Team leadership during medical emergencies like cardiac arrest resuscitation is cognitively demanding, especially for trainees. These cognitive processes remain poorly characterized due to measurement challenges. Using virtual reality simulation, this study aimed to elucidate and compare communication and cognitive processes-such as decision-making, cognitive load, perceived pitfalls, and strategies-between expert and novice code team leaders to inform strategies for accelerating proficiency development.

A simulation-based mixed methods approach was utilized within a single large academic medical center, involving twelve standardized virtual reality cardiac arrest simulations. These 10- to 15-minutes simulation sessions were performed by seven experts and five novices. Following the simulations, a cognitive task analysis was conducted using a cued-recall protocol to identify the challenges, decision-making processes, and cognitive load experienced across the seven stages of each simulation.

The analysis revealed 250 unique cognitive processes. In terms of reasoning patterns, experts used inductive reasoning, while novices tended to use deductive reasoning, considering treatments before assessments. Experts also demonstrated earlier consideration of potential reversible causes of cardiac arrest. Regarding team communication, experts reported more critical communications, with no shared subthemes between groups. Experts identified more teamwork pitfalls, and suggested more strategies compared to novices. For cognitive load, experts reported lower median cognitive load (53) compared to novices (80) across all stages, with the exception of the initial presentation phase.

The identified patterns of expert performance - superior teamwork skills, inductive clinical reasoning, and distributed cognitive strategiesn - can inform training programs aimed at accelerating expertise development.

It is already known that age and some chronic diseases are associated with worse outcomes after in-hospital cardiac arrest (IHCA). Usually, patients with two or more chronic diseases are treated with multiple medicines, which is commonly referred as polypharmacy (five or more medications). The objective of this study was to evaluate the association between polypharmacy and antidepressant use before hospital admission with return of spontaneous circulation (ROSC) and in-hospital mortality in IHCA.

This retrospective study included patients over 18 years of age with IHCA, attended by the rapid response team in hospital wards, from March 2018 to September 2023. The exclusion criteria were the absence of information regarding polypharmacy, pregnancy, and the presence of an express "do-not-resuscitate order". Data were collected from the electronic medical records.

A total of 578 patients with IHCA were evaluated; 42 patients were excluded due to the absence of information regarding polypharmacy and 24 due to "natural death permission". Thus, we included 512 patients in the analysis. The mean age was 64.4 ± 14.9 years; 52.3% were male, and 54.5% were older people. Polypharmacy was prescribed for 50.8% of patients, 48.4% had ROSC, and in-hospital mortality was 92.0%. In logistic regression models, the polypharmacy regimen use in the older population was not associated with ROSC (odds ratio [OR]: 1.122; 95% confidence interval [CI]: 0.660-1.906; p: 0.672) or mortality (OR: 1.185; 95% CI: 0.170-8.260; p: 0.864). Regarding antidepressant use, it was associated with lower rates of ROSC (OR: 0.412; 95% CI: 0.183-0.925; p: 0.032) but was not associated with mortality in older people (OR: 1.682; 95% CI: 0.129-21.996; p: 0.692).

In conclusion, polypharmacy regimen was not associated with ROSC and in-hospital mortality; however, antidepressant use was associated with lower rates of ROSC only in older patients.

Clinically, patients in a coma after cardiac arrest are given the prognosis of "neurological recovery" to minimize discrepancies in opinions and reduce judgment errors. This study aimed to analyze the background patterns of electroencephalogram (EEG) signals from such patients to identify the key indicators for assessing the prognosis after coma.

Standard machine learning models were applied sequentially as feature selectors and filters. CatBoost demonstrated superior performance as a classification method compared to other approaches. In addition, Shapley additive explanation (SHAP) values were utilized to rank and analyze the importance of the features.

Our results indicated that the three different EEG features helped achieve a fivefold cross-validation receiver-operating characteristic (ROC) of 0.87. Our evaluation revealed that functional connectivity features contribute the most to classification at 70%. Among these, low-frequency long-distance functional connectivity (45%) was associated with a poor prognosis, whereas high-frequency short-distance functional connectivity (25%) was linked with a good prognosis. Burst suppression ratio is 20%, concentrated in the left frontal-temporal and right occipital-temporal regions at high thresholds (10/15 mV), demonstrating its strong discriminative power.

Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. These findings provide a clinically actionable framework for advancing neurological prognosis and optimizing patient care.

Automated external defibrillators (AEDs) are critical in emergency cardiac care. However, significant gaps in nurses' knowledge and confidence in using AEDs limit their effectiveness. This study explores the current status of AED training and its impact on nurses' emergency response capabilities across municipal-level and county-level hospitals in China.

A cross-sectional survey was conducted from May 20 to August 1, 2024, via telephone and electronic questionnaires in the emergency, intensive care unit (ICU), and general wards of 12 municipal and county-affiliated hospitals. A total of 451 questionnaires were distributed, with 440 valid responses (response rate: 97.6%). Data were analyzed using chi-square tests and multivariate logistic regression in SPSS 24.0 (P < 0.05 considered significant). Key factors influencing AED knowledge and confidence included additional training, hands-on drills, and defibrillation experience.

Only 17.5% of nurses demonstrated sufficient AED knowledge, and 15.9% reported confidence in defibrillation. In municipal-level hospitals, nurses outperformed county hospitals in training access and proficiency. Additional training (OR = 24.50, p < 0.0001) and practical drills (OR = 12.36, p < 0.0001) were strongly associated with improved knowledge and confidence.

The study highlights the need for enhanced AED training, emphasizing practical drills and scenario-based simulations, particularly in resource-limited county hospitals. Targeted strategies could significantly improve nurses' emergency response capabilities, contributing to better cardiac arrest outcomes.

In-hospital cardiac arrest (IHCA) refers to the occurrence of cardiac arrest in hospitalized patients requiring chest compressions and/or defibrillation, with only about one-third of patients achieving return of spontaneous circulation (ROSC) after cardiopulmonary resuscitation. Pan-immune-inflammation value (PIIV) is an indicator assessing the overall inflammatory status within the body, but the relationship between PIIV and ROSC remains unclear.

This study aims to analyze the occurrence of ROSC and its influencing factors, and investigate the predictive value of PIIV, in order to provide insights for clinical prevention and treatment.

Clinical data of IHCA patients admitted to our hospital were retrospectively collected. Patients were divided into the ROSC group and non-ROSC group based on whether spontaneous circulation was restored after cardiopulmonary resuscitation. Multivariate logistic regression was used to analyze factors affecting ROSC, and the receiver operating characteristic (ROC) curve was employed to calculate the area under the curve (AUC) to evaluate the predictive value of PIIV.

168 patients' clinical data were collected, including 62 patients with ROSC and 106 with non-ROSC. The results of multivariate logistic regression analysis showed that the duration of cardiopulmonary resuscitation, adrenaline dosage, blood lactate (Lac), and PIIV were independent influencing factors for ROSC in IHCA patients (P < 0.05). The ROC curve analysis revealed that the AUC of PIIV for predicting ROSC in IHCA patients was 0.805 (95% CI: 0.720-0.891), with an optimal cutoff value of 395.3, sensitivity of 83.33%, and specificity of 70.37%.

PIIV demonstrates valuable application in predicting ROSC in IHCA patients.

Mortality in cardiogenic shock (CS) remains high. Significant interhospital heterogeneity in critical care therapies has been described, which reflects the lack of high-quality evidence to guide optimal treatment. We aimed to describe differences in practices and clinical outcomes among patients with CS in the United States and Canada.

The Critical Care Cardiology Trials Network (CCCTN) is a research network of tertiary cardiac intensive care units (CICUs). Data collection spanned from 2017 to 2022. The analysis included 34 American and 8 Canadian sites. The outcomes of interest included baseline clinical differences, use of critical care monitoring and therapies, and all-cause in-hospital mortality between patients with CS in the United States and Canada admitted to CICUs.

