Predicting atrial fibrillation episodes with rapid ventricular rates associated with low levels of activity

Abstract Background Rapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity. Methods This study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity. Results Among the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67–0.78. Conclusion The DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.http://deepblue.lib.umich.edu/bitstream/2027.42/173608/1/12911_2021_Article_1723.pd

Interleukin-1 blockade in cardiac sarcoidosis: study design of the multimodality assessment of granulomas in cardiac sarcoidosis: Anakinra Randomized Trial (MAGiC-ART)

Abstract Background Sarcoidosis is an inflammatory disease characterized by the formation of granulomas, which involve the heart in up to 25% of patients. Cardiac sarcoidosis can lead to life threatening arrhythmias and heart failure. While corticosteroids have been used as a treatment for over 50 years, they are associated with hypertension, diabetes, and weight gain, further increasing cardiovascular risk. Interleukin-1 (IL-1) is the prototypical proinflammatory cytokine that works to activate the nuclear transcription factor NF-kB, one of the targets of glucocorticoids. IL-1 also plays an important role also in the pathophysiology of heart disease including atherosclerosis, myocardial infarction, and myocarditis. Methods Building on a network of research collaborators developed in the Cardiac Sarcoidosis Consortium, we will investigate the feasibility and tolerability of treatment of CS with anakinra at two National Institute of Health Clinical and Translational Science Award (CTSA) hubs with expertise in cardiac sarcoidosis. In this pilot study, up to 28 patients with cardiac sarcoidosis will be recruited to compare the administration of an IL-1 blocker, anakinra, 100 mg daily on top of standard of care versus standard of care only for 28 days and followed for 180 days. Utilizing surrogate endpoints of changes in systemic inflammatory biomarkers and cardiac imaging, we aim to determine whether IL-1 blockade with anakinra can combat systemic and cardiac inflammation in patients with cardiac sarcoidosis. Discussion The current trial demonstrates an innovative collaborative approach to clinical trial development in a rare, understudied disease that disproportionately affects females and minorities. Trial Registration The trial was registered prospectively with ClinicalTrials.gov on July 12, 2019, identifier NCT04017936.http://deepblue.lib.umich.edu/bitstream/2027.42/173742/1/12967_2021_Article_3130.pd