Search Results (69)

Background/Objectives: Early neuroprognostication after cardiac arrest is essential for guiding treatment strategies and providing accurate prognostic information to families. While several early risk scores have been proposed, few have incorporated a wide range of variables in large cohorts. This study aimed to develop and validate a novel prognostic model, the KORHN risk score, and to compare its performance with established tools including MIRACLE, TTM, CAHP, C-GRApH, and OHCA scores; Methods: We conducted a prospective multicenter observational study using data from the KORean Hypothermia Network registry. Risk variables identified in previous studies, along with extensive data from 1371 patients in the KORHN registry, were analyzed. The primary endpoint was poor neurological outcome at 6 months; Results: Key predictors included low-flow time, diastolic shock index, cardiac etiology, bilateral absence of pupil reflex, shockable initial rhythm, Glasgow Coma Scale motor response, epinephrine use, and age. Compared with established risk scores, the KORHN score demonstrated superior performance (AUC 0.925 vs. 0.827–0.902 with all variables, and AUC 0.914 vs. 0.85–0.903 with the top five variables with identical cut-off). External validation in a non-KORHN cohort (AUC 0.890) confirmed its robustness; Conclusions: The KORHN score provides a simple, accurate tool for early neuroprognostication, supporting clinical decision-making and family communication. Full article

Background: Cardiopulmonary resuscitation (CPR) is a highly effort-intensive intervention and, in cases of cardiac arrest, the ability to predict a return of spontaneous circulation (ROSC) is of great importance for the efficient use of resources. This real-time assessment approach offers a practical advantage by increasing the applicability of prognostic models during acute resuscitation in an emergency department. Method: In this study, the data of patients who underwent CPR in the emergency department of a tertiary care hospital between 1 June 2019 and 1 June 2024 and underwent cardiopulmonary resuscitation were retrospectively analyzed. The patients’ demographics, comorbidities, CPR characteristics, and laboratory findings were evaluated using logistic regression and ROC curve analysis to identify the predictors of ROSC. Result: Our study revealed that cases with shockable rhythms and a shorter CPR duration were more likely to achieve ROSC. Elevated levels of albumin, creatine kinase, glucose, hemoglobin, and white blood cells were significantly associated with ROSC, while higher levels of creatinine, base excess, and eosinophils were more common in non-survivors. Atrial fibrillation and neurodegenerative disease were associated with lower ROSC rates. Conclusions: Although the criteria for the termination of cardiac arrest resuscitation are not definitive, certain patient characteristics and laboratory findings may guide the prediction of ROSC or the identification of cases requiring prolonged CPR. The integration of these real-time predictors into clinical algorithms may support decision making in crowded emergency departments. Full article

Background: The coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has had a profound impact on global health, extending beyond pulmonary complications. Cardiovascular involvement in COVID-19 is multifactorial and may be influenced by viral load, inflammatory response, and pre-existing comorbidities. Discussion: Acute complications include myocardial injury, arrhythmias, acute coronary syndromes (ACS), heart failure, Takotsubo cardiomyopathy, myopericarditis, and cardiac arrest. Notably, atrial fibrillation (AF) emerges as a frequent arrhythmic complication, particularly among critically ill patients, and is associated with increased mortality. COVID-19-patients with concomitant ACS present more severe clinical profiles and higher rates of thrombotic events, including stent thrombosis. Cardiac arrest predominantly presents with non-shockable rhythms and is associated with dismal outcomes. COVID-19 also exacerbates heart failure, both by aggravating existing cardiac dysfunction or by precipitating de novo heart failure. Takotsubo cardiomyopathy and myocarditis, although less frequent, have been reported and are often underdiagnosed due to subtle clinical presentations. Right ventricular dysfunction, linked to pulmonary involvement, has emerged as a key prognostic marker. Post-COVID-19 syndrome include persistent cardiac abnormalities such as reduced ventricular function and myocardial inflammation. Cardiac magnetic resonance imaging and strain echocardiography have proven useful in identifying subclinical cardiac involvement. Conclusions: Early recognition and monitoring of cardiovascular complications are crucial for improving outcomes in patients affected by COVID-19. This review summarizes current evidence regarding cardiovascular manifestations associated with COVID-19. Full article

