Evidence map›Paper›PMID 32065827›Full record

Trial reportJAMA2020

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.

Marije Wijnberge, Bart F Geerts, Liselotte Hol, Nikki Lemmers, Marijn P Mulder, Patrick Berge, Jimmy Schenk, Lotte E Terwindt, Markus W Hollmann, Alexander P Vlaar and 1 more

15 registry-linked trialsOpen access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JAMA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 15 registered trials, which are not on this map. Cited by 263 papers, 16 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
263citing papers in PubMed, 16 pooled it
55.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03376347 nacompletednot on this map

HYPE Trial. Intraoperative Implementation of the Hypotension Probability Indicator (HPI) Algorithm: a Pilot Randomized Controlled Clinical Trial

TypeinterventionalSponsorAcademisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)Ran2017 to 2019Enrolled100ConditionsIntraoperative HypotensionArmsFlotracIQ with HPI algorithm
NCT05010941 completednot on this mapstarted 2021, after this paper: background citation

Performance of the Hypotension Prediction Index in Living Donor Liver Transplantation

TypeobservationalSponsorSeoul National University HospitalRan2021 to 2022Enrolled20ConditionsLiving Donor Liver Transplantation, HypotensionArmsHypotension Prediction Index
NCT05117151 completednot on this mapstarted 2022, after this paper: background citation

Ability of Hypotension Prediction Index to Predict Future Hypotension Events in Each Phase of Thoracic Surgery: an Observational Pilot Study

TypeobservationalSponsorNational Taiwan University HospitalRan2022 to 2023Enrolled40ConditionsPerioperative/Postoperative ComplicationsArmshypotension prediction index
NCT05274412 naunknown statusnot on this mapstarted 2022, after this paper: background citation

Clinical Efficacy of Hypotension Prediction Index Based Perioperative Protocolized Hemodynamic Management in Geriatric Orthopedic Surgery

TypeinterventionalSponsorNational Taiwan University HospitalRan2022 to 2024Enrolled280ConditionsOrthopedic Disorder, Hemodynamic InstabilityArmshypotension prediction index(HPI), ordinary goal-directed hemodynamic therapy
NCT05569265 nacompletednot on this mapstarted 2022, after this paper: background citation

Open-label, Multicenter, Randomized, Controlled Trial of Hemodynamic Optimization Based on the Hypotension Prediction Index (HPI) Compared to Standard Practice in Adult Patients Undergoing Elective Major Abdominal Surgery.

TypeinterventionalSponsorMaria José Clara Colomina SolerRan2022 to 2024Enrolled958ConditionsPostoperative Acute Kidney Injury, Postoperative ComplicationsArmsIntraoperative hemodynamic management
NCT05738603 narecruitingnot on this mapstarted 2024, after this paper: background citation

The Hypotension Prediction Index in Free Flap Transplant in Head and Neck Surgery - a Prospective Randomized Controlled Trial.

TypeinterventionalSponsorJakub SzramaRan2024 to 2026Enrolled206ConditionsHypotension, Head and Neck Cancer, Perioperative Complication, Postoperative ComplicationsArmsHypotension Prediction Index
NCT05821647 nacompletednot on this mapstarted 2021, after this paper: background citation

Effect of a Machine Learning-Derived Early Warning System for Hypotension vs Standard Care on Depth and Duration of Intra- and Postoperative Hypotension in Elective Cardiac Surgery

TypeinterventionalSponsorAcademisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)Ran2021 to 2023Enrolled130ConditionsIntraoperative Hypotension, Postoperative Hypotension, HypotensionArmsHPI guided treatment advice
NCT05840432 unknown statusnot on this mapstarted 2023, after this paper: background citation

Description of the Performance of a New Index for Hypotension Prediction During the Immediate Postoperative Period of Cardiac Surgery in the Intensive Care Unit

Typeobservational_patient_registrySponsorInstituto de Investigación Sanitaria de la Fundación Jiménez DíazRan2023 to 2024Enrolled41ConditionsPostoperative HypotensionArmsPostoperative hypotension prediction index
NCT06097052 completednot on this mapstarted 2023, after this paper: background citation

The Effect of Hypotension Predictive Index on Intraoperative Hypotension Events and Duration During Pancreatic Surgery. A Retrospective Pre-post Observational Study.

