Evidence map›Paper›PMID 36233455›Full record

ArticleJournal of clinical medicine2022

Hypotension Prediction Index Software to Prevent Intraoperative Hypotension during Major Non-Cardiac Surgery: Protocol for a European Multicenter Prospective Observational Registry (EU-HYPROTECT).

Manuel Ignacio Monge García, Daniel García-López, Étienne Gayat, Michael Sander, Peter Bramlage, Elisabetta Cerutti, Simon James Davies, Abele Donati, Gaetano Draisci, Ulrich H Frey and 4 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.6field-weighted citation impact, top 10% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 14 institutions in 7 countries.

Manuel Ignacio Monge GarcíaMedical Affairs Department, Critical Care Europe, Edwards Lifesciences, 1260 Nyon, Switzerland.ORCID 0000-0003-1849-2114
Daniel García-LópezDepartment of Anaesthesiology and Reanimation, University Hospital Marqués de Valdecilla, 39008 Santander, Spain.ORCID 0000-0001-8361-8372
Étienne GayatUniversité Paris Cité, INSERM, 75006 Paris, France.
Michael SanderDepartment of Anaesthesiology, Intensive Care Medicine and Pain Medicine, University Hospital Giessen, Justus-Liebig University Giessen, 35392 Giessen, Germany.ORCID 0000-0003-1677-3609
Peter BramlageInstitute for Pharmacology and Preventive Medicine, 49661 Cloppenburg, Germany.
Elisabetta CeruttiDepartment of Anesthesia, Transplant and Surgical Intensive Care, Azienda Ospedaliero Universitaria Ancona, 60126 Ancona, Italy.
Simon James DaviesYork and Scarborough Teaching Hospitals NHS Foundation Trust, York YO31 8HE, UK.
Abele DonatiDepartment of Biomedical Sciences and Public Health, Università Politecnica delle Marche, 60126 Ancona, Italy.
Gaetano DraisciUnit of Obstetric and Gynecologic Anesthesia, IRCCS Fondazione Policlinico Universitario Agostino Gemelli, 00168 Rome, Italy.
Ulrich H FreyDepartment of Anesthesiology, Intensive Care, Pain and Palliative Care, Marien Hospital Herne, Ruhr-University Bochum, 44801 Bochum, Germany.ORCID 0000-0003-2961-6629
Eric NollDepartment of Anesthesiology and Intensive Care, Les Hôpitaux Universitaires de Strasbourg, 67098 Strasbourg, France.
Javier Ripollés-MelchorAnesthesia and Critical Care Department, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain.
Hinnerk WulfDepartment of Anaesthesiology-Intensive Care, University Hospital Marburg, 35043 Marburg, Germany.
Bernd SaugelDepartment of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany.ORCID 0000-0001-6619-0722
Agostino Gemelli University Polyclinic · ITEdwards Lifesciences (Switzerland) · CHHôpitaux Universitaires de Strasbourg · FRHospital Universitario Infanta Leonor · ESInserm · FRIPPMed (Germany) · DEJustus-Liebig-Universität Gießen · DEMarche Polytechnic University · ITMarqués de Valdecilla University Hospital · ESOspedali Riuniti di Ancona · ITPhilipps University of Marburg · DEUniversität Hamburg · DEUniversitätsklinik Marien Hospital Herne · DEYork Teaching Hospital NHS Foundation Trust · GB

Funding

Edwards Lifesciences (Switzerland) N/A
6 · The paper itself

Abstract

Background: Intraoperative hypotension is common in patients having non-cardiac surgery and associated with postoperative acute myocardial injury, acute kidney injury, and mortality. Avoiding intraoperative hypotension is a complex task for anesthesiologists. Using artificial intelligence to predict hypotension from clinical and hemodynamic data is an innovative and intriguing approach. The AcumenTM Hypotension Prediction Index (HPI) software (Edwards Lifesciences; Irvine, CA, USA) was developed using artificial intelligence—specifically machine learning—and predicts hypotension from blood pressure waveform features. We aimed to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery. Methods: We built up a European, multicenter, prospective, observational registry including at least 700 evaluable patients from five European countries. The registry includes consenting adults (≥18 years) who were scheduled for elective major non-cardiac surgery under general anesthesia that was expected to last at least 120 min and in whom arterial catheter placement and HPI monitoring was planned. The major objectives are to quantify and characterize intraoperative hypotension (defined as a mean arterial pressure [MAP] < 65 mmHg) when using HPI monitoring. This includes the time-weighted average (TWA) MAP < 65 mmHg, area under a MAP of 65 mmHg, the number of episodes of a MAP < 65 mmHg, the proportion of patients with at least one episode (1 min or more) of a MAP < 65 mmHg, and the absolute maximum decrease below a MAP of 65 mmHg. In addition, we will assess causes of intraoperative hypotension and investigate associations between intraoperative hypotension and postoperative outcomes. Discussion: There are only sparse data on the effect of using HPI monitoring on intraoperative hypotension in patients having elective major non-cardiac surgery. Therefore, we built up a European, multicenter, prospective, observational registry to describe the incidence, duration, severity, and causes of intraoperative hypotension when using HPI monitoring in patients having elective major non-cardiac surgery.

Indexed as

advanced hemodynamic monitoringartificial intelligenceblood pressurehemodynamic instabilitymachine learningpostoperative complications

Identifiers

PMID36233455
PMCPMC9571548
OpenAlexW4297923307

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.