Details

Autor(en) / Beteiligte

• Rodriguez, Pablo O.
• Tiribelli, Norberto
• Gogniat, Emiliano
• Plotnikow, Gustavo A.
• Fredes, Sebastian
• Fernandez Ceballos, Ignacio
• Pratto, Romina A.
• Madorno, Matias
• Ilutovich, Santiago
• San Roman, Eduardo
• Bonelli, Ignacio
• Guaymas, María
• Raimondi, Alejandro C.
• Maskin, Luis P.
• Setten, Mariano

Titel

Automatic detection of reverse-triggering related asynchronies during mechanical ventilation in ARDS patients using flow and pressure signals

Ist Teil von

• Journal of clinical monitoring and computing, 2020-12, Vol.34 (6), p.1239-1246

Ort / Verlag

Dordrecht: Springer Netherlands

Erscheinungsjahr

2020

Quelle

SpringerLink

Beschreibungen/Notizen

• Asynchrony 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.