Evidence of an association between atrial fibrillation (AF) and sudden cardiac arrest (SCA) in young adults is limited. In this study, we aim to evaluate this association in a general population aged between 20 and 39 years. Young adults who underwent health check-ups between 2009 and 2012 were screened from a nationwide healthcare database in South Korea. A history of AF diagnosis before the health check-ups was identified based on the relevant International Classification of Diseases, 10th edition codes reported in the database. Associations between an established diagnosis of AF and the risk of SCA during follow-up were examined. A total of 6 345 162 young people were analysed with a mean follow-up duration of 9.4 years. The mean age was 30.9 ± 5.0 years, and 5875 (0.09%) individuals were diagnosed with AF. During follow-up, SCA occurred in 5352 (0.08%) individuals, and the crude incidence was 0.56 and 0.09 events per 1000 person-years for participants with and without AF, respectively. Individuals with AF had a 3.0-fold higher risk in a multivariate model adjusted for age, sex, lifestyle, anthropometric data, and medical comorbidities (adjusted hazard ratio 2.96, 95% confidence interval 1.99–4.41, P < 0.001). Both incident and prevalent AFs were associated with an increased risk of SCA, with no significant differences between the two groups. Atrial fibrillation was associated with a significantly higher risk of SCA developing in healthy young adults. Whether the rate or rhythm control influences the risk of SCA in young patients with AF remains to be examined. AF, atrial fibrillation; CI, confidence interval; SCA, sudden cardiac arrest.

Background: Metabolic syndrome is associated with a significantly increased risk of sudden cardiac death (SCD). However, whether temporal changes in the metabolic syndrome status are associated with SCD is unknown. We aimed to determine whether metabolic syndrome and gamma-glutamyl transferase (ɣ-GTP), including their temporal changes, are associated with the risk of SCD. Methods: We performed a nationwide population-based analysis using the Korean National Health Insurance Service. People who underwent a national health check-up in 2009 and 2011 were enrolled. The influence of metabolic syndrome and ɣ-GTP on SCD risk was evaluated. Results: In 2009, 4,056,423 (848,498 with metabolic syndrome) people underwent health screenings, 2,706,788 of whom underwent follow-up health screenings in 2011. Metabolic syndrome was associated with a 50.7% increased SCD risk (adjusted hazard ratio (aHR) = 1.507; p < 0.001). The SCD risk increased linearly as the metabolic syndrome diagnostic criteria increased. The ɣ-GTP significantly impacted the SCD risk; the highest quartile had a 51.9% increased risk versus the lowest quartile (aHR = 1.519; p < 0.001). A temporal change in the metabolic syndrome status and ɣ-GTP between 2009 and 2011 was significantly correlated with the SCD risk. Having metabolic syndrome in 2009 or 2011 indicated a lower SCD risk than having metabolic syndrome in 2009 and 2011 but a higher risk than having no metabolic syndrome. People with a ≥20-unit increase in ɣ-GTP between 2009 and 2011 had an 81.0% increased SCD risk versus those with a change ≤5 units (aHR = 1.810; p < 0.001). Conclusions: Metabolic syndrome and ɣ-GTP significantly correlated with an increased SCD risk. SCD was also influenced by temporal changes in the metabolic syndrome status and ɣ-GTP, suggesting that appropriate medical treatment and lifestyle modifications may reduce future SCD risk.

Aims: Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence–based algorithms in predicting LV systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR). Methods and results: This study is an external validation of a pre-existing deep learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF ≤ 40%, assessed using 12-lead electrocardiography (ECG). Secondary outcome involved predicting LVSD using 1-lead ECG (Lead I). Among 423 patients, 241 with available echocardiography data within 2 months were evaluated, of whom 54 (22.4%) were confirmed to have LVSD. Deep learning algorithm demonstrated fair performance in predicting LVSD [area under the curve (AUC) 0.78]. Negative predictive value for excluding LVSD was 0.88. Deep learning algorithm resulted competent performance in predicting LVSD compared with N-terminal prohormone of brain natriuretic peptide (AUC 0.78 vs. 0.70, P = 0.12). Predictive performance of the deep learning algorithm was lower in Lead I (AUC 0.68); however, negative predictive value remained consistent (0.88). Conclusion: Deep learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence–based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR.

