Identifying and managing patient–ventilator asynchrony: An international survey / Identificación y manejo de la asincronía paciente-ventilador: encuesta internacional

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. (AU)

Objetivo Describir los factores asociados al correcto reconocimiento y manejo de la asincronía paciente-ventilador (APV). Diseño Estudio analítico transversal. Ámbito Estudio internacional realizado en 20 países mediante una encuesta a través de Internet. Participantes Médicos, terapeutas respiratorios, enfermeras/os y fisioterapeutas que trabajan actualmente en unidades de cuidados intensivos (UCI). Principales variables de interés Se utilizó un análisis uni y multivariado para describir la asociación entre todas las variables (profesión, formación en ventilación mecánica, tipo de programa de formación, años de experiencia y características de la UCI en la cual trabajan los profesionales) con la correcta identificación y manejo de 6 APV. Resultados Un total de 431 profesionales respondieron una encuesta validada previamente. Los factores asociados a una correcta identificación de 6 APV fueron: haber completado un programa de formación específico sobre ventilación mecánica (OR: 2,27; IC 95%: 1,14-4,52; p=0,019), programa de formación con más de 100h (OR: 2,28; IC 95%: 1,29-4,03; p=0,005) y el número de camas de UCI (OR: 1,037; IC 95%: 1,01-1,06; p=0,005). El principal factor asociado a un adecuado manejo de la APV fue la correcta identificación de 6 APV (OR: 118,98; IC 95%: 35,25-401,58; p<0,001). Conclusiones La identificación y el manejo de la asincronía paciente-ventilador, mediante el análisis de las curvas del ventilador está influenciada por programas de formación, específicos sobre ventilación mecánica, el número de camas de la UCI y el número de asincronías identificadas. (AU)

Identifying and managing patient-ventilator asynchrony: An international survey.

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.