Among 23,299 admissions, 19% had CS (n = 4336, 88% United States vs 12% Canada). The proportion of patient who received invasive hemodynamics (United States: 80.8% vs Canada: 74.8%, P = 0.0015), vasoactive medications (United States: 88.9% vs Canada: 82.1%, P < 0.0001), temporary mechanical circulatory support (tMCS) (United States: 39.4% vs Canada: 23.1%, P < 0.0001) were more frequent in US centres. Intra-aortic balloon pump was the most common tMCS device in both countries. After multivariable adjustment, in-hospital mortality was higher in Canada vs United States (37.1% vs 29.4%, odds ratio [OR]: 1.47; 95% confidence interval [CI], 1.18-1.83).

In a contemporary registry, management of CS was heterogenous between the United States and Canada, with higher use of invasive monitoring and MCS in the US. Although adjusted mortality was lower in the United States, the effects of these treatments cannot be reliably determined without randomized trial data.

Objective: Cardiac arrest (CA) is one of the most severe complications in patients with intracerebral hemorrhage (ICH), increasing the risk of death. This study explored the factors influencing CA occurrence and its resuscitation characteristics in ICH patients. Methods: Data were retrieved from the Chinese Stroke Center Alliance database. The primary outcome was CA, and the secondary outcomes were in-hospital death and survival post- CA. Absolute standardized and rate differences were utilized for intergroup comparisons, while logistic regression was employed for correlation analysis. Results: A total of 85,105 patients were enrolled in this study. Among them, 1651 (1.9%) patients experienced CA, of whom 1032 (62.5%) died in hospital. At baseline, prehospital notification from the emergency medical service system was a co-factor influencing CA occurrence and the presence of a death outcome (OR: 1.71, 95% CI: 1.47-1.98, P < 0.001; OR: 0.50, 95% CI: 0.41-0.62, P < 0.001). In terms of complications, posthospital hematoma expansion and swallowing dysfunction were co-factors influencing CA occurrence and the presence of a death outcome (OR: 3.78, 95% CI: 3.20-4.47, P < 0.001, OR: 1.39, 95% CI: 1.11-1.76; P < 0.001; OR: 7.66, 95% CI: 5.48-10.70, P < 0.001, OR: 1.66, 95% CI: 1.08-2.57, P < 0.001). The incidence of CA in ICH patients decreased annually from 2015 to 2019, while survival after CA increased annually ( P < 0.001). Conclusions: Prehospital notification from the emergency medical service system, posthospital hematoma expansion, and swallowing dysfunction were identified as co-factors contributing to CA occurrence and post-CA mortality following ICH. The proportion of CA patients following ICH decreased, while survival rates improved annually from 2015 to 2019.

Post-cardiac arrest brain injury (PCABI) is a leading cause of death in cardiac arrest/cardiopulmonary resuscitation (CA/CPR) victims and long-term disability in CA/CPR survivors. Despite previous evidence indicating that the microbiota-gut-brain axis is critically involved in many neurological disorders, no research has hitherto established a connection between the gut microbiota and PCABI through this axis. This study aims to explore the biological roles of microbial tryptophan metabolites in the progression of PCABI.

To achieve this, we pretreated rats with a cocktail of broad-spectrum antibiotics (Abx) to eradicate the gut microbiota before establishing a 7-min asphyxia-CA/CPR model.

Remarkably, the 24-h survival rate and neurological outcomes improved in Abx/CPR rats. Fecal 16s rDNA sequencing and PICRUSt2 analysis revealed that Abx reshaped the microbial community and elevated the proportion of microbial tryptophan metabolism in rats. Metabolomic profiling suggested that Abx shifted the phenotype of microbial tryptophan metabolism from the indole pathway to the kynurenine pathway, thereby increasing the levels of the neuroprotective metabolite kynurenine in the feces, circulation, and ultimately the brain. Furthermore, the hippocampal expression of aryl hydrocarbon receptor (AhR), an endogenous receptor of kynurenine, was upregulated in Abx/CPR rats. In vitro experiments further demonstrated that the neuroprotective effects of kynurenine are AhR-dependent and that AhR activation could negatively regulate the NLRP3 protein expression. Supporting this, results from qRT-PCR, immunohistochemistry, and immunofluorescence in the rat cerebral cortex exhibited that L-kynurenine inhibited NLRP3-induced pyroptosis.

Our study provides a direct clue to the essential participation of the microbiota-gut-brain axis in the progression of PCABI. It demonstrates that kynurenine might attenuate PCABI by inhibiting NLRP3-induced pyroptosis.

The potential benefit of combining adrenaline, vasopressin, and corticosteroids in in-hospital cardiac arrest (IHCA) needs to be confirmed in a large clinical trial. This pilot study assesses feasibility and safety of randomising patients to this combination therapy compared to standard care.

A randomised, double-blind, placebo-controlled pilot study was conducted from December 2022 to June 2024 across three Swedish hospitals (NCT05139849). Witnessed IHCAs meeting criteria for adrenaline were randomised 1:1 to adrenaline, vasopressin, and corticosteroids (intervention) or adrenaline and placebo (control). Primary outcomes included feasibility (e.g., protocol adherence, event times, enrolment rate), and safety. Secondary outcome was return of spontaneous circulation.

Of 183 screened IHCAs, 39 patients (median age 77, 64% male) were randomised (16 intervention, 23 control), with an enrolment rate of 0.8 patients/hospital bed/month. Most cardiac arrests occurred in general wards (n = 17/39, 44%). In the feasibility analysis, four patients at the scene of the arrest and three patients in the intensive care unit experienced protocol deviations. Median time (minutes) from cardiac arrest to rapid response team arrival was similar between groups. Median time to adrenaline administration was 7:00 (IQR 3:00-10:00) (intervention) vs 5:00 (IQR 2:30-8:30) (control) and to vasopressin/placebo 10:30 (IQR 9:30-12:15) vs 9:00 (IQR 5:00-11:00). Return of spontaneous circulation occurred in 38% (6/16) in the intervention group and 17% (4/23) in controls.

In this IHCA pilot study, randomisation to adrenaline, vasopressin, and corticosteroids compared to controls was safe, but feasibility needs improvement for adequate enrolment in the VAST-A main study.

Objective: In-hospital cardiac arrest (IHCA) is a common event with high morbidity and mortality. This study seeks to evaluate advance care planning (ACP) among hospitalized patients who experienced IHCA. Design: Single center retrospective cohort study. Measurement and Main Results: The primary objective was to compare whether certain clinical characteristics are associated with a physician's likelihood of having an ACP discussion with patients who subsequently have IHCA. We found that older age, White race, and higher GO-FAR score were associated with increased ACP documentation. In multivariate regression modeling, numerically higher GO-FAR score, ICU patients, hospitalization for ≥7 days, and having a normal mental status were consistently associated with ACP documentation (OR ∼2 for all). There was a persistent trend, significant in some models, to lower likelihood of ACP documentation for non-White patients. Among patients who had predicted low-to-very low likelihood of IHCA survival, most (56%) had no ACP documentation prior to IHCA. Conclusions: We found that the factors associated with an increased likelihood of ACP were age, ICU location, longer LOS prior to IHCA, higher GO-FAR score and normal mental status before IHCA. There was a worrying trend toward lower rates of ACP documentation among non-White patients. The overall rate of completion of ACP was low in patients with poor predicted IHCA outcomes. Ongoing efforts should continue to engage all patients in ACP irrespective of demographics, and there may be a role for utilizing standardized prognostication models to encourage ACP.

Currently, there is a lack of effective early assessment tools for predicting the onset and development of cardiac arrest (CA). With the increasing attention of clinical researchers on machine learning (ML), some researchers have developed ML models for predicting the occurrence and prognosis of CA, with certain models appearing to outperform traditional scoring tools. However, these models still lack systematic evidence to substantiate their efficacy.

