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

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 access

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

DOI 10.1213/ane.0000000000004121

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1213/ane.0000000000004121

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

Relationship between Intraoperative Mean Arterial Pressure and Clinical Outcomes after Noncardiac Surgery
Walsh, M. W., Devereaux, P. J., Garg, A. X., Kurz, A. M., Turan, A., Rodseth, R. N., Cywiński, J. B., Thabane, L., & Sessler, D. I. (2013) · Anesthesiology · doi:10.1097/aln.0b013e3182a10e26 · Cited by 1,485
Relationship between Intraoperative Hypotension, Defined by Either Reduction from Baseline or Absolute Thresholds, and Acute Kidney and Myocardial Injury after Noncardiac Surgery
Salmasi, V., Maheshwari, K. K., Yang, D., Mascha, E. J., Singh, A., Sessler, D. I., & Kurz, A. M. (2016) · Anesthesiology · doi:10.1097/aln.0000000000001432 · Cited by 1,135
Effect of Individualized vs Standard Blood Pressure Management Strategies on Postoperative Organ Dysfunction Among High-Risk Patients Undergoing Major Surgery
Futier, E., Lefrant, J., Guinot, P., Godet, T., Lorne, E., Cuvillon, P., Bertran, S., Léone, M., Pastène, B., Piriou, V., et al. (2017) · JAMA · doi:10.1001/jama.2017.14172 · Cited by 931
Incidence of Intraoperative Hypotension as a Function of the Chosen Definition
Bijker, J. B., van Klei, W. A., Kappen, T. H., van Wolfswinkel, L., Moons, K. G. M., & Kalkman, C. J. (2007) · Anesthesiology · doi:10.1097/01.anes.0000270724.40897.8e · Cited by 712
Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis
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-citation
Post-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
Kendale, S. M., Kulkarni, P., Rosenberg, A. D., & Wang, J. (2018) · Anesthesiology · doi:10.1097/aln.0000000000002374 · Cited by 251
Shock index in diagnosing early acute hypovolemia
Birkhahn, R., Gaeta, T. J., Terry, D., Bove, J. J., & Tloczkowski, J. (2005) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2005.02.029 · Cited by 223
Guidelines for Reporting Health Research: The EQUATOR Network's Survey of Guideline Authors
Simera, I., Altman, D. G., Moher, D., Schulz, K. F., & Hoey, J. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050139 · Cited by 220
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

Show all 23 references

Sorted by citations

Items 101–200 of 219

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices
Jeong, H., Kim, D.-H., Kim, D. W., Baek, S., Lee, H., Kim, Y., & Ahn, H. J. (2024) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-024-01206-6 · Cited by 8
“However, HPI has limited generalizability because it requires an invasive arterial line or a specialized finger cuff for non-invasive measurement (Acumen IQ cuff ™, Edwards Lifesciences, Irvine, CA, USA) [ 4, 5, 13 ].”
Hypotension prediction index in the prediction of better outcomes: a systematic review and meta-analysis
Liu, Y., Liu, B., Xiong, W., Wang, C., Yang, K., Ma, W., Lan, L., Wei, M., Jiang, N., & Feng, X. (2025) · Korean Journal of Anesthesiology · doi:10.4097/kja.25249 · Cited by 8
“A large number of studies have validated its sensitivity and specificity for predicting hypotension [39–41].”
The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
Denn, S. M., Schneck, E., Jablawi, F., Bender, M., Schmidt, G., Habicher, M., Uhl, E., & Sander, M. (2022) · Journal of Medical Case Reports · doi:10.1186/s13256-022-03653-8 · Cited by 8
“outline a significant reduction of IOH by using an HPI-based GDT protocol [9].”
Method
Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study
Benson, B. E., Belle, A., Lee, S., Bassin, B. S., Medlin, R. P., Sjoding, M. W., & Ward, K. R. (2023) · BMC Anesthesiology · doi:10.1186/s12871-023-02283-x · Cited by 8
“10 Lifesciences, Irvine CA) uses features in the arterial waveform (collected from IAP monitoring) 182 to predict the occurrence of hypotension during surgery.(25,26) However, these approaches 183 require separate hardware.”
