title: Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation.
abstract: Asynchronous Events (AEs) during mechanical ventilation (MV) result in increased work of breathing and potential poor patient outcomes. Thus, it is important to automate AE detection. In this study, an AE detection method, Automated Logging of Inspiratory and Expiratory Non-synchronized breathing (ALIEN) was developed and compared between standard manual detection in 11 MV patients. A total of 5701 breaths were analyzed (median [IQR]: 500 [469-573] per patient). The Asynchrony Index (AI) was 51% [28-78]%. The AE detection yielded sensitivity of 90.3% and specificity of 88.3%. Automated AE detection methods can potentially provide clinicians with real-time information on patient-ventilator interaction.
authors.forename: Yeong Shiong
authors.initials: YS
authors.lastname: Chiew
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Pretty
authors.forename: Alex
authors.initials: A
authors.lastname: Beatson
authors.forename: Daniel
authors.initials: D
authors.lastname: Glassenbury
authors.forename: Vincent
authors.initials: V
authors.lastname: Major
authors.forename: Simon
authors.initials: S
authors.lastname: Corbett
authors.forename: Daniel
authors.initials: D
authors.lastname: Redmond
authors.forename: Akos
authors.initials: A
authors.lastname: Szlavecz
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Shaw
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
mesh_terms: D001331:Automation; D045853:Exhalation; D006801:Humans; D012119:Respiration; D012121:Respiration, Artificial / Q000379:methods*