A new technique inducing mitral valve regurgitation as an experimental porcine model of volume-overload induced heart failure
Year: 2026
Type: article
Abstract: Heart failure (HF) is a common disease resulting in high morbidity, mortality, and healthcare costs. An important cause of HF is mitral valve regurgitation (MR), which induces left ventricular remodeling and volume overload. For HF research, a reproducible and reliable large animal model is crucial. Several MR models have been developed, but they often show high variability in MR severity and MR jet characteristics, as well as an underlying ischemic cardiomyopathy, which may increase the risk of complications during follow-up. We present a straightforward and uniform porcine model of primary MR-induced HF. A custom-made retractor was used to induce severe and uniform MR by chordal rupture. After four weeks, severe MR led to significant left ventricular remodeling compared to the sham group, with a significant increase in left ventricular end-diastolic (+34.5 ± 6.8 ml vs. +8.4 ± 5.3 ml, p = 0.023) and end-systolic volume (+29.9 ± 4.0 ml vs. +4.2 ± 2.9 ml, p = 0.001), a decrease in left ventricular ejection fraction (-11.9 ± 1.9% vs. -0.7 ± 0.9%, p < 0.001) and fractional shortening (-12.2 ± 1.3% vs. -2.2 ± 0.8%, p < 0.001), and a higher amount of left ventricular fibrosis (12.6 ± 1.0% vs. 6.4 ± 0.9%, p = 0.001). This porcine model allows a straightforward and reproducible induction of primary MR-induced HF, with a high success rate. It could be valuable in basic research to study the underlying pathophysiology and in translational research to develop novel diagnostics and therapeutics targeting volume-overload induced HF and/or primary MR.
Source: Scientific Reports
Institutions: University of Antwerp, Antwerp University Hospital
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: Universiteit Antwerpen, Vlaamse regering
Award: G6743352091
OpenAlex ID: W7135397232
