About: Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods

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About: Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods
Creator	Grasso, Salvatore Latronico, Nicola Bernardini, Achille Buizza, Barbara Ciabatti, Gianni Lippolis, Valeria Natalini, Giuseppe Pisani, Luigi Rosano, Antonio Antonelli, · Achille Bernardini, · Eros, · Granato, ·
source	PMC
abstract	Pressure support ventilation (PSV) should be titrated considering the pressure developed by the respiratory muscles (P(musc)) to prevent under- and over-assistance. The esophageal pressure (P(es)) is the clinical gold standard for P(musc) assessment, but its use is limited by alleged invasiveness and complexity. The least square fitting method and the end-inspiratory occlusion method have been proposed as non-invasive alternatives for P(musc) assessment. The aims of this study were: (1) to compare the accuracy of P(musc) estimation using the end-inspiration occlusion (P(musc,index)) and the least square fitting (P(musc,lsf)) against the reference method based on P(es); (2) to test the accuracy of P(musc,lsf) and of P(musc,index) to detect overassistance, defined as P(musc) ≤ 1 cmH(2)O. We studied 18 patients at three different PSV levels. At each PSV level, P(musc), P(musc,lsf), P(musc,index) were calculated on the same breaths. Differences among P(musc), P(musc,lsf), P(musc,index) were analyzed with linear mixed effects models. Bias and agreement were assessed by Bland–Altman analysis for repeated measures. The ability of P(musc,lsf) and P(musc,index) to detect overassistance was assessed by the area under the receiver operating characteristics curve. Positive and negative predictive values were calculated using cutoff values that maximized the sum of sensitivity and specificity. At each PSV level, P(musc,lsf) was not different from P(musc) (p = 0.96), whereas P(musc,index) was significantly lower than P(musc). The bias between P(musc) and P(musc,lsf) was zero, whereas P(musc,index) systematically underestimated P(musc) of 6 cmH(2)O. The limits of agreement between P(musc) and P(musc,lsf) and between P(musc) and P(musc,index) were ± 12 cmH(2)O across bias. Both P(musc,lsf) ≤ 4 cmH(2)O and P(musc,index) ≤ 1 cmH(2)O had excellent negative predictive value [0.98 (95% CI 0.94–1) and 0.96 (95% CI 0.91–0.99), respectively)] to identify over-assistance. The inspiratory effort during PSV could not be accurately estimated by the least square fitting or end-inspiratory occlusion method because the limits of agreement were far above the signal size. These non-invasive approaches, however, could be used to screen patients at risk for absent or minimal respiratory muscles activation to prevent the ventilator-induced diaphragmatic dysfunction.
has issue date	2020-07-02(xsd:dateTime)
bibo:doi	10.1007/s10877-020-00552-5
has license	no-cc
sha1sum (hex)	deefd286ba0ad399fab800e2a9edfc158a92e36f
schema:url	https://doi.org/10.1007/s10877-020-00552-5
resource representing a document's title	Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods
has PubMed Central identifier	PMC7330529
schema:publication	J Clin Monit Comput
resource representing a document's body	covid:deefd286ba0ad399fab800e2a9edfc158a92e36f#body_text
is schema:about of	named entity 'musc' named entity 'musc' named entity 'musc' named entity 'bias' named entity 'receiver' named entity '95% CI' named entity 'Bias' named entity 'cmH' named entity 'invasiveness' named entity 'musc' named entity 'plateau' named entity 'activity' named entity 'calculated' named entity 'prevent' named entity 'assessed' named entity 'PSV' named entity 'musc' named entity 'pressure' named entity 'musc' named entity 'predictive' named entity 'developed' named entity '95% CI' named entity 'bias' named entity 'limits' named entity 'limited' named entity 'musc' named entity 'titrated' named entity 'lsf' named entity 'lsf' named entity 'regression coefficient' named entity 'atrophy' named entity 'Brescia' named entity 'transduced' named entity 'patients' named entity 'effects' named entity 'estimation' named entity 'systematically' named entity 'respiratory muscles' named entity 'non-invasive' named entity 'titrated' named entity 'Non-invasive' named entity 'musc' named entity 'musc' named entity 'tidal volume' named entity 'abdomen' named entity 'diaphragm' named entity 'attending physician' named entity 'fatigue' named entity 'cerebral ischemia' named entity 'fixed effects' named entity 'morbidity' named entity '1.2' named entity 'spontaneous breathing trial' named entity 'Confidence interval' named entity 'mmHg' named entity 'systolic' named entity 'mechanical ventilation' named entity 'contraindication' named entity 'Vienna' named entity 'arterial pressure' named entity 'PEEP' named entity 'respiratory mechanics' named entity 'Statistical Computing' named entity 'Chest wall' named entity 'PEEP' named entity 'elastic recoil' named entity 'Finland' named entity 'Helsinki' named entity 'Tidal volume' named entity 'informed consent' named entity 'PEEP'

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