title: Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks.
abstract: Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEPapp) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEPtot) is the sum of PEEPappand intrinsic PEEP (PEEPi), a hidden component. To measure PEEPtot, ventilation must be discontinued with an end-expiratory hold maneuver (EEHM). We hypothesized that artificial neural networks (ANN) could estimate the PEEPtotfrom flow and pressure tracings during ongoing mechanical ventilation. Ten pigs were mechanically ventilated, and the time constant of their respiratory system (τRS) was measured. We shortened their expiratory time (TE) according to multiples of τRS, obtaining different respiratory patterns (Rpat). Pressure (PAW) 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 Rpatincluded an EEHM, which was used to compute the reference PEEPtot. 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 PEEPtot, from the tracings without an EEHM. ANN agreement with reference PEEPtotwas 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 PEEPtotwell at different levels of PEEPappunder dynamic conditions, opening up new possibilities in monitoring PEEPiin critically ill patients who require ventilator treatment.
authors.affiliation: Department of Emergency and Organ Transplant, Section of Anaesthesia and Intensive Care Medicine, University of Bari, c/o Centro di Rianimazione - Policlinico Hospital, Piazza Giulio Cesare, 11, 70124, Bari, Italy. gaetano.perchiazzi@uniba.it.|Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden. gaetano.perchiazzi@uniba.it.
authors.forename: Gaetano
authors.initials: G
authors.lastname: Perchiazzi
authors.affiliation: Department of Anaesthesia and Intensive Care Medicine, Sahlgrenska University Hospital, Göteborg, Sweden.
authors.forename: Christian
authors.initials: C
authors.lastname: Rylander
authors.affiliation: Department of Emergency and Organ Transplant, Section of Anaesthesia and Intensive Care Medicine, University of Bari, c/o Centro di Rianimazione - Policlinico Hospital, Piazza Giulio Cesare, 11, 70124, Bari, Italy.|Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
authors.forename: Mariangela
authors.initials: M
authors.lastname: Pellegrini
authors.affiliation: Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Larsson
authors.affiliation: Hedenstierna Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
authors.forename: Göran
authors.initials: G
authors.lastname: Hedenstierna
mesh_terms: D055371:Acute Lung Injury / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy*; D000818:Animals; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D008365:Manometry / Q000379:methods*; D008991:Monitoring, Physiologic / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011175:Positive-Pressure Respiration / Q000379:methods*; D015203:Reproducibility of Results; D012121:Respiration, Artificial / Q000379:methods; D012680:Sensitivity and Specificity; D013552:Swine; D013813:Therapy, Computer-Assisted / Q000379:methods*; D016896:Treatment Outcome
keywords: Acute lung injury; Animal model; Artificial neural networks; Intrinsic positive end expiratory pressure