Browsing by Author "Blanch, Lluis"

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    Automated detection and quantification of reverse triggering effort under mechanical ventilation
    (2021) Pham, Tài; Montanya, Jaume; Telias, Irene; Piraino, Thomas; Magrans, Rudys; Coudroy, Rémi; Damiani Rebolledo, L. Felipe; Mellado Artigas, Ricard; Madorno, Matías; Blanch, Lluis
    Abstract Background Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and flow. A secondary objective was to describe the magnitude of the efforts generated during RT. Methods We developed algorithms for detection of RT using Paw and flow waveforms. Experts having Paw, flow and esophageal pressure (Pes) assessed automatic detection accuracy by comparison against visual assessment. Muscular pressure (Pmus) was measured from Pes during RT, triggered breaths and ineffective efforts. Results Tracings from 20 hypoxemic patients were used (mean age 65 ± 12 years, 65% male, ICU survival 75%). RT was present in 24% of the breaths ranging from 0 (patients paralyzed or in pressure support ventilation) to 93.3%. Automatic detection accuracy was 95.5%: sensitivity 83.1%, specificity 99.4%, positive predictive value 97.6%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH20, with a median of 8.7 cmH20. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. Conclusion An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH2O with important variability between and within patients. Trial registration BEARDS, NCT03447288.
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    Specific Training Improves the Detection and Management of Patient-Ventilator Asynchrony
    (2024) Ramirez, Ivan I.; Gutierrez-Arias, Ruvistay; Damiani, L. Felipe; Adasme, Rodrigo S.; Arellano, Daniel H.; Salinas, Francisco A.; Roncalli, Angelo; Nunez-Silveira, Juan; Santillan-Zuta, Milton; Sepulveda-Barisich, Patrick; Gordo-Vidal, Federico; Blanch, Lluis
    BACKGROUND: Patient -ventilator asynchrony is common in patients undergoing mechanical ventilation. The proportion of health-care professionals capable of identifying and effectively managing different types of patient -ventilator asynchronies is limited. A few studies have developed specific training programs, but they mainly focused on improving patient -ventilator asynchrony detection without assessing the ability of health-care professionals to determine the possible causes. METHODS: We conducted a 36-h training program focused on patient -ventilator asynchrony detection and management for health-care professionals from 20 hospitals in Latin America and Spain. The training program included 6 h of a live online lesson during which 120 patient -ventilator asynchrony cases were presented. After the 6-h training lesson, health-care professionals were required to complete a 1-h training session per day for the subsequent 30 d. A 30 -question assessment tool was developed and used to assess health-care professionals before training, immediately after the 6-h training lecture, and after the 30 d of training (1 -month follow-up).RESULTS: One hundred sixteen health-care professionals participated in the study. The median (interquartile range) of the total number of correct answers in the pre -training, post -training, and 1 -month follow-up were significantly different (12 [8.75-15], 18 [13.75-22], and 18.5 [14-23], respectively). The percentages of correct answers also differed significantly between the time assessments. Study participants significantly improved their performance between pre -training and post -training (P < .001). This performance was maintained after a 1 -month follow-up (P 5 .95) for the questions related to the detection, determination of cause, and management of patient -ventilator asynchrony. CONCLUSIONS: A specific 36-h training program significantly improved the ability of health-care professionals to detect patient -ventilator asynchrony, determine the possible causes of patient -ventilator asynchrony, and properly manage different types of patient -ventilator asynchrony.