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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
Open accessAbstract
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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In the citing paper
“AI could thus improve RCTs by allowing the enrolment of very subtle subgroups of patients with hundreds of specific inclusion criteria over dozens of centres, a task impossible to perform by humans in real-time practice, improving trial efficiency in enrolling enriched populations [65–67].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [67]
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“It was developed using an ML, logistic regression-based model analyzing components of the arterial waveform (41, 43, 44).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [44]
In the citing paper
“The Hypotension Prediction Index (HPI; Edwards Lifesciences, Irvine, CA) is an algorithm created to aid in the early detection of intraoperative hypotension, defined as mean arterial pressure <65 mmHg for non-cardiac surgeries (41, 43, 44).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [44]
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Metadata
- 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
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2020-03-17
- Volume
- 323
- Issue
- 11
- Pages
- 1052
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- 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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