No abstract

Gas exchange capabilities of polymethylpentene membrane lungs (MLs) worsen over time. ML deterioration is related to protein deposit and clot formation. Condensation and trapping of water vapor inside ML hollow fibers might affect ML performances as well. Increasing sweep gas flow (GF) could remove such fluid. The purpose of this study was to evaluate the effects on ML gas exchange of a recruitment maneuver (RM) based on a brief increase in GF, during veno-venous ECMO support. Short-term (15 min) effects of 20 RMs were assessed. RM raised ML CO2 removal from 149 ± 37 to 174 ± 41 ml/min (p < 0.001). Conversely, RM did not improve ML O2 transfer (155 ± 31 and 158 ± 31 ml/min before and after RM, respectively). ML outlet pCO2 decreased after RM from 51.2 ± 5.8 to 45.8 ± 5.4 mmHg (p < 0.001), while ML outlet pO2 increased from 520 ± 61 to 555 ± 51 mmHg (p < 0.001). Both ML dead space and shunt fractions decreased from 47.8 ± 15.3 to 29.6 ± 14.7 % (p < 0.001) and from 8.8 ± 4.2 to 7.0 ± 3.8 % (p < 0.001), respectively. Furthermore, a subset of 5 RMs was evaluated on a 6-h time frame. The beneficial effects on ML performances due to the RM gradually diminished and waned over a 6-h interval after the RM. The RM improved ML CO2 removal substantially, albeit temporarily. ML oxygenation performance was marginally affected.

This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we can update our initial belief about data with the evidence obtained from any kind of acquired data. It provides a theoretical framework and a statistical method to use pre-existing information within the context of new evidence. Several authors have described the Bayesian approach as capable of dealing with uncertainty in medical decision-making. This review describes the Bayes theorem and how it is used in clinical studies in anesthesia and critical care. It starts with a general introduction to the theorem and its related concepts of prior and posterior probabilities. Second, there is an explanation of the basic concepts of the Bayesian statistical inference. Last, a summary of the applicability of some of the Bayesian statistics in current literature is provided, such as Bayesian analysis of clinical trials and PKPD modeling.