Validation of a novel system to assess end-expiratory lung volume and alveolar recruitment in an ARDS model

Abstract

Background Personalizing mechanical ventilation requires the development of reliable bedside monitoring techniques. The multiple-breaths nitrogen washin–washout (MBNW) technique is currently available to measure end-expiratory lung volume (EELV MBNW ), but the precision of the technique may be poor, with percentage errors ranging from 28 to 57%. The primary aim of the study was to evaluate the reliability of a novel MBNW bedside system using fast mainstream sensors to assess EELV in an experimental acute respiratory distress syndrome (ARDS) model, using computed tomography (CT) as the gold standard. The secondary aims of the study were: (1) to evaluate trending ability of the novel system to assess EELV; (2) to evaluate the reliability of estimated alveolar recruitment induced by positive end-expiratory pressure (PEEP) changes computed from EELV MBNW , using CT as the gold standard. Results Seven pigs were studied in 6 experimental conditions: at baseline, after experimental ARDS and during a decremental PEEP trial at PEEP 16, 12, 6 and 2 cmH 2 O. EELV was computed at each PEEP step by both the MBNW technique (EELV MBNW ) and CT (EELV CT ). Repeatability was assessed by performing replicate measurements. Alveolar recruitment between two consecutive PEEP levels after lung injury was measured with CT (Vrec CT ), and computed from EELV measurements (Vrec MBNW ) as ΔEELV minus the product of ΔPEEP by static compliance. EELV MBNW and EELV CT were significantly correlated ( R 2 \u2009=\u20090.97). An acceptable non-constant bias between methods was identified, slightly decreasing toward more negative values as EELV increased. The conversion equation between EELV MBNW and EELV CT was: EELV MBNW \u2009=\u20090.92\u2009×\u2009EELV CT \u2009+\u200936. The 95% prediction interval of the bias amounted to\u2009±\u200986\xa0mL and the percentage error between both methods amounted to 13.7%. The median least significant change between repeated measurements amounted to 8% [CI 95% : 4–10%]. EELV MBNW adequately tracked EELV CT changes over time (concordance rate amounting to 100% [CI 95% : 87%–100%] and angular bias amounting to −\u20092°\u2009±\u200910°). Vrec MBNW and Vrec CT were significantly correlated ( R 2 \u2009=\u20090.92). A non-constant bias between methods was identified, slightly increasing toward more positive values as Vrec increased. Conclusions We report a new bedside MBNW technique that reliably assesses EELV in an experimental ARDS model with high precision and excellent trending ability.