Precision Cardiovascular Medicine

Future Strategies to Prevent Heart Failure

by Prateek Chopra | May 22, 2026 | Cardiology Conferences | ESC-HF 2026 Heart failure (HF) prevention is increasingly focused on early intervention targeting cardiometabolic, inflammatory, renal, and neurohormonal pathways before the development of symptomatic disease. Emerging evidence suggests that integrated preventive strategies can substantially reduce incident HF, cardiovascular (CV) events, and progression of chronic kidney disease (CKD). The findings were […]

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Personalised Prevention for Patients with Dyslipidaemia, Hypertension, and Elevated hs-CRP: The Key to Optimal Cardiovascular Care

by Prateek Chopra | June 16, 2026 | Cardiology Conferences | ESH 2026 Background and Rationale: Cardiovascular disease (CVD) prevention is increasingly shifting from a population-based model to a personalized approach that integrates genetic risk, traditional risk factors, and inflammatory biomarkers. Precision medicine can enhance prevention strategies in patients with dyslipidemia, hypertension, and elevated inflammatory burden. Genetic Risk Assessment: Hemodynamic-Guided Hypertension

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Integrated Polygenic Scores: Are They Ready for Clinical Practice?

by Prateek Chopra | June 7, 2026 | Cardiology Conferences | ESH 2026 Precision Cardiovascular Prevention: Integrated polygenic risk scores (PRS) enhance risk stratification beyond traditional risk factors. PRS predicts coronary artery disease (CAD) 2–4 times more strongly than conventional risk factors and identifies high-risk individuals not captured by family history or standard clinical assessments. High-Risk Populations: Evidence from Clinical Trials:

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AI in Cardiovascular Medicine – Moving Beyond the Hype

by Prateek Chopra | June 6, 2026 | Cardiology Conferences | ESH 2026 Transforming Cardiovascular Care: AI is reshaping risk prediction, diagnostics, imaging, and personalized cardiovascular management. Machine-learning models integrating EHRs, imaging, genomics, proteomics, biomarkers, and wearable-device data outperform traditional risk scores. Large-scale datasets (>100,000 individuals) improve risk stratification across diverse populations, while plasma proteomic profiling enhances prediction of cardiovascular events

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