Pham et al. Crit Care (2021) 25:60 https://doi.org/10.1186/s13054-020-03387-3 RESEARCH Open Access Automated detection and quantification of reverse triggering effort under mechanical ventilation Tài Pham1,2,3* , Jaume Montanya4, Irene Telias1,2,5,6, Thomas Piraino7,8 , Rudys Magrans4, Rémi Coudroy1,2,9,10, L. Felipe Damiani1,2,11 , Ricard Mellado Artigas1,2,12, Matías Madorno13, Lluis Blanch14,15† and Laurent Brochard1,2† the BEARDS study investigators 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 challeng- ing. 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. Mus- cular 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%, nega- tive predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 c­ mH20, 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. Keywords: Reverse triggering, Dyssynchrony, Mechanical ventilation, Lung and diaphragm protection, Respiratory muscles Background mechanical ventilation or mortality [1–4]. The causality Patient–ventilator dyssynchrony is often associated with has not been demonstrated, and it is not clear yet whether poor patient-centered outcomes such as duration of and when some types of dyssynchrony can directly cause harm or discomfort, or whether others are simply mark- ers of suboptimal ventilator settings or more severe *Correspondence: tai.pham@aphp.fr † Lluis Blanch, Laurent Brochard: Co-Senior authors. underlying conditions. Poor patient–ventilator interac- 1 Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge tion is, however, a major reason for administering seda- Institute, St. Michael’s Hospital, 30 Bond St, Toronto, ON M5B 1W8, Canada tion in the ICU, and therefore this phenomenon deserves Full list of author information is available at the end of the article © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat​iveco​mmons​.org/licen​ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat​iveco​ mmons​.org/publi​cdoma​in/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Pham et al. Crit Care (2021) 25:60 Page 2 of 10 attention and a more granular description than refer- point for the current algorithm. In a pre-validation ring to dyssynchrony in general [5]. Of major interest, phase, we created, developed and tested algorithms for reverse triggering (RT) is a specific form of dyssynchrony RT based on airway pressure (Paw) and flow (e-sup- defined by the presence of a respiratory muscle contrac- plement). Our main objective was to validate the auto- tion following a passive mechanical insufflation as if the matic detection of RT by the software using only Paw contraction was “triggered by” the ventilator [6]. It has and flow against a visual assessment of the same trac- been described in intubated patients receiving sedation ings by experts having Paw, flow and esophageal pres- under controlled ventilation and seems to be very fre- sure (Pes). A secondary objective was to describe the quent [7–15]. This phenomenon might constitute a regu- magnitude of the efforts generated during RT. lar entrainment (phase locking) of the respiratory rhythm to periodic insufflation, as described in animals [16, 17] Methods and healthy humans [18], but it may also be more irregu- Definitions lar and can even occur in brain-dead patients [10]. When A breath was defined as an insufflation followed by an the effort generated is strong enough, it induces breath expiration, even if short and incomplete. A respira- stacking, often misinterpreted to be caused by double tory cycle started at the beginning of an insufflation and triggering (in which the same patient’s inspiratory effort ended at the beginning of the next insufflation as shown would trigger the first and second mechanical insuffla- in Fig. 1. If an early second insufflation happened before tion). Reverse triggering could impact patients’ outcomes complete exhalation, we counted it as a different breath through several mechanisms, such as increased tidal (and labeled it as a stacked breath). volume during inspiration, breath stacking, or through A machine-triggered breath was characterized by the pendelluft during the inspiratory phase [14]. On the one absence of patient’s effort before the machine insufflation. hand, it can generate diaphragm injury when generat- Reverse triggering was defined as an active contraction ing strong eccentric contractions during exhalation [19], of the respiratory muscles including the diaphragm (using but on the other hand, when small, diaphragmatic con- esophageal pressure or EAdi) starting more than 0.1 s tractions related to RT could be beneficial by preventing after the start of a machine-triggered insufflation (Fig. 1); muscle disuse and atrophy in sedated patients. also, the maximal Pes deflection (or the EAdi peak) had Detection of dyssynchrony in general and RT in par- to happen less than 1.5 s after the machine insufflation ticular is challenging requiring additional physiological had started. Although the effort starts during insufflation, signals and/or careful attention to the waveforms on the peak of the effort can still be during insufflation or the ventilator screens and