Background/Objectives: Initial cardiac rhythm is a known prognostic indicator in out-of-hospital cardiac arrest (OHCA). However, the impact of rhythm conversion during cardiopulmonary resuscitation (CPR) on outcomes in patients undergoing extracorporeal CPR (ECPR) remains unclear. This study evaluated the association between initial and converted cardiac rhythms and outcomes in patients with refractory OHCA treated with ECPR. Methods: This nationwide retrospective observational study analyzed data from the Out-of-Hospital Cardiac Arrest Surveillance registry in South Korea (2008–2022). Patients were categorized into three groups: initial shockable rhythm (SR), non-shockable rhythm (NSR) converted to SR, and refractory NSR. The primary outcome was survival to hospital discharge; the secondary outcome was favorable neurological status (CPC 1–2). Results: Among 681 patients, 161 had initial SR, 345 had converted SR, and 175 had refractory NSR. Before matching, survival and CPC 1–2 rates were highest in the initial SR group (21.1% and 15.5%), followed by the converted SR group (19.4% and 11.6%), and lowest in the refractory NSR group (9.7% and 4.0%) (p < 0.01). After matching, CPC 1–2 remained significantly higher in the initial SR group (14.4%) and in the converted SR group (9.3%) vs. the refractory NSR group (5.1%, p = 0.016; 3.7%, p = 0.042). Persistent NSR was independently associated with poor neurological outcomes compared to both initial SR (AOR 0.337, p = 0.037) and converted SR (AOR 0.283, p = 0.020). Conclusions: Rhythm conversion from NSR to SR before ECPR was associated with significantly improved neurological outcomes. Rhythm conversion may serve as a prognostic marker and resuscitation target to guide ECPR decisions. Full article

Background: Pediatric in-hospital cardiac arrest (IHCA) remains a critical health challenge with high mortality rates. Limited data from Western China prompted this study to investigate the characteristics of IHCA using the Utstein style. Methods: A retrospective analysis of 456 pediatric patients with IHCA (2018–2022) at the Children’s Hospital of Chongqing Medical University assessed demographics, arrest characteristics, outcomes and mortality risk factors. The primary outcome was survival to discharge; the secondary outcomes included return of spontaneous circulation (ROSC) > 20 min, 24 h survival, and favorable neurological outcomes. Logistic regression was used to identify the mortality risk factors. Results: ROSC > 20 min was achieved in 78.07% of cases, with 37.94% surviving to discharge (86.13% of survivors had favorable neurological outcomes). Etiological stratification identified general medical conditions (52.63%) as the predominant diagnoses, with surgical cardiac patients demonstrating superior resuscitation outcomes (ROSC > 20 min: 86.84%, discharge survival: 64.04%). Initial arrest rhythms predominantly featured non-shockable patterns, specifically bradycardia with poor perfusion (79.39%), whereas shockable rhythms (ventricular fibrillation/pulseless ventricular tachycardia) constituted only 4.17% of cases. Multivariable regression analysis identified five independent risk factors: vasoactive infusion before arrest (OR = 7.69), CPR > 35 min (OR = 13.92), emergency intubation (OR = 5.17), administration of >2 epinephrine doses (OR = 3.12), and rearrest (OR = 8.48). Notably, prolonged CPR (>35 min) correlated with higher mortality (8.96% survival vs. 48.54% for 1–15 min), yet all six survivors with CPR > 35 min had favorable neurological outcomes. Conclusions: These findings underscore the persistent challenges in pediatric IHCA management while challenging the conventional CPR duration thresholds for futility. The identified mortality risk factors inform resuscitation decision making and future studies. Full article

