Search Results (2)

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. Full article

A 14-year-old boy with hypertrophic cardiomyopathy (HCM) diagnosed at the age of 1 year and with massive left ventricular hypertrophy suffered an episode of ventricular fibrillation during mild effort. He underwent a dual-chamber implantable cardioverter defibrillator (ICD) implantation. The defibrillation threshold testing (DFT) was ineffective. Subcutaneous multi-coli arrays tunneled into the left postero-lateral position and connected to the superior vena cava (SVC) port of the dual-chamber ICD were added to increase the myocardial mass involved in the defibrillation shock pathway. A new DFT was unsuccessful. The patient was transferred to our hospital for myectomy. An epicardial defibrillation patch was placed on the left ventricular lateral wall, but again, DFT testing was ineffective using the right ventricular (RV) coil to lateral patch as shock pathway. Another epicardial defibrillation patch was then placed on the inferior wall. In this case, DFT testing was effective with a defibrillation pathway between the two patches and the can. In November 2015, a high shock impedance alarm was recorded through remote monitoring, thus compromising the safety of the ICD shock pathway. The patient underwent the implant of a new trans-venous defibrillation coil lead in the azygos vein. After few months, the patient developed symptomatic severe aortic regurgitation and underwent an aortic valve replacement. During the operation, DFT testing was performed and was successful. Our case illustrates that azygous vein ICD lead implantation is efficacious in HCM with massive hypertrophy and high DFT, and prompts further studies to systematically investigate its efficacy in this particular subgroup of the HCM population. Full article