Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University.
Circ J. 2021 Apr 23;85(5):576-583. doi: 10.1253/circj.CJ-20-1299. Epub 2021 Mar 2.
Clinical risk stratification is a key strategy used to identify low- and high-risk subjects to optimize the management, ranging from pharmacological treatment to palliative care, of patients with heart failure (HF). Using statistical modeling techniques, many HF risk prediction models that combine predictors to assess the risk of specific endpoints, including death or worsening HF, have been developed. However, most risk prediction models have not been well-integrated into the clinical setting because of their inadequacy and diverse predictive performance. To improve the performance of such models, several factors, including optimal sampling and biomarkers, need to be considered when deriving the models; however, given the large heterogeneity of HF, the currently advocated one-size-fits-all approach is not appropriate for every patient. Recent advances in techniques to analyze biological "omics" information could allow for the development of a personalized medicine platform, and there is growing awareness that an integrated approach based on the concept of system biology may be an excessively naïve view of the multiple contributors and complexity of an individual's HF phenotype. This review article describes the progress in risk stratification strategies and perspectives of emerging precision medicine in the field of HF management.
临床风险分层是一种用于识别低风险和高风险患者的关键策略，旨在优化心力衰竭（HF）患者的管理，从药物治疗到姑息治疗不等。使用统计建模技术，已经开发出许多 HF 风险预测模型，这些模型结合了预测因子来评估特定终点（包括死亡或 HF 恶化）的风险。然而，由于其不充分性和不同的预测性能，大多数风险预测模型尚未很好地融入临床环境。为了提高此类模型的性能，在推导模型时需要考虑包括最佳采样和生物标志物在内的几个因素；然而，鉴于 HF 的巨大异质性，目前提倡的一刀切的方法并不适合每个患者。分析生物“组学”信息的技术的最新进展可以为个性化医疗平台的开发提供可能，人们越来越意识到，基于系统生物学概念的综合方法可能是对个体 HF 表型的多个贡献者和复杂性的过于天真的看法。本文综述了 HF 管理领域中风险分层策略和新兴精准医学的进展。