Load distributing band (LDB) mechanical chest compression (CC) devices are used to treat out-of-hospital cardiac arrest (OHCA) patients. Mechanical CCs induce artifacts in the electrocardiogram (ECG) recorded by defibrillators, potentially leading to inaccurate cardiac rhythm analysis. A reliable analysis of the cardiac rhythm is essential for guiding resuscitation treatment and understanding, retrospectively, the patients’ response to treatment. The aim of this study was to design a deep learning (DL)-based framework for cardiac automatic multiclass rhythm classification in the presence of CC artifacts during OHCA. Concretely, an automatic multiclass cardiac rhythm classification was addressed to distinguish the following types of rhythms: shockable (Sh), asystole (AS), and organized (OR) rhythms. A total of 15,479 segments (2406 Sh, 5481 AS, and 7592 OR) were extracted from 2058 patients during LDB CCs, whereof 9666 were used to train the algorithms and 5813 to assess the performance. The proposed architecture consists of an adaptive filter for CC artifact suppression and a multiclass rhythm classifier. Two DL alternatives were considered for the multiclass classifier: convolutional neuronal networks (CNNs) and residual networks (ResNets). A traditional machine learning-based classifier, which incorporates the research conducted over the past two decades in ECG rhythm analysis using more than 90 state-of-the-art features, was used as a point of comparison. The unweighted mean of sensitivities, the unweighted mean of ${\mathrm{F}}_1$-Scores, and the accuracy of the best method (ResNets) were 88.3%, 88.3%, and 88.2%, respectively. These results highlight the potential of DL-based methods to provide accurate cardiac rhythm diagnoses without interrupting mechanical CC therapy. Full article

To develop and evaluate deep learning models for cardiac arrest rhythm classification during cardiopulmonary resuscitation (CPR), we analyzed 508 electrocardiogram (ECG) segments (each 4 s in duration, recorded at 250 Hz) from 131 cardiac arrest patients. Compression-affected segments were recorded during chest compressions, while non-compression segments were extracted during compression pauses or immediately after return of spontaneous circulation (ROSC) declaration. One-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) models were employed for four binary classification tasks: (1) shockable rhythms (VF and pVT) versus non-shockable rhythms (asystole and PEA) in all ECG segments; (2) the same classification in compression-affected ECG segments; (3) pulse-generating rhythms (ROSC rhythm) versus non-pulse-generating rhythms (asystole, PEA, VF and pVT) in all ECG segments; and (4) the same classification in compression-affected ECG segments. The 1D-CNN model consistently outperformed the RNN model across all classification tasks. For shockable versus non-shockable rhythm classification, the 1D-CNN achieved accuracies of 91.3% and 89.8% for all ECG segments and compression-affected ECG segments, respectively, compared to 50.6% and 54.5% for the RNN. In detecting pulse-generating rhythms, the 1D-CNN demonstrated accuracies of 90.9% and 85.7% for all ECG segments and compression-affected ECG segments, respectively, while the RNN achieved 92.2% and 84.4%. The 1D-CNN model demonstrated superior performance in cardiac arrest rhythm classification, maintaining high accuracy even with compression-affected ECG data. Full article

Background/Objectives: Out-of-hospital cardiac arrests (OHCAs) are common, with return of spontaneous circulation (ROSC) achieved in approximately 25% of patients. However, it remains unknown whether post-ROSC care delivered by a pre-hospital critical care team (CCT) improves patient outcomes. We therefore aimed to investigate this in OHCA patients admitted to our intensive care unit (ICU). Methods: In this retrospective observational study, consecutive adults with ROSC after non-traumatic OHCA admitted to our ICU between 1 September 2019 and 31 August 2022 were included. We compared patients who received post-ROSC care from a CCT to those who received standard care. The primary outcome was a good neurological outcome on hospital discharge (defined as Cerebral Performance Category 1–2). Descriptive statistics, Area Under the Receiver Operating Characteristic Curve (AUC) values, and adjusted Odds Ratios (ORs) are reported. We constructed multivariable logistic regression models that adjusted for the component variables of the MIRACLE2 score. Results: We included 126 OHCAs (median age 63 years, 69% male), which were largely witnessed (82%), involved bystander cardiopulmonary resuscitation (87%), and had an initial shockable rhythm (61%). The prevalence of good neurological outcomes was higher in patients who received post-ROSC care from a pre-hospital CCT (37% vs. 17%, p = 0.012). The MIRACLE2 score was a strong predictor of good neurological outcomes (AUC 0.932), and in our multivariable analysis, good neurological outcome was associated with both CCT presence post-ROSC (aOR 3.77, 95% CI 1.02–13.89) and the delivery of PHEA (aOR 4.10, 95% CI 1.10–15.27, p = 0.035). Furthermore, in patients meeting the Utstein criteria (n = 69), good neurological outcomes were also more prevalent with CCT presence post-ROSC (62% vs. 29%, p < 0.001). Conclusions: We found that post-ROSC care delivered by a pre-hospital CCT was associated with good neurological outcomes on hospital discharge. Full article

