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Background: Atrioventricular nodal re-entrant tachycardia (AVNRT) is the most common paroxysmal supraventricular tachycardia. We sought to investigate the incidence of atrial fibrillation in patients with electrophysiologically confirmed/ablated AVNRT and its association with transient ischemic attack (TIA)/stroke as well as mortality during long-term follow-up. Methods: From the Karolinska Ablation Registry, 2855 consecutive patients with a first-time ablation for AVNRT between 2005 and 2018 were analyzed. Results: Patients were 52.1 ± 15.9 years old and 59.3% were women. During follow-up of up to 10 years (median 6.0 years; interquartile range 3.3 to 9.2 years), new onset or recurrence of atrial fibrillation occurred in 317 (11.1%) patients (incidence rate 19 cases per 1000 person-years). Excluding those with history of atrial fibrillation, new onset of atrial fibrillation occurred in 153 (6.1%) patients. In multivariable analysis, history of atrial fibrillation, arterial hypertension, history of TIA/stroke, and heart failure remained independently associated with new onset or recurrence of atrial fibrillation during follow-up. Death of any cause and TIA/stroke occurred in 141 (4.9%) patients and 107 (3.7%) patients, respectively. In multivariable analysis, occurrence of atrial fibrillation during follow-up remained independently associated with both outcomes. The prevalence of atrial fibrillation according to age at the end of follow-up was high among young patients (

Background: The aim of this study was to evaluate the implementation of a novel extra corporeal cardiopulmonary (ECPR) program in the greater Stockholm area with focus on feasibility, safety aspects and clinical outcomes. Methods: Prospective observational study of ECPR program including patients with OHCA from January 2020 to December 2022, fulfilling ECPR criteria: age 18–65 years, initial shockable rhythm or pulseless electrical activity, witnessed arrest, bystander cardiopulmonary resuscitation and refractory arrest after three cycles of advance cardiac life support. The predefined time threshold from collapse to extracorporeal membrane oxygenation (ECMO) initiation was set at 60 min. Results: We included 95 patients. Of these, 22/95 (23%) had return of spontaneous circulation before ECMO initiation, 39/95 (41%) were excluded for ECMO and 34/95 (36%) had ECMO initiated out of which 23 patients were admitted alive to the ICU. ECMO-initiation within 60 min was met in 9%. In 6 patients vascular access was complicated, 2 patients had severe bleeding at access site requiring intervention. Survival to discharge among all cases was 25% (24/95). Among patients admitted to ICU on ECMO 39% (9/23) survived to discharge, of these 78% had cerebral performance category scale score 1–2 within 12 months. 8 out of 9 survivors had time from OHCA to ECMO-initiation >60 min. Conclusion: The implementation of an ECPR protocol was feasible without any major, unexpected safety aspects but did not meet the intended target time intervals. Despite this, survival rates were similar to previous studies although most survivors had >60 min to ECMO-initiation.

Background: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. Objective: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). Methods: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. Results: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. Conclusion: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients’ risk of mortality or MACE. Our models’ accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.