Method
Con: Routine Use of the Hypotension Prediction Index in Cardiac, Thoracic, and Vascular Surgery
Shehata, I. M., Alcodray, G., Essandoh, M. K., & Bhandary, S. P. (2020) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2020.09.128 · Cited by 8
Pro: Routine Use of the Hypotension Prediction Index (HPI) in Cardiac, Thoracic, and Vascular Surgery
Awadallah, D., Thomas, G., Saklayen, S. L., Dalton, R., & Awad, H. (2020) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2020.11.048 · Cited by 8
Clinical Implication of the Acumen Hypotension Prediction Index for Reducing Intraoperative Haemorrhage in Patients Undergoing Lumbar Spinal Fusion Surgery: A Prospective Randomised Controlled Single-Blinded Trial
Koo, J. M., Choi, H., Hwang, W., Hong, S. H., Kim, S.-I., Kim, Y.-H., Choi, S., Kim, C. J., & Chae, M. S. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11164646 · Cited by 8
“It predicts hypotension 15 min in advance and is faster than cardiac output, stroke volume, MAP, pulse pressure, heart rate, stroke volume variation, and the shock index [8].”
Background
Diagnostic Efficacy of Carotid Ultrasound for Predicting the Risk of Perioperative Hypotension or Fluid Responsiveness: A Meta-Analysis
Hung, K., Huang, Y., Tsai, W.-W., Tan, P.-H., Wu, J., Huang, P., Liu, T., Chen, I., & Sun, C. (2023) · Diagnostics · doi:10.3390/diagnostics13132290 · Cited by 9
“7%, respectively, for predicting hypotension 10 min before the occurrence of events [35].”
Background
Blood Pressure Goals in Critically Ill Patients
Rajkumar, K. P., Hicks, M. H., Marchant, B. E., & Khanna, A. K. (2023) · Methodist DeBakey Cardiovascular Journal · doi:10.14797/mdcvj.1260 · Cited by 7
Impact of Predictive Hemodynamic Monitoring on Intraoperative Hypotension and Postoperative Complications in Multi-level Spinal Fusion Surgery
Carreon, L. Y., Glassman, S. D., Chappell, D., Garvin, S., Lavelle, A. M., Gum, J. L., Djurasovic, M., & Saasouh, W. (2024) · Spine · doi:10.1097/brs.0000000000005121 · Cited by 7
“17 This includes a predictive algorithm, the Hypotension Prediction Index (HPI), developed using AI and machine learning which identi ﬁ es impending hypotension by analyzing arterial waveform changes over time.”
Comparing the Hypotension Prediction Index to Mean Arterial Pressure and Linear Extrapolated Mean Arterial Pressure for the Prediction of Intraoperative Hypotension: A Secondary Analysis
Massari, D., de Keijzer, I. N., & Vos, J. J. (2024) · Anesthesiology · doi:10.1097/aln.0000000000005198 · Cited by 7
Reversible intraoperative neuromonitoring changes in thoracic ossification of the posterior longitudinal ligament and/or ossification of the ligamentum flavum surgery
Liu, T., Wang, W., Qi, H., Li, J., Guo, B., Zhao, S., Wang, J., Jiang, K., Wu, G., & Wang, G. (2025) · European Spine Journal · doi:10.1007/s00586-025-09100-8 · Cited by 7
“Treatment with vasopressors (e.g., ephedrine) is the other method of increasing MAP [ 23].”
Effect of subclavian vein diameter combined with perioperative fluid therapy on preventing post-induction hypotension in patients with ASA status I or II
Wang, B., Hui, K., Xiong, J., Yang, C., Cao, X., Zhu, G., Ang, Y., & Duan, M. (2024) · BMC Anesthesiology · doi:10.1186/s12871-024-02514-9 · Cited by 7
“In a previous study, approximately 87% of the subjects experienced one or more hypotensive events [4].”
Background
The use of Hypotension Prediction Index in cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC)
Gangakhedkar, G. R., Solanki, S. L., & Divatia, J. V. (2022) · Indian Journal of Anaesthesia · doi:10.4103/ija.ija_102_22 · Cited by 7
“[8] Various studies document the successful use of HPI in cardiac and non-cardiac surgeries.[9-12]”
Background
Opportunities of AI-powered applications in anesthesiology to enhance patient safety
Kovacheva, V., & Nagle, B. (2024) · International Anesthesiology Clinics · doi:10.1097/aia.0000000000000437 · Cited by 7
“37,38 42 These results may be due to a selection bias, and thus, additional investigation is needed.”
Background
Towards reliable prediction of intraoperative hypotension: a cross-center evaluation of deep learning-based and MAP-derived methods
Chaari, N., Winski, G., Hallbäck, M., Lundström, N., Björne, H., & Jacobsson, M. (2025) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-025-01357-0 · Cited by 6
“MAP threshold (MAPthr) [6]: This method evaluates predefined MAP thresholds (e.g., 55–85 mmHg) selected to cover the possible MAP values at which hypotension or non-hypotension events may occur 5 minutes later.”