expertise to properly inter- plateau but often takes place during early expiration pret the signals. Additionally, to have an estimate of [e-Fig. 1 and e-Fig. 2].We did not incorporate any element the real burden of dyssynchrony 24/7 inspection of the of regular entrainment as we have observed multiple waveforms would be required [20, 21]. The reference examples where a true entrainment is disrupted and technique to detect respiratory muscle activity and becomes complex to recognize. We initially used record- accurately diagnose dyssynchrony needs an esophageal ings either with EAdi or esophageal pressure tracings to catheter or a catheter that captures the electrical activ- develop the algorithms, and only tracings with Pes for the ity of the diaphragm (EAdi). Preliminary data suggest a validation. We defined a minimal effort before machine high incidence of frequent RT (> 30%) in patients under insufflation to be considered a patient triggered breath as assist-control ventilation [22]. Automatic machine being a Pes drop of more than 2 ­cmH20 as compared to learning techniques that do not require visual inspec- baseline or an EAdi > 0.5 µV above the baseline with a tion are needed for understanding the phenomenon Paw drop > 0.3 ­cmH20. For each tracing, the rate of RT sum of all RTs and helping the clinician to optimize patient–ventila- was defined as the following: sum of all breaths . tor interactions [23]. As the first step of a prospective Breath stacking was the occurrence of a second insuf- multicenter observational study that aims at establish- flation before complete exhalation of the volume insuf- ing the incidence, determinants and consequences flated in the preceding breath (Fig. 1 and e-Fig. 3). of various dyssynchronies during early acute hypox- An active contraction of the inspiratory muscles (Pes emic respiratory failure (BEARDS, NCT03447288), we negative swing) during the late expiration phase of a developed and validated an automated algorithm to patient-triggered breath and not able to trigger a breath detect RT only from ventilator signals, i.e., airway pres- defined an Ineffective effort during expiration. If this hap- sure and flow. Some of the authors (JM, RM, LBl) had pened after a machine-triggered inspiration, the maxi- previous experience in developing a dedicated software mal peak deflection had to occur more than 1.5 s after application to detect other types of dyssynchrony (https​ the machine insufflation to differentiate ineffective effort ://bette​rcare​.es/); this platform was used as a starting from reverse triggering. Pham et al. Crit Care (2021) 25:60 Page 3 of 10 Fig. 1 Breath count and events classification methods. This figure shows an example of tracing with breath counts, reverse triggering and breath stacking. Top waveform (blue): airway pressure (Paw); middle waveform (red): flow; bottom waveform (green): esophageal pressure (Pes). A breath is an insufflation followed by an expiration (even if short and incomplete); nine breaths are present on the figure. There are two reverse triggering on this figure, and the second reverse triggering causes breath stacking (definitions in the text). Algorithm for detection of reverse triggering training dataset). It comprised 20 tracings from 20 differ- Several methods of digital signal processing and super- ent patients among the first 109 patients enrolled in the vised machine learning have been combined to detect RT BEARDS study and the 1087 tracings available: 4 tracings events from flow and Paw waveforms depending on the without visible RT, 4 with a low visible rate of RT, 4 with mode of mechanical ventilation and the phase of the res- a moderate rate of RT, 4 with a high rate of RT and 4 on piratory cycle where the event occurred. These methods PSV. All patients met the criteria for moderate or severe intended to account for the different ways RT impacts hypoxemic respiratory failure (PaO2/FiO2 < 200 mmHg on either or both waveforms. For instance, during con- and deep sedation at inclusion) with unilateral injury stant flow, the impact during inspiration will be mainly or ARDS. Ventilator waveforms and esophageal pres- on Paw, while during pressure controlled ventilation the sure were synchronously recorded over the first 7 days impact will be both on Paw and on flow. In both situa- of mechanical ventilation. We selected 20 patients in tions there may also be an impact on expiratory flow. The order to represent a full spectrum of RT occurrence. A signals were smoothed to avoid artifactual detections. semiquantitative analysis allowed to classify the tracings The mechanism that initiated the respiratory cycle was available (in volume or pressure assist-control mode) automatically detected (i.e., machine/time or patient trig- according to the estimated frequency of RT: (1) no vis- gered). The strategies implemented are summarized in ible RT; (2) low rate of detectable RT: less than 4 RTs Fig. 2, and the details regarding the algorithm are given in per minute; (3) moderate rate of RT: between 4 and 10 the online supplement. RTs per minute; (4) high rate of