This systematic review and meta-analysis was conducted to evaluate the prediction value of ML in CA for occurrence, good neurological prognosis, mortality, and the return of spontaneous circulation (ROSC), thereby providing evidence-based support for the development and refinement of applicable clinical tools.

PubMed, Embase, the Cochrane Library, and Web of Science were systematically searched from their establishment until May 17, 2024. The risk of bias in all prediction models was assessed using the Prediction Model Risk of Bias Assessment Tool.

In total, 93 studies were selected, encompassing 5,729,721 in-hospital and out-of-hospital patients. The meta-analysis revealed that, for predicting CA, the pooled C-index, sensitivity, and specificity derived from the imbalanced validation dataset were 0.90 (95% CI 0.87-0.93), 0.83 (95% CI 0.79-0.87), and 0.93 (95% CI 0.88-0.96), respectively. On the basis of the balanced validation dataset, the pooled C-index, sensitivity, and specificity were 0.88 (95% CI 0.86-0.90), 0.72 (95% CI 0.49-0.95), and 0.79 (95% CI 0.68-0.91), respectively. For predicting the good cerebral performance category score 1 to 2, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.86 (95% CI 0.85-0.87), 0.72 (95% CI 0.61-0.81), and 0.79 (95% CI 0.66-0.88), respectively. For predicting CA mortality, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.85 (95% CI 0.82-0.87), 0.83 (95% CI 0.79-0.87), and 0.79 (95% CI 0.74-0.83), respectively. For predicting ROSC, the pooled C-index, sensitivity, and specificity based on the validation dataset were 0.77 (95% CI 0.74-0.80), 0.53 (95% CI 0.31-0.74), and 0.88 (95% CI 0.71-0.96), respectively. In predicting CA, the most significant modeling variables were respiratory rate, blood pressure, age, and temperature. In predicting a good cerebral performance category score 1 to 2, the most significant modeling variables in the in-hospital CA group were rhythm (shockable or nonshockable), age, medication use, and gender; the most significant modeling variables in the out-of-hospital CA group were age, rhythm (shockable or nonshockable), medication use, and ROSC.

ML represents a currently promising approach for predicting the occurrence and outcomes of CA. Therefore, in future research on CA, we may attempt to systematically update traditional scoring tools based on the superior performance of ML in specific outcomes, achieving artificial intelligence-driven enhancements.

PROSPERO International Prospective Register of Systematic Reviews CRD42024518949; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=518949.

Background/Objectives: This study sought to evaluate the clinical profile, in-hospital management, prognosis, and survival of patients treated for cardiac arrest using extracorporeal therapy in a third-level Spanish hospital before and after the therapy was protocolised. Methods: This study is a historical single-centre cohort study that was conducted from January 2009 to February 2024. In 2019, an in-hospital extracorporeal reanimation therapy protocol was established in the centre's Coronary Intensive Care Unit. As a result, the cohort was split into two groups: the Pre-Protocol group (between 2009 and December 2018) and the Post-Protocol group (between 2019 and February 2024). Results: A total of 26 patients were recruited, i.e., 10 in the first cohort and 16 in the second, with acute myocardial infarction being the most prevalent cause in both cohorts. A 30% (3) to 43.65% (7) increase in survival was observed between the two cohorts (p = 0.48), with CPC 1-2 neurological functionality exceeding 85% of cases in both cohorts (p = 0.7). The mean time from cardiac arrest to the application of extracorporeal therapy decreased from 104.1 min to 41.87 min (p = 0.09). The longer duration of ECMO (p = 0.03) and the longer hospital stay (p = 0.002) are due to a higher survival. Conclusions: The results show a trend in improvement outcomes. The small cohort size makes it difficult to draw robust conclusions, but we want to highlight the importance of applying a specific protocol based on standardised patient selection criteria and the establishment of extracorporeal reanimation therapy.

Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and perioperative management plans, but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study developed and validated a machine learning (ML) model using accessible preoperative clinical data to predict perioperative MACEs in stable coronary artery disease (SCAD) patients undergoing noncardiac surgery (NCS).

We collected data from 9171 adult SCAD patients who underwent NCS and extracted 64 preoperative variables. First, the optimal data imputation, resampling, and feature selection methods were compared and selected to deal with missing data values and imbalances. Then, nine independent machine learning models (logistic regression (LR), support vector machine, Gaussian Naive Bayes (GNB), random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine, categorical boosting (CatBoost), and deep neural network) and a stacking ensemble model were constructed and compared with the validated Revised Cardiac Risk Index's (RCRI) model for predictive performance, which was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), calibration curve, and decision curve analysis (DCA). To reduce overfitting and enhance robustness, we performed hyperparameter tuning and 5-fold cross-validation. Finally, the Shapley additive interpretation (SHAP) method and a partial dependence plot (PDP) were used to determine the optimal ML model.

Of the 9,171 patients, 514 (5.6 %) developed MACEs. 24 significant preoperative features were selected for model development and evaluation. All ML models performed well, with AUROC above 0.88 and AUPRC above 0.39, outperforming the AUROC (0.716) and AUPRC (0.185) of RCRI (P < 0.001). The best independent model was XGBoost (AUROC = 0.898, AUPRC = 0.479). The calibration curve accurately predicted the risk of MACEs (Brier score = 0.040), and the DCA results showed that XGBoost had a high net benefit for predicting MACEs. The top-ranked stacking ensemble model, consisting of CatBoost, GBDT, GNB, and LR, proved to be the best (AUROC 0.894, AUPRC 0.485). We identified the top 20 most important features using the mean absolute SHAP values and depicted their effects on model predictions using PDP.

This study combined missing-value imputation, feature screening, unbalanced data processing, and advanced machine learning methods to successfully develop and verify the first ML-based perioperative MACEs prediction model for patients with SCAD, which is more accurate than RCRI and enables effective identification of high-risk patients and implementation of targeted interventions to reduce the incidence of MACEs.

Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles. We aimed to improve IHCA predictions by combining vital sign indicators with laboratory test results and, optionally, International Classification of Disease-10 block for diagnosis (ICD10BD).

We conducted a retrospective cohort study in the general ward (GW) and intensive care unit (ICU) of a 680-bed secondary healthcare institution. We included 62,061 adults admitted to the Department of Internal Medicine from January 2010 to August 2022. IHCAs were identified based on cardiopulmonary resuscitation prescriptions. Patient-days within three days preceding IHCAs were labeled as case days; all others were control days. The eXtreme Gradient Boosting (XGBoost) model was trained using daily vital signs, 14 laboratory test results, and ICD10BD.

In the GW, among 1,299,448 patient-days from 62,038 patients, 1,367 days linked to 713 patients were cases. In the ICU, among 117,190 patient-days from 16,881 patients, 1,119 days from 444 patients were cases. The area under the ROC curve for IHCA prediction model was 0.934 and 0.896 in the GW and ICU, respectively, using the combination of vital signs, laboratory test results, and ICD10BD; 0.925 and 0.878, respectively, with vital signs and laboratory test results; and 0.839 and 0.828, respectively, with only vital signs.

Incorporating laboratory test results or combining laboratory test results and ICD10BD with vital signs as predictor variables in the XGBoost model potentially enhances clinical decision-making and improves patient outcomes in hospital settings.