Although obesity is a well-established risk factor of cardiovascular event, the linkage between obesity and sudden cardiac arrest (SCA) is not fully understood. Based on a nationwide health insurance database, this study investigated the impact of body weight status, measured by body-mass index (BMI) and waist circumference, on the SCA risk. A total of 4,234,341 participants who underwent medical check-ups in 2009 were included, and the influence of risk factors (age, sex, social habits, and metabolic disorders) was analyzed. For 33,345,378 person-years follow-up, SCA occurred in 16,352 cases. The BMI resulted in a J-shaped association with SCA risk, in which the obese group (BMI ≥ 30) had a 20.8% increased risk of SCA compared with the normal body weight group (18.5 ≤ BMI < 23.0) (p < 0.001). Waist circumference showed a linear association with the risk of SCA, with a 2.69-fold increased risk of SCA in the highest waist circumference group compared with the lowest waist circumference group (p < 0.001). However, after adjustment of risk factors, neither BMI nor waist circumference was associated with the SCA risk. In conclusion, obesity is not independently associated with SCA risk based on the consideration of various confounders. Rather than confining the findings to obesity itself, comprehensive consideration of metabolic disorders as well as demographics and social habits might provide better understanding and prevention of SCA.

Background: Diabetes mellitus (DM) can cause various atherosclerotic cardiovascular disease including sudden cardiac death (SCD). The impact of being underweight on the risk of SCD in people with DM remains to be revealed. We aimed to evaluate the risk of SCD according to body-mass index (BMI; kg/m²) level in DM population. Methods: We used a nationwide healthcare insurance database to conduct this study. We identified people with DM among those who underwent nationwide health screening during 2009 to 2012. Medical follow-up data was available until December 2018. Results: A total of 2,602,577 people with DM with a 17,851,797 person*year follow-up were analyzed. The underweight group (BMI < 18.5) showed 2.4-fold increased risk of SCD during follow-up (adjusted-hazard ratio [HR] = 2.40; 95% confidence interval [CI] = 2.26–2.56; p < 0.001). When normal-BMI group (18.5 ≤ BMI < 23) was set as a reference, underweight group (adjusted-HR = 2.01; 95% CI = 1.88–2.14) showed even higher risk of SCD compared with the obesity group (BMI ≥ 30; adjusted-HR = 0.89; 95% CI = 0.84–0.94). When BMI was stratified by one unit, BMI and SCD risk showed a U-curve association with the highest risk observed at low BMI levels. The lowest risk was observed in 27 ≤ BMI < 28 group. The association between being underweight and increased SCD risk in DM people was maintained throughout various subgroups. Conclusions: Being underweight is significantly associated with an increased risk of SCD in the DM population. A steep rise in the risk of SCD was observed as the BMI level decreased below 23. The lowest risk of SCD was observed in 27 ≤ BMI < 28 group.

Background: Clinical outcome of ischemic cardiogenic shock (CS) requiring extracorporeal membrane oxygenation is highly variable, necessitating appropriate assessment of prognosis. However, a systemic predictive model estimating the mortality of refractory ischemic CS is lacking. The PRECISE (Prediction of In‐Hospital Mortality for Patients With Refractory Ischemic Cardiogenic Shock Requiring Veno‐Arterial Extracorporeal Membrane Oxygenation Support) score was developed to predict the prognosis of refractory ischemic CS due to acute myocardial infarction. Methods and Results: Data were obtained from the multicenter CS registry RESCUE (Retrospective and Prospective Observational Study to Investigate Clinical Outcomes and Efficacy of Left Ventricular Assist Device for Korean Patients With Cardiogenic Shock) that consists of 322 patients with acute myocardial infarction complicated by refractory ischemic CS requiring extracorporeal membrane oxygenation support. Fifteen parameters were selected to assess in‐hospital mortality. The developed model was validated internally and externally using an independent external cohort (n=138). Among 322 patients, 138 (42.9%) survived postdischarge. Fifteen predictors were included for model development: age, diastolic blood pressure, hypertension, chronic kidney disease, peak lactic acid, serum creatinine, lowest left ventricular ejection fraction, vasoactive inotropic score, shock to extracorporeal membrane oxygenation insertion time, extracorporeal cardiopulmonary resuscitation, use of intra‐aortic balloon pump, continuous renal replacement therapy, mechanical ventilator, successful coronary revascularization, and staged percutaneous coronary intervention. The PRECISE score yielded a high area under the receiver‐operating characteristic curve (0.894 [95% CI, 0.860–0.927]). External validation and calibration resulted in competent sensitivity (area under the receiver‐operating characteristic curve, 0.895 [95% CI, 0.853–0.930]). Conclusions: The PRECISE score demonstrated high predictive performance and directly translates into the expected in‐hospital mortality rate. The PRECISE score may be used to support clinical decision‐making in ischemic CS ( www.theprecisescore.com ). Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02985008.