RTs: more than 10 RTs per minute. We discarded tracings with technical issues Validation phase (excessive cardiac artifacts, leaks, uncertain calibration A training set was based on 20 to 40 minutes long trac- procedure, oscillations impairing the tracing assessment) ings displaying visually detectable RT; these tracings and randomly selected 4 tracings in each category for mostly came from a previous study for 9 recordings the validation. For the 4 tracings with no RT, we pur- (DIVIP, NCT02434016) and 3 from the BEARDS study. posely selected recordings collected when the patient was The final validation dataset was only based on recordings receiving neuromuscular blocking agents. Additionally, from the BEARDS study (and with no patient used in the we randomly selected 4 tracings from the 246 recordings Pham et al. Crit Care (2021) 25:60 Page 4 of 10 Fig. 2 Diagram of the diverse methods implemented for RT events detection. This figure shows the step-by-step approach of the automatic detection algorithm to detect reverse triggering according to the mode of mechanical ventilation and the time of the RT occurrence. MV = mechanical ventilation; PT = patient-triggered breath; MT = machine-triggered breath; VCV = volume-controlled ventilation; PCV = pressure-controlled ventilation; PS = pressure support; Paw = airway pressure; ∆Ppeak = variation between the current peak pressure (Ppeak) value and the average of the most immediate previous Ppeak values; ∆Pplat = variation between the current plateau pressure (Pplat) value and the average of the most immediate previous Pplat values; ∆Ppp = difference between ∆Ppeak and ∆Pplat values; PEF = peak expiratory flow; Prob = RT probability values predicted by the logistic regression model; cutoff = optimal probability value for RT event classification; RT insp = reverse triggering during inspiration in VCV and PCV modes, and (*) in auto-triggered breaths in PS; RT pause = reverse triggering during the pause; RT exp1 = reverse triggering late at inspiration producing airflow deformation at the onset of exhalation; RT exp = reverse triggering during expiration occurring within 1.5 s after controlled inspirations; RT BS = reverse triggering producing breath stacking. collected while patients were ventilated in pressure sup- and discrepancies breath by breath. All discrepancies port ventilation (PSV). Tracings collected during paraly- were reviewed by a third researcher (RC) blinded to sis or PSV acted as a control to ensure that the algorithm the results obtained with visual or automatic detection. would not have false positives: RT should not occur in This step confirmed or infirmed the discrepancies. these situations because there is either no diaphragm contraction (paralysis) or because the patient triggers all the breaths and there is no passive insufflation (PSV). Quantification of effort during reverse triggering Two researchers (TPh and TPi) visually reviewed One important question regarding the potential risk 10 min of each of these 20 selected tracings and cat- of RT (for the lung or diaphragm) relates to the ampli- egorized each breath according to the type of trigger tude of efforts generated by RT. Therefore, we calcu- (machine vs patient), the presence or absence of RT and lated the muscular pressure (Pmus) of each breath to the presence or absence of a stacked breath (early addi- estimate the range of effort generated during RT and tional breath during the exhalation phase) using Paw, compare it to the Pmus generated during pressure sup- flow and Pes. This assessment was used as the gold stand- port ventilation [24]. We aimed at estimating the range ard for breaths classification. of effort that generates a deformation of flow and Paw Independently, and without the Pes signal, the same waveforms, as detected by our algorithm. Pmus was breaths were analyzed by the automatic detection soft- calculated as the maximum difference between the ware. The results of the visual and automatic detections Pes and the chest wall relaxation curve according to were merged on a spreadsheet to review agreement volume. The latter was calculated using the chest wall Pham et al. Crit Care (2021) 25:60 Page 5 of 10 same patient. The FluxMed® software was used for compliance measured during passive breaths in the Table 1 Patients characteristics. Categorial variables are expressed as N (%) and numerical variables as mean ± SD this calculation. Inspiratory efforts with artifact in the N = 20 Pes signal that prevented from accurate measurement of Pmus were discarded (N = 34 in tracings during Female Gender 7 (35.0%) assist-control and N = 14 in tracings during pressure Age, years 65 ± 12 support). Height, cm 168 ± 10 Weight, kg 79 ± 30 Comorbidities Statistics Hypertension 10 (50.0%) Agreement between researchers and automatic detec- COPD 4 (20.0%) tion for the diagnosis of RT was determined with the Diabetes 6 (30.0%) Kappa statistic [25, 26]. True positives were breaths Chronic kidney disease 4 (20.0%) considered as RT both by visual assessment and by Chronic cardiac failure 3 (15.0%) automatic detection. True