Eficacia del protocolo Start to move en funcionalidad, DA-UCI y delirio: ensayo clínico aleatorizado / Efficacy of the «Start to move» protocol on functionality, ICU-acquired weakness and delirium: Randomized clinical trial

Objetivo Evaluar la eficacia del protocolo Start to move comparado con el tratamiento convencional en sujetos mayores de 15 años hospitalizados en la UCI sobre una mejoría en funcionalidad, disminución de debilidad adquirida en la UCI (DA-UCI), incidencia de delirio, días de ventilación mecánica (VM), estadía en la UCI y mortalidad a los 28 días. Diseño Ensayo clínico controlado aleatorizado. Ámbito Unidad de paciente crítico. Participantes Incluye adultos mayores a 15 años con VMI mayor a 48h, asignación aleatoria. Intervenciones Protocolo «Start to move» y tratamiento convencional. Variables de interés principales Se analizó funcionalidad, incidencia DA-UCI, incidencia delirio, días VM, estadía UCI y mortalidad-28 días, ClinicalTrials.gov número, NCT05053724. Resultados Sesenta y nueve sujetos fueron ingresados al estudio, 33 al grupo Start to move y 36 a tratamiento convencional, comparables clínico y sociodemograficamente. En el grupo Start to move la incidencia DAUCI al egreso de la UCI fue de 35,7 vs. 80,7% grupo tratamiento convencional (p=0,001). La funcionalidad (FSS-ICU) al egreso de la UCI corresponde a 26 vs. 17 puntos a favor del grupo Start to move (p=0,001). La diferencia en Barthel al egreso de la UCI fue del 20% a favor del grupo Start to move (p=0,006). No hubo diferencias significativas en incidencia de delirio, días de VM, estadía UCI y mortalidad-28 días. El estudio no reportó eventos adversos, ni suspensión de protocolo. Conclusiones La aplicación del protocolo Start to move en la UCI se asoció reducción en la incidencia DA-UCI, aumento en funcionalidad y menor caída en puntaje Barthel al egreso. (AU)

Objective To evaluate the efficacy of the Start to move protocol compared to conventional treatment in subjects over 15 years of age hospitalized in the ICU on an improvement in functionality, decrease in ICU-acquired weakness (IUCD), incidence of delirium, days of mechanical ventilation (MV), length of stay in ICU and mortality at 28 days. Design Randomized controlled clinical trial. Setting Intensive care unit. Participants Includes adults older than 15 years with invasive mechanical ventilation more than 48h, randomized allocation. Interventions Start to move protocol and conventional treatment. Main variables of interest Functionality, incidence of ICU-acquired weakness, incidence of delirium, days on mechanical ventilation, ICU stay and mortality-28 days, ClinicalTrials.gov number, NCT05053724. Results Sixty-nine subjects were admitted to the study, 33 to the Start to move group and 36 to conventional treatment, clinically and sociodemographic comparable. In the “Start to move” group, the incidence of IUCD at ICU discharge was 35.7% vs. 80.7% in the “conventional treatment” group (P=.001). Functionality (FSS-ICU) at ICU discharge corresponds to 26 vs. 17 points in favor of the “Start to move” group (P=.001). The difference in Barthel at ICU discharge was 20% in favor of the “Start to move” group (P=.006). There were no significant differences in the incidence of delirium, days of mechanical ventilation, ICU stay and 28-day mortality. The study did not report adverse events or protocol suspension. Conclusions The application of the “Start to move” protocol in ICU showed a reduction in the incidence of IUCD, an increase in functionality and a smaller decrease in Barthel score at discharge. (AU)

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.