Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks

Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEP_app) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEP_tot) is the sum of PEEP_app and intrinsic PEEP (PEEP_i), a hidden component. To measure PEEP_tot, ventilation must be discontinued with an end-expiratory hold maneuver (EEHM). We hypothesized that artificial neural networks (ANN) could estimate the PEEP_tot from flow and pressure tracings during ongoing mechanical ventilation. Ten pigs were mechanically ventilated, and the time constant of their respiratory system (τ_RS) was measured. We shortened their expiratory time (TE) according to multiples of τ_RS, obtaining different respiratory patterns (R_pat). Pressure (P_AW) and flow (V′_AW) at the airway opening during ongoing mechanical ventilation were simultaneously recorded, with and without the addition of external resistance. The last breath of each R_pat included an EEHM, which was used to compute the reference PEEP_tot. The entire protocol was repeated after the induction of ALI with i.v. injection of oleic acid, and 382 tracings were obtained. The ANN had to extract the PEEP_tot, from the tracings without an EEHM. ANN agreement with reference PEEP_tot was assessed with the Bland–Altman method. Bland Altman analysis of estimation error by ANN showed −0.40 ± 2.84 (expressed as bias ± precision) and ±5.58 as limits of agreement (data expressed as cmH₂O). The ANNs estimated the PEEP_tot well at different levels of PEEP_app under dynamic conditions, opening up new possibilities in monitoring PEEP_i in critically ill patients who require ventilator treatment.