New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation.

To identify radiomic and clinical features associated with post-ablation recurrence of AF, given that cardiac morphologic changes are associated with persistent atrial fibrillation (AF), and initiating triggers of AF often arise from the pulmonary veins which are targeted in ablation. Subjects with pre-ablation contrast CT scans prior to first-time catheter ablation for AF between 2014-2016 were retrospectively identified. A training dataset (D) was constructed from left atrial and pulmonary vein morphometric features extracted from equal numbers of consecutively included subjects with and without AF recurrence determined at 1 year.

A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

Objective: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.

Methods: This retrospective study included 68 AF patients who had pre-catheter ablation cardiac CT scans with contrast. AF recurrence was defined at 1 year, excluding a 3-month post-ablation blanking period.

Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.

[Figure: see text].

Chembio DPP COVID-19 IgM/IgG Serological Testing: Laboratory Validation, Guidelines, and Recommendations.

Background: On January 30, 2020, WHO declared COVID-19 a pandemic. In this article we describe our experience at Richmond University Medical Center with Chembio serological IgM, IgG testing.

Methods: In this prospective cohort study of patients and hospital employees, we utilized Chembio COVID-19 IgM/IgG serological testing in addition to Cepheid RT-PCR analysis.

Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Background: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.

Methods: We analyzed CRT patients from Cleveland Clinic and Johns Hopkins.