Background: Therapeutic hypothermia (TH) represents a critical therapeutic intervention for patients with cardiac arrest, although treatment efficacy and prognostic factors may vary between individuals. Precise, personalized outcome predictions can empower better clinical decisions. Methods: In this multi-center retrospective cohort study involving nine medical centers in Taiwan, we developed machine learning algorithms to predict neurological outcomes in patients who experienced cardiac arrest with shockable rhythms and underwent TH. The study cohort comprised 209 patients treated between January 2014 and September 2019. The models were trained on patients’ pre-treatment characteristics collected during this study period. The optimal artificial neural network (ANN) model was interpretable using the SHapley Additive exPlanations (SHAP) method. Results: Among the 209 enrolled patients, 79 (37.80%) demonstrated favorable neurological outcomes at discharge. The ANN model achieved an area under the curve value of 0.9089 (accuracy = 0.8330, precision = 0.7984, recall = 0.7492, specificity = 0.8846) for outcome prediction. SHAP analysis identified vital predictive features, including the dose of epinephrine during resuscitation, diabetes status, body temperature at return of spontaneous circulation (ROSC), whether the cardiac arrest was witnessed, and diastolic blood pressure at ROSC. Using real-life case examples, we demonstrated how the ANN model provides personalized prognostic predictions tailored to individuals’ distinct profiles. Conclusion: Our machine learning approach delivers personalized forecasts of TH outcomes in cardiac arrest patients with shockable rhythms. By accounting for each patient’s unique health history and cardiac arrest event details, the ANN model empowers more precise risk stratification, tailoring clinical decision-making regarding TH prognostication and optimizing personalized treatment planning. Full article

Background/Objectives: Minimizing interruptions in chest compressions is very important when resuscitating patients with cardiac arrest. Recently, research has analyzed electrocardiograms (ECGs) during chest compressions using convolutional neural networks (CNNs). This study aimed to compare the accuracy of deeper neural networks and more advanced structures. Methods: ECGs with chest compression artifacts were obtained from patients who visited the emergency department of Korea University Ansan Hospital from September 2019 to February 2024. We compared the accuracy of a deeper CNN, long short-term memory (LSTM), and a CNN with an attention mechanism and residual block against a reference model. The input of the model was 5 s ECG segments with compression artifacts, and the output was the probability that the ECG with the artifacts was a shockable rhythm. Results: A total of 1889 ECGs with compression artifacts from 172 patients were included in this study. There were 969 ECGs annotated as shockable and 920 as non-shockable. The area under the receiver operating characteristic curve (AUROC) of the reference model was 0.8672. The AUROCs of the deeper CNN for five and seven layers were 0.7374 and 0.6950, respectively. The AUROCs of LSTM and bidirectional LSTM were 0.7719 and 0.8287, respectively. The AUROC of the CNN with the attention mechanism and residual block was 0.7759. Conclusions: CNNs with deeper layers or those incorporating attention mechanisms, residual blocks, and LSTM architectures did not exhibit better accuracy. To improve the model accuracy, a larger dataset or advanced augmentation techniques may be required, rather than complicating the structure of the model. Full article