negatives were breaths con- Hematologic malignancy 1 (5.0%) sidered as not being RT both by visual assessment and Immunosuppression 1 (5.0%) by automatic detection. False positives were breaths Chronic liver disease 2 (10.0%) considered as RT by automatic detection but not by Cause for intubation visual assessment. False negatives were breaths consid- Hypoxemia 18 (90.0%) ered as RT by visual assessment but not by automatic Hypercapnia 1 (5.0%) detection. Standard formulas were used to calculate Shock 2 (10.0%) sensitivity, specificity, positive predictive value (PPV) Cardiac arrest 2 (10.0%) and negative predictive value (NPV) [27, 28]. General- Surgery 1 (5.0%) ized estimating equation was used to compare the mag- Risk factor for ARDS nitude of inspiratory effort for different types of breaths Pneumonia 17 (85.0%) considering each patient a cluster. Aspiration 2 (10.0%) Sample size calculation: Our primary objective was to Pulmonary contusion 1 (5.0%) validate the automatic detection versus visual assessment Non-pulmonary sepsis 1 (5.0%) of RT aiming at a Kappa above 0.8 that can be considered Severity at inclusion an almost perfect agreement. To obtain a kappa of 0.85 APACHE III 82 ± 29 with a 95% confidence interval [0.83;0.87] and assuming a SOFA 9.5 ± 4.0 rate of RT of 20% with an alpha risk of 0.05, 4655 breaths pH 7.34 ± 0.09 were estimated to be necessary [25]. Using 20 record- PaO2/FiO2 ratio, mmHg 148 ± 42 ings at a respiratory rate of 20/min, the analysis of 10 min PaCO2, mmHg 45 ± 10 would provide approximately 5000 breaths. Outcomes Discharged alive from the ICU 15 (75.0%) Results Discharged alive from the hospital 11 (55.0%) Patients and tracings characteristics The 109 patients included in the BEARDS study had a median [IQR] of 12 [9;15] tracings collected and 58 (53%) a median [IQR] Richmond Agitation Sedation Scale [29] of the patients had at least one of their tracings display- of − 4 [− 5;− 2]. ing reverse triggering. Tracings from 20 of these patients were randomly selected for the validation (e-Fig. 4). The Accuracy of reverse triggering detection main characteristics of these patients are presented in A total of 4509 breaths from the 20 recordings were Table 1. The tracings used for this validation study were assessed, and 1073 (24%) were considered as RT as collected at a median [IQR] of 3 [2;4] days from inclusion. per gold standard (visual assessment); almost all RTs Among the 20 tracings selected, 9 (45%) were in pressure (N = 1070) occurred during controlled ventilation and assist-control, 7 (35%) in volume assist-control, 4 (20%) in only 3 following auto-triggered breaths in PSV. Among pressure support ventilation mode and 4 were collected patients in assisted-control mode presenting at least one in paralyzed patients. At the time of recording, 8 (40%) RT, the rate of RT ranged from 14.8% to 93.3%. The con- patients were receiving a vasopressor, 12 (60%) patients fusion matrix between visual assessment and the auto- were sedated with midazolam, 4 (20%) with propofol, 17 matic detection is shown in Table 2, and the ROC curve (85%) were receiving an opioid infusion and patients had is displayed in the e-supplement (e-Fig. 5): There were Pham et al. Crit Care (2021) 25:60 Page 6 of 10 Table 2 Agreement matrix Visual assessment Total RT No RT Automatic detection RT 892 22 914 No RT 181 3414 3595 Total 1073 3436 4509 20% true positive, 76% true negative, 4% false negative and 0.5% false positive. Therefore, automatic detection achieved a global accuracy of 95.5%, sensitivity of 83.1%, specificity of 99.3%, a positive predictive value of 97.6%, a negative predictive value of 94.9% and kappa index of 86.9 [85.1;88.7]. At a tracing level, RT rates (expressed as a percentage of all ventilator breaths) ranged from 0 (patients paralyzed or in PSV) to 93.4%; the median [IQR] was 17.0% [0%;43.3%]. Individually, the accuracy ranged from 73.8 to 100% (median [IQR] 98.1% [95.5%; 100%]). Fig. 3 Amplitude of Pmus generated during RT with breath stacking A low accuracy (73.8%) was observed in one patient with (RT with BS N = 206), without breath stacking (N = 841), during efforts numerous but very weak reverse triggering efforts (mag- triggering the ventilator in pressure support ventilation (synchronous, nitude ~ 3–4 cm ­H2O) frequently not strong enough to N = 682) and during ineffective efforts during expiration (IEE, N = 33). induce a visible deformation on Paw or Flow; this patient RT = reverse triggering, BS = breath stacking, IEE = ineffective efforts had been sedated and had no sign of wakefulness. during expiration, ACV = assist-control ventilation, PSV = pressure support ventilation Range of patients’ effort corresponding to RT To evaluate the range of efforts generated by RT together with its variability, the muscular pressure (Pmus) was ­cmH2O min−1 (vs 172.7 [135.5, 213.2] ­cmH2O min−1 for calculated for each breath in 1047 breaths with RT dur- patient-triggered breath). ing assist-control ventilation (5 in volume control, 7 in pressure control) and was compared to 715 breaths from Discussion 4 tracings during