Real time noninvasive estimation of work of breathing using facemask leak-corrected tidal volume during noninvasive pressure support: validation study.
We describe a real time, noninvasive method of estimating work of breathing (esophageal balloon not required) during noninvasive pressure support (PS) that uses an artificial neural network (ANN) combined with a leak correction (LC) algorithm, programmed to ignore asynchronous breaths, that corrects...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 30; no. 3; pp. 285 - 295 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jun2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115098877&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115098877 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Jun2016 vid: 30 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115098877 115098877 NLM26070542 115098877 10.1007/s10877-015-9716-5 NLM26070542 115098877 ppf: 285 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Real time noninvasive estimation of work of breathing using facemask leak-corrected tidal volume during noninvasive pressure support: validation study. aug: au: Banner, Michael Tams, Carl Euliano, Neil Stephan, Paul Leavitt, Trevor Martin, A. Al-Rawas, Nawar Gabrielli, Andrea Banner, Michael J Tams, Carl G Euliano, Neil R Stephan, Paul J Leavitt, Trevor J Martin, A Daniel affil: Department of Anesthesiology, University of Florida College of Medicine, 1600 SW Archer Road Gainesville 32610 USA sug: subj: Tidal Volume Physiology Work of Breathing Physiology Monitoring, Physiologic Statistics and Numerical Data Algorithms Neural Networks (Computer) Pressure Human Computer Systems Acute Lung Injury Physiopathology Acute Lung Injury Therapy Respiration, Artificial Statistics and Numerical Data Respiration, Artificial Equipment and Supplies Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: We describe a real time, noninvasive method of estimating work of breathing (esophageal balloon not required) during noninvasive pressure support (PS) that uses an artificial neural network (ANN) combined with a leak correction (LC) algorithm, programmed to ignore asynchronous breaths, that corrects for differences in inhaled and exhaled tidal volume (VT) from facemask leaks (WOBANN,LC/min). Validation studies of WOBANN,LC/min were performed. Using a dedicated and popular noninvasive ventilation ventilator (V60, Philips), in vitro studies using PS (5 and 10 cm H2O) at various inspiratory flow rate demands were simulated with a lung model. WOBANN,LC/min was compared with the actual work of breathing, determined under conditions of no facemask leaks and estimated using an ANN (WOBANN/min). Using the same ventilator, an in vivo study of healthy adults (n = 8) receiving combinations of PS (3-10 cm H2O) and expiratory positive airway pressure was done. WOBANN,LC/min was compared with physiologic work of breathing/min (WOBPHYS/min), determined from changes in esophageal pressure and VT applied to a Campbell diagram. For the in vitro studies, WOBANN,LC/min and WOBANN/min ranged from 2.4 to 11.9 J/min and there was an excellent relationship between WOBANN,LC/breath and WOBANN/breath, r = 0.99, r(2) = 0.98 (p < 0.01). There were essentially no differences between WOBANN,LC/min and WOBANN/min. For the in vivo study, WOBANN,LC/min and WOBPHYS/min ranged from 3 to 12 J/min and there was an excellent relationship between WOBANN,LC/breath and WOBPHYS/breath, r = 0.93, r(2) = 0.86 (p < 0.01). An ANN combined with a facemask LC algorithm provides noninvasive and valid estimates of work of breathing during noninvasive PS. WOBANN,LC/min, automatically and continuously estimated, may be useful for assessing inspiratory muscle loads and guiding noninvasive PS settings as in a decision support system to appropriately unload inspiratory muscles. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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