Publicação
Automation of sub-aortic velocity time integral measurements by transthoracic echocardiography: clinical evaluation of an artificial intelligence-enabled tool in critically ill patients
| dc.contributor.author | Gonzalez, Filipe | |
| dc.contributor.author | Varudo, Rita | |
| dc.contributor.author | Leote, João | |
| dc.contributor.author | Martins, Cristina | |
| dc.contributor.author | Bacariza, Jacobo | |
| dc.contributor.author | Fernandes, Antero | |
| dc.contributor.author | Michard, Frederic | |
| dc.date.accessioned | 2022-10-26T14:50:47Z | |
| dc.date.available | 2022-10-26T14:50:47Z | |
| dc.date.issued | 2022 | |
| dc.description | © 2022 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved. | pt_PT |
| dc.description.abstract | Point-of-care ultrasound techniques are increasingly used for the bedside assessment of cardiac function and haemodynamics in critically ill patients. The sub-aortic or left ventricular outflow tract velocity time integral (VTI) can be measured using pulsed-Doppler ultrasonography from a transthoracic apical 5-chamber view. Quantifying VTI is useful to discriminate between vasoplegic states (hypotension with normal/high VTI) and low flow states (low VTI). Measuring VTI is also useful to predict fluid responsiveness, either by quantifying the respiratory swings in VTI when patients are mechanically ventilated, or by quantifying VTI changes during a passive leg raising manoeuvre or a fluid challenge. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Br J Anaesth. 2022 Nov;129(5):e116-e119 | pt_PT |
| dc.identifier.doi | 10.1016/j.bja.2022.07.037 | pt_PT |
| dc.identifier.eissn | 1471-6771 | |
| dc.identifier.issn | 0007-0912 | |
| dc.identifier.uri | http://hdl.handle.net/10451/54923 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation.publisherversion | https://www.sciencedirect.com/journal/british-journal-of-anaesthesia | pt_PT |
| dc.subject | Artificial intelligence | pt_PT |
| dc.subject | Automation | pt_PT |
| dc.subject | Cardiac output | pt_PT |
| dc.subject | Critical care | pt_PT |
| dc.subject | Echocardiography | pt_PT |
| dc.subject | Velocity time integral | pt_PT |
| dc.title | Automation of sub-aortic velocity time integral measurements by transthoracic echocardiography: clinical evaluation of an artificial intelligence-enabled tool in critically ill patients | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | e119 | pt_PT |
| oaire.citation.issue | 5 | pt_PT |
| oaire.citation.startPage | e116 | pt_PT |
| oaire.citation.title | British Journal of Anaesthesia | pt_PT |
| oaire.citation.volume | 129 | pt_PT |
| person.familyName | Fernandes | |
| person.givenName | Antero do Vale | |
| person.identifier.ciencia-id | DE19-9ED8-D782 | |
| person.identifier.orcid | 0000-0002-6986-083X | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 9778c0e0-e42c-4da3-8d0f-1513f0e80dbb | |
| relation.isAuthorOfPublication.latestForDiscovery | 9778c0e0-e42c-4da3-8d0f-1513f0e80dbb |