pressure support ventilation includ- We showed that an automated machine learning detec- ing 33 expiratory ineffective efforts occurring within tion using only airway pressure and flow tracings was triggered breaths. RT efforts were separated into those able to diagnose reverse triggering with a high specific- inducing breath stacking (n = 206) and those without ity, positive and negative predictive values resulting in an breath stacking (n = 841). The results are presented in excellent accuracy. Despite some false negatives, sensi- Fig. 3. The median [25th–75th percentiles] of Pmus for tivity remained very good and specificity was strong. In RT was 8.7 [5.6;9.9] c­mH20, ranging from 1.3 to 36.8 this selected set of tracings, RT was present in 23% of ­cmH20. Between- and within-patient variability in RT the breaths and generated a median muscle pressure of effort was considerable with median values ranging 9 ­cmH2O with important variability between and within from 4.2 to 33.0 cm ­H2O and within-patient coefficient patients ranging from 1 to 36 ­cmH2O. of variabilities ranging from 5 to 106% (Fig. 4). RT with breath stacking corresponded to the highest levels of Accuracy of the algorithm Pmus, while breaths with RT but no breath stacking and RT is a phenomenon that has been described recently triggered breaths during pressure support were of simi- and is very likely underdiagnosed at the bedside [6, 30]. lar magnitude for Pmus. Ineffective efforts during pres- Visual detection of dyssynchrony is impractical and chal- sure support corresponded to the lowest Pmus. Within lenging due to clinicians’ lack of time and/or expertise, each tracing, the median rate of RT was 5.5 [4.0;11.0] and there is an urgent need for reliable automatic detec- per minute (vs 16.6 [11.0, 20.3] for patient-triggered tion. We reasoned that any diagnostic tool implemented breaths) and the corresponding product of the amplitude for daily practice would require an algorithm that diag- of RT and rate (Pmus x rate of RT) was 67.3 [49.2;109.0] noses mostly impactful events, i.e., with potential clinical Pham et al. Crit Care (2021) 25:60 Page 7 of 10 Fig. 4 Variability and distribution of the amplitude of inspiratory effort for each patient (indicated by ID number). Amplitude of Pmus calculated for each breath during RT and during patient-triggered synchronous efforts on pressure support ventilation for each patient. RT with BS are represented with orange triangles, RT without BS are seen in green circles, and those synchronous patient-triggered efforts with violet circles consequences, while avoiding false positives that would invasive device. Paw and Flow are readily available on all impair interpretation. The algorithms validated in this ventilators, and the system can communicate and capture study fulfill these major conditions by displaying a very data from all ventilators for online analysis or subsequent strong specificity (when a RT is detected, it is actually review. Continuous assessment of RT is relevant to iden- a RT and not a normal breath or an artifact) and a high tify the changes along the patients’ course of mechani- sensitivity (most RTs are detected). The false negatives cal ventilation and to quantify clusters of dyssynchrony found in this study were reviewed and corresponded to that might have an impact on the outcome. It would also weak efforts close to the limits for detection. Modifying allow to obtain reliable epidemiologic data and to exam- the threshold for detection of these weak efforts could ine relationship between RT and treatments or relation- reclassify these breaths as RT but could increase the risk ship with other biological phenomena in a continuous of false positive that really needs to be avoided. PPV and manner. A few other studies have previously evaluated NPV had very high values in our study but are depend- the accuracy of automatic detections of some types of ent on the prevalence of the event (The higher the preva- dyssynchronies [31–34] but only one evaluated RT [35]. lence, the higher the PPV and the lower the NPV and vice The latter found excellent algorithm performance but versa). The rate of RT in unselected invasively ventilated only evaluated patients with volume-controlled ventila- patients is still unclear, but recent reports mentioned tion for ARDS during a very short period (20 consecutive high incidence between 25 and 50% (manuscript in breaths per patient) with only 27% of the breaths of this revision). Use of these automatic detection algorithms validation study having Pes available for the evaluation by will help obtaining a precise quantification of this likely a single expert. For the remaining breaths (72.6%) evalu- underestimated phenomenon. ated by a group of 7 experts, they did not reach complete agreement in 27% of the breaths and did not have access Continuous assessment and quantification to Pes or EAdi signals to confirm their classification. A major asset of the technique is the possibility to quan- tify RT continuously with no additional catheter or Pham et al. Crit Care (2021) 25:60 Page 8 of 10 