End-tidal to arterial PCO2 ratio: a bedside meter of the overall gas exchanger performance

Bonifazi, Matteo; Romitti, Federica; Busana, Mattia; Palumbo, Maria Michela; Steinberg, Irene; Gattarello, Simone; Palermo, Paola; Saager, Leif; Meissner, Konrad; Quintel, Michael; Chiumello, Davide; Gattinoni, Luciano

doi:10.1186/s40635-021-00377-9

Cited by 20 publications

(15 citation statements)

References 9 publications

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“…However, dead space is not routinely measured in critical care practice, because of the difficulties in interpreting capnograms and the different calculation methods. The ventilation ratio and P ET CO 2 /PaCO 2 ratio, though less precise, appear to be an excellent surrogate for physiologic dead space [ 59 , 60 ].…”

Section: Introductionmentioning

confidence: 99%

Personalized mechanical ventilation in acute respiratory distress syndrome

et al. 2021

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A personalized mechanical ventilation approach for patients with adult respiratory distress syndrome (ARDS) based on lung physiology and morphology, ARDS etiology, lung imaging, and biological phenotypes may improve ventilation practice and outcome. However, additional research is warranted before personalized mechanical ventilation strategies can be applied at the bedside. Ventilatory parameters should be titrated based on close monitoring of targeted physiologic variables and individualized goals. Although low tidal volume (VT) is a standard of care, further individualization of VT may necessitate the evaluation of lung volume reserve (e.g., inspiratory capacity). Low driving pressures provide a target for clinicians to adjust VT and possibly to optimize positive end-expiratory pressure (PEEP), while maintaining plateau pressures below safety thresholds. Esophageal pressure monitoring allows estimation of transpulmonary pressure, but its use requires technical skill and correct physiologic interpretation for clinical application at the bedside. Mechanical power considers ventilatory parameters as a whole in the optimization of ventilation setting, but further studies are necessary to assess its clinical relevance. The identification of recruitability in patients with ARDS is essential to titrate and individualize PEEP. To define gas-exchange targets for individual patients, clinicians should consider issues related to oxygen transport and dead space. In this review, we discuss the rationale for personalized approaches to mechanical ventilation for patients with ARDS, the role of lung imaging, phenotype identification, physiologically based individualized approaches to ventilation, and a future research agenda.

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Section: Introductionmentioning

confidence: 99%

Personalized mechanical ventilation in acute respiratory distress syndrome

et al. 2021

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“…The use of capnography in the ICU and the monitoring of dead space estimates have been long advocated 13,25 , given their role as a safety measure (assessment of endotracheal tube placement), as a marker of the adequacy of ventilation and global perfusion, and their prognostic role in specific situations, such as ARDS.…”

Section: Discussionmentioning

confidence: 99%

Physiologic dead space is independently associated with mortality and discharge of mechanically ventilated patients with COVID-19 ARDS: a retrospective study

Mollura

Baroncelli

Mandelli

et al. 2023

Sci Rep

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Physiologic dead space is a well-established independent predictor of death in patients with acute respiratory distress syndrome (ARDS). Here, we explore the association between a surrogate measure of dead space (DS) and early outcomes of mechanically ventilated patients admitted to Intensive Care Unit (ICU) because of COVID-19-associated ARDS. Retrospective cohort study on data derived from Italian ICUs during the first year of the COVID-19 epidemic. A competing risk Cox proportional hazard model was applied to test for the association of DS with two competing outcomes (death or discharge from the ICU) while adjusting for confounders. The final population consisted of 401 patients from seven ICUs. A significant association of DS with both death (HR 1.204; CI 1.019–1.423; p = 0.029) and discharge (HR 0.434; CI 0.414–0.456; p$$< 0.001$$ < 0.001 ) was noticed even when correcting for confounding factors (age, sex, chronic obstructive pulmonary disease, diabetes, PaO$$_{2}$$ 2 /FiO$$_{2}$$ 2 , tidal volume, positive end-expiratory pressure, and systolic blood pressure). These results confirm the important association between DS and death or ICU discharge in mechanically ventilated patients with COVID-19-associated ARDS. Further work is needed to identify the optimal role of DS monitoring in this setting and to understand the physiological mechanisms underlying these associations.

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“…Corresponding mean PACO 2 values could be calculated from the model with relatively minor adjustments. This factor prompted our selection of mean PACO 2 over end-tidal PCO 2 , notwithstanding the promise shown by the latter measurement in severity stratification of acute respiratory distress syndrome (ARDS) [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Single-FiO2 lung modelling with machine learning: a computer simulation incorporating volumetric capnography

Morgan

Scott

Langley

et al. 2023

J Clin Monit Comput

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We investigated whether machine learning (ML) analysis of ICU monitoring data incorporating volumetric capnography measurements of mean alveolar PCO2 can partition venous admixture (VenAd) into its shunt and low V/Q components without manipulating the inspired oxygen fraction (FiO2). From a 21-compartment ventilation / perfusion (V/Q) model of pulmonary blood flow we generated blood gas and mean alveolar PCO2 data in simulated scenarios with shunt values from 7.3% to 36.5% and a range of FiO2 settings, indirect calorimetry and cardiac output measurements and acid- base and hemoglobin oxygen affinity conditions. A ‘deep learning’ ML application, trained and validated solely on single FiO2 bedside monitoring data from 14,736 scenarios, then recovered shunt values in 500 test scenarios with true shunt values ‘held back’. ML shunt estimates versus true values (n = 500) produced a linear regression model with slope = 0.987, intercept = -0.001 and R2 = 0.999. Kernel density estimate and error plots confirmed close agreement. With corresponding VenAd values calculated from the same bedside data, low V/Q flow can be reported as VenAd—shunt. ML analysis of blood gas, indirect calorimetry, volumetric capnography and cardiac output measurements can quantify pulmonary oxygenation deficits as percentage shunt flow (V/Q = 0) versus percentage low V/Q flow (V/Q > 0). High fidelity reports are possible from analysis of data collected solely at the operating FiO2.

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End-tidal to arterial PCO2 ratio: a bedside meter of the overall gas exchanger performance

Cited by 20 publications

References 9 publications

Personalized mechanical ventilation in acute respiratory distress syndrome

Personalized mechanical ventilation in acute respiratory distress syndrome

Physiologic dead space is independently associated with mortality and discharge of mechanically ventilated patients with COVID-19 ARDS: a retrospective study

Single-FiO2 lung modelling with machine learning: a computer simulation incorporating volumetric capnography

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