pressure waveform, such as shape and variability, and outputs a dimensionless number from 0 to 100, re fl ecting the probability of hypotension occurring in the near future, with a score of > 85 considered high [12].”
Piccioni, F., Rigo, S., Pagot, E., Nisi, F., Giustiniano, E., Grosso, S., Capretti, G., Nappo, G., Zerbi, A., & Cecconi, M. (2026) · Frontiers in Anesthesiology · doi:10.3389/fanes.2026.1816846
Zhang, P., Wu, L., Liao, Y., & Li, H. (2026) · Journal of International Medical Research · doi:10.1177/03000605261454051
Dolnimitsakul, S., Chaichalothorn, M., & Songchuen, N. (2026) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2026.06.049
Niu, T., Li, J., Shi, L., Han, Z., Mao, X., Zheng, M., Deng, X., Yang, G., Ji, M., Liang, X., et al. (2026) · Current Problems in Surgery · doi:10.1016/j.cpsurg.2026.102156
Pharmacokinetic State Space Models for Unbiased Prediction of Haemodynamic Collapse
Nagaraj, R., Chindula, S., & Das, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-35387-0_22
“Enevoldsen and Vistisen [6] demonstrated that HPI [9] and subsequent validation studies [2] evaluated performance using observation windows not strictly separated from hypotensive events by a designated blind spot interval (lead-gap), leading to performance overestimation due to selection bias.”
Aspide, R., & Biasucci, D. G. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16100503
“Similarly, arterial waveform analysis and machine-learning-based systems such as the Hypotension Prediction Index have been investigated as tools for anticipating hypotensive events and potentially supporting earlier intervention [22,23].”
Long, B., Ye, Y., Xing, H., & Yang, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1884174
“This contrasts with the Hypotension Prediction Index and similar algorithms that rely on population-derived waveform features without explicit patient-specific referencing (8, 9).”
Task-Window Validity of Pre-Induction Arterial Pressure for Hypotension Modeling in VitalDB
Gao, G. (2026) · IEEE Access · doi:10.1109/access.2026.3742514
“[15], [16], [17] Additional evaluation and review literature has placed HPI performance in the context of signal quality, threshold selection, and transportability.”
Technology and Innovation in Practice Management
Huang, E. C., Huang, J. J., & Huang, J. (2026) · Anesthesiology Clinics · doi:10.1016/j.anclin.2026.09.007
Duan, J., Chen, Z., & Wang, J. (2026) · Journal of Clinical Anesthesia · doi:10.1016/j.jclinane.2026.112353
Metadata
- 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
- Venue
- Anesthesia & Analgesia
- Published
- 2020-02
- Volume
- 130
- Issue
- 2
- Pages
- 352-359
- ISSN
- 0003-2999 (ISSN-L)
- 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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