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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., Author(s): Michele Introna [sup.1] [sup.2], Johannes P. van den Berg [sup.1], Douglas J. Eleveld [sup.1], Michel M. R. F. Struys [sup.1] [sup.3] Author Affiliations: (1) grid.4830.f, 0000 0004 0407 1981, [...]

Background: The quality of care given by EMS staff is in part monitored by routine review of deployment reports that the providers must complete after each deployment. The assessment consists of determining whether a deployment was performed in a medically correct manner and thus whether the clinical reasoning process was appropriate. The time used during the deployment is also measured, as there are often time limits for performing different parts of the deployment and these must be adhered to. One might question whether measuring the time spent really gives insight into the quality of care, and if it might not be more useful to see how that time was spent. We therefore conceived a study to map the EMS care process and evaluate this process in relation to the time used. Methods: We used a focused ethnographic study design. Video recordings of EMS deployments in combination with semi-structured interviews of the EMS providers involved, were thematically analysed. This led to insights into the EMS care process and the embedded clinical reasoning. By categorising the professional activities we identified we were able to define a model that describes in general the EMS care process. We reported the first part of this study in which we developed this so called SPART model in a separate paper. In this second part of the study we determined the execution time within a deployment and measured the frequency of occurrence and the duration of the identified activities. We interpreted the operational content and the time variables both qualitatively and quantitatively. We also determined the distribution of activities over the three deployment operational periods (response, on-scene and transport period). Results: Using the SPART model, we gained insight into the different activities' nature, order, and duration. We could qualitatively judge the effectiveness of the clinical reasoning process, i.e., the quality of care delivered. Generally, the studied cases were followable, and the clinical reasoning process was medically logical. The diagnostic process sometimes continued after the clinical decision, which was not medically logical. Remarkably, this never changed the clinical decision. Although this could negatively affect the quality of care, we found no clinical evidence that this was the case. Conclusion: Our findings demonstrated that the quality of care in EMS can be measured by using the SPART model to evaluate EMS deployments. We concluded that qualitative judgment was more important than quantitative evaluation. Interpreting the order of different activities led to the clearest understanding of the clinical reasoning process. It was concluded that knowledge of the exact time used per activity and, in total, had the least impact on understanding the clinical reasoning process. [ABSTRACT FROM AUTHOR]

Only some subpopulations of natural killer (NK) cells have cytotoxic functionality, and the effects of anesthetics on these subpopulations are unknown. This study aimed to evaluate the in vitro effects of various anesthetics, both alone and in combination, on the distribution and cytotoxic function of NK cells and their subpopulations. Peripheral blood mononuclear cells (PBMCs) from eight healthy volunteers were treated for 4 h in vitro with dexmedetomidine, remifentanil, lidocaine, propofol, sevoflurane, and combinations in clinically relevant concentrations or left untreated. Flow cytometry was used to quantify the percentage of sampled NK cells and evaluate their distribution (CD56brightCD16neg, CD56brightCD16dim, CD56dimCD16neg, CD56dimCD16bright, and CD56negCD16bright) and cytotoxicity (Granzyme B (GrzB) and perforin) of NK cell subpopulations. Although the percentage of total NK cells did not change following exposure to anesthesia, the most important cytotoxic subpopulation (CD56dimCD16bright NK cells) decreased after exposure to both propofol (−3.58%, p = 0.045) and sevoflurane (−16.10%, p = 0.008) alone, and most combinations, especially in combination with lidocaine (propofol with lidocaine (−9.66%, p = 0.002) and sevoflurane with lidocaine (−21.90%, p < 0.001)). Dexmedetomidine and remifentanil had no effect on CD56dimCD16bright NK cells. Furthermore, no anesthetic regimen or combination altered the expression of GrzB and perforin in NK cells or NK cell subpopulations. In short, propofol and sevoflurane suppressed the highly cytotoxic phenotype (CD56dimCD16bright) of NK cells, with those exposed to sevoflurane combinations showing greater reductions. Immunosuppression was intensified with the inclusion of lidocaine in the anesthetic regimen. [ABSTRACT FROM AUTHOR]

(1) Background: Wearable sensors support healthcare professionals in clinical decision-making by measuring vital parameters such as heart rate (HR), respiration rate (RR), and blood oxygenation saturation (SpO2). This study assessed the validity and reliability of two types of wearable sensors, based on electrocardiogram or photoplethysmography, compared with continuous monitoring of patients recovering from trauma surgery at the postanesthesia care unit. (2) Methods: In a prospective observational study, HR, RR, SpO2, and temperature of patients were simultaneously recorded with the VitalPatch and Radius PPG and compared with reference monitoring. Outcome measures were formulated as correlation coefficient for validity and mean difference with 95% limits of agreement for reliability for four random data pairs and 30-min pairs per vital sign per patient. (3) Results: Included were 60 patients. Correlation coefficients for VitalPatch were 0.57 to 0.85 for HR and 0.08 to 0.16 for RR, and for Radius PPG, correlation coefficients were 0.60 to 0.83 for HR, 0.20 to 0.12 for RR, and 0.57 to 0.61 for SpO2. Both sensors presented mean differences within the cutoff values of acceptable difference. (4) Conclusions: Moderate to strong correlations for HR and SpO2 were demonstrated. Although mean differences were within acceptable cutoff values for all vital signs, only limits of agreement for HR measured by electrocardiography were considered clinically acceptable. [ABSTRACT FROM AUTHOR]

Background: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep using multichannel electroencephalogram (EEG) recordings. Methods: Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) scores. Results: The performance of the model was poor (AUC = 0.55–0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation—dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80. Conclusions: We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards. [ABSTRACT FROM AUTHOR]