Background
Patient–ventilator dyssynchrony is often associated with poor patient-centered outcomes such as duration of mechanical ventilation or mortality [
1‐
4]. The causality has not been demonstrated, and it is not clear yet whether and when some types of dyssynchrony can directly cause harm or discomfort, or whether others are simply markers of suboptimal ventilator settings or more severe underlying conditions. Poor patient–ventilator interaction is, however, a major reason for administering sedation in the ICU, and therefore this phenomenon deserves attention and a more granular description than referring to dyssynchrony in general [
5]. Of major interest, reverse triggering (RT) is a specific form of dyssynchrony defined by the presence of a respiratory muscle contraction following a passive mechanical insufflation as if the contraction was “triggered by” the ventilator [
6]. It has been described in intubated patients receiving sedation under controlled ventilation and seems to be very frequent [
7‐
15]. This phenomenon might constitute a regular entrainment (phase locking) of the respiratory rhythm to periodic insufflation, as described in animals [
16,
17] and healthy humans [
18], but it may also be more irregular and can even occur in brain-dead patients [
10]. When the effort generated is strong enough, it induces breath stacking, often misinterpreted to be caused by double triggering (in which the same patient’s inspiratory effort would trigger the first and second mechanical insufflation). Reverse triggering could impact patients’ outcomes through several mechanisms, such as increased tidal volume during inspiration, breath stacking, or through pendelluft during the inspiratory phase [
14]. On the one hand, it can generate diaphragm injury when generating strong eccentric contractions during exhalation [
19], but on the other hand, when small, diaphragmatic contractions related to RT could be beneficial by preventing muscle disuse and atrophy in sedated patients.
Detection of dyssynchrony in general and RT in particular is challenging requiring additional physiological signals and/or careful attention to the waveforms on the ventilator screens and expertise to properly interpret the signals. Additionally, to have an estimate of the real burden of dyssynchrony 24/7 inspection of the waveforms would be required [
20,
21]. The reference technique to detect respiratory muscle activity and accurately diagnose dyssynchrony needs an esophageal catheter or a catheter that captures the electrical activity of the diaphragm (EAdi). Preliminary data suggest a high incidence of frequent RT (> 30%) in patients under assist-control ventilation [
22]. Automatic machine learning techniques that do not require visual inspection are needed for understanding the phenomenon and helping the clinician to optimize patient–ventilator interactions [
23]. As the first step of a prospective multicenter observational study that aims at establishing the incidence, determinants and consequences of various dyssynchronies during early acute hypoxemic respiratory failure (BEARDS, NCT03447288), we developed and validated an automated algorithm to detect RT only from ventilator signals, i.e., airway pressure and flow. Some of the authors (JM, RM, LBl) had previous experience in developing a dedicated software application to detect other types of dyssynchrony (
https://bettercare.es/); this platform was used as a starting point for the current algorithm. In a pre-validation phase, we created, developed and tested algorithms for RT based on airway pressure (Paw) and flow (Additional file
1). Our main objective was to validate the automatic detection of RT by the software using only Paw and flow against a visual assessment of the same tracings by experts having Paw, flow and esophageal pressure (Pes). A secondary objective was to describe the magnitude of the efforts generated during RT.
Ackowlegements
List of the BEARDS investigators: (1) Saint Michael’s Hospital, Toronto, Ontario, Canada: Laurent Brochard, Irene Telias, Felipe Damiani, Ricard Artigas, Cesar Santis, Tài Pham. (2) Dipartimento di Anestesia, Rianimazione ed Emergenza-Urgenza, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Università degli studi di Milano, Milan, Italy: Tommaso Mauri, Elena Spinelli, Giacomo Grasselli. (3) Department of Morphology, Surgery and Experimental Medicine, Intensive Care Unit University of Ferrara, Sant'Anna Hospital, Ferrara, Italy: Savino Spadaro, Carlo Alberto Volta. (4) Anesthesia and Intensive Care, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Policlinico San Matteo, Pavia, Italy: Francesco Mojoli. (5) Department of Intensive Care Medicine, University Hospital of Heraklion and School of Medicine, University of Crete, Heraklion, Crete, Greece: Dimitris Georgopoulos, Eumorfia Kondili, Stella Soundoulounaki. (6) Department of Anesthesiology and Intensive Care Medicine, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany: Tobias Becher, Norbert Weiler, Dirk Schaedler. (7) Critical Care Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute and Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain: Oriol Roca, Manel Santafe. (8) Intensive Care Medicine, Hospital de Sant Pau, Barcelona, Spain: Jordi Mancebo, Nuria Rodríguez. (9) Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, The Netherlands: Leo Heunks, Heder de Vries. (10) National Cheng Kung University Hospital, College of Medicine, National Cheng-Kung University, Tainan, Taiwan: Chang-Wen Chen. (11) Department of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China: Jian-Xin Zhou周建新, Guang-Qiang Chen 陈光强. (12) Division of Respiratory Diseases and Tuberculosis, Mahidol University Faculty of Medicine Siriraj Hospital, Bangkok, Thailand: Nuttapol Rittayamai. (13) Complejo Médico de la Policía Federal Argentina Churruca Visca, Buenos Aires, Argentina: Norberto Tiribelli. (14) Sanatorio de la Trinidad Mitre, Buenos Aires, Argentina: Sebastian Fredes. (15) Hospital Clinic, Barcelona, Spain : Ricard Mellado Artigas, Carlos Ferrando Ortolá. (16) Medical Intensive Care Unit, University Hospital of Angers, Angers, France: François Beloncle, Alain Mercat. (17) Service de Réanimation Polyvalente, Hôpital Sainte Musse, Toulon, France: Jean-Michel Arnal. (18) Medical Intensive Care Unit Hôpital Européen Georges Pompidou Assistance Publique-Hôpitaux de Paris, Paris, France: Jean-Luc Diehl. (19) AP-HP, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Service de Pneumologie, Médecine intensive - Réanimation (Département 'R3S'), Paris, France: Alexandre Demoule, Martin Dres, Quentin Fossé. (20) Groupe Hospitalier Sud Ile-De-France, Centre Hospitalier de Melun, Melun, France: Sébastien Jochmans, Jonathan Chelly. (21) Médecine Intensive Réanimation, C.H.U de Grenoble-Alpes, Grenoble, France: Nicolas Terzi, Claude Guérin. (22) Division of Pulmonary and Critical Care, Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA: E Baedorf Kassis. (23) Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University College of Physicians and Surgeons, NewYork-Presbyterian Hospital, New York, New York, USA: Jeremy Beitler. (24) Anesthesia and Intensive Care, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Policlinico San Matteo, Pavia, Italy: Davide Chiumello, Erica Ferrari Luca Bolgiaghi. (25) Médecine Intensive Réanimation, Centre Hospitalier Universitaire de Poitiers, Poitiers, France : Arnaud W Thille, Rémi Coudroy. (26) Médecine Intensive Réanimation, Hôpital Nord, Hôpitaux de Marseille, Chemin des Bourrely, 13015, Marseille, France : Laurent Papazian
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