2023 International consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations: summary from the basic life support; advanced life support; pediatric life support; neonatal life support; education, implementation, and teams; and first aid task forces

The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates

Use of augmented and virtual reality in resuscitation training: A systematic review.

OBJECTIVES To evaluate the effectiveness of augmented reality (AR) and virtual reality (VR), compared with other instructional methods, for basic and advanced life support training. METHODS This systematic review was part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation (ILCOR) and reported based on the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines and registered with PROSPERO (CRD42023376751). MEDLINE, EMBASE, and SCOPUS were searched from inception to January 16, 2024. We included all published studies comparing virtual or augmented reality to other methods of resuscitation training evaluating knowledge acquisition and retention, skills acquisition and retention, skill performance in real resuscitation, willingness to help, bystander CPR rate, and patients' survival. RESULTS Our initial literature search identified 1807 citations. After removing duplicates, reviewing the titles and abstracts of the remaining 1301 articles, full text review of 74 articles and searching references lists of relevant articles, 19 studies were identified for analysis. AR was used in 4 studies to provide real-time feedback during CPR, demonstrating improved CPR performance compared to groups trained with no feedback, but no difference when compared to other sources of CPR feedback. VR use in resuscitation training was explored in 15 studies, with the majority of studies that assessed CPR skills favoring other interventions over VR, or showing no difference between groups. CONCLUSION Augmented and virtual reality can be used to support resuscitation training of lay people and healthcare professionals, however current evidence does not clearly demonstrate a consistent benefit when compared to other methods of training

Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data

This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm (lasso) regularization was used in penalized logistic regression to predict the onset of surgical site infection occurrence based on available patient blood testing results up to the day of surgery. Prior knowledge of predictors (blood tests) was integrated in the modelling by introduction of penalty factors depending on blood test prices and an early stopping parameter limiting the maximum number of selected features used in predictive modelling. Finally, solutions resulting in higher interpretability and cost-effectiveness were demonstrated. Using repeated holdout cross-validation, the baseline C-reactive protein (CRP) classifier achieved a mean AUC of 0.801, whereas our best full lasso model achieved a mean AUC of 0.956. Best model testing results were achieved for full lasso model with maximum number of features limited at 20 features with an AUC of 0.967. Presented models showed the potential to not only support domain experts in their decision making but could also prove invaluable for improvement in prediction of SSI occurrence, which may even help setting new guidelines in the field of preoperative SSI prevention and surveillance

Ranked list of the most frequent positive coefficients including comorbidity terms for both proposed approaches.

<p>Ranked list of the most frequent positive coefficients including comorbidity terms for both proposed approaches.</p

Ranked list of the most frequent negative coefficients including comorbidity terms for both proposed approaches.

<p>Ranked list of the most frequent negative coefficients including comorbidity terms for both proposed approaches.</p

Complexity of the three observed approaches.

<p>Comparison of model complexity, measured as number of selected features, for the three compared approaches and four different settings of “Number of Discovered Interactions” (NDI).</p