Cardiac arrest (CA) is a critical medical emergency that can occur in both patients with preexisting conditions and otherwise healthy individuals. Despite successful resuscitation through cardiopulmonary resuscitation (CPR), many survivors are at significant risk of developing post-cardiac arrest syndrome (PCAS), a complex systemic response to CA that includes brain injury as a major component. Phosphoenolpyruvate carboxykinase 1 (PCK1), the first rate-limiting enzyme in gluconeogenesis, has been implicated in various diseases. However, its role in neuronal damage following CA/CPR remains unclear. To investigate the role of PCK1 in neuronal damage after CA/CPR, we established the CA/CPR animal model and hypoxia/re‑oxygenation (H/R) cell model, manipulated PCK1 expression both in vivo and in vitro. We found increased expression of PCK1 in cortical neurons after CA/CPR. In vivo PCK1 overexpression exacerbated brain injury after CA/CPR via augmenting neuroinflammation and neuronal apoptosis. RNA-sequencing suggested PCK1-OE disturbed the neuronal metabolism while immunoprecipitation/mass spectrometry (IP/MS) revealed that PCK1 contributed to the mitochondrial dysfunction via binding to Voltage-dependent anion-selective channel 1 (VDAC1) and promoting its oligomerization and cytochrome c release. Besides, we confirmed that 3-Mercaptopicolinic acid (3-MPA), the PCK1 inhibitor, could ameliorate the mitochondrial dysfunction and apoptosis of neurons both in vitro and in vivo. For the first time, we identified the detrimental role of PCK1 in post-CA brain injury. During CA/CPR, excessive PCK1 binds to VDAC1, promoting its oligomerization and cytochrome c release which leading to neuronal apoptosis and eventually PCAS. Utilization of 3-MPA during CPR could effectively improve the survival rate and prognosis of mice after CA, which may provide a novel strategy for CA/CPR treatment.

In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)-based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging. Traditional EDCA prediction methods primarily rely on structured vital signs or electrocardiogram (ECG) signals, which require additional preprocessing or specialized devices. This study introduces a novel approach using image-based 12-lead ECG data obtained at ED triage, leveraging the inherent richness of visual ECG patterns to enhance prediction and integration into clinical workflows.

This study aims to address the challenge of early detection of EDCA by developing an innovative deep learning model, the ECG-Image-Aware Network (EIANet), which uses 12-lead ECG images for early prediction of EDCA. By focusing on readily available triage ECG images, this research seeks to create a practical and accessible solution that seamlessly integrates into real-world ED workflows.

For adult patients with EDCA (cases), 12-lead ECG images at ED triage were obtained from 2 independent data sets: National Taiwan University Hospital (NTUH) and Far Eastern Memorial Hospital (FEMH). Control ECGs were randomly selected from adult ED patients without cardiac arrest during the same study period. In EIANet, ECG images were first converted to binary form, followed by noise reduction, connected component analysis, and morphological opening. A spatial attention module was incorporated into the ResNet50 architecture to enhance feature extraction, and a custom binary recall loss (BRLoss) was used to balance precision and recall, addressing slight data set imbalance. The model was developed and internally validated on the NTUH-ECG data set and was externally validated on an independent FEMH-ECG data set. The model performance was evaluated using the F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC).

There were 571 case ECGs and 826 control ECGs in the NTUH data set and 378 case ECGs and 713 control ECGs in the FEMH data set. The novel EIANet model achieved an F1-score of 0.805, AUROC of 0.896, and AUPRC of 0.842 on the NTUH-ECG data set with a 40% positive sample ratio. It achieved an F1-score of 0.650, AUROC of 0.803, and AUPRC of 0.678 on the FEMH-ECG data set with a 34.6% positive sample ratio. The feature map showed that the region of interest in the ECG was the ST segment.

EIANet demonstrates promising potential for accurately predicting EDCA using triage ECG images, offering an effective solution for early detection of high-risk cases in emergency settings. This approach may enhance the ability of health care professionals to make timely decisions, with the potential to improve patient outcomes by enabling earlier interventions for EDCA.

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Background and Objectives: The development of acute kidney injury (AKI) in the post-cardiopulmonary resuscitation (post-CPR) period is a common pathology that has not been adequately investigated but contributes significantly to morbidity and mortality. We aimed to investigate the causes of AKI in the early post-CPR period. Materials and Methods: This study was performed retrospectively in 82 adult patients who survived for at least 2 days out of 312 patients admitted to the intensive care unit after cardiac arrest in 2013-2022. AKI developed in 40 (48.7%) of these 82 patients (AKI 1-3 patient, respectively: 14, 13, 13). Binary logistic regression analysis was performed separately to determine the risk factors for AKI and mortality. Results: Each unit increase in BMI increased the risk of developing AKI by 1.272-fold, and the increase was statistically significant [OR (95%CI) = 1.272 (1.089-1486); p = 0.002]. The use of VSP and INO treatment alone increased the risk of AKI by approximately 14-fold, and this increase was statistically significant [OR (95%CI) = 14.225 (1.172-172.669); p = 0.037]. The combined use of VSP and INO treatment increased the risk of AKI by approximately 42-fold, and this increase was statistically significant [OR (95%CI) = 42.089 (2.683-660.201); p = 0.008]. The COVID-19 period alone increased the risk of developing AKI by 2.8-fold compared to the non-COVID-19 period, but the statistical significance of this increase was limited [OR (95%CI) = 2.801 (0.859-9.126); p = 0.088]. The development of AKI was not associated with mortality [OR (95%CI) = 2.194 (0.700-6.872); p = 0.178]. Conclusions: Having VSP and/or INO support and high BMI in the post-CPR period are the most important reasons for the development of AKI. COVID-19 may also increase the risk of developing AKI.

Cardiac arrest (CA) is a leading cause of mortality and morbidity, with survivors often developing post-cardiac arrest syndrome (PCAS), characterized by systemic inflammation, ischemia-reperfusion injury (IRI), and multiorgan dysfunction. Acute kidney injury (AKI), a frequent complication, is associated with increased mortality and prolonged intensive care unit (ICU) stays. This study evaluates AKI incidence and progression in cardiac arrest patients managed with different temperature protocols and explores urinary biomarkers' predictive value for AKI risk.

A prospective, single-center observational study was conducted, including patients with Return of Spontaneous Circulation (ROSC) post-cardiac arrest. Patients were stratified into three groups: therapeutic hypothermia (TH) at 33 °C, Targeted Temperature Management (TTM) at 35 °C, and no temperature management (No TTM). AKI was defined using KDIGO criteria, with serum creatinine and urinary biomarkers (TIMP-2 and IGFBP7) measured at regular intervals during ICU stay.

AKI incidence at 72 h was 31%, varying across protocols. It was higher in the No TTM group at 24 h and in the TH and TTM groups during rewarming. Persistent serum creatinine elevation and fluid imbalance were notable in the TH group. Biomarkers indicated moderate tubular stress in the TTM and No TTM groups.

AKI is a frequent complication post-cardiac arrest, with the rewarming phase identified as critical for renal vulnerability. Tailored renal monitoring, biomarker-guided risk assessment, and precise temperature protocols are essential to improve outcomes.

Objective: The purpose of this study was to evaluate the utility of end-tidal carbon dioxide (ETCO2) values as a predictive marker of in-hospital cardiac arrest (IHCA). This was achieved by comparing the trends of ETCO2 values in mechanically ventilated ICU patients that experienced an IHCA versus patients that did not. Methods: A single-center, retrospective, observational, and comparative cohort study at an academic medical center. Mechanically ventilated adults in the ICU who received continuous ETCO2 monitoring were included. Patients who were transferred to our facility already intubated, experienced an out-of-hospital cardiac arrest, or had a do-not-resuscitate order were excluded. Extracted data points included demographics, comorbidities, vitals, labs, and outcomes. Patients were grouped into IHCA and non-IHCA cohorts, and the trends of ETCO2 values were compared at multiple time points for 48 h before the IHCA or after intubation (time zero) for the groups, respectively. An ROC curve was constructed to determine the predictive value of ETCO2 for IHCA. Results: A total of 207 patients were included, of which 104 (50.2%) had an IHCA and 103 (49.8%) did not. There were no differences in the mean SOFA scores at the initiation of mechanical ventilation (8.5 vs. 7.6). The ETCO2 values were decreased in the IHCA cohort, and significantly different at each time point analyzed from 300 min until immediately prior to the arrest (p < 0.001). The ETCO2 values were a mean of 20.0 mmHg in the IHCA cohort at the index time vs. 34.7 mmHg in the non-IHCA cohort (p < 0.001). The ROC analysis demonstrated moderate reliability, with an AUC = 0.687 (p < 0.0001, 95% CI 0.613-0.761) and with an ETCO2 of less than 23 mmHg, demonstrating a 67% sensitivity and a 71% specificity, as well as a 70% PPV for predicting the IHCA from our sample. Conclusions: Patients typically have rapid clinical deteriorations prior to cardiac arrest, and monitoring ETCO2 is easily achieved at the bedside while aiding in clinical decision making. The ETCO2 values in our study were significantly decreased in the IHCA cohort prior to cardiac arrest compared to the stable values in those that did not experience an IHCA, indicating that ETCO2 monitoring may have utility in predicting cardiac arrest. Further study is warranted to evaluate if predictive models utilizing ETCO2 can be constructed to predict IHCAs in mechanically ventilated ICU patients.