A comparison between invasive and noninvasive measurement of the Hypotension Prediction Index
Rellum, S. R., Kho, E., Schenk, J., van der Ster, B. J. P., Vlaar, A. P. J., & Veelo, D. P. (2024) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000002082 · Cited by 6
The role of compression in large scale data transfer and storage of typical biomedical signals at hospitals
Jacobsson, M., Seoane, F., & Abtahi, F. (2023) · Health Informatics Journal · doi:10.1177/14604582231213846 · Cited by 5
Artificial intelligence-based predictive hemodynamic monitoring in conjunction with goal-directed therapy reduces duration, frequency, and severity of intraoperative hypotension in major maxillofacial and otolaryngological surgery—a prospective randomized controlled pilot trial
Akbari, A. A., Koch, C., Schmidt, G., Schmermund, D., Langer, C., Edinger, F., Denn, S. M., Markmann, M., Sander, M., & Habicher, M. (2025) · Journal of Anesthesia Analgesia and Critical Care · doi:10.1186/s44158-025-00331-1 · Cited by 5
“The Hypotension Prediction Index (HPI; Edwards Lifes-ciences, Irvine, CA, USA) uses features derived from the arterial pressure waveform to estimate the likelihood of IOH (MAP < 65 mmHg) before it occurs [12, 13].”
Application of AI Models for Preventing Surgical Complications: Scoping Review of Clinical Readiness and Barriers to Implementation
Mevik, K., Woldaregay, A. Z., Jonsson, E. L., Hernández, M. Á. T., & Temple‐Oberle, C. F. (2026) · JMIR AI · doi:10.2196/75064 · Cited by 6
“Regarding hypotension specifically, there have been several evaluations of HPI [77,78], including an RCT showing the efficacy of the HPI [70].”
Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost–Benefit Derivatives of Digital Medical Devices
Frassanito, L., Bidino, R. D., Vassalli, F., Michnacs, K., Giuri, P. P., Zanfini, B. A., Catarci, S., Filetici, N., Sonnino, C., Cicchetti, A., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm14010058 · Cited by 6
“The HPI has been validated on surgical patients with a high sensitivity and specificity in predicting hypotension 5, 10 and 15 min before the event [14,15].”
Method
Forecasting the fall: the role of machine learning in predicting intraoperative hypotension, a scoping review
Koh, A., Dhanya, B. A. B. Y., Martis, W. R., & Daniel Capurro, N. (2025) · Minerva Anestesiologica · doi:10.23736/s0375-9393.25.19197-9 · Cited by 5
A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning
Lu, F., Li, W., Zhou, Z., Song, C., Sun, Y., Zhang, Y., Ren, Y., Liao, X., Jin, H., Luo, A., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26681 · Cited by 5
“• HPI (Hatib et al. 2018; Davies et al. 2020): A commercial system with proprietary monitors and machine learning algorithms was developed and used HPI (Hypotension Prediction Index) to predict IOH events.”
BackgroundMethodInfluential
Machine Learning of Physiologic Waveforms and Electronic Health Record Data
Kim, S., Kwon, S., Rudas, Á., Pal, R., Markey, M. K., Bovik, A. C., & Cannesson, M. P. (2023) · Critical Care Clinics · doi:10.1016/j.ccc.2023.03.003 · Cited by 5
Effect of machine learning‐guided haemodynamic optimization on postoperative free flap perfusion in reconstructive maxillofacial surgery: A study protocol
Pražetina, M., Šribar, A., Jurinjak, I. S., Matošević, J., & Peršec, J. (2023) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15942 · Cited by 4
Characterization of intraoperative hemodynamic instability in patients undergoing general anesthesia
Ripollés‐Melchor, J., Valbuena-Bueno, M. A., Fernández-Valdés-Bango, P., Rodríguez-Herrero, A., Tomé-Roca, J. L., Olvera-García, M., García-López, D., Ruiz-Escobar, A., Carrasco-Sánchez, L., Gurumeta, A. A., et al. (2024) · Frontiers in Anesthesiology · doi:10.3389/fanes.2024.1405405 · Cited by 4
Forecasting intraoperative hypotension during hepatobiliary surgery
Cata, J. P., Soni, B., Bhavsar, S., Pillai, P. S., Rypinski, T. A., Deva, A., Siewerdsen, J. H., & Soliz, J. (2024) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-024-01223-5 · Cited by 5
“In that regard, significant research efforts are underway to apply ML in the complex interplay between surgery, patient demographics, anaesthetic factors, and their relationship with perioperative outcomes [15–17].”
