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无创压力支持期间使用面罩漏气校正潮气量进行呼吸功的实时无创估计:验证研究

Real time noninvasive estimation of work of breathing using facemask leak-corrected tidal volume during noninvasive pressure support: validation study.

作者信息

Banner Michael J, Tams Carl G, Euliano Neil R, Stephan Paul J, Leavitt Trevor J, Martin A Daniel, Al-Rawas Nawar, Gabrielli Andrea

机构信息

Department of Anesthesiology, University of Florida College of Medicine, 1600 SW Archer Road, PO Box 100254, Gainesville, FL, 32610, USA.

Convergent Engineering, 107 SW 140th Terrace, #1, Newberry, FL, 32669, USA.

出版信息

J Clin Monit Comput. 2016 Jun;30(3):285-94. doi: 10.1007/s10877-015-9716-5. Epub 2015 Jun 13.

Abstract

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.

摘要

我们描述了一种实时、无创的方法,用于在无创压力支持(PS)期间估计呼吸功(无需食管气囊),该方法使用人工神经网络(ANN)结合泄漏校正(LC)算法,编程以忽略异步呼吸,校正因面罩泄漏导致的吸入和呼出潮气量(VT)差异(WOBANN,LC/min)。对WOBANN,LC/min进行了验证研究。使用一台专用且常用的无创通气呼吸机(V60,飞利浦),在体外研究中,用肺模型模拟了在不同吸气流量需求下使用PS(5和10厘米水柱)的情况。将WOBANN,LC/min与在无面罩泄漏条件下测定并使用人工神经网络估计的实际呼吸功(WOBANN/min)进行比较。使用同一台呼吸机,对8名接受PS(3 - 10厘米水柱)和呼气末正压联合治疗的健康成年人进行了体内研究。将WOBANN,LC/min与根据食管压力变化和应用于坎贝尔图的VT确定的生理呼吸功/分钟(WOBPHYS/min)进行比较。对于体外研究,WOBANN,LC/min和WOBANN/min范围为2.4至11.9焦耳/分钟,且WOBANN,LC/呼吸与WOBANN/呼吸之间存在极好的关系,r = 0.99,r² = 0.98(p < 0.01)。WOBANN,LC/min和WOBANN/min之间基本无差异。对于体内研究,WOBANN,LC/min和WOBPHYS/min范围为3至12焦耳/分钟,且WOBANN,LC/呼吸与WOBPHYS/呼吸之间存在极好的关系,r = 0.93,r² = 0.86(p < 0.01)。人工神经网络结合面罩LC算法可在无创PS期间提供无创且有效的呼吸功估计值。自动且连续估计的WOBANN,LC/min可能有助于评估吸气肌负荷,并像在决策支持系统中那样指导无创PS设置,以适当减轻吸气肌负荷。

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