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

The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.

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 access
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
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1001/jama.2020.0592

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

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

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 26 references

1 of the 26 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. Pinsky, 2004, Functional Hemodynamic Monitoring: Update in Intensive Care and Emergency Medicine, 381

Cited by

Sorted by citations

Items 101–200 of 585

Related

See all

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
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
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

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