Automated detection and quantification of reverse triggering effort under mechanical ventilation

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dc.contributor.authorPham, Tài
dc.contributor.authorMontanya, Jaume
dc.contributor.authorTelias, Irene
dc.contributor.authorPiraino, Thomas
dc.contributor.authorMagrans, Rudys
dc.contributor.authorCoudroy, Rémi
dc.contributor.authorDamiani Rebolledo, L. Felipe
dc.contributor.authorMellado Artigas, Ricard
dc.contributor.authorMadorno, Matías
dc.contributor.authorBlanch, Lluis
dc.date.accessioned2021-03-08T11:11:52Z
dc.date.available2021-03-08T11:11:52Z
dc.date.issued2021
dc.date.updated2021-02-21T01:03:21Z
dc.description.abstractAbstract Background Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and flow. A secondary objective was to describe the magnitude of the efforts generated during RT. Methods We developed algorithms for detection of RT using Paw and flow waveforms. Experts having Paw, flow and esophageal pressure (Pes) assessed automatic detection accuracy by comparison against visual assessment. Muscular pressure (Pmus) was measured from Pes during RT, triggered breaths and ineffective efforts. Results Tracings from 20 hypoxemic patients were used (mean age 65 ± 12 years, 65% male, ICU survival 75%). RT was present in 24% of the breaths ranging from 0 (patients paralyzed or in pressure support ventilation) to 93.3%. Automatic detection accuracy was 95.5%: sensitivity 83.1%, specificity 99.4%, positive predictive value 97.6%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH20, with a median of 8.7 cmH20. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. Conclusion An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH2O with important variability between and within patients. Trial registration BEARDS, NCT03447288.
dc.format.extent10 páginas
dc.identifier.citationCritical Care. 2021 Feb 15;25(1):60
dc.identifier.doi10.1186/s13054-020-03387-3
dc.identifier.urihttps://doi.org/10.1186/s13054-020-03387-3
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/52693
dc.issue.numeroNo. 60
dc.language.isoen
dc.nota.accesoContenido completo
dc.pagina.final10
dc.pagina.inicio1
dc.revistaCritical Carees_ES
dc.rightsacceso abierto
dc.rights.holderThe Author(s)
dc.subjectReverse triggeringes_ES
dc.subjectDyssynchronyes_ES
dc.subjectMechanical ventilationes_ES
dc.subjectLung and diaphragm protectiones_ES
dc.subjectRespiratory muscleses_ES
dc.subject.ddc615.83620284
dc.subject.deweyMedicina y saludes_ES
dc.titleAutomated detection and quantification of reverse triggering effort under mechanical ventilationes_ES
dc.typeartículo
dc.volumenVol. 25
sipa.codpersvinculados237645
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