Influential
The hypotension prediction index in major abdominal surgery – A prospective randomised clinical trial protocol
Szrama, J., Gradys, A., Nowak, Z., Lohani, A., Zwoliński, K., Bartkowiak, T., Woźniak, A., Koszel, T., & Kusza, K. (2024) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2024.101417 · Cited by 4
Evaluation of hypotension prediction index software in patients undergoing orthotopic liver transplantation: retrospective observational study
Cywiński, J. B., Li, Y., Israelian, L. A., Sreedharan, R., Perez-Protto, S. E., & Maheshwari, K. K. (2025) · Brazilian Journal of Anesthesiology (English Edition) · doi:10.1016/j.bjane.2025.844589 · Cited by 4
Continuous Intraoperative Hemodynamic Monitoring Decreases In-Hospital Costs
Carreon, L. Y., Glassman, S. D., Chappell, D., Djurasovic, M., Mathew, J., Ward, T. W., & Gum, J. L. (2025) · Spine · doi:10.1097/brs.0000000000005373 · Cited by 4
Journal of Clinical Monitoring and Computing end of year summary 2019: hemodynamic monitoring and management
Saugel, B., Critchley, L. A. H., Kaufmann, T., Flick, M., Kouz, K., Vistisen, S. T., & Scheeren, T. (2020) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-020-00496-w · Cited by 4
Self-citation
Application of perioperative hemodynamics today and potentials for tomorrow
Ramsingh, D. S., Staab, J., & Flynn, B. C. (2021) · Best practice & research. Clinical anaesthesiology · doi:10.1016/j.bpa.2021.01.005 · Cited by 4
Central Hypovolemia Detection During Environmental Stress—A Role for Artificial Intelligence?
van der Ster, B. J. P., Kim, Y.-S., Westerhof, B. E., & LIESHOUT, J. J. V. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.784413 · Cited by 4
“It is based on arterial waveform features and has been claimed to predict intraoperative hypotension; the available evidence from recent clinical studies is summarized below (Davies et al., 2020; Maheshwari et al., 2020; Schneck et al., 2020; Wijnberge et al., 2020; Schenk et al., 2021).”
BackgroundMethod
Valor del índice de predicción de la hipotensión y el dP/dtmax para predecir y tratar la hipotensión en un paciente con miocardiopatía dilatada
Solares, G., Barredo, F., & García, M. I. M. (2020) · Revista Española de Anestesiología y Reanimación · doi:10.1016/j.redar.2020.02.011 · Cited by 4
Ability of the hypotension prediction index to predict hypotension in patients with septic shock in the intensive care unit
Khwannimit, B., Sathaporn, N., & Vattanavanit, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-24270-7 · Cited by 3
“Time-weight average (TWA) of area under the MAP 65 mmHg is a combination of severity and duration of hypotension that calculated by sum of area under the MAP 65 mmHg/duration of monitoring 8 .”
Influential
Intraoperative hemodynamic management in abdominal aortic surgery guided by the Hypotension Prediction Index: the Hemas multicentric observational study
Giustiniano, E., Nisi, F., Ferrod, F., Lionetti, G., Viscido, C., Reda, A., Piccioni, F., Buono, G., & Cecconi, M. (2025) · Journal of Anesthesia Analgesia and Critical Care · doi:10.1186/s44158-024-00222-x · Cited by 3
Goal-directed therapy: what is the goal again?
Yerdon, A., Taylor, K., Woodfin, K., Richey, R., McMullan, S. P., & Chappell, D. (2025) · Perioperative Medicine · doi:10.1186/s13741-025-00533-9 · Cited by 3
“Recent innovations in advanced hemodynamic monitoring using artificial intelligence and machine learning make this possible (Davies et al. 2020; Edwards Lifesciences 2024; Frassanito et al. 2023; Hatib et al. 2018; Kouz et al. 2023b; Li et al. 2022; Saugel et al. 2019; Wijnberge et al. 2020).”
Ephedrine vs. phenylephrine effect on sublingual microcirculation in elderly patients undergoing laparoscopic rectal cancer surgery
Zhang, Y., Jin, L., Liu, H., Meng, X., & Ji, F. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.969654 · Cited by 3
Progress and Perspective of Artificial Intelligence and Machine Learning of Prediction in Anesthesiology
Xia, M., Xu, T., & Jiang, H. (2021) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-021-2331-3 · Cited by 4
Development and validation of a predictive model for PACU hypotension in elderly patients undergoing sedated gastrointestinal endoscopy
Wang, Z., Ma, J., Liu, X., & Gao, J. (2024) · Aging Clinical and Experimental Research · doi:10.1007/s40520-024-02807-6 · Cited by 2
“To date, research efforts aimed at developing and validating predictive models for perioperative hypotension have largely focused on the intraoperative period [21–25].”
Hypotension prediction index: comparison between invasive and non-invasive pressure inputs
Sarhadi, K., Hamman, J., Ávila, J., Jian, Z., & Fleming, N. (2025) · BMC Anesthesiology · doi:10.1186/s12871-025-03086-y · Cited by 2
“The Hypotension Prediction Index (HPI) is a newer parameter that provides advanced warning of critical hypotension [11, 12] facilitating earlier interventions, thus decreasing the incidence and magnitude of intra-operative hypotension [13].”