Efforts generated during RT operational definition for RT not taking into account This is the first study providing a quantification of the entrainment pattern. Entrainment patterns might result muscular efforts exerted during different forms of RT and from different underlying causes and mechanisms. Vali- compared to triggered breaths and ineffective efforts. We dation of an automatic detection of this dyssynchrony identified a broad range of efforts from almost negligible is the first step to better describe RT and grasp the full to huge contractions showing considerable between- and spectrum of its physiologic mechanism, consequences within-patient variability. Their impact on lung and dia- and prevention. phragm may therefore vary considerably. Physiological [36–38] and epidemiological [39] data showed that the Conclusion intensity of breathing effort has variable consequences on An algorithm for automatic detection for reverse trig- the lung by generating varying levels of stress and strain, gering showed excellent performances with usual modes and on the diaphragm by modifying the risk of disuse of MV. This monitoring tool allows accurate continuous atrophy versus load-induced injury. In these randomly assessment and diagnosis of RT which is the first step to selected tracings, the amplitude of effort during RT better understand this phenomenon underlying physi- without breath stacking occurring during assist-control ologic mechanism, assess its impact and ultimately pro- modes was similar to that occurring during synchronous pose treatment to prevent clinical complications. efforts on pressure support. Interestingly, despite the lev- els of breathing effort during RT being considerable, they Abbreviations are often missed by the majority of clinicians relying on EAdi: Electrical activity of the diaphragm; ICU: Intensive care unit; NPV: Nega- standard monitoring techniques to detect the presence tive predictive value; Paw: Airway pressure; Pes: Esophageal pressure; Pmus: of breathing effort (e.g., total rate on the ventilator equals Muscular pressure; PPV: Positive predictive value; RT: Reverse triggering. the set rate). As expected, RT associated with breath Ackowlegements stacking corresponded to higher levels of efforts, there- List of the BEARDS investigators: (1) Saint Michael’s Hospital, Toronto, Ontario, fore being potentially the most injurious for the lung and Canada: Laurent Brochard, Irene Telias, Felipe Damiani, Ricard Artigas, Cesar Santis, Tài Pham. (2) Dipartimento di Anestesia, Rianimazione ed Emergenza- the diaphragm. Additionally, the timing of RT within Urgenza, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, the respiratory cycle (i.e., occurring during inspiration Università degli studi di Milano, Milan, Italy: Tommaso Mauri, Elena Spinelli, or expiration) might also lead to specific mechanisms of Giacomo Grasselli. (3) Department of Morphology, Surgery and Experimental Medicine, Intensive Care Unit University of Ferrara, Sant’Anna Hospital, Ferrara, injury such as eccentric diaphragmatic contractions or Italy: Savino Spadaro, Carlo Alberto Volta. (4) Anesthesia and Intensive Care, pendelluft [14, 40]. Other important aspects of breath- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Policlinico ing effort during RT might also have an impact on the San Matteo, Pavia, Italy: Francesco Mojoli. (5) Department of Intensive Care Medicine, University Hospital of Heraklion and School of Medicine, University lung and the diaphragm such as duration and the overall of Crete, Heraklion, Crete, Greece: Dimitris Georgopoulos, Eumorfia Kondili, frequency of the events. Results from future study (e.g., Stella Soundoulounaki. (6) Department of Anesthesiology and Intensive BEARDS) are needed to provide additional information Care Medicine, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany: Tobias Becher, Norbert Weiler, Dirk Schaedler. (7) Critical Care regarding outcome. Department, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute and Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Limitations Spain: Oriol Roca, Manel Santafe. (8) Intensive Care Medicine, Hospital de Sant Pau, Barcelona, Spain: Jordi Mancebo, Nuria Rodríguez. (9) Department Our main goal was to evaluate the accuracy of the auto- of Intensive Care Medicine, Amsterdam UMC, Amsterdam, The Netherlands: matic detection on different types of tracings in terms Leo Heunks, Heder de Vries. (10) National Cheng Kung University Hospital, of modes and patient’s effort, and we assessed a large College of Medicine, National Cheng-Kung University, Tainan, Taiwan: Chang- Wen Chen. (11) Department of Critical Care Medicine, Beijing Tiantan Hospital, number of breaths but in a relatively limited number of Capital Medical University, Beijing, China: Jian-Xin Zhou周建新, Guang-Qiang patients. Our tracing selection included RT rates ranging Chen 陈光强. (12) Division of Respiratory Diseases and Tuberculosis, Mahidol from 0 to 93.3% representing a broad spectrum of clinical University Faculty of Medicine Siriraj Hospital, Bangkok, Thailand: Nuttapol