dc.contributor.author | Monge García, Manuel Ignacio | es_ES |
dc.contributor.author | García López, Daniel | es_ES |
dc.contributor.author | Gayat, Étienne | es_ES |
dc.contributor.author | SAnder, Michael | es_ES |
dc.contributor.author | Bramlage, Peter | es_ES |
dc.contributor.author | Cerutti, Elisabetta | es_ES |
dc.contributor.author | Davies, Simon James | es_ES |
dc.contributor.author | Donati, Abele | es_ES |
dc.contributor.author | Draisci, Gaetano | es_ES |
dc.contributor.author | Frey, Ulrich H. | es_ES |
dc.contributor.author | Noll, Eric | es_ES |
dc.contributor.author | Ripollés-Melchor, Javier | es_ES |
dc.contributor.author | Wulf, Hinnerk | es_ES |
dc.contributor.author | Saugel, Bernd | es_ES |
dc.contributor.other | Universidad de Cantabria | es_ES |
dc.date.accessioned | 2023-02-24T15:16:04Z | |
dc.date.available | 2023-02-24T15:16:04Z | |
dc.date.issued | 2022-09-23 | es_ES |
dc.identifier.issn | 2077-0383 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10902/27883 | |
dc.description.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. | es_ES |
dc.description.sponsorship | Funding: Edwards Lifesciences SA, Department of Critical Care, Route de l’Etraz 70, 1260 Nyon, Switzerland funded the study and acts as the legal sponsor. The sponsor/funder had an active role in the design of the study. The collection, analysis, and interpretation of the data will be a collaborative effort of all investigators, who will also write the manuscript.
Acknowledgments: We acknowledge the support of all participating patients and their physicians. We also acknowledge the tremendous contribution of the staff at Edwards Lifesciences, especially Edward Hembrow, Tim van den Boom, Anne Halfmann, Pierre Sibileau, Barbara Plasschaert, Volker Haag, Giulia Torricella and Alessia Longo. We further appreciate the excellent project management secured by Daniel Greinert, Marie Zielinksi and Claudia Lüske at the Institute for Pharmacology and Preventive Medicine (Cloppenburg, Germany). Data are captured using the s4trials software provided by Software for Trials Europe GmbH (Berlin, Germany). | es_ES |
dc.format.extent | 12 p. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Attribution 4.0 International | * |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.source | Journal of clinical medicine 2022, 11, 5585 | es_ES |
dc.subject.other | Advanced hemodynamic monitoring | es_ES |
dc.subject.other | Artificial intelligence | es_ES |
dc.subject.other | Blood pressure | es_ES |
dc.subject.other | Hemodynamic instability | es_ES |
dc.subject.other | Machine learning | es_ES |
dc.subject.other | Postoperative complications | es_ES |
dc.title | Hypotension prediction index software to prevent intraoperative hypotension during major non-cardiac surgery: protocol for a european multicenter prospective observational registry (EU-HYPROTECT) | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherVersion | https://doi.org/10.3390/jcm11195585 | es_ES |
dc.rights.accessRights | openAccess | es_ES |
dc.identifier.DOI | 10.3390/jcm11195585 | es_ES |
dc.type.version | publishedVersion | es_ES |