Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence-powered analysis of 12-lead intake electrocardiogram.

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.

Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning.

Objective: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. We aimed to derive HFpEF phenotype-based groups ('phenogroups') based on clinical and echocardiogram data using machine learning, and to compare clinical characteristics, proteomics and outcomes across the phenogroups.

Methods: We applied model-based clustering to 32 echocardiogram and 11 clinical and laboratory variables collected in stable condition from 320 HFpEF outpatients in the Karolinska-Rennes cohort study (56% female, median 78 years (IQR: 71-83)).

Sequential biventricular pacing: evaluation of safety and efficacy.

The study evaluated the clinical safety, performance, and efficacy of sequential biventricular pacing in the InSync III (Model 8042) biventricular stimulator in a multicenter, prospective 3-month study and assessed the proper functioning of features aiming at improving biventricular AV therapy delivery. The system was successfully implanted in 189 (95.9%) of 198 patients with symptomatic systolic heart failure and a prolonged QRS complex duration.