title: Robustness of two different methods of monitoring respiratory system compliance during mechanical ventilation.
abstract: Robustness measures the performance of estimation methods when they work under non-ideal conditions. We compared the robustness of artificial neural networks (ANNs) and multilinear fitting (MLF) methods in estimating respiratory system compliance (CRS) during mechanical ventilation (MV). Twenty-four anaesthetized pigs underwent MV. Airway pressure, flow and volume were recorded at fixed intervals after the induction of acute lung injury. After consecutive mechanical breaths, an inspiratory pause (BIP) was applied in order to calculate CRSusing the interrupter technique. From the breath preceding the BIP, ANN and MLF had to compute CRSin the presence of two types of perturbations: transient sensor disconnection (TD) and random noise (RN). Performance of the two methods was assessed according to Bland and Altman. The ANN presented a higher bias and scatter than MLF during the application of RN, except when RN was lower than 2% of peak airway pressure. During TD, MLF algorithm showed a higher bias and scatter than ANN. After the application of RN, ANN and MLF maintain a stable performance, although MLF shows better results. ANNs have a more stable performance and yield a more robust estimation of CRSthan MLF in conditions of transient sensor disconnection.
authors.affiliation: Department of Emergency and Organ Transplant, Bari University, Bari, Italy. gaetano.perchiazzi@uniba.it.|Hedenstierna Laboratory, Surgical Sciences, Uppsala University, Akademiska Sjukhuset ing.40 tr.3, 75185, Uppsala, Sweden. gaetano.perchiazzi@uniba.it.
authors.forename: Gaetano
authors.identifier: http://orcid.org/0000-0001-6834-6399
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, Bari University, Bari, Italy.|Hedenstierna Laboratory, Surgical Sciences, Uppsala University, Akademiska Sjukhuset ing.40 tr.3, 75185, Uppsala, Sweden.
authors.forename: Mariangela
authors.initials: M
authors.lastname: Pellegrini
authors.affiliation: Hedenstierna Laboratory, Surgical Sciences, Uppsala University, Akademiska Sjukhuset ing.40 tr.3, 75185, Uppsala, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Larsson
authors.affiliation: Hedenstierna Laboratory, Medical Sciences, Uppsala University, Uppsala, Sweden.
authors.forename: Göran
authors.initials: G
authors.lastname: Hedenstierna
mesh_terms: D000465:Algorithms; D000818:Animals; D016571:Neural Networks, Computer; D011312:Pressure; D012121:Respiration, Artificial / Q000379:methods; D012129:Respiratory Function Tests / Q000379:methods; D015656:Respiratory Mechanics / Q000502:physiology*; D012137:Respiratory System / Q000503:physiopathology*; D013552:Swine; D013990:Tidal Volume / Q000502:physiology
keywords: Acute lung injury; Lung compliance; Mechanical ventilation; Neural networks; Robustness