Background: Extracorporeal cardiopulmonary resuscitation (ECPR) has the potential to improve neurological outcomes in patients with refractory out-of-hospital cardiac arrest (OHCA), offering an alternative to conventional cardiopulmonary resuscitation (CCPR). However, its effectiveness in OHCA remains controversial despite advancements in resuscitation techniques. Methods: This retrospective single-center study compared neurological outcomes and 30-day survival between ECPR and CCPR patients from January 2014 to January 2022. Patients aged 18–75 with witnessed OHCA, minimal no flow and low flow times, and cardiac arrests occurring at home or in public places were included. All patients were transported directly to our institution, a tertiary medical center serving the southeastern region of Seoul, where extracorporeal membrane oxygenation implantation was consistently performed in the emergency department. Neurological outcomes were assessed using Cerebral Performance Category scores, with good outcomes defined as scores of 1–2. Statistical analyses included logistic regression models and Kaplan–Meier survival curves, adjusted for confounders using inverse probability of treatment weighting. Results: ECPR was associated with significantly better neurological outcomes than CCPR (p < 0.001). Factors predicting poor outcomes included older age and longer low flow times, while male sex and shockable rhythms were protective. No significant difference was found in 30-day survival between the ECPR and CCPR groups, although a trend toward better survival was noted with ECPR. Conclusions: ECPR may improve neurological outcomes in patients with refractory OHCA compared to CCPR, although it does not significantly affect 30-day survival. Further studies are necessary to validate these findings and explore the long-term impacts of ECPR. Full article

Background: Out-of-hospital cardiac arrest (OHCA) is a critical health issue with survival influenced by multiple factors. This study analyzed resuscitation outcomes at the County Clinical Emergency Hospital of Sibiu, Romania, during pre-COVID-19 and pandemic periods. Methods: A retrospective analysis of 508 OHCA patients (2017–2020) assessed the return of spontaneous circulation (ROSC) as the primary endpoint. Statistical methods included decision tree analysis, logistic regression and ROC curve analysis to evaluate the predictive value of adrenaline dose and patient factors. Results: The mortality rate was 68.7%, with non-shockable rhythms predominant among fatalities. Rural patients, though younger, had lower ROSC rates than urban counterparts. Logistic regression showed that lower adrenaline doses (≤4 mg, OR 11.835 [95% CI: 6.726–20.27]; 4–6 mg, OR 2.990 [95% CI: 1.773–5.042]) were associated with better ROSC outcomes. Conclusions: A multivariable model (AUC = 0.773) incorporating demographics and pandemic status outperformed adrenaline dose alone (AUC = 0.711). Full article

Background: Out-of-hospital cardiac arrest (OHCA) is a critical global health challenge and a leading cause of mortality. This study investigates the combined effect of initial cardiac arrest rhythm, patient age, and location on the return of spontaneous circulation (ROSC) in OHCA patients. Methods: This retrospective study analyzed medical records from the National Emergency Medical Service (EMS) in Poland between January 2021 and June 2022. Data from 33,636 patients with OHCA who received cardiopulmonary resuscitation (CPR) at the scene were included. Results: Public incidents were associated with higher ROSC rates (54.10% vs. 31.53%, p < 0.001). Initial shockable rhythms (VF/pVT) significantly increased the odds of ROSC (OR = 3.74, 95% CI 3.39–4.13, p < 0.001). Obesity decreased the odds of ROSC in at-home cases (OR = 0.85, 95% CI 0.73–0.99, p = 0.036) but had no significant effect in public cases. The effect of age on ROSC outcomes varied significantly depending on the location. In patients younger than 60 years, better ROSC outcomes were observed in at-home cases, while for those older than 60 years, the odds of ROSC were higher in public locations. Each additional year of age decreased the odds of ROSC by 1.62% in at-home incidents (p < 0.001) and by 0.40% in public incidents (p = 0.009). Sex differences were significant in public locations, with women having higher odds of ROSC compared to men (OR = 0.57, 95% CI 0.37–0.87, p = 0.009 for VF/pVT). Conclusions: The interaction between the location of OHCA, initial cardiac rhythm, and patient age significantly impacts ROSC outcomes. Public locations show higher ROSC rates, especially in cases with shockable rhythms (VF/pVT). Age modifies ROSC outcomes, with younger patients benefiting more at home, and older patients showing better outcomes in public places. Full article

