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Identifying and Managing Patient-ventilator Asynchrony: An International Survey

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Specialty Critical Care
Date 2019 Nov 1
PMID 31668560
Citations 9
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Abstract

Objective: To describe the main factors associated with proper recognition and management of patient-ventilator asynchrony (PVA).

Design: An analytical cross-sectional study was carried out.

Setting: An international study conducted in 20 countries through an online survey.

Participants: Physicians, respiratory therapists, nurses and physiotherapists currently working in the Intensive Care Unit (ICU).

Main Variables Of Interest: Univariate and multivariate logistic regression models were used to establish associations between all variables (profession, training in mechanical ventilation, type of training program, years of experience and ICU characteristics) and the ability of HCPs to correctly identify and manage 6 PVA.

Results: A total of 431 healthcare professionals answered a validated survey. The main factors associated to proper recognition of PVA were: specific training program in mechanical ventilation (MV) (OR 2.27; 95%CI 1.14-4.52; p=0.019), courses with more than 100h completed (OR 2.28; 95%CI 1.29-4.03; p=0.005), and the number of ICU beds (OR 1.037; 95%CI 1.01-1.06; p=0.005). The main factor influencing the management of PVA was the correct recognition of 6 PVAs (OR 118.98; 95%CI 35.25-401.58; p<0.001).

Conclusion: Identifying and managing PVA using ventilator waveform analysis is influenced by many factors, including specific training programs in MV, the number of ICU beds, and the number of recognized PVAs.

Citing Articles

Emergency and critical care medicine residents' competency to identify patient ventilator asynchrony using a mechanical ventilator waveform analysis in Addis Ababa, Ethiopia: a multicenter cross-sectional study.

Bogale W, Kefyalew M, Debebe F BMC Med Educ. 2025; 25(1):180.

PMID: 39905426 PMC: 11796057. DOI: 10.1186/s12909-025-06748-0.


Knowledge and associated factors of healthcare professionals in detecting patient-ventilator asynchrony using waveform analysis at intensive care units of the federal public hospitals in Addis Ababa, Ethiopia, 2023.

Zelalem H, Sibhat M, Yeshidinber A, Kehali H BMC Nurs. 2024; 23(1):398.

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Survey of Ventilator Waveform Interpretation Among ICU Professionals.

Liu P, Lyu S, Mireles-Cabodevila E, Miller A, Albuainain F, Ibarra-Estrada M Respir Care. 2024; 69(7):773-781.

PMID: 38653558 PMC: 11285504. DOI: 10.4187/respcare.11677.


The Ability of Critical Care Physicians to Identify Patient-Ventilator Asynchrony Using Waveform Analysis: A National Survey.

Chelbi R, Thabet F, Ennouri E, Meddeb K, Toumi R, Zghidi M Respir Care. 2024; 69(2):176-183.

PMID: 38267232 PMC: 10898468. DOI: 10.4187/respcare.11360.


Specific Training Improves the Detection and Management of Patient-Ventilator Asynchrony.

Ramirez I, Gutierrez-Arias R, Damiani L, Adasme R, Arellano D, Salinas F Respir Care. 2024; 69(2):166-175.

PMID: 38267230 PMC: 10898470. DOI: 10.4187/respcare.11329.