Automated detection and quantification of reverse triggering effort under mechanical ventilation

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 challenging. 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. Muscular 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%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH20, 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

Multimodal non-invasive monitoring to apply an open lung approach strategy in morbidly obese patients during bariatric surgery

To evaluate the use of non-invasive variables for monitoring an open-lung approach (OLA) strategy in bariatric surgery. Twelve morbidly obese patients undergoing bariatric surgery received a baseline protective ventilation with 8 cmH2O of positive-end expiratory pressure (PEEP). Then, the OLA strategy was applied consisting in lung recruitment followed by a decremental PEEP trial, from 20 to 8 cmH2O, in steps of 2 cmH2O to find the lung’s closing pressure. Baseline ventilation was then resumed setting open lung PEEP (OL-PEEP) at 2 cmH2O above this pressure. The multimodal non-invasive variables used for monitoring OLA consisted in pulse oximetry (SpO2), respiratory compliance (Crs), end-expiratory lung volume measured by a capnodynamic method (EELVCO2), and esophageal manometry. OL-PEEP was detected at 15.9 ± 1.7 cmH2O corresponding to a positive end-expiratory transpulmonary pressure (PL,ee) of 0.9 ± 1.1 cmH2O. ROC analysis showed that SpO2 was more accurate (AUC 0.92, IC95% 0.87–0.97) than Crs (AUC 0.76, IC95% 0.87–0.97) and EELVCO2 (AUC 0.73, IC95% 0.64–0.82) to detect the lung’s closing pressure according to the change of PL,ee from positive to negative values. Compared to baseline ventilation with 8 cmH2O of PEEP, OLA increased EELVCO2 (1309 ± 517 vs. 2177 ± 679 mL) and decreased driving pressure (18.3 ± 2.2 vs. 10.1 ± 1.7 cmH2O), estimated shunt (17.7 ± 3.4 vs. 4.2 ± 1.4%), lung strain (0.39 ± 0.07 vs. 0.22 ± 0.06) and lung elastance (28.4 ± 5.8 vs. 15.3 ± 4.3 cmH2O/L), respectively; all p < 0.0001. The OLA strategy can be monitored using noninvasive variables during bariatric surgery. This strategy decreased lung strain, elastance and driving pressure compared with standard protective ventilatory settings.Fil: Tusman, Gerardo. Fundación Medica de Mar del Plata. Hospital Privado de Comunidad; ArgentinaFil: Acosta, Cecilia Maria. Fundación Medica de Mar del Plata. Hospital Privado de Comunidad; ArgentinaFil: Ochoa, Marcos Raúl. Fundación Medica de Mar del Plata. Hospital Privado de Comunidad; ArgentinaFil: Böhm, Stephan H.. Universität Rostock; AlemaniaFil: Gogniat, Emiliano. Sociedad Argentina de Cuidados Intensivos; ArgentinaFil: Martinez Arca, Jorge. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Ingeniería Eléctrica. Laboratorio de Bioingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Scandurra, Adriana Gabriela. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Ingeniería Eléctrica. Laboratorio de Bioingeniería; ArgentinaFil: Madorno, Matías. Instituto Tecnológico de Buenos Aires; ArgentinaFil: Ferrando, Carlos. Hospital Clínico Barcelona; EspañaFil: Suarez Sipmann, Fernando. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Centro de Investigación Biomédica en Red de Enfermedades Respiratorias; España. Uppsala Universitet; Sueci