Canagliflozin can reduce the risk of cardiovascular disease in patients except for its targeted antidiabetic effects. However, it remains unknown whether canagliflozin alleviates the post-resuscitation myocardial dysfunction (PRMD) in type 2 diabetes mellitus.

To explore the effects and potential mechanisms of canagliflozin on myocardial function after cardiac arrest (CA) and cardiopulmonary resuscitation (CPR) in a type 2 diabetic rat model.

Twenty-four type 2 diabetic rats were randomized into four groups: (1) sham + canagliflozin, (2) sham + placebo, (3) CPR + placebo, and (4) CPR + canagliflozin. Except for the sham + canagliflozin and placebo groups, both the CPR + placebo and canagliflozin groups underwent 8 min of CPR after the induction of ventricular fibrillation for 6 min. Myocardial function and hemodynamics were assessed at baseline and within 6 h after autonomous circulation (ROSC) return. Left ventricular tissues were sampled to determine the expressions of relevant proteins in the NLRP3 inflammasome pathway.

The results demonstrated that the mean arterial pressure (MAP) was significantly improved in the CPR + canagliflozin group after ROSC compared with the CPR + placebo group (p < 0.05). Meanwhile, both ejection fraction (EF) and fraction shortening (FS) were dramatically increased in the CPR + canagliflozin group when compared with the CPR + placebo group at 2h, 4h, and 6h after ROSC (p < 0.05). In addition, the levels of NT-proBNP, cTn-I, and NLRP3 inflammatory inflammasome-associated proteins were significantly decreased in the CPR + canagliflozin group compared with the CPR + placebo group.

In type 2 diabetic rats, pretreatment of canagliflozin alleviates PRMD. The potential mechanisms may include inhibition of the NLRP3/caspase-1 signaling pathway.

Multiple studies have shown that extracorporeal membrane oxygenation (ECMO) is used clinically more often in men than women. Because clinical trials provide the basis for identifying patients who are likely to benefit from medical therapies, we hypothesized that sex-based imbalances in ECMO trial enrollment may both reflect and perpetuate these observed disparities. Our objective was to determine whether sex-based enrollment imbalances exist within the published ECMO clinical trial literature.

Cross-sectional analysis.

Randomized controlled trials published between 2003 and 2023 that either tested ECMO as a treatment modality or tested another intervention among patients receiving ECMO.

Not applicable.

None.

We used the Cochrane Highly Sensitive Search Strategy to search PubMed for eligible trials. Data on participant demographics, trial characteristics, and journal information were abstracted for each publication. The primary outcome of interest was the proportion of male and female participants in each published trial. The initial literature search identified 774 articles. Of these, 31 were eligible for inclusion, and 28 provided data on the sex of study participants. Twenty-six of these 28 trials (93%) enrolled more men than women, and in aggregate women comprised 28% (95% CI, 26-30%) of all trial participants (551/1956 subjects). Trials involving extracorporeal cardiopulmonary resuscitation (ECPR) exhibited the largest sex-based enrollment differences (83% men) followed by venoarterial ECMO for cardiogenic shock (73% men). Among trials published in journals with impact factors of ten or higher 74% (95% CI, 72-76%) of participants were men. Only two trials (7%) provided data on the race or ethnicity of study participants.

Substantial sex-based disparity exists in published ECMO clinical trials. Underrepresentation of women relative to disease prevalence is most significant among trials utilizing venoarterial ECMO for cardiogenic shock and ECPR for cardiac arrest, limiting the applicability of findings from these trials for women.

This study investigated the predictive value of albumin-related inflammatory markers for short-term outcomes in in-hospital cardiac arrest (IHCA) patients.

A linear mixed model investigated the dynamic changes of markers within 72 hours after return of spontaneous circulation (ROSC). Time-Dependent COX regression explored the predictive value. Mediation analysis quantified the association of markers with organ dysfunctions and adverse outcomes.

Prognostic Nutritional Index (PNI) and RDW-Albumin Ratio (RAR) slightly changed (p > 0.05). Procalcitonin-Albumin Ratio (PAR1) initially increased and then slowly decreased. Neutrophil-Albumin Ratio (NAR) and Platelet-Albumin Ratio (PAR2) decreased slightly during 24-48 hours (all p<0.05). PNI (HR = 1.646, 95%CI (1.033,2.623)), PAR1 (HR = 1.69, 95%CI (1.057,2.701)), RAR (HR = 1.752,95%CI (1.103,2.783)) and NAR (HR = 1.724,95%CI (1.078,2.759)) were independently associated with in-hospital mortality. PNI (PM = 45.64%, 95%CI (17.05%,87.02%)), RAR (PM = 45.07%,95%CI (14.59%,93.70%)) and NAR (PM = 46.23%,95%CI (14.59%,93.70%)) indirectly influenced in-hospital mortality by increasing SOFA (central) scores. PNI (PM = 21.75%, 95%CI(0.67%,67.75%)) may also indirectly influenced outcome by increasing SOFA (renal) scores (all p < 0.05).

Within 72 hours after ROSC, albumin-related inflammatory markers (PNI, PAR1, RAR, and NAR) were identified as potential predictors of short-term prognosis in IHCA patients. They may mediate the adverse outcomes of patients by causing damages to the central nervous system and renal function.

Research on predictive models for hospital mortality in patients who have survived 24 h following cardiopulmonary resuscitation (CPR) is limited. We aim to explore the factors associated with hospital mortality in these patients and develop a predictive model to aid clinical decision-making and enhance the survival rates of patients post-resuscitation.

We sourced the data from a retrospective study within the Dryad dataset, dividing patients who suffered cardiac arrest following CPR into a training set and a validation set at a 7:3 ratio. We identified variables linked to hospital mortality in the training set using Least Absolute Shrinkage and Selection Operator (LASSO) regression, as well as univariate and multivariate logistic analyses. Utilizing these variables, we developed a prognostic nomogram for predicting mortality post-CPR. Calibration curves, the area under receiver operating curves (ROC), decision curve analysis (DCA), and clinical impact curve were used to assess the discriminability, accuracy, and clinical utility of the nomogram.

The study population comprised 374 patients, with 262 allocated to the training group and 112 to the validation group. Of these, 213 patients were dead in the hospital. Multivariate logistic analysis revealed age (OR 1.05, 95% CI: 1.03-1.08), witnessed arrest (OR 0.28, 95% CI: 0.11-0.73), time to return of spontaneous circulation (ROSC) (OR 1.05, 95% CI: 1.02-1.08), non-shockable rhythm (OR 3.41, 95% CI: 1.61-7.18), alkaline phosphatase (OR 1.01, 95% CI: 1-1.01), and sequential organ failure assessment (SOFA) (OR 1.27, 95% CI: 1.15-1.4) were independent risk factors for hospital mortality for patients who survived 24 h after CPR. ROC of the nomogram showed the AUC in the training and validation group was 0.827 and 0.817, respectively. Calibration curves, DCA, and clinical impact curve demonstrated the nomogram with good accuracy and clinical utility.