Self-citation
The impact of hypotension prediction index on intraoperative hypotension in elderly patients during pancreaticoduodenectomy: A randomized controlled trial
Lai, C.-J., Yeh, Y., Wang, J., Han, Y., Shiu, T.-R., & Chien, K. (2025) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2025.10.030 · Cited by 2
Artificial intelligence and EEG during anesthesia: ideal match or fleeting bond?
Introna, M., Karippacheril, J. G., Pilla, S., Trimarchi, D., Gemma, M. F., Martino, D., & Carozzi, C. (2026) · Artificial Intelligence Surgery · doi:10.20517/ais.2025.68 · Cited by 2
Comparison of the Hypotension Prediction Index and pulse pressure variation-guided haemodynamic management for intra-operative hypotension during kidney transplant: A randomised controlled trial
Aditya, A. S., Kajal, K., Sethi, S., Premkumar, M., Naik, N. M., & Sharma, A. (2026) · Indian Journal of Anaesthesia · doi:10.4103/ija.ija_1519_25 · Cited by 2
Hypotension prediction index in surgical patients: A systematic review and meta-analysis of randomized controlled trials
Xie, C.-M., Shen, M., Yao, Y.-T., Yang, K., He, L.-X., & Dai, Z. (2026) · Medicine · doi:10.1097/md.0000000000049257 · Cited by 2
“[10,11] Davies et al reported sensitivities of 85.8%, 81.7%, and 80.6% at 5, 10, and 15-minute prediction windows, with corresponding high specificity.”
Influential
The Hemodynamic Management and Postoperative Outcomes After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: A Prospective Observational Study
Solanki, S. L., Agarwal, V., Ambulkar, R. P., Joshi, M. P., Chawathey, S., Rudrappa, S. P., BHANDARE, M. S., & Saklani, A. P. (2024) · Critical Care Research and Practice · doi:10.1155/ccrp/8815211 · Cited by 2
AIM in Anesthesiology
Komorowski, M., & Joosten, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_246-1 · Cited by 2
Monitorage non-invasif de la pression artérielle en anesthésie-réanimation : un état de l’art
Lakhal, K., & Dauvergne, J. E. (2022) · Anesthésie & Réanimation · doi:10.1016/j.anrea.2022.01.006 · Cited by 2
Con: Hypotension Prediction Index—A New Tool to Predict Hypotension in Cardiac Surgery?
Smith, A. D., & Túróczi, Z. (2023) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2023.05.046 · Cited by 2
Validation of Preload Assessment Technologies at Altitude in a Porcine Model of Hemorrhage
Baucom, M. R., Wallen, T. E., Price, A. D., Caskey, C., Schuster, R. M., Smith, M. P., Blakeman, T. C., Strilka, R. J., & Goodman, M. D. (2023) · Journal of Surgical Research · doi:10.1016/j.jss.2023.07.046 · Cited by 1
Intraoperative Hypotonie beim Kind – Messung und Therapie
Bratke, S., Schmid, S., Sabharwal, V., Jungwirth, B., & Becke, K. (2024) · Die Anaesthesiologie · doi:10.1007/s00101-024-01461-x · Cited by 1
The Hypotension Prediction Index in Free Flap Transplant in Head and Neck Surgery: Protocol of a Prospective Randomized Controlled Trial
Szrama, J., Gradys, A., Woźniak, A., Nowak, Z., Bartkowiak, T., Lohani, A., Zwoliński, K., Koszel, T., & Kusza, K. (2025) · Life · doi:10.3390/life15030400 · Cited by 1
Association between intraoperative hypotension and motor evoked potential change in ossification of posterior longitudinal ligament and/or ossification of ligamentum flavum surgery
Liu, T., Wang, W., Qi, H., Li, J., Guo, B., Jiang, K., Zhao, S., Wang, R., Zhang, J., Wu, G., et al. (2025) · North American Spine Society Journal (NASSJ) · doi:10.1016/j.xnsj.2025.100820 · Cited by 1
“…our previous studies, MEP signals were obtained before and after critical surgical procedures or according to our speciﬁc needs as followings [ 4 , 6 ]: (1) Intraoperative signals were documented even when MEP remained unchanged, particularly when the surgical team suspected potential iatrogenic…”
Bayesian parameter inference and uncertainty-informed sensitivity analysis in a 0D cardiovascular model for intraoperative hypotension
Thiel, J., Žličar, M., Steinseifer, U., Kirn, B., & Neidlin, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111371 · Cited by 1
Intérêt de l’intelligence artificielle en anesthésie-réanimation
Ariès, P., Belot, F., Py, N., Humbert, P., & Dubost, C. (2025) · EMC-Anesthésie réanimation/Encyclopédie médico-chirurgicale. Anesthésie, réanimation · doi:10.1016/s0246-0289(25)48411-4 · Cited by 1
Comparison of Intraoperative Hypotension Outcomes with and Without Hypotension Prediction Index Tool in Patients Undergoing Brain Tumour Surgery: A Study Protocol for a Randomised Controlled Trial
Francis, T., Sriganesh, K., Kumar, R. M., & Chakrabarti, D. (2026) · Neurology India · doi:10.4103/neurol-india.neurol-india-d-25-00367 · Cited by 1
Performance of the Hypotension Prediction Index as an indicator of hypotensive haemodynamic instability in surgical patients
Davies, S., Buddi, S. P., Jian, Z., Fleming, N., Cannesson, M., Sander, M., Veelo, D. P., Vlaar, A. P. J., Scheeren, T., Mythen, M. G., et al. (2026) · Perioperative Medicine · doi:10.1186/s13741-026-00661-w · Cited by 1
“The arterial line dataset included 871 surgical patients from six previously reported studies (Hatib et al., 2018; Davies et al., 2020; Schneck et al., 2019; Shin et al., 2021; Wijnberge et al., 2020).”