Rit- tayamai. (13) Complejo Médico de la Policía Federal Argentina Churruca Visca, situations. We evaluated pressure control, volume con- Buenos Aires, Argentina: Norberto Tiribelli. (14) Sanatorio de la Trinidad Mitre, trol and pressure support ventilation, which are the most Buenos Aires, Argentina: Sebastian Fredes. (15) Hospital Clinic, Barcelona, Spain frequently used modes worldwide [41, 42]. We cannot : Ricard Mellado Artigas, Carlos Ferrando Ortolá. (16) Medical Intensive Care Unit, University Hospital of Angers, Angers, France: François Beloncle, Alain extrapolate the algorithm performances to other modes. Mercat. (17) Service de Réanimation Polyvalente, Hôpital Sainte Musse, Toulon, We arbitrarily limited our definition of RT to patients’ France: Jean-Michel Arnal. (18) Medical Intensive Care Unit Hôpital Européen efforts that reached maximum intensity within 1.5 s after Georges Pompidou Assistance Publique-Hôpitaux de Paris, Paris, France: Jean- Luc Diehl. (19) AP-HP, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Service the start of mandatory breath to differentiate RT from de Pneumologie, Médecine intensive - Réanimation (Département ’R3S’), Paris, ineffective effort during expiration. Some patients have France: Alexandre Demoule, Martin Dres, Quentin Fossé. (20) Groupe Hospital- a fluctuating respiratory drive and intermittently trig- ier Sud Ile-De-France, Centre Hospitalier de Melun, Melun, France: Sébastien Jochmans, Jonathan Chelly. (21) Médecine Intensive Réanimation, C.H.U de ger the ventilator. Reverse-triggered breaths usually keep Grenoble-Alpes, Grenoble, France: Nicolas Terzi, Claude Guérin. (22) Division the same characteristics of phase lag. We used a simple of Pulmonary and Critical Care, Beth Israel Deaconess Medical Center and Pham et al. Crit Care (2021) 25:60 Page 9 of 10 Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA: E 209 Victoria St, Toronto, ON M5B 1T8, Canada. 3 Université Paris-Saclay, AP-HP, Baedorf Kassis. (23) Division of Pulmonary, Allergy, and Critical Care Medicine, Service de médecine intensive-réanimation, Hôpital de Bicêtre, DMU COR- Columbia University College of Physicians and Surgeons, NewYork-Presbyte- REVE, FHU SEPSIS, Groupe de recherche clinique CARMAS, Le Kremlin‑Bicêtre, rian Hospital, New York, New York, USA: Jeremy Beitler. (24) Anesthesia and France. 4 Better Care SL, Sabadell, Spain. 5 Division of Respirology, Department Intensive Care, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, of Medicine, University Health Network, Toronto, Canada. 6 Sinai Health Sys- Policlinico San Matteo, Pavia, Italy: Davide Chiumello, Erica Ferrari Luca Bol- tem, Toronto, Canada. 7 St. Michael’s Hospital, Unity Health Toronto, Toronto, giaghi. (25) Médecine Intensive Réanimation, Centre Hospitalier Universitaire Canada. 8 Division of Critical Care, Department of Anesthesia, McMaster de Poitiers, Poitiers, France : Arnaud W Thille, Rémi Coudroy. (26) Médecine University, Hamilton, Canada. 9 Médecine Intensive Réanimation, CHU de Intensive Réanimation, Hôpital Nord, Hôpitaux de Marseille, Chemin des Bour- Poitiers, Poitiers, France. 10 INSERM CIC 1402, Groupe ALIVE, Université de rely, 13015, Marseille, France : Laurent Papazian Poitiers, Poitiers, France. 11 Departamento Ciencias de la Salud, Carrera de Kinesiología, Faculdad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile. 12 Surgical ICU, Department of Anesthesia, Hospital Clínic, Authors’ contributions Barcelona, Spain. 13 Instituto Tecnológico de Buenos Aires (ITBA), Buenos The study was designed by LBr, LBl, TPh and IT. Machine learning process Aires, Argentina. 14 Critical Care Center, Hospital Universitari Parc Taulí, Institut was developed by JM, RM and LBl. Visual assessment of the tracings was per- D’Investigació I Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, formed by TPh, TPi and RC. Assessment of inspiratory effort was performed by Sabadell, Spain. 15 Biomedical Research Networking Center in Respiratory IT and MM. All authors participated in the manuscript drafting and approved Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain. 16 Saint Michael’s the submitted version. Hospital, Toronto, ON, Canada. 17 Dipartimento di Anestesia, Rianimazione ed Emergenza‑Urgenza, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Funding Policlinico, Università degli studi di Milano, Milan, Italy. 18 Department of Mor- Tài Pham received a mobility award from the French Intensive Care Society phology, Surgery and Experimental Medicine, Intensive Care Unit University (FICS/SRLF), the European Society of Intensive Care Medicine (ESICM) young of Ferrara, Sant’Anna Hospital, Ferrara, Italy. 19 Anesthesia and Intensive Care, investigator award and a Society of Critical Care Medicine (SCCM) grant for this Fondazione Istituto Di Ricovero E Cura a Carattere Scientifico, Policlinico project. Rémi Coudroy received grants from the French Ministry of Health, Le San Matteo, Pavia, Italy. 20 Department of Intensive Care Medicine, School Nouveau Souffle, AADAIRC, French Intensive Care Society and European Res- of Medicine, University Hospital of Heraklion, University of Crete, Heraklion, piratory Society, and nonfinancial support from Fisher & Paykel Healthcare and Crete, Greece. 