July 14, 2026International Journal of Angiology0 citations
Recent Advances in the Management of Heart Failure, Including the Role of AI and Advances in Gene Therapy
Key Result
Recent advances in heart failure management, including novel pharmacotherapies, transcatheter interventions, AI, and gene therapies, expand phenotype-directed options but face implementation barriers.
Key Points
- This research aims to synthesize recent advances in heart failure (HF) management, specifically focusing on artificial intelligence (AI) and gene therapy.
- Systematic review of original research from 2020 to January 2026 using PubMed, Embase, Web of Science, and Google Scholar.
- Focused on recent advances in heart failure management including pharmacotherapies and intervention techniques.
- Explored the emerging roles of AI and gene therapies in the treatment of HF.
- Guideline-directed management and therapy (GDMT) improve mortality and hospitalization in patients with heart failure with reduced ejection fraction (HFrEF), but barriers remain such as noncompliance and delayed initiation.
- Transcatheter mitral interventions significantly improve symptoms and quality of life for selected patients despite optimized GDMT.
- AI tools and gene therapies display promising results needing further studies for validation.
Study Design
Type
Systematic Review
Structured PICO
P
Population
Patients with heart failure, including HFrEF, obesity-related HFpEF, and advanced HF.
I
Intervention
Recent advances in heart failure management, including guideline-directed medical therapy (GDMT), finerenone, SGLT2 inhibitors, GLP-1 receptor agonists, transcatheter mitral/tricuspid repair/replacement, AI tools, and gene therapies.
Recent advances in heart failure management have expanded phenotype-directed therapies and transcatheter interventions, though emerging technologies like AI and gene therapy still require large-scale validation.
Limitations
- AI tools and gene therapies require further large-scale studies for validation and clinical adoption
- Clinical adoption is limited by optimal implementation and access barriers
Abstract
Abstract Heart failure (HF) remains a major cause of morbidity and mortality despite significant advances in its management. This study aimed to synthesize recent advances in HF management, including the role of artificial intelligence (AI) and advances in gene therapy. PubMed, Embase, Web of Science, and Google Scholar were searched for original research (2020–January 2026) on recent advances in HF management, including AI and gene-based therapies. While guideline-directed management and therapy (GDMT) substantially reduce mortality and hospitalization in heart failure with reduced ejection fraction (HFrEF), utilization of such therapies is frequently constrained by delayed initiation, incomplete titration, noncompliance, and cost. Beyond foundational therapy, finerenone, sodium glucose cotransporter-2 inhibitors (SGLT2i) across the ejection fraction (EF) spectrum, and glucagon-like peptide-1 receptor agonist (GLP-1 RA) for obesity-related heart failure with preserved ejection fraction (HFpEF) extend phenotype-directed options. Transcatheter mitral and tricuspid repair/replacement has shifted mitral and tricuspid regurgitation from a bystander lesion to a treatable contributor to improvement of symptoms and quality of life. Adjunctive transcatheter mitral interventions have expanded treatment options for selected symptomatic HFrEF patients with secondary mitral regurgitation (MR) despite optimized GDMT, improving MR severity, functional status, and reducing HF hospitalizations. Donor-derived cell-free DNA rejection surveillance, donation after circulatory death to expand donor supply, and refinement of left ventricular assist device (LVAD) antithrombotic strategies are evolving in advanced HF. AI tools and gene therapies are showing promising results but require further large-scale studies for validation and clinical adoption. Contemporary HF management is increasingly phenotype- and trajectory-guided, but its clinical adoption is limited by optimal implementation and access barriers. Future research should focus on outcomes-based validation with a clear patient-selection framework to enable durable real-world implementation to improve outcomes further.