Our prediction model had accurate predictive value for hospital mortality in patients who survived 24 h after CPR, which will be beneficial for assisting in identifying high-risk patients and intervention. Further confirmation of the model's accuracy required external validation data.

In-hospital cardiac arrest (IHCA) is a critical emergency, occurring at rates of 1-6 events per 1000 hospital admissions, necessitating immediate and efficient resuscitation efforts. This study aims to determine the frequency, demographic characteristics, and outcomes of Code Blue activations in a tertiary teaching hospital in a low-middle-income country.

This retrospective observational study was conducted at in National Referral and Teaching Hospital in a middle income country in Asia, covering data from January 1, 2017, to December 31, 2023. The study included 2184 Code Blue activations, with data on Early Warning Scores (EWS) and palliative scores available from 2021 onwards. Statistical analyses were performed to evaluate the relationship between these scores and patient outcomes.

Out of 2184 Code Blue activations, 713 cases included both EWS and palliative scores. The highest number of activations was recorded in 2019 (535 cases), and the lowest in 2021 (152 cases). Calculated incidence where 5.46 per 1000 visits. The return of spontaneous circulation (ROSC) rates ranged from 11% to 27.6%, with an average of 17.7% per year. The mean EWS and palliative scores for Code Blue activations were 9.2 (SD ± 2.3) and 7.8 (SD ± 1.9), respectively.

The findings highlight trends in IHCA incidence, causes, and outcomes, emphasizing the importance of early identification and management of patients at risk. The study underscores the need for continuous monitoring and early intervention, particularly for patients with high EWS. Additionally, the integration of palliative care considerations into hospital protocols is crucial for improving patient outcomes and resource allocation.

Early warning system and palliative care scoring may predict code blue activation and if managed can reduce its number.

Conflicting data exist regarding sex-specific outcomes after cardiac arrest. This study investigates sex disparities in the provision of critical care and outcomes of in-hospital (IHCA) and out-of-hospital cardiac arrest (OHCA) patients.

Analysis of adult cardiac arrest patients admitted to certified Swiss intensive care units (ICUs) (01/2008-12/2022) using the nationwide prospective ICU registry. The primary outcome was ICU mortality, with secondary outcomes including ICU admission probability and advanced treatment provision.

Among 41,733 individuals (34.9% women), 21,692 patients (30.6% women) were admitted to ICUs (16,571 OHCA patients/5121 IHCA patients). Women were less likely to be admitted to the ICU than men (incidence rate ratio 0.82 [95% CI 0.80-0.85] and had a higher ICU mortality (41.8% vs 36.2%; p < 0.001). Mortality differences were more pronounced in OHCA patients (unadjusted HR: 1.35 [95% CI 1.28-1.43]; adjusted HR: 1.19 [95% CI 1.12-1.25]). In IHCA patients, mortality differences were less pronounced (unadjusted HR: 1.14 [95% CI 1.04-1.25]) and vanished after adjustment for confounders: adjusted HR: 1.03 [95% CI 0.94-1.13]). Women after cardiac arrest were older, more severely ill, and received fewer interventions before (44.7% vs 54.0%; p < 0.001) and during ICU stay. A subgroup analysis of 11,202 patients revealed that treatment limitations were more frequent in women (46.7% vs 38.7%; p < 0.001). However, these limitations were associated with an increased risk of death in both sexes.

This study highlights sex disparities in short-term mortality and ICU resource allocation among cardiac arrest patients, with women potentially facing disadvantages, in particular after OHCA. The limitations of ICU registry data, particularly the lack of detailed cardiac arrest-specific and comorbidity information, restrict definitive conclusions. Future research should prioritize prospective studies with more granular data to better understand and address these disparities.

In-hospital cardiac arrest (IHCA) poses a considerable threat to hospitalized patients, leading to high mortality rates and severe neurological deficits among survivors. Despite the advancements in resuscitation practices, the prognosis of IHCA remains poor, and comprehensive studies exploring nationwide trends and long-term survival are scarce, particularly in the Asian populations.

Utilizing data from the Taiwan National Health Insurance Research Database, we conducted a nationwide cohort study to analyze the IHCA events among adult patients between 2003 and 2020. The outcomes of interest in this study included the temporal trend in the IHCA incidence, in-hospital mortality, and median survival after discharge for overall hospitalizations.

Over the 18-year period, the IHCA incidence in Taiwan declined by 70%, from an annual incidence of 7.1 per 1,000 admissions to a lower rate in 2020, accompanied by a 14% reduction in the in-hospital mortality rate, with an average of 86.5%. The overall long-term survival rate for discharged survivors was 63.9%. We observed a substantial 125% increase in the median survival duration of discharged survivors, rising from 1.56 years in 2003 to 3.51 years in 2015. Favorable in-hospital survival rates and extended life expectancy were notably seen in the patients with shockable rhythms, those with a cardiac primary diagnosis, women, and younger patients.

Our study data revealed significant declines in the IHCA incidence and in-hospital mortality in Taiwan, along with improved long-term survival among survivors, particularly among specific subgroups. Women exhibited significantly better long-term survival as compared to men, underscoring the need to avoid sex-based treatment biases. Improvements in discharge survival rates and life expectancy were less pronounced in older survivors, indicating that age alone may not be sufficient to guide IHCA management decisions. Proactive resuscitation should be carefully considered for older patients, particularly those with mild frailty and potentially reversible conditions. Trial registration the institutional review board of Chang Gung Memorial Hospital approved all data usage and the study protocol (Registration number: 202301625B0, Registered 7 November 2023).

Improving the resuscitation and teamwork skills of residents is key to better outcomes of in-hospital cardiac arrest events. This study aims to explore the effects of regular low-dose simulation combined with a booster workshop on the progression and retention of resuscitation skills and teamwork among residents.

This comparative study took place at a teaching hospital in Northern Taiwan from August 2019 to June 2021. Residents were divided into two groups: the control group received regular low-dose simulation with faculty-led debriefing at the 1st, 6th, and 11th months, while the intervention group participated in an additional 3-hour simulation workshop one week before the 6th-month scenario. The workshop focused on resuscitation and teamwork skills. The study evaluated medical task performance, teamwork, patient safety attitudes, and timing of key resuscitation actions through standardized simulation scenarios, with the assessments occurring at the 1st, 6th, and 11th months (pre-test, mid-test, post-test, respectively).

Outcome measures in medical task performance and team leader behavior showed statistically significant improvement in both groups (p < 0.05) from pre-test to mid-test. After the intervention, the exposure group demonstrated significantly better results in medical task performance, team leader behavior, patient safety attitudes, and the timing of chest compression initiation compared to the control group (p < 0.05). Five months after the intervention, a significant decline in most measured outcomes was observed in the exposure group (p < 0.05). Despite this decline, the exposure group still performed significantly higher than the control group across most measured outcomes.

This study demonstrates the potential and advantages of integrating simulation-based training into the clinical training curriculum for residents to improve medical task performance and teamwork behaviors for handling in-hospital cardiac arrest scenarios. The observed decline in skills over time supports the inclusion of regular refresher courses to maintain and advance these vital competencies.

Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making.

For this study, comprehensive data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 2.0 database. We excluded patients under 18 years old, those not initially admitted to the intensive care unit (ICU), or treated in the ICU for less than 72 h. A total of 57 clinical parameters relevant to CA patients were selected for analysis. These included demographic data, vital signs, and laboratory parameters. After an extensive literature review and expert consultations, key factors such as temperature (T), sodium (Na), creatinine (CR), glucose (GLU), heart rate (HR), PaO2/FiO2 ratio (P/F), hemoglobin (HB), mean arterial pressure (MAP), platelets (PLT), and white blood cell count (WBC) were identified as the most significant for cluster analysis. Consensus cluster analysis was utilized to examine the mean values of these routine clinical parameters within the first 24 h post-ICU admission to categorize patient classes. Furthermore, in-hospital and 28-day mortality rates of patients across different CA subphenotypes were assessed using multivariate logistic and Cox regression analysis.