Background: Extracorporeal cardiopulmonary resuscitation (eCPR) offers cardiorespiratory support to patients experiencing cardiac arrest. However, this technology is not yet considered a standard treatment, and the evidence on eCPR criteria and its association with survival and good neurological outcomes remains scarce. Therefore, we aimed to investigate the overall mortality and risk factors for mortality. Moreover, we provide a comparison of demographic, clinical, and laboratory characteristics of patients, including neurological outcomes and adverse events during support. Methods: This retrospective analysis included in-hospital and out-of-hospital cardiac arrest patients who received eCPR and were admitted between January 2008 and June 2022 at a tertiary and trauma one-level university hospital in Austria. Results: In total, 90 patients fulfilled inclusion criteria, 41 (46%) patients survived until intensive care unit discharge, and 39 (43%) survived until hospital discharge. The most common cause of cardiac arrest was myocardial infarction (42, 47%), and non-shockable initial rhythm was reported in 50 patients (56%). Of 33 survivors with documented outcomes, 30 had a good recovery as measured with Cerebral Performance Category score, 2 suffered severe disability, and 1 remained in a persistent vegetative state. Finally, multivariate analysis identified asystole as initial rhythm (HR 2.88, p = 0.049), prolonged CPR (HR 1.02, p = 0.043), and CPR on the weekend (HR 2.57, p = 0.032) as factors with a higher risk of mortality. Conclusions: eCPR-related decision-making could be additionally supported by the comprehension of the reported risk factors for mortality and severe disability. Further studies are needed to elucidate the impact of peri-arrest variables on outcomes, aiming to improve patient selection. Full article

Introduction: Cardiac arrests are traditionally classified according to the setting in which they occur, including out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA). However, cardiac arrests that occur in the emergency department (EDCA) could constitute a third category, due to the peculiar characteristics of the emergency department (ED). In recent years, the need to study EDCAs separately from other intra-hospital events has emerged. The aim of this study was to describe the characteristics and outcomes of a cohort of patients experiencing EDCA in an Italian hospital over a 14-year period. Methods: This was a single-centre retrospective observational study conducted in the ED of the Santa Croce e Carle Hospital in Cuneo, Italy. All adult patients who experienced EDCA between 1 January 2010 and 30 June 2023 were included. OHCA patients, those arriving in the ED with on-going resuscitation measures, patients with EDCA not undergoing resuscitation, and patients with post-traumatic cardiac arrest were excluded from the study. The main outcome of the study was survival at hospital discharge with a favourable neurological outcome. Results: 350 cases of EDCA were included. The median age was 78 (63–85) years, and the median Charlson Comorbidity Index score was 5 (3–6). A total of 35 patients (10%) survived to hospital discharge with a cerebral performance category (CPC) Score of 1–2; survival in the ED was 28.3%. The causes of cardiac arrests were identified in 212 cases (60.6%) and included coronary thrombosis (35%), hypoxia (22%), hypovolemia (17%), pulmonary embolism (11%), metabolic (8%), cardiac tamponade (4%), toxins (2%) and hypothermia (1%). Variables associated with survival with a favourable neurological outcome were young age, a lower Charlson Comorbidity Index, coronary thrombosis as the primary EDCA cause, and shockable presenting rhythm; however, only the latter was associated with the outcome in a multivariate age-weighted model. Conclusions: In a cohort of patients with EDCA over a period of more than a decade, the most frequent cause identified was coronary thrombosis; 10% of patients survived with a good neurological status, and the only factor associated with the best prognosis was presenting a shockable rhythm. EDCA should be considered an independent category in order to fully understand its characteristics and outcomes. Full article