Ádám Pál-Jakab

Introduction: Population aging in Europe is ongoing and linked to poorer outcomes after out-of-hospital cardiac arrest (OHCA), yet age alone should not guide treatment. We aimed to describe age-related survival, identify independent predictors, and develop a predictive model using EMS data. Methods: We analyzed 147,962 adult OHCA cases from the Hungarian National EMS registry. Variables included initial rhythm, witness status, location, and sex. The primary outcome was survival to hospital admission. Multivariable logistic regression assessed independent predictors and age × treatment interactions; performance was evaluated with AUC, Brier score, and cross-validation. Results: Overall survival was 8.8%; elderly patients had lower survival (7.3%) than non-elderly (11.7%, p < 0.001). VF/VT (adjusted OR 5.34), medical personnel witness (OR 4.52), and AED shock (OR 3.52) were the strongest predictors. Age attenuated the survival benefit of VF/VT (interaction OR 0.914) and the protective effect of female sex (interaction OR 0.882; both p < 0.001). Model performance was good (AUC 0.784; Brier 0.0705). Conclusions: Age independently predicts survival after OHCA, but substantial treatment benefits persist in the elderly. Age–treatment interactions support geriatric-tailored resuscitation strategies and potential integration of this high-performing model into clinical decision support systems.

Background: Early defibrillation improves outcomes in cardiac arrest, but the optimal defibrillation strategy and energy requirements remain debated. This study investigated whether arterial blood gas (ABG) parameters could predict optimal defibrillation energy requirements for achieving the highest first-shock success rates in an animal model. Our study focused on clinical scenarios where ABG measurements are readily available, such as ventricular tachycardia and ventricular fibrillation storms requiring multiple shock deliveries. Materials and Methods: In the experimental setting, ventricular fibrillation was induced by 50 Hz direct current (DC), and the defibrillation threshold (DFT) was determined using a stepwise defibrillation protocol. ABG parameters were measured before each defibrillation attempt, recording partial arterial pressure of carbon dioxide (PaCO₂) and oxygen (PaO₂), pH, hematocrit (Hct), sodium (Na⁺), potassium (K⁺), and bicarbonate (HCO₃⁻) levels. The relationships between ABG parameters and the DFT were analyzed for 15 subjects using classical data analysis techniques and machine learning (ML) algorithms. Multiple ML models were trained and tested to predict the higher energy needed for successful defibrillation based on the ABG parameters. Results: Statistically significant differences were found in Hct and Na⁺ levels between the two DFT categories, above 130 Joules (J) and below 40 J (p < 0.01). The DFT negatively correlated with PaO₂ and positively correlated with Hct and Na⁺. However, other ABG parameters did not show significant correlations with DFT. Using ML, we predicted cases requiring higher defibrillation E. Our best-performing model, the Extra Trees Classifier, achieved 83% overall accuracy, with 100% and 67% precision rates for higher and lower DFT categories, respectively. We validated the model using bootstrap resampling and 10-fold cross-validation, confirming consistent performance. We identified Hct, PaCO₂, and PaO₂ as significant contributors to model prediction based on the feature importance value. Conclusions: Modern data analysis techniques applied to ABG parameters may guide personalized defibrillation energy selection, particularly in controlled clinical environments such as catheterization laboratories and intensive care units where ABG measurements are readily available.

ECMO has become a therapeutic modality for in- and out-of-hospital scenarios and is also suitable as a bridging therapy until further decisions and interventions can be made. Case report: A 27-year-old male patient with mechanical aortic valve prothesis had a sudden cardiac arrest (SCA). ROSC had been achieved after more than 60 min of CPR and eight DC shocks due to ventricular fibrillation (VF). The National Ambulance Service unit transported the patient to our clinic for further treatment. Due to the trauma and therapeutic INR, a CT scan was performed and ruled out bleeding. Echocardiography described severely decreased left ventricular function. Coronary angiography was negative. Due to the therapeutic refractory circulatory and respiratory failure against intensive care, VA-ECMO implantation was indicated. After four days of ECMO treatment, the patient’s circulation was stabilized without neurological deficit, and the functions of the end organs were normalized. Cardiac MRI showed no exact etiology behind SCA. ICD was implanted due to VF and SCA. The patient was discharged after 19 days of hospitalization. Conclusion: This case report points out that the early application of mechanical circulatory support could be an outcome-determinant therapeutic modality. Post-resuscitation care includes cardiorespiratory stabilization, treatment of reversible causes of malignant arrhythmia, and secondary prevention.

Remote monitoring (RM) is the newest function of cardiac implantable electronic devices (CIEDs). In our observational retrospective analysis, we aimed to assess whether telecardiology could be a safe alternative to routine outpatient examinations during the COVID-19 pandemic. The in- and outpatient visits, the number of acute cardiac decompensation episodes, the RM data from CIEDs, and general condition were examined via questionnaires (KCCQ, EQ-5D-5L). Regarding the enrolled 85 patients, the number of personal patient appearances was significantly lower in the year following the pandemic outbreak compared to the previous year (1.4 ± 1.4 and 1.9 ± 1.2, p = 0.0077). The number of acute decompensation events was five before and seven during lockdown (p = 0.6). Based on the RM data, there was no significant difference in heart failure (HF) markers (all related p > 0.05); only patient activity increased after restrictions were lifted compared to that before the lockdown (p = 0.03). During restrictions, patients reported increased anxiety and depression compared to their previous state (p < 0.001). There was no subjective change in the perception of HF symptoms (p = 0.7). Based on the subjective perception and CIED data, the quality of life of patients with CIED did not deteriorate during the pandemic, but their anxiety and depression intensified. Telecardiology may be a safe alternative to routine inpatient examination.