Chang Alex J, Liang Yilin, Girouard Michael P, Bhatt Ankeet S, Sandhu Alexander T, Sauer Andrew J, Greene Stephen J, Harrington Josephine, Go Alan S, Ambrosy Andrew P
Department of Medicine, Kaiser Permanente San Francisco Medical Center, 2425 Geary Boulevard, San Francisco, CA, 94115, USA.
Department of Cardiology, Kaiser Permanente San Francisco Medical Center, 2425 Geary Boulevard, San Francisco, CA, 94115, USA.
Heart Fail Rev. 2025 Jan;30(1):177-189. doi: 10.1007/s10741-024-10454-2. Epub 2024 Oct 23.
Heart failure (HF) poses a major global health challenge with rising prevalence, significant morbidity and mortality, and substantial associated healthcare costs. With aging of the population and an increasing burden of comorbidities, the complex interplay between cardiovascular, kidney, and metabolic risk factors have been thrust into the spotlight and have broadened the traditional focus from HF treatment to an increased emphasis on prevention. In recognition of the evolving HF landscape, the American Heart Association released the PREVENT models which are comprehensive risk assessment tools that estimate 10- and 30-year risk of incident cardiovascular disease and its subtypes, including atherosclerotic cardiovascular disease (ASCVD) and, for the first time, HF. While it is an accurate risk estimation tool and represents a step forward in improving risk stratification for primary prevention of HF, there remain several limitations and unknowns like model performance across disaggregated racial and ethnic groups, the role of traditional ASCVD vs. HF-specific risk factors, HF prediction among those with known ASCVD, and the use of traditional regression techniques in lieu of potentially more powerful machine learning-based modeling approaches. Furthermore, it remains unclear how to optimize risk estimation in clinical care. The emergence of multiple novel pharmacological therapies that prevent incident HF, including sodium-glucose co-transporter 2 (SGLT2) inhibitors, glucagon-like peptide 1 (GLP1) receptor agonists, and nonsteroidal mineralocorticoid receptor antagonists (MRAs), highlights the importance of accurate HF risk prediction. To provide HF prevention with these effective but costly therapies, we must understand the optimal strategy in sequencing and combining these therapies and prioritize patients at highest risk. Such implementation requires both accurate risk stratification and a better understanding of how to communicate risk to patients and providers. This state-of-the-art review aims to provide a comprehensive overview of recent trends in HF prevention, including risk assessment, care management strategies, and emerging and novel treatments.
心力衰竭(HF)是一项重大的全球健康挑战,其患病率不断上升,发病率和死亡率高,且相关医疗费用巨大。随着人口老龄化和合并症负担的增加,心血管、肾脏和代谢风险因素之间复杂的相互作用已成为焦点,并将传统重点从HF治疗扩大到更加强调预防。认识到HF领域的不断演变,美国心脏协会发布了PREVENT模型,这是一种全面的风险评估工具,可估计心血管疾病及其亚型(包括动脉粥样硬化性心血管疾病(ASCVD),首次包括HF)的10年和30年发病风险。虽然它是一种准确的风险估计工具,代表了在改善HF一级预防风险分层方面的一大进步,但仍存在一些局限性和未知因素,如不同种族和族裔群体的模型性能、传统ASCVD与HF特异性风险因素的作用、已知ASCVD患者中的HF预测,以及使用传统回归技术代替可能更强大的基于机器学习的建模方法。此外,尚不清楚如何在临床护理中优化风险估计。多种预防HF的新型药物疗法的出现,包括钠-葡萄糖协同转运蛋白2(SGLT2)抑制剂、胰高血糖素样肽1(GLP1)受体激动剂和非甾体盐皮质激素受体拮抗剂(MRAs),凸显了准确HF风险预测的重要性。为了用这些有效但昂贵的疗法预防HF,我们必须了解这些疗法的最佳排序和联合策略,并对风险最高的患者进行优先排序。这种实施既需要准确的风险分层,也需要更好地理解如何向患者和医疗服务提供者传达风险。这篇综述旨在全面概述HF预防的最新趋势,包括风险评估、护理管理策略以及新兴和新型治疗方法。