Self-citation
Künstliche Intelligenz in der Anästhesiologie – Schwerpunkt Hämodynamik
Heringlake, M., Schemke, S., & Muras, L. (2026) · AINS - Anästhesiologie · Intensivmedizin · Notfallmedizin · Schmerztherapie · doi:10.1055/a-2683-2991 · Cited by 1
Predictive value of Hypotension Prediction Index in liver transplantation: a prospective study
Yeh, B.-H., Yu-Chieh, L. U., Liu, F.-C., Tsai, H., Chih-Chung, L. I. N., Chun-Yu, C. H. E. N., Lee, W.-C., & Tsai, Y. (2026) · Minerva Anestesiologica · doi:10.23736/s0375-9393.25.19432-7 · Cited by 1
Does Hypotension Prediction Index limit the occurrence of postoperative complications associated with intraoperative hypotension? A systematic review and model-averaged Bayesian meta-analysis of statistically sound studies
Piscopo, R., Giustiniano, E., Meco, M., Nisi, F., Corte, F. D., Piccioni, F., & Cecconi, M. (2026) · Journal of Anesthesia · doi:10.1007/s00540-026-03741-9 · Cited by 1
“While initial studies suggested that HPI may be beneficial in preventing intraoperative hypotension [10, 11], its effectiveness across various clinical settings remains uncertain.”
The Effectiveness of Private and Public Blockchain Frameworks for Predicting Hypotension in Edge Computing Systems
Dharani, D., & Anitha, K. (2021) · International Conference on Combinatorial and Optimization (ICCAP) · doi:10.4108/eai.7-12-2021.2314550 · Cited by 1
“Hypotension can lead to dizziness and faintness (Davies, et al 2020).”
Background
AIM in Anesthesiology
Komorowski, M., & Joosten, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_246 · Cited by 1
Pro: Hypotension Prediction Index—A New Tool to Predict Hypotension in Cardiac Surgery?
Parsons, H. M., & Zilahi, G. (2023) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2023.05.023 · Cited by 1
Non-invasive waveform analysis for emergency triage via simulated hemorrhage: An experimental study using novel dynamic lower body negative pressure model
Nesaragi, N., Høiseth, L. Ø., Qadir, H. A., Rosseland, L. A., Halvorsen, P. S., & Balasingham, I. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.06.002 · Cited by 1
Big Data und künstliche Intelligenz in der Anästhesie
Sander, J., Simon, P., & Hinske, L. C. (2023) · Die Anaesthesiologie · doi:10.1007/s00101-023-01362-5 · Cited by 1
Early Autocalibrated Arterial Waveform Analysis for the Management of Burn Shock—A Cohort Study
Kruse, M., Liesenborghs, K. E., Josuttis, D., Plettig, P., Gümbel, D., Lenz, I. K., Güthoff, C., Gebhardt, V., & Schmittner, M. D. (2024) · Journal of Intensive Care Medicine · doi:10.1177/08850666231224388 · Cited by 1
“16,17 The bene ﬁ ts of aEHM devices are especially appreciated, when the establishment of a calibrated system is not practical, even though determining the cardiac pre-and afterload values of a patient are of critical importance.”
Background
LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization
Liu, L., Hang, Y., Chen, R., He, X., Jin, X., Wu, D., & Li, Y. (2024) · Physiological Measurement · doi:10.1088/1361-6579/ad4e92 · Cited by 1
“For example, Davies et al (2020) achieved a recall rate of 85.8% when predicting AHE 5 min in advance, while our model can achieve a recall rate of 94.24%.”