21 Department of Anesthesiology and Intensive Care Medicine, MSD outside the submitted work. L. Felipe Damiani acknowledges partial sup- University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany. 22 port from CONICYT-PFCHA/Doctorado Nacional/2017-folio 21171551. Irene Critical Care Department, Vall D’Hebron University Hospital, Vall D’Hebron Telias is suported by a grant from the Canadian Institutes for Health Research Research Institute and Ciber Enfermedades Respiratorias, Instituto de Salud in the form of a Post-Doctoral Fellowship. Lluis Blanch and Rudys Magrans Carlos III, Madrid, Spain. 23 Intensive Care Medicine, Hospital de Sant Pau, received a grant from Ministerio de Ciencia e Innovación (Spain) for project Barcelona, Spain. 24 Department of Intensive Care Medicine, Amsterdam UMC, RTC-2017-6193-1 (AEI/FEDER UE). Amsterdam, The Netherlands. 25 College of Medicine, National Cheng Kung University Hospital, National Cheng-Kung University, Tainan, Taiwan. 26 Depart- Availability of data and materials ment of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical Data and material can be shared on request. University, Beijing, China. 27 Division of Respiratory Diseases and Tuberculosis, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. 28 Code availability Complejo Médico de La Policía Federal Argentina Churruca Visca, Buenos Custom code can be shared if requested. Aires, Argentina. 29 Sanatorio de La Trinidad Mitre, Buenos Aires, Argentina. 30 Hospital Clinic, Barcelona, Spain. 31 Medical Intensive Care Unit, University Ethics approval and consent to participate Hospital of Angers, Angers, France. 32 Service de Réanimation Polyvalente, This study was approved by St. Michael’s Hospital Research Ethics Board Hôpital Sainte Musse, Toulon, France. 33 Medical Intensive Care Unit, Hôpital (Toronto, Ontario): REB Approval—Study #17-182. All patients or their surro- Européen Georges Pompidou Assistance Publique-Hôpitaux de Paris, Paris, gate decision-makers provided informed consent to participate in the study. France. 34 AP-HP, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, Service de Pneumologie, Médecine Intensive - Réanimation (Département ‘R3S’), Paris, Consent for publication France. 35 Groupe Hospitalier Sud Ile‑De‑France, Centre Hospitalier de Melun, Not applicable. Melun, France. 36 Médecine Intensive Réanimation, C.H.U de Grenoble-Alpes, Grenoble, France. 37 Division of Pulmonary and Critical Care, Beth Israel Competing interests Deaconess Medical Center, Massachusetts General Hospital, Harvard Medical Dr. Telias reports personal fees from Covidien, Argentina, and MBMed SA, School, Boston, MA, USA. 38 Division of Pulmonary, Allergy, and Critical Care Argentina, Dr. Madorno is the partner and manager of MBMed S.A. and had Medicine, Columbia University College of Physicians and Surgeons, NewYork- received grants for development of medical technology from the Argentinean Presbyterian Hospital, New York, NY, USA. 39 Department of Surgical Science, Ministry of Science and Technology outside of the submitted work. Thomas Division of Anesthesia and Critical Care Medicine, University of Turin, AOU Piraino reports Speaker fees and consulting from Drager, Philips and Fisher& Città della Salute e della Scienza di Torino, Turin, Italy. 40 Médecine Intensive Paykel. Lluis Blanch has been named in a provisional European patent applica- Réanimation, Centre Hospitalier Universitaire de Poitiers, Poitiers, France. 41 tion number EP19383116 owned by Corporació Sanitària Parc Taulí: “A device Médecine Intensive Réanimation, Hôpital Nord, Hôpitaux de Marseille, and method for respiratory monitoring in mechanically ventilated patients.” Chemin des Bourrely, 13015 Marseille, France. Lluis Blanch is the inventor of a US patent owned by Corporació Sanitària Parc Taulí: “Method and system for managed related patient parameters provided Received: 25 August 2020 Accepted: 12 November 2020 by a monitoring device,” US Patent No. 12/538,940. Jaume Montanya and Lluis Blanch own stock options of BetterCare S.L., a research and development spinoff of Corporació Sanitària Parc Taulí. Ricard Mellado Artigas received speaker fees from Medtronic. Laurent Brochard reports research grants from Covidien Medtronic, equipment support from Philips, equipment support References from Sentec, equipment support from Air Liquide, grants and equipment sup- 1. Thille AW, Rodriguez P, Cabello B, Lellouche F, Brochard L. Patient-venti- port from Fisher Paykel, grants from General Electric, outside the submitted lator asynchrony during assisted mechanical ventilation. Intensive Care work. Med. 2006;32:1515–22. 2. Blanch L, Villagra A, Sales B, Montanya J, Lucangelo U, Luján M, et al. Asyn- Author details chronies during mechanical ventilation are associated with mortality. 1 Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Intensive Care Med. 2015;41:633–41. 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