TypeobservationalSponsorIstituto Clinico HumanitasRan2023 to 2024Enrolled48ConditionsHypotension During Surgery, Perioperative ComplicationArmsHypotension Predictive Index
NCT06158542 completednot on this mapstarted 2023, after this paper: background citation

Development of an Artificial Intelligence Algorithm to Predict Hypotension Risk After Induction in Cesarean Sections With Spinal Anesthesia

TypeobservationalSponsorHacettepe UniversityRan2023 to 2023Enrolled370ConditionsSpinal Anesthesia Induced Hypotension, Caeserian SectionArmsSpinal Anesthesia, Blood Sampling
NCT06291714 narecruitingnot on this mapstarted 2024, after this paper: background citation

Prospective Interventional Study Investigating a Goal-directed Therapy Based on Non-invasive Measurement of the Hypotension Prediction Index for the Reduction of Hypotension in Patients Undergoing Major Orthopedic and Trauma Surgery

TypeinterventionalSponsorUniversity of GiessenRan2024 to 2026Enrolled150ConditionsIntraoperative Hypotension, Acute Kidney Injury, Myocardial Injury After Non-cardiac SurgeryArmsGDT-based hemodynamic management based on Clearsight device
NCT06347211 nacompletednot on this mapstarted 2024, after this paper: background citation

Is the Hypotension Probability Indicator with Goal Directed Haemodynamic Treatment Useful in Predicting and Treating Hypotension in Patients Undergoing Transcatheter Aortic Valve Implantation(TAVI) or MitraClip

TypeinterventionalSponsorAttikon HospitalRan2024 to 2024Enrolled100ConditionsHypotension During Surgery, Prevention of HypotensionArmsAcumen IQ sensor with Hemosphere monitor incorporating the HPI algorithm
NCT06982001 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

Importance of Arterial Measurement Sites on Intraoperative Hemodynamic Management of Major Abdominal Surgeries and Artificial Intelligence-based Prediction of Arterial Gradient Using Peripheral Radial Pressure Waveform Analysis

TypeinterventionalSponsorCiusss de L'Est de l'Île de MontréalRan2025 to 2028Enrolled204ConditionsPerioperative Care, Hemodynamics, Monitoring Blood Pressure, Arterial LinesArmsBrachial line guided hemodynamic management, Radial line guided hemodynamic management, Radial line catheterization, Brachial line catheterization
NCT07070973 narecruitingnot on this mapstarted 2025, after this paper: background citation

HEMOdynamic Proactive Management During Cytoreductive Surgery and Intraperitoneal hyPErthermic Chemotherapy for Advanced Ovarian Cancer

TypeinterventionalSponsorFondazione Policlinico Universitario Agostino Gemelli IRCCSRan2025 to 2027Enrolled52ConditionsOvarian Cancer, Hemodynamic Instability, Fluid OverloadArmsAssisted Fluid Management, Goal Directed Fluid Therapy (GDT)
NCT07301307 narecruitingnot on this mapstarted 2026, after this paper: background citation

The Use of the Hypotension Prediction Index (HPI) Combined With the Assisted Fluid Management (AFM) Software for Perioperative Hemodynamic Optimization in Patients Undergoing General Anesthesia.

TypeinterventionalSponsorNational and Kapodistrian University of AthensRan2026 to 2027Enrolled100ConditionsIntraoperative HypotensionArmsAcumen IQ sensor with Hypotension Prediction Index (HPI) and Assisted Fluid Management (AFM) software, Conventional Intraoperative Hemodynamic management
3 · Its place in the literature

Who cites it

263 citing papers in PubMed, 16 syntheses or guidelines pooled it, 517 citations in OpenAlex.

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203 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Marije WijnbergeDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Bart F GeertsDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Liselotte HolDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Nikki LemmersDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Marijn P MulderDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Patrick BergeDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Jimmy SchenkDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Lotte E TerwindtDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Markus W HollmannDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Alexander P VlaarDepartment of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Denise P VeeloDepartment of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Amsterdam University Medical Centers · NLUniversity of Twente · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Elective Surgical ProceduresMachine LearningAgedAnesthesia, GeneralBlood Pressure DeterminationFemaleHumansHypotensionIntraoperative ComplicationsMaleMiddle AgedOperative TimeTertiary Care CentersTime Factors

Identifiers

PMID32065827
PMCPMC7078808
OpenAlexW3006079219

What OpenQuestion holds

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Registered trials

and 9 more above

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.