Palaparthi Elizabeth Caroline, Padala Tanvi, Singamaneni Reva, Manaswini Rachakatla, Kantula Abhigna, Aditya Reddy Palle, Chandini Punuri, Sathwika Eliana Addanki, Siri Samhita Papasani, Patnaik Prashanth Kumar
Department of Internal Medicine, Shasta Regional Medical Center, California, USA.
Department of Pharmacology, RVM Institute of Medical Sciences and Research Center, Laxmakkapally, IND.
Cureus. 2025 Apr 1;17(4):e81573. doi: 10.7759/cureus.81573. eCollection 2025 Apr.
Heart failure (HF) is a complex clinical syndrome characterized by the heart's inability to meet the body's metabolic demands. HF remains a global health challenge with high morbidity and mortality. Outcomes of beta-blockers, angiotensin receptor-neprilysin inhibitors (ARNIs), and mineralocorticoid receptor antagonists (MRAs) in HF remain suboptimal. HF is a heterogeneous syndrome driven by neurohormonal dysregulation, fibrosis, metabolic disturbances, and inflammation, contributing to symptoms like dyspnea, fatigue, and fluid retention. Recent advances in pharmacological therapies, including sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors), soluble guanylate cyclase stimulators (sGC stimulators), and cardiac myosin activators, have shown promise in HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), offering mechanism-specific interventions. Moreover, molecular-targeted therapies, such as clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) gene editing, RNA-based therapeutics, and adeno-associated virus serotype 9-sarcoplasmic reticulum calcium ATPase 2a (AAV9-SERCA2a gene) therapy, are emerging as potential disease-modifying treatments aimed at addressing genetic and inflammatory drivers of cardiomyopathies. Artificial intelligence (AI) is transforming HF care by enhancing predictive modelling, risk stratification, and precision medicine, with applications in multi-omics data integration. AI-driven tools, including machine learning (ML) algorithms, improve echocardiographic phenotyping, optimize treatment strategies, and refine patient selection for therapies. Despite these promising developments, challenges such as data quality, standardization, scalability, and regulatory barriers remain. Furthermore, gene therapies' long-term safety and efficacy are still uncertain, with concerns about immune responses, off-target effects, and sustained gene expression. Regenerative medicine strategies, including induced pluripotent stem cells (iPSC)-derived cardiomyocytes, extracellular vesicles (EVs), and 3D-bioprinted cardiac patches, offer potential solutions for myocardial repair. However, immune rejection, graft integration, and long-term viability remain significant obstacles. Additionally, high costs associated with novel biologics and gene-based therapies limit accessibility, particularly in low-resource settings. The future of HF management depends on overcoming these translational challenges. Key steps include validating AI-driven phenotyping tools in clinical trials, advancing scalable biomanufacturing technologies, and refining regulatory frameworks to facilitate clinical integration. By addressing these barriers, precision medicine, AI, and regenerative therapies can transform HF management, providing more personalized, effective, and accessible treatments and ultimately improving patient outcomes globally.
心力衰竭(HF)是一种复杂的临床综合征,其特征是心脏无法满足身体的代谢需求。HF仍然是一个全球性的健康挑战,发病率和死亡率都很高。β受体阻滞剂、血管紧张素受体脑啡肽酶抑制剂(ARNI)和盐皮质激素受体拮抗剂(MRA)在HF治疗中的效果仍不尽人意。HF是一种异质性综合征,由神经激素失调、纤维化、代谢紊乱和炎症驱动,导致呼吸困难、疲劳和液体潴留等症状。包括钠-葡萄糖协同转运蛋白2抑制剂(SGLT2抑制剂)、可溶性鸟苷酸环化酶刺激剂(sGC刺激剂)和心肌肌球蛋白激活剂在内的药物治疗的最新进展,已在射血分数降低的心力衰竭(HFrEF)和射血分数保留的心力衰竭(HFpEF)治疗中显示出前景,提供了针对特定机制的干预措施。此外,分子靶向治疗,如成簇规律间隔短回文重复序列(CRISPR)相关蛋白9(Cas9)基因编辑、基于RNA的治疗和9型腺相关病毒-肌浆网钙ATP酶2a(AAV9-SERCA2a基因)治疗,正作为潜在的疾病修饰治疗方法出现,旨在解决心肌病的遗传和炎症驱动因素。人工智能(AI)正在通过增强预测模型、风险分层和精准医学来改变HF治疗,应用于多组学数据整合。包括机器学习(ML)算法在内的AI驱动工具可改善超声心动图表型分析、优化治疗策略并完善治疗的患者选择。尽管有这些有前景的进展,但数据质量、标准化、可扩展性和监管障碍等挑战仍然存在。此外,基因治疗的长期安全性和有效性仍不确定,人们担心免疫反应、脱靶效应和持续的基因表达。再生医学策略,包括诱导多能干细胞(iPSC)衍生的心肌细胞、细胞外囊泡(EV)和3D生物打印心脏补片,为心肌修复提供了潜在的解决方案。然而,免疫排斥、移植物整合和长期生存能力仍然是重大障碍。此外,新型生物制剂和基于基因的治疗的高成本限制了可及性,尤其是在资源匮乏的地区。HF管理的未来取决于克服这些转化挑战。关键步骤包括在临床试验中验证AI驱动的表型分析工具、推进可扩展的生物制造技术以及完善监管框架以促进临床整合。通过解决这些障碍,精准医学、AI和再生疗法可以改变HF管理,提供更个性化、有效和可及的治疗方法,并最终改善全球患者的治疗效果。