Influential
A retrospective study of the effects of a vasopressor bolus on systolic slope (dP/dt) and dynamic arterial elastance (Eadyn)
Abdallah, A. C., Song, S. H., & Fleming, N. (2024) · BMC Anesthesiology · doi:10.1186/s12871-024-02574-x · Cited by 1
“The HPI software has been validated as a reliable predictive index in both cardiac [10] and non-cardiac [11] surgical procedures and has been shown to decrease the total duration of intraoperative hypotension [12].”
Method
Artificial Intelligence and Predictive Analytics
de Keijzer, I. N., Vistisen, S. T., & Scheeren, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-71752-0_29 · Cited by 1
Self-citation
DO SHOCK INDEX, MODIFIED SHOCK INDEX, AND SHOCK INDEX BY AGE HAVE A PREDICTIVE VALUE IN DETERMINING THE RISK OF POST-SPINAL HYPOTENSION IN ELDERLY PATIENTS
Aytaç, İ. (2021) · The Turkish Journal of Geriatrics · doi:10.31086/tjgeri.2021.233 · Cited by 1
Predictive medicine, machine learning, and anesthesia
Llevot, J. M. R. (2020) · Revista Española de Anestesiología y Reanimación (English Edition) · doi:10.1016/j.redare.2020.03.004 · Cited by 1
Why chest compressions should start when systolic arterial blood pressure is below 50 mm Hg in the anaesthetised patient. Reply to Br J Anaesth 2020; 124: 234–8
Khandelwal, A., Singh, G. P., & Chaturvedi, A. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.03.019 · Cited by 1
Performance of Hypotension Prediction Index for Prevention of Hypotension and Results on Biomarkers of Organ Injury and Oxidative Stress During Elective Non-Cardiac Surgery: A Randomized Controlled TrialPreprint
Murabito, P., Astuto, M., Sanfilippo, F., Via, L. L., Basile, F., Cappellani, A., Longhitano, L., Distefano, A., Volti, G. L., & Vasile, F. (2021) · Research Square · doi:10.21203/rs.3.rs-851209/v1 · Cited by 1
Conversations with the Editors: Artificial Intelligence–Based Technologies Leading the Innovation in Surgical Care
Ravera, L., Scheeren, T., Piaggio, D., & Federici, C. (2022) · Clinical Therapeutics · doi:10.1016/j.clinthera.2022.05.004 · Cited by 1
Self-citation
Management of Hypotension: Implications for Noncardiac Surgery and Intensive Care
Schneck, E., Saugel, B., & Sander, M. (2020) · Annual update in intensive care and emergency medicine · doi:10.1007/978-3-030-37323-8_15
The value of hypotensive prediction index and dP/dtmax to predict and treat hypotension in a patient with a dilated cardiomyopathy
Solares, G., Barredo, F., & García, M. I. M. (2020) · Revista Española de Anestesiología y Reanimación (English Edition) · doi:10.1016/j.redare.2020.02.004
Medicina predictiva, aprendizaje automático y anestesia
Llevot, J. M. R. (2020) · Revista Española de Anestesiología y Reanimación · doi:10.1016/j.redar.2020.03.001
Zielgerichtetes hämodynamisches Monitoring im OP: Wozu und womit?
Lorenzen, U., & Grünewald, M. (2022) · AINS - Anästhesiologie · Intensivmedizin · Notfallmedizin · Schmerztherapie · doi:10.1055/a-1472-4285
Applicability of hypotension prediction index to surgical patients
Lee, K., & Jung, K. T. (2022) · Medical Biological Science and Engineering · doi:10.30579/mbse.2022.5.2.57
Nouveaux développements en monitorage hémodynamique
Riad, Z., Didier, L., & Fellahi, J.-L. (2022) · Le Praticien en Anesthésie Réanimation · doi:10.1016/j.pratan.2022.09.004
Optimization of a laparoscopic procedure for advanced intrahepatic cholangiocarcinoma based on the concept of “waiting time”: a preliminary report
Liao, C., Wang, D., Jiang, B.-H., Huang, L., Lin, T.-S., Qiu, F., Zhou, S., Wang, Y., Zheng, X., Tian, Y., et al. (2022) · BMC Cancer · doi:10.1186/s12885-022-10323-x
Artificial Intelligence in Prediction for Intraoperative Hypotension and Hypoxemia
Cao, S. (2023) · Springer eBooks · doi:10.1007/978-981-99-5925-9_6
Validation and clinical applicability of the Hypotension Prediction Index in a general ICU population: a prospective observational cohort study Study acronym Prediction of Hemodynamic Instability in Patients Admitted to the ICU; the PHYSIC studyPreprint
Terwindt, L. E., Veelo, D. P., Ligtenberg, M., Schuurmans, J., Hollmann, M. W., Schenk, J., Vlaar, A. P. J., & van der Ster, B. J. P. (2024) · Research Square · doi:10.21203/rs.3.rs-4169157/v1
Progress of the Limitation and Inadequacy of Hypotension Prediction Index in Clinical Application
周, 函. (2024) · Advances in Clinical Medicine · doi:10.12677/acm.2024.1441181
The hidden cost of hypotension: redefining hemodynamic management to improve patient outcomes
Lineburger, E. B., Tempe, D. K., da Costa, L. G. V., Mackensen, G. B., Papa, F. D. V., Galhardo, C., Tahan, M. R. E., Salgado‐Filho, M. F., Díaz, R., & Schmidt, A. P. (2024) · Brazilian Journal of Anesthesiology (English Edition) · doi:10.1016/j.bjane.2024.844581
“23 − 26 Thus, it is clear that maintaining BP levels above intraoperative hypotension thresholds is challenging.”
