Die Anaesthesiologie最新文献

Background: A regression model to estimate the duration from the onset of resuscitation efforts to the return of spontaneous circulation (ROSC) could help improving both resuscitation care and the quality control of registries. This study aims to evaluate the prediction accuracy and to identify challenges for future model development.

Method: Regression models based on M5P, random forest (RF) algorithms and a linear regression (LR) modified using M5P were retrospectively developed using a Belgian cohort of 84 individuals in whom ROSC was achieved. Model performance was assessed using quality metrics, such as the correlation coefficient (CC), coefficient of determination (R²), and root mean square error (RMSE) in a cross-validation approach.

Results: In the cohort 61.9% were male with a mean age of 65.7 years. A shockable rhythm was present in 27.7% of cases and the bystander cardiopulmonary resuscitation (CPR) rate was 48.2%. The no-flow time averaged 5.13 min. The mean time from CPR onset to first defibrillation was 7.81 min and to first medication administration 11.31 min. The ROSC occurred after an average of 16.8 min, the LR showed the highest correlation (0.73, 95% confidence interval, CI 0.72-0.74) and R² (0.53 [0.52-0.55]) along with the lowest RMSE (6.76 min [6.63-6.90]). The M5P yielded similar not significantly different values (CC 0.72 [0.70-0.73], R² 0.52 [0.50-0.53], RMSE 6.84 min [6.69-6.99]). In contrast, RF performed significantly worse (CC 0.62 [0.61-0.63], R² 0.38 [0.37-0.40], RMSE 7.89 min [7.82-7.96], all p < 0.01). Only LR showed no significant difference between predicted and actual values in terms of mean (p = 0.75) and variance (p = 0.15). The proportion of potentially prematurely terminated resuscitation attempts, defined as cases with actual ROSC occurring later than predicted ROSC plus RMSE, ranged from 13% (M5P) to 18% (LR).

Conclusion: The duration from the start of CPR to ROSC appears to be a process that is suitable for modelling with machine learning algorithms. At this early stage of development, the individual regression models did not demonstrate sufficient validity possibly due to low sample size and simplified data structure; however, the findings indicated potential for an application as a quality assurance tool to compare actual vs. predicted time to ROSC. Therefore, to increase the robustness the results require further evaluation in a larger cohort with additional variables and improved data quality based on the Utstein criteria.

Background: Balancing effective postoperative analgesia with reduced opioid use is crucial in the context of the opioid crisis.

Objective: This meta-analysis compares intrathecal morphine (ITM) and truncal nerve blocks (TNBs) to address this challenge.

Methods: Randomized controlled trials (RCTs) comparing ITM with TNBs were included. Databases searched included CENTRAL, Embase, PubMed, Scopus and Web of Science. Primary outcomes were total opioid consumption within 24 h postoperatively, with secondary outcomes including 48‑h opioid use, pain intensity and postoperative nausea and vomiting (PONV). Effect sizes were estimated using a random effects model with mean difference (MD), standardized mean difference (SMD), or relative risk (RR) and 95% confidence intervals (CIs).

Results: In total, 13 RCTs involving 834 participants were included. The use of ITM significantly reduced opioid consumption at 24 h (SMD: -1.27, 95% CI -2.32, -0.21, P = 0.0185) and 48 h (SMD: -1.01, 95% CI -1.89, -0.13, P = 0.0248) and also lowered resting pain scores at 12 h (MD: -1.05, 95% CI -1.74, -0.36, P = 0.0029) and 24 h (MD: -0.62, 95% CI -1.07, -0.18, P = 0.0062) postoperatively. For dynamic pain, ITM was more effective than TNB at 12 h (MD: -1.33, 95% CI -2.09, -0.57, P = 0.0006) and 24 h (MD: -0.59, 95% CI -1.05, -0.13, P = 0.0128); however, ITM was associated with a higher incidence of PONV (RR: 3.26, 95% CI 1.55, 6.83, P = 0.0018).

Conclusion: In comparison to TNBs the use of ITM in relatively high dosages (5-10 μg/kg) provides statistically superior analgesia and reduces opioid use within 24 h postoperatively but its association with increased PONV warrants cautious interpretation and further study.

Volatile anesthetics represent a relevant yet largely avoidable source of greenhouse gas emissions in the healthcare system. In Germany, their use accounts for approximately 69 kt of CO₂ equivalents annually. While desflurane is used in only about 3% of inhalation anesthesia procedures, it is responsible for more than 50% of the associated emissions due to its extremely high global warming potential. Modelling studies indicate that complete substitution of desflurane with sevoflurane would reduce total emissions by approximately 53%; replacing isoflurane as well would increase the reduction to 65%. From a clinical perspective, desflurane offers no proven advantage over sevoflurane with respect to patient safety or postoperative outcomes. Isoflurane likewise shows no clinical superiority. This creates substantial scope for substitution without compromising quality of care. Under minimum alveolar concentration (MAC)-adjusted conditions, desflurane causes an approximately 34-fold higher global warming per anesthesia hour than sevoflurane. The contrast is even greater when compared to total intravenous anesthesia (TIVA). Of particular relevance is the short-term climate impact. Due to its high radiative efficiency, desflurane exerts most of its warming effect within a few decades, precisely the critical period up to 2050. In addition, volatile anesthetics contribute to environmental contamination with persistent perfluoroalkyl and polyfluoroalkyl substances (PFAS), posing potential long-term risks to ecosystems and human health. Eliminating desflurane requires no new infrastructure, is economically rational and can be implemented immediately. It hence represents a rare opportunity in the healthcare system to achieve rapid and substantial emission reductions through a simple clinical decision, while maintaining patient safety and quality of care.