Banner Michael J, Tams Carl G, Euliano Neil R, Stephan Paul J, Leavitt Trevor J, Martin A Daniel, Al-Rawas Nawar, Gabrielli Andrea
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.
J Clin Monit Comput. 2016 Jun;30(3):285-94. doi: 10.1007/s10877-015-9716-5. Epub 2015 Jun 13.
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.
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。