Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning.

There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching >85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain when it comes to multi-center deployment. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution is fine-tuned to be utilized to classify ECGs at another institution.

Short-term outcomes after sodium-glucose cotransporter-2 inhibitor initiation in a cohort of heart failure patients.

Aims: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) decrease mortality and risk of hospitalization in patients with heart failure with reduced ejection fraction (HFrEF). SGLT2i have a natriuretic effect shortly after initiation, followed by a lasting osmotic diuretic effect. We sought to evaluate rates of acute kidney injury (AKI) and therapy discontinuation with SGLT2i initiation in a real-world cohort of HFrEF patients.