Long-term follow-up of the TRED-HF trial: Implications for therapy in patients with dilated cardiomyopathy and heart failure remission.

Aims: In TRED-HF, 40% of patients with recovered dilated cardiomyopathy (DCM) relapsed in the short term after therapy withdrawal. This follow-up investigates the longer-term effects of therapy withdrawal.

Methods And Results: TRED-HF was a randomized trial investigating heart failure therapy withdrawal in patients with recovered DCM over 6 months.

Precision prediction of heart failure events in patients with dilated cardiomyopathy and mildly reduced ejection fraction using multi-parametric cardiovascular magnetic resonance.

Aims: To assess whether left ventricular (LV) global longitudinal strain (GLS), derived from cardiovascular magnetic resonance (CMR), is associated with (i) progressive heart failure (HF), and (ii) sudden cardiac death (SCD) in patients with dilated cardiomyopathy with mildly reduced ejection fraction (DCMmrEF).

Methods And Results: We conducted a prospective observational cohort study of patients with DCM and LV ejection fraction (LVEF) ≥40% assessed by CMR, including feature-tracking to assess LV GLS and late gadolinium enhancement (LGE). Long-term adjudicated follow-up included (i) HF hospitalization, LV assist device implantation or HF death, and (ii) SCD or aborted SCD (aSCD).

Your Heart Function Has Normalized-What Next After TRED-HF?

Purpose Of Review: With the widespread implementation of contemporary disease-modifying heart failure therapy, the rates of normalization of ejection fraction are continuously increasing. The TRED-HF trial confirmed that heart failure remission rather than complete recovery is typical in patients with dilated cardiomyopathy who respond to therapy. The present review outlines key points related to the management and knowledge gaps of this growing patient group, focusing on patients with non-ischaemic dilated cardiomyopathy.

Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition.

The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features.