• Pablo O Rodriguez, Norberto Tiribelli, Emiliano Gogniat, Gustavo A Plotnikow, Sebastian Fredes, Fernandez CeballosIgnacioIHospital Italiano de Buenos Aires (HIBA), Buenos Aires, Argentina., Romina A Pratto, Matias Madorno, Santiago Ilutovich, San RomanEduardoEHospital Italiano de Buenos Aires (HIBA), Buenos Aires, Argentina., Ignacio Bonelli, María Guaymas, Alejandro C Raimondi, Luis P Maskin, Mariano Setten, and GRAAVEplus (Grupo Argentino de estudio de Asincronías en la VEntilación mecanica y temas relacionados a los cuidados críticos).
• Centro de Educación Médica e Investigaciones Clínicas "Norberto Quirno" (CEMIC), Av. Cnel. Díaz 2423, Buenos Aires, 1425, Argentina. prodriguez@cemic.edu.ar.
• J Clin Monit Comput. 2020 Dec 1; 34 (6): 1239-1246.

AbstractAsynchrony due to reverse-triggering (RT) may appear in ARDS patients. The objective of this study is to validate an algorithm developed to detect these alterations in patient-ventilator interaction. We developed an algorithm that uses flow and airway pressure signals to classify breaths as normal, RT with or without breath stacking (BS) and patient initiated double-triggering (DT). The diagnostic performance of the algorithm was validated using two datasets of breaths, that are classified as stated above. The first dataset classification was based on visual inspection of esophageal pressure (Pes) signal from 699 breaths recorded from 11 ARDS patients. The other classification was obtained by vote of a group of 7 experts (2 physicians and 5 respiratory therapists, who were trained in ICU), who evaluated 1881 breaths gathered from recordings from 99 subjects. Experts used airway pressure and flow signals for breaths classification. The RT with or without BS represented 19% and 37% of breaths in Pes dataset while their frequency in the expert's dataset were 3% and 12%, respectively. The DT was very infrequent in both datasets. Algorithm classification accuracy was 0.92 (95% CI 0.89-0.94, P < 0.001) and 0.96 (95% CI 0.95-0.97, P < 0.001), in comparison with Pes and experts' opinion. Kappa statistics were 0.86 and 0.84, respectively. The algorithm precision, sensitivity and specificity for individual asynchronies were excellent. The algorithm yields an excellent accuracy for detecting clinically relevant asynchronies related to RT.

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