Cited by 586Hemodynamic Monitoring and TherapyCardiac, Anesthesia and Surgical OutcomesHealthcare Technology and Patient Monitoring

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Effect of a Machine Learning–Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial

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Wijnberge, Marije, Geerts, Bart F., Hol, Liselotte, Lemmers, Nikki, Mulder, Marijn P., Berge, Patrick, Schenk, Jimmy, Terwindt, Lotte E., Hollmann, Markus W., Vlaar, Alexander P., Veelo, Denise P.·2020·JAMA

DOI 10.1001/jama.2020.0592PMC PMC7078808

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Abstract

Importance: Intraoperative hypotension is associated with increased morbidity and mortality. A machine learning-derived early warning system to predict hypotension shortly before it occurs has been developed and validated. Objective: To test whether the clinical application of the early warning system in combination with a hemodynamic diagnostic guidance and treatment protocol reduces intraoperative hypotension. Design, Setting, and Participants: Preliminary unblinded randomized clinical trial performed in a tertiary center in Amsterdam, the Netherlands, among adult patients scheduled for elective noncardiac surgery under general anesthesia and an indication for continuous invasive blood pressure monitoring, who were enrolled between May 2018 and March 2019. Hypotension was defined as a mean arterial pressure (MAP) below 65 mm Hg for at least 1 minute. Interventions: Patients were randomly assigned to receive either the early warning system (n = 34) or standard care (n = 34), with a goal MAP of at least 65 mm Hg in both groups. Main Outcomes and Measures: The primary outcome was time-weighted average of hypotension during surgery, with a unit of measure of millimeters of mercury. This was calculated as the depth of hypotension below a MAP of 65 mm Hg (in millimeters of mercury) × time spent below a MAP of 65 mm Hg (in minutes) divided by total duration of operation (in minutes). Results: Among 68 randomized patients, 60 (88%) completed the trial (median age, 64 [interquartile range {IQR}, 57-70] years; 26 [43%] women). The median length of surgery was 256 minutes (IQR, 213-430 minutes). The median time-weighted average of hypotension was 0.10 mm Hg (IQR, 0.01-0.43 mm Hg) in the intervention group vs 0.44 mm Hg (IQR, 0.23-0.72 mm Hg) in the control group, for a median difference of 0.38 mm Hg (95% CI, 0.14-0.43 mm Hg; P = .001). The median time of hypotension per patient was 8.0 minutes (IQR, 1.33-26.00 minutes) in the intervention group vs 32.7 minutes (IQR, 11.5-59.7 minutes) in the control group, for a median difference of 16.7 minutes (95% CI, 7.7-31.0 minutes; P < .001). In the intervention group, 0 serious adverse events resulting in death occurred vs 2 (7%) in the control group. Conclusions and Relevance: In this single-center preliminary study of patients undergoing elective noncardiac surgery, the use of a machine learning-derived early warning system compared with standard care resulted in less intraoperative hypotension. Further research with larger study populations in diverse settings is needed to understand the effect on additional patient outcomes and to fully assess safety and generalizability. Trial Registration: ClinicalTrials.gov Identifier: NCT03376347.

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Additional file 4 of AcumenTM hypotension prediction index guidance for prevention and treatment of hypotension in noncardiac surgery: a prospective, single-arm, multicenter trial
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Additional file 1 of Postoperative delirium prediction using machine learning models and preoperative electronic health record data
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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
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Additional file 1 of Early prediction of hemodynamic interventions in the intensive care unit using machine learning
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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
Additional file 2 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.26500399

Authors: Wijnberge, M., Geerts, B. F., Hol, L., Lemmers, N., Mulder, M. P., Berge, P., Schenk, J., Terwindt, L. E., Hollmann, M. W., Vlaar, A. P., & Veelo, D. P.
Affiliations: Amsterdam UMC Location University of Amsterdam (NL)
Corresponding author: Vlaar, Alexander P.
ORCID: Wijnberge, Marije
Geerts, Bart F.
Hol, Liselotte
Mulder, Marijn P.
Schenk, Jimmy
Terwindt, Lotte E.
Hollmann, Markus W.
Vlaar, Alexander P.
Veelo, Denise P.
Venue: JAMA
Publisher: American Medical Association (AMA)
Published: 2020-03-17
Volume: 323
Issue: 11
Pages: 1052
ISSN: 0002-9955 (ISSN-L)
0098-7484
DOI: 10.1001/jama.2020.0592
PMC: PMC7078808
OpenAlex: W3006079219
Cited by: 586
References: 26
Citations by year: 2026: 111 · 2025: 101 · 2024: 109 · 2023: 88 · 2022: 81 · 2021: 59 · 2020: 36
FWCI: 60.27
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: JAMA (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion, other-oa)
University of Twente Research Information (publishedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion)
University of Twente Research Information (submittedVersion, other-oa)
Funders: Edwards Lifesciences
Topics: Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
Keywords: intraoperative hypotension, early warning systems, machine learning prediction, non-cardiac surgery, general anesthesia, hemodynamic monitoring, mean arterial pressure, clinical decision support, hypotension prevention, non-invasive blood pressure monitoring
MeSH terms: Machine Learning, Aged, Anesthesia, General, Blood Pressure Determination, Female, Humans, Hypotension, Intraoperative Complications, Male, Middle Aged, Time Factors, Elective Surgical Procedures, Operative Time, Tertiary Care Centers

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Effect of a Machine Learning–Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial

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