Magnitude of Synchronous and Dyssynchronous Inspiratory Efforts during Mechanical Ventilation: A Novel Method

Telias, Irene; Madorno, Matías; Pham, Tài; Piraino, Thomas; Coudroy, Rémi; Kondili, Eumorfia; Spadaro, Savino; Becher, Tobias; Chen, Chang-Wen; Mauri, Tommaso; Piquilloud, Lise; Brochard, Laurent; Damiani, Felipe; Santis, César; Sklar, Michael C.; Kim, Audery; Abbott, Megan; Spinelli, Elena; Grasselli, Giacomo; Corcione, Nadia; Corte, Francesca Dalla; Volta, Carlo Alberto; Mojoli, Francesco; Georgopoulos, Dimitris; Soundoulounaki, Stella; Weiler, Norbert; Schaedler, Dirk; Roca, Oriol; Santafé, Manel M.; Mancebo, Jordi; Rodríguez, Núria; Heunks, Leo; Vries, Heder de; Chen, Chang Wen; Zhou, Jianxin; Chen, Guang-Qiang; Rittayamai, Nuttapol; Tiribelli, Norberto; Fredes, Sebastián; Mellado, Ricard Artigas; Ortolá, C. Ferrando; Beloncle, François; Mercat, Alain; Arnal, Jean-Michel; Diehl, Jean‐Luc; Younan, Romy; Demoule, Alexandre; Dres, Martin; Fossé, Quentin; Jochmans, Sébastien; Chelly, Jonathan; Terzi, Nicolas; Guérin, Claude; Kassis, Elias Baedorf; Beitler, Jeremy R.; Chiumello, Davide; Bolgiaghi, Erica Ferrari Luca; Thille, Arnaud W.; Papazian, Laurent; Guervilly, Christophe; Goligher, Ewan C.; Ferguson, Niall D.

doi:10.1164/rccm.202211-2086le

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…Currently, the gold standard for the identification and quantification of strong inspiratory efforts is the measurement of P es swing. However, it is not commonly used due to its complexity and invasiveness [ 32 – 34 ]. Similarly to our study, Telias et al [ 34 ] have recently developed an automated algorithm based on P es measurements that accurately generates and quantifies the muscular pressure for synchronous and dyssynchronous inspiratory efforts.…”

Section: Discussionmentioning

confidence: 99%

“…However, it is not commonly used due to its complexity and invasiveness [ 32 – 34 ]. Similarly to our study, Telias et al [ 34 ] have recently developed an automated algorithm based on P es measurements that accurately generates and quantifies the muscular pressure for synchronous and dyssynchronous inspiratory efforts. They suggest that those patients with strong efforts detected by the algorithm might benefit from P es monitoring.…”

Section: Discussionmentioning

confidence: 99%

“…Double triggering was present exclusively in breaths with a severe P aw deformation (8.8% of them) [ 34 ]. Double triggering is one of the most potentially injurious patient-ventilator asynchronies in assisted volume-controlled ventilation, due to the high P aw and very high tidal volume resulting from the accumulation of two consecutive breaths [ 39 – 41 ].…”

Section: Discussionmentioning

confidence: 99%

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Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques

de Haro,

Santos-Pulpón,

Telías

et al. 2024

Crit Care

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Background Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients’ ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. Methods Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. Results 6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6–88.3], and 86.8% [86.6–87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O. Conclusions Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques

de Haro,

Santos-Pulpón,

Telías

et al. 2024

Crit Care

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“…The first algorithmic approaches for patient effort segmentation in have been described by Castellote et al [ 16 ]. Recently, Telias et al [ 17 ] have automatically detected asynchrony based on esophageal pressure.…”

Section: Introductionmentioning

confidence: 99%

Automated characterization of patient–ventilator interaction using surface electromyography

Sauer,

Graßhoff,

Carbon

et al. 2024

Ann. Intensive Care

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Background Characterizing patient–ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient. Methods In this study, we recorded surface electromyography ($$\textrm{sEMG}$$ sEMG ) signals from the diaphragm and intercostal muscles and esophageal pressure ($$P_{\textrm{es}}$$ P es ) in mechanically ventilated patients with ARDS. The sEMG recordings were preprocessed, and two different algorithms (triangle algorithm and adaptive thresholding algorithm) were used to automatically detect inspiratory patient effort. Based on the detected inspirations, major asynchronies (ineffective, auto-, and double triggers and double efforts), delayed and synchronous triggers were computationally classified. Reverse triggers were not considered in this study. Subsequently, asynchrony indices were calculated. For the validation of detected efforts, two experts manually annotated inspiratory patient activity in $$P_{\textrm{es}}$$ P es , blinded toward each other, the $$\textrm{sEMG}$$ sEMG signals, and the algorithmic results. We also classified patient–ventilator interaction and calculated asynchrony indices with manually detected inspirations in $$P_{\textrm{es}}$$ P es as a reference for automated asynchrony classification and asynchrony index calculation. Results Spontaneous breathing activity was recognized in 22 out of the 36 patients included in the study. Evaluation of the accuracy of the algorithms using 3057 inspiratory efforts in $$P_{\textrm{es}}$$ P es demonstrated reliable detection performance for both methods. Across all datasets, we found a high sensitivity (triangle algorithm/adaptive thresholding algorithm: 0.93/0.97) and a high positive predictive value (0.94/0.89) against expert annotations in $$P_{\textrm{es}}$$ P es . The average delay of automatically detected inspiratory onset to the $$P_{\textrm{es}}$$ P es reference was $$-$$ - 79 ms/29 ms for the two algorithms. Our findings also indicate that automatic asynchrony index prediction is reliable. For both algorithms, we found the same deviation of $$0.06\pm 0.13$$ 0.06 ± 0.13 to the $$P_{\textrm{es}}$$ P es -based reference. Conclusions Our study demonstrates the feasibility of automating the quantification of patient–ventilator asynchrony in critically ill patients using noninvasive sEMG. This may facilitate more frequent diagnosis of asynchrony and support improving patient–ventilator interaction.

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“…Third, the authors report overall effect sizes of each type of event on surrogates of lung stress and strain, accounting for patients’ characteristics but without focusing on the variable consequences within each category of event. Changes in lung stress and strain due to breathing effort depend on the timing of breathing effort as shown by the authors (i.e., synchronous or dyssynchronous) but also, and, probably most importantly, on the magnitude of breathing effort (muscular pressure [13]), particularly when considering events without double-triggering. Importantly, in the study by Sottile et al (5), the presence of “normal spontaneous breaths” was associated with a larger increase in ΔPL, dyn than most other events, highlighting the relevance of the magnitude of effort over synchrony in some situations.…”

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confidence: 99%

Potentially Injurious Patient-Ventilator Interactions, Challenges Beyond Excess Stress and Strain*

Castellví-Font,

Rodrigues,

Telias

2024

Critical Care Medicine

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Magnitude of Synchronous and Dyssynchronous Inspiratory Efforts during Mechanical Ventilation: A Novel Method

Cited by 8 publications

References 9 publications

Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques

Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques

Automated characterization of patient–ventilator interaction using surface electromyography

Potentially Injurious Patient-Ventilator Interactions, Challenges Beyond Excess Stress and Strain*

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