PDF(Pubmed)
Abstract：
Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.
摘要：
改善心源性休克（CS）结局的进展可能因未能涵盖驱动潜在综合征的病理生理过程的异质性而受到限制。为了更好地理解CS群体固有的变异性，描述潜在CS疾病亚表型的最新算法已得到描述和验证.这些策略希望确定对标准疗法有更有利反应的特定患者亚组。以及那些需要新治疗方法的人。本文是由两部分组成的最新评论的第二部分。在第二篇文章中，我们提出了基于机器学习的统计方法来识别亚表型，并讨论了它们的优势和局限性，以及其他危重病综合征和CS新兴应用的证据。然后，我们讨论如何在CS临床试验中考虑分期和分层，最后考虑这一新兴研究领域的未来方向。