After applying exclusion criteria, 719 CA patients were included in the study, with a median age of 67.22 years (IQR: 55.50-79.34), of whom 63.28% were male. The analysis delineated two distinct subphenotypes: Subphenotype 1 (SP1) and Subphenotype 2 (SP2). Compared to SP1, patients in SP2 exhibited significantly higher levels of P/F, HB, MAP, PLT, and Na, but lower levels of T, HR, GLU, WBC, and CR. SP2 patients had a notably higher in-hospital mortality rate compared to SP1 (53.01% for SP2 vs. 39.36% for SP1, P < 0.001). 28-day mortality decreased continuously for both subphenotypes, with a more rapid decline in SP2. These differences remained significant after adjusting for potential covariates (adjusted OR = 1.82, 95% CI: 1.26-2.64, P = 0.002; HR = 1.84, 95% CI: 1.40-2.41, P < 0.001).

The study successfully identified two distinct clinical subphenotypes of CA by analyzing routine clinical data from the first 24 h following ICU admission. SP1 was characterized by a lower rate of in-hospital and 28-day mortality when compared to SP2. This differentiation could play a crucial role in tailoring patient care, assessing prognosis, and guiding more targeted treatment strategies for CA patients.

Cardiopulmonary resuscitation (CPR) quality is often substandard to guidelines for resuscitation teams. We aimed to investigate if the use of a CPR coach as part of the resuscitation team can improve teamwork, quality of care, and patient outcomes during simulated and clinical cardiac arrest resuscitation.

We searched PubMed, Embase, and Cochrane from inception until October 9, 2024 for randomized trials and observational studies. We assessed risk of bias using Cochrane tools and assessed the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation approach. PROSPERO CRD42024603212.

We screened 505 records and included 7 studies. Overall, 6 were randomized studies involving pediatric resuscitation of which 4 studies were secondary analyses of one simulation-based trial, and one was an observational study on adult out-of-hospital cardiac arrest. Reported outcomes were: CPR performance in a simulated setting (n = 3), workload in a simulated setting (n = 2), adherence to guidelines in a simulated setting (n = 1), team communication in a simulated setting (n = 1), and clinical CPR performance (n = 1). All studies suggested improved CPR quality and guideline adherence when using a CPR coach compared to not using a coach. Risk of bias varied from low to critical and the certainty of evidence across outcomes was low or very low.

We identified low- to very-low certainty of evidence supporting the use of a CPR coach as part of the resuscitation team in order to improve CPR quality and guideline adherence. However, further research is needed, in particular for clinical performance and patient outcomes.

Nonshockable cardiac arrest rhythms have demonstrably poor outcomes. Pseudo-pulseless electrical activity (PEA), a subset of PEA in which visible cardiac contractility is present, is being described more frequently in recent literature. Physiology suggests that presence of cardiac motion even without a palpable pulse is energetically more favorable than true PEA, which is more like asystole. Therefore, we hypothesize that there is an increase in the survivability of PEA compared with asystole which may in part be due to a subset of pseudo-PEA.

A PICOST research question was generated which guided the composition of a systematic review and meta-analysis in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist.

A total of 494,355 patients were identified from 12 pieces of literature. Meta-analyses revealed an overall increased survivability of PEA compared with asystole (odds ratio [OR] 2.08, 95% confidence interval [CI] 1.52-2.86). When differentiating between location of arrest, PEA was more survivable in both in-hospital cardiac arrest and out-of-hospital cardiac arrest than asystole (out-of-hospital cardiac arrest OR 4.17, 95% CI 3.78-4.60, and in-hospital cardiac arrest OR 1.60, 95% CI 1.42-1.79). Finally, when comparing neurological outcome of PEA with asystole, PEA was more favorable (OR 3.32, 95% CI 1.39-7.94).

Pseudo-PEA may be one of the explanations attributed to better outcomes of PEA, especially neurological, due to the presence of cerebral and coronary flow. The presence of PEA likely requires evidence-based tailored management with presence of pseudo-PEA being more like a profound shock state. More evidence is required to investigate the true incidence of pseudo-PEA and its outcomes compared with true PEA.

Retrospective analysis of clinical characteristics and outcomes of patients with out-of-hospital cardiac arrest (OHCA) treated with extracorporeal cardiopulmonary resuscitation (ECPR) requiring extracorporeal membrane oxygenation (ECMO) reinsertion or not.

Data from the Study of Advanced Life Support for Ventricular Fibrillation with Extracorporeal Circulation in the Japan II database were reviewed. Patients who received ECPR after OHCA between January 2015 and July 2021 and underwent ECPR weaning were divided into reinsertion and no-reinsertion groups. The primary outcome was the 30-day survival rate.

Data from 1011 patients who underwent ECMO weaning ≥1 time and survived were analyzed (12 [1.2%], reinsertion; 999 [98.8%] no-reinsertion). The reinsertion group had a longer time to first ECMO weaning (median [interquartile range, IQR]: 3.0 [2.0-5.0] vs. 4.5 [3.2-6.8] days; p = 0.02). The survival rates at 30 days (25.0% vs. 55.1%; p = 0.08) and favorable neurological outcomes at discharge (8.3% vs. 30.5%; p = 0.18) tended to be lower in the reinsertion group. Among patients who died within 30 days, medical costs were significantly higher in the reinsertion group (median [IQR]: $36,628.2 [26,012.9-45,885.6] vs. $16,456.6 [9341.2-24,880.6]; p < 0.01). Intensive care unit (ICU) stay and mechanical ventilation duration were significantly longer in the reinsertion group.

Patients requiring ECMO reinsertion tended to have poor clinical outcomes and higher healthcare costs, highlighting the need for large-scale studies to develop ECPR protocols and optimize clinical benefits and resource allocation.

Patients who achieve return of spontaneous circulation (ROSC) after in-hospital cardiac arrest (IHCA) may re-arrest. This phenomenon has not been sufficiently investigated. The aim of this study was to examine the immediate (1-min) and short-term (20-min) risks of re-arrest in IHCA.

We retrospectively analyzed four datasets of IHCA episodes, comprising defibrillator recordings collected between 2002 and 2022. Re-arrest was defined as the resumption of chest compressions following a period of ROSC after cardiac arrest of any duration. Parametric models were applied to calculate the immediate risk of re-arrest. In addition, we estimated the short-term risk of re-arrest within 20 min.

In 763 episodes of IHCA, we observed 316 re-arrests: 68% to pulseless electrical activity (PEA), 25% to ventricular fibrillation/ventricular tachycardia (VF/VT), and 7% to asystole. Most re-arrests occurred with the same rhythm as in the initial arrest. When ROSC was achieved from a non-shockable rhythm, the risk of re-arrest to a non-shockable rhythm was initially 2% per minute and decreased to 1% per minute after 9 min. The corresponding risk of re-arrest to VF/VT was constant at 2% per minute. If ROSC was obtained from a shockable rhythm, the risk of re-arrest to a shockable rhythm was initially 5% per minute, decreasing to 4% per minute after 9 min. The corresponding risk to a non-shockable rhythm was constant at 1% per minute. The risk of re-arrest within 20 min was 27%, and the overall risk of at least one re-arrest per episode was 33%.

The immediate risk of re-arrest was approximately 2% per minute, with the highest risk occurring as a reversion to VF/VT if ROSC was obtained from VF/VT. The risk of re-arrest within 20 min of the initial arrest was 27%, and the overall risk of at least one re-arrest per episode was 33%.

