Artificial Intelligence for the Detection of Patient–Ventilator Asynchrony.
Patient–ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized by misalignment of ventilatory support and patient respiratory effort. PVA is highly prevalent and associated with adverse clinical outcomes, including increased work of breathing, oxygen consumption,...
| Publicado en: | Respiratory Care Vol. 70; no. 5; pp. 583 - 593 |
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| Autores principales: | , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Mary Ann Liebert, Inc.
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184950594&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184950594 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00201324 4GG jtl: Respiratory Care issn: 00201324 maglogo: N pubinfo: dt: May2025 vid: 70 iid: 5 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 184950594 184950594 184950594 10.1089/respcare.12540 184950594 ppf: 583 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence for the Detection of Patient–Ventilator Asynchrony. aug: au: Tlimat, Abdulhakim Fowler, Cosmo Safadi, Sami Johnson, Robert B. Bodduluri, Sandeep Morris, Peter Bhatt, Surya P. affil: Drs. Tlimat, Bodduluri, Morris, and Bhatt are affiliated with the Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA. sug: subj: Patient-Ventilator Asynchrony Diagnosis Artificial Intelligence Utilization Respiration, Artificial Diagnosis, Computer Assisted Prediction Models Human Scoping Review PubMed Embase Machine Learning Breath Tests Deep Learning Patient-Ventilator Asynchrony Classification Sensitivity and Specificity ab: Patient–ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized by misalignment of ventilatory support and patient respiratory effort. PVA is highly prevalent and associated with adverse clinical outcomes, including increased work of breathing, oxygen consumption, and risk of barotrauma. Artificial intelligence (AI) is a potentially transformative solution offering capabilities for automated detection of PVA. This narrative review characterizes the landscape of AI models designed for PVA detection and quantification. A comprehensive literature search identified 13 studies, spanning diverse settings and patient populations. Machine learning (ML) techniques, derivation datasets, types of asynchronies detected, and performance metrics were assessed to provide a contemporary view of AI in this domain. We reviewed 166 articles published between 1989 and April 2024, of which 13 were included, encompassing 332 participants and analyzing >5.8 million breaths. Patient counts ranged between 8 and 107 and breath data ranged between 1,375 and 4.2 M. The reason for invasive mechanical ventilation use was given as ARDS in three articles, whereas the remainder had different invasive mechanical ventilation indications. Various ML methods as well as newer deep learning techniques were used to address PVA types. Sensitivity and specificity of 10 of the 13 models were >0.9, and 8 models reported accuracy of >0.9. AI models have significant potential to address PVA in invasive mechanical ventilation, displaying high accuracy across various populations and asynchrony types. This showcases their potential to accurately detect and quantify PVA. Future work should focus on model validation in diverse clinical settings and patient populations. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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