AI & Machine Learning in Heart Failure Management

Contemporary Applications of AI and Machine Learning for Heart Failure Management

Heart failure (HF) is still a major problem in cardiovascular medicine, leading to high morbidity, frequent hospitalizations, and a growing economic burden. Precision medicine and artificial intelligence (AI) advancements offer new ways to understand, diagnose, and manage HF, especially in early stages.

This issue of Frontiers in Cardiovascular Medicine includes four original studies about genetic biomarkers and predictive AI tools. They show how machine learning and artificial intelligence are being used to manage heart failure, which could help personalize and improve cardiovascular care.

Early Markers for Acute Decompensated Heart Failure

One study used bioinformatics and machine learning to find four genes—SMOC2, OGN, FCN3, and SERPINA3—that could be early markers for acute decompensated heart failure (ADHF) caused by venous congestion. These genes performed well in diagnostics and could help detect worsening HF earlier.

As more research is done, highly accurate biomarkers will become essential for managing heart failure, which affects over 64 million people worldwide and is linked to high rates of hospitalization and mortality. This could reduce hospitalizations, improve patients' social, psychological, and clinical condition, lower healthcare costs, and improve patients' quality of life.

Gene Expression Profiles and Heart Failure Subtypes

Another study identified four candidate genes (FCN3, FREM1, MNS1, and SMOC2) for diagnosing HF and investigated how gene expression profiles divide HF patients into subtypes. One group (C3) had an immune-related gene signature. These genes are also connected to cancer development, suggesting shared pathways between heart failure and tumor biology. This connection between cardiology and oncology could lead to new research in immunology and precision medicine.

This was shown using machine learning and lab methods like qPCR and Western blot. SDSL's overexpression led to increased apoptosis, while silencing it reduced cellular damage. These results highlight a novel gene with strong potential as a therapeutic target-not just a diagnostic marker.

AI Model for Predicting Hospitalizations

Another study developed an AI model to remotely monitor patients and predict HF-related hospitalizations within seven days. Using data from the TIM-HF2 clinical trial, the model outperformed conventional systems. The system could have caught 95% of upcoming hospitalizations by monitoring only the top one-third of patients with the highest predicted risk. This shows how AI can improve care and focus clinical attention where it's most needed.

These studies use advanced analytics to make heart failure care more proactive, precise, and efficient. These approaches align with the future of cardiovascular medicine by identifying early molecular signals, characterizing patient subtypes, and predicting hospitalization risk.

There are still challenges in translating these tools into clinical workflows, training healthcare teams, and ensuring access for all patients. Without equity, technological progress could worsen healthcare disparities.

This collection of research combines science with innovation to solve real-world problems. HF remains a major challenge, but the tools to address it are emerging.