It was widely accepted that malignant ventricular arrhythmias (VA) are the main direct initial cause for cardiac arrest and sudden cardiac death (SCD), but diverging data tended to demonstrate that asystole or pulseless activity were becoming the most prevalent cardiac rhythms at the time of cardiac arrest. We challenge here these conceptions and reinforce the persisting prominent role of VA in SCD.

To assess short- and long-term outcomes of acute myocardial infarction (AMI) complicated by out-of-hospital cardiac arrest (OHCA) or in-hospital cardiac arrest (IHCA) in a nationwide cohort.

Cohort study of AMI patients admitted to hospitals in Norway 2013-22 registered in the Norwegian Myocardial Infarction Registry. Outcomes were in-hospital and long-term mortality. Cumulative mortality was assessed with the Kaplan-Meier and the life-table methods. Cox regression was used for risk comparisons. Among 105 439 AMI patients (35% women), we identified 3638 (3.5%) patients with OHCA and 2559 (2.4%) with IHCA. The mean age was 65.7 (13.2), 70.9 (12.6), and 70.7 (13.6) years for OHCA, IHCA, and AMI without cardiac arrest (CA), respectively. The median follow-up time was 3.3 (25th, 75th percentile: 1.1, 6.3) years. In-hospital mortality was 28, 49, and 5%, in OHCA, IHCA, and AMI without CA, and the estimated 5-year cumulative mortality was 48% [95% confidence interval (CI) 46-50%], 69% (95% CI 67-71%), and 35% (95% CI 34-35%), respectively. Among patients surviving to hospital discharge, no significant difference in mortality during follow-up was found between OHCA and AMI without CA [adjusted hazard ratio (HR) 1.04, 95% CI 0.96-1.13], while the long-term mortality of AMI patients with IHCA was higher (age-adjusted HR 1.31, 95% CI 1.19-1.45).

In this large, contemporary cohort of AMI patients, in-hospital mortality of patients with OHCA or IHCA was still high. Among patients surviving to hospital discharge, long-term mortality was comparable between OHCA and AMI without CA, while the outcome of patients with IHCA was significantly worse.

Out-of-hospital cardiac arrest (OHCA) is characterized by the cessation of mechanical cardiac activity and voluntary circulation occurring outside of a hospital setting, making it the leading cause of death worldwide. Recently, the optimal approach to airway management has been a subject of controversy.

Follow PRISMA guidelines for systematic evaluation and meta-analysis. The primary outcome was survival assessed by 4 measures: Restoration of spontaneous circulation, survival to hospital or emergency department, evaluation of functional recovery after PCR (measured at both discharge and 3 months after PCR), and neurological function score at discharge.

A total of 6 RCTs (14,205 patients) were included in the systematic review and 4 RCTs (13,053 patients) were included in the meta-analysis. 5 studies (83.3%)of RCTs with ETI controls were notable for their high quality, with low risk of bias judged in all 7 domains of the risk assessment scale. Showed an advantage of SGA (compared to ETI) with potential for ROSC (95% CI [1.02 to 1.18], I2 = 48%, p = 0.01) and survival to hospital or emergency department(95% CI [1.01 to 1.17], I2 = 12%, p = 0.02).

This systematic review and meta-analysis found a significant association between SGA and the possibility of obtaining ROSC and reaching the hospital or emergency department after CPR in OHCA.

This study aimed to comprehensively examine the factors influencing healthcare providers' decision-making for initiation of resuscitation (IOR).

In-hospital resuscitation survival hinges on timely and effective interventions. Despite guidelines, decision-making during resuscitation remains challenging, impacted by both clinical and non-clinical factors.

A mixed-methods systematic review (MMSR) was conducted, searching PubMed, Web of Science, Scopus, and Embase in May 2024. Twenty peer-reviewed studies of adult in-hospital resuscitation decision-making (≥ 18 years) were included. Data were extracted and synthesized using the Joanna Briggs Institute (JBI) convergent integrated approach.

A database search yielded 4398 studies, of which 1216 were duplicates. After screening 3182 unique studies, 20 articles (five qualitative, 12 quantitative, three mixed methods) were included. Data synthesis identified three overarching themes: patient, provider, and system factors. These themes encompassed barriers and facilitators to IOR.

This review underscores the importance of understanding patient-related, provider-related, and system-related factors influencing IOR. By addressing these factors, healthcare organizations can improve resuscitation practices and outcomes. Future research should focus on enhancing collaboration, communication, and resource availability while considering non-medical factors in decision-making for IOR.

Understanding the multifaceted barriers and facilitators identified in this study can enhance the effectiveness of resuscitation protocols and ultimately improve patient outcomes during critical care situations.

Advanced cardiac life support (ACLS) skills are essential for nurses. During the COVID-19 pandemic, augmented reality (AR) technologies were incorporated into medical education to increase learning motivation and accessibility.

This study aims to determine whether AR for educational applications can significantly improve crash cart learning, learning motivation, cognitive load, and system usability. It focused on a subgroup of nurses with less than 2 years of experience.

This randomized controlled trial study was conducted in a medical center in southern Taiwan. An ACLS cart training course was developed using AR technologies in the first stage. Additionally, the efficacy of the developed ACLS training course was evaluated. The AR group used a crash cart learning system developed with AR technology, while the control group received traditional lecture-based instruction. Both groups were evaluated immediately after the course. Performance was assessed through learning outcomes related to overall ACLS and crash cart use. The Instructional Materials Motivation Survey, System Usability Scale, and Cognitive Load Theory Questionnaire were also used to assess secondary outcomes in the AR group. Subgroup analyses were performed for nurses with less than 2 years of experience.

All 102 nurses completed the course, with 43 nurses in the AR group and 59 nurses in the control group. The AR group outperformed the control group regarding overall ACLS outcomes and crash cart learning outcomes (P=.002; P=.01). The improvement rate was the largest for new staff regardless of the overall learning effect and the crash cart effect. Subgroup analysis revealed that nurses with less than 2 years of experience in the AR group showed more significant improvements in both overall learning (P<.001) and crash cart outcomes (P<.001) compared to their counterparts in the control group. For nurses with more than 2 years of experience, no significant differences were found between the AR and control groups in posttraining learning outcomes for the crash cart (P=.32). The AR group demonstrated high scores for motivation (Instructional Materials Motivation Survey mean score 141.65, SD 19.25) and system usability (System Usability Scale mean score 90.47, SD 11.91), as well as a low score for cognitive load (Cognitive Load Theory Questionnaire mean score 15.42, SD 5.76).

AR-based learning significantly improves ACLS knowledge and skills, especially for nurses with less experience, compared to traditional methods. The high usability and motivational benefits of AR suggest its potential for broader applications in nursing education.

ClinicalTrials.gov NCT06057285; https://clinicaltrials.gov/ct2/show/NCT06057285.

suicide is a global public health issue, with over 800 000 people taking their own lives every year. However, most suicide attempts do not result in death. Hanging is the most common method used in France, often leading to post-hanging coma (PHC). The prognosis for patients admitted in intensive care unit (ICU) following PHC is poor, yet predictive criteria of mortality have been poorly evaluated.

we retrospectively collected prehospital and in-hospital data from 65 patients hospitalized in 2 French ICU for PHC, between first March 2010 and first August 2023, and compared characteristics between patients alive and dead.

hospital mortality was 52%. Among baseline characteristics, SAPSII and pre-hospital cardiac arrest were associated with mortality, respectively 47 versus 62 (P = .005) and 32% versus 85% (P = .001). Concerning neuroprognostication, abnormal pupillary light reflex (PLR) was more frequent in patients who died (14% vs 56%, P = .002), as abnormal EEG (0% vs 32%, P = .002) and abnormal transcranial doppler (10% vs 35%, P = .031).

we identified several poor prognostic factors associated with hospital mortality after PHC. Further larger-scale studies are needed to supplement these findings.