Big Data und künstliche Intelligenz in der Anästhesie
Sander, J., Simon, P., & Hinske, L. C. (2025) · Wiener klinisches Magazin · doi:10.1007/s00740-024-00546-y
Feasibility of Hypotension Prediction Index-Guided Monitoring for Epidural Labor Analgesia: A Randomized Controlled Trial
Aloziem, O. U., Lin, H. S., Kelly, K., Nicholas, A. K., Romeo, R. C., Smith, C. T., Yu, X., & Lim, G. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14145037
“Other studies have similarly demonstrated the effectiveness of HPI in predicting hypotension in perioperative settings [10–12].”
Smart Healthcare: The Catalytic Role of AI in Transforming Diagnosis and Care
Dutta, D., Samantaray, S., Raut, A., & Mohanty, A. K. (2025) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic64892.2025.11158086
“Logistic Regression was used to formulate an index (Hypotension Prediction Index) which measured the arterial blood flow and is used for measuring the intraoperative change in blood pressure and monitor it during the early stages of hypotension[10].”
Feasibility of continuous high-resolution bioreactance monitoring during cesarean delivery under spinal anesthesia
Gaik, C., Bahle, T., Keil, C. N., Wulf, H., & Vojnar, B. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1647102
Advanced Haemodynamic Monitoring During Transfemoral Aortic Valve Replacement: A Prospective Pilot Study
Bergmann, A., Woldt, P., Steins, L., Hulde, N., Fliegenschmidt, J., Piper, C., Rudolph, T. K., & von Dossow, V. (2025) · Life · doi:10.3390/life15111714
“%) for forecasting hypotension five minutes before onset, as reported in validation studies [9,10].”
Blood Pressure Optimization During Fetoscopic Repair of Open Spinal Dysraphism: Insights from Advanced Hemodynamic Monitoring
Vojnar, B., Belfort, M. A., Sutton, C. D., Keil, C. N., Bedei, I. A., Kalmus, G., Wulf, H., Köhler, S., & Gaik, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14228055
“The HPI is an algorithm-based machine learning model that analyzes arterial pressure waveforms to estimate the likelihood of an impending hypotensive event, defined as a mean arterial pressure (MAP) < 65 mmHg lasting for at least one minute [23–26].”
Additional file 1 of Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis
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.26500334
Additional file 1 of Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study
Benson, B. E., Belle, A., Lee, S., Bassin, B. S., Medlin, R. P., Sjoding, M. W., & Ward, K. R. (2024) · Figshare · doi:10.6084/m9.figshare.26619043
Additional file 1 of Hypotension prediction index for prevention of intraoperative hypotension in patients undergoing general anesthesia: a randomized controlled trial
Lai, C.-J., Cheng, Y.-J., Han, Y.-Y., Hsiao, P.-N., Lin, P.-L., Chiu, C.-T., Lee, J.-M., Tien, Y.-W., & Chien, K. (2024) · Figshare · doi:10.6084/m9.figshare.26045479
Additional file 2 of 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. E., Drover, D. R., Pennington, M. W., Domino, K. B., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26688707
Additional file 1 of The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
Denn, S. M., Schneck, E., Jablawi, F., Bender, M., Schmidt, G., Habicher, M., Uhl, E., & Sander, M. (2022) · Figshare · doi:10.6084/m9.figshare.21546852
Additional file 4 of 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. E., Drover, D. R., Pennington, M. W., Domino, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26688713
Additional file 1 of 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. E., Drover, D. R., Pennington, M. W., Domino, K. B., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26688704
Additional file 3 of Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis
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.26757757
Additional file 3 of 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. E., Drover, D. R., Pennington, M. W., Domino, K. B., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26688710

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks