Risk stratification in acute heart failure: rationale and design of the STRATIFY and DECIDE studies.
Risk stratification in acute heart failure: rationale and design of the STRATIFY and DECIDE studies.
复制标题
急性心力衰竭的风险分层：分层和决策研究的基本原理和设计。
DOI：
10.1016/j.ahj.2012.07.033
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发表时间：
2012-12
影响因子：
4.8
通讯作者：
Storrow, Alan B.
中科院分区：
文献类型：
--
作者：
Collins, Sean P.;Lindsell, Christopher J.;Jenkins, Cathy A.;Harrell, Frank E.;Fermann, Gregory J.;Miller, Karen F.;Roll, Sue N.;Sperling, Matthew I.;Maron, David J.;Naftilan, Allen J.;McPherson, John A.;Weintraub, Neal L.;Sawyer, Douglas B.;Storrow, Alan B.
医生面对急性心力衰竭（AHF）体征和症状的患者时面临的一个关键挑战是如何以及在何处最好地管理它们。目前，大多数接受AHF评估的患者都被送进医院，但并非所有患者都需要住院治疗。高达50%的住院可能会避免，许多住院患者可以在短期观察和治疗后出院。缺乏识别可以尽早回家的病人的方法。提高医生识别和安全管理低风险患者的能力对于避免不必要地使用医院病床至关重要。两项研究（STRATIFY和DECIDE）由国家心肺和血液研究所资助，目的是制定预测规则，以促进AHF的早期决策。使用从急性环境（STRATIFY）和早期住院（DECIDE）前瞻性收集的评估和治疗数据，将生成预测死亡和严重并发症风险的规则。随后的研究将被设计为测试这些预测规则在不同的急性护理环境中的外部有效性，实用性，普遍性和成本效益，代表种族和社会经济上不同的患者人群。一个主要的创新是预测5天以及30天的结果，克服了30天的结果高度依赖于不可预测的，访视后的患者和提供者的行为的限制。该项目的一个新方面是使用全面的心脏病学审查，以正确分配急性表现的治疗后结果。最后，一个严格的分析计划已经制定，以构建预测规则，将最大限度地提取每个数据元素的统计和临床属性。本研究完成后，我们随后将在异质性患者队列中对预测规则进行外部测试。
A critical challenge for physicians facing patients presenting with signs and symptoms of acute heart failure (AHF) is how and where to best manage them. Currently, most patients evaluated for AHF are admitted to the hospital, yet not all warrant inpatient care. Up to 50% of admissions could be potentially avoided and many admitted patients could be discharged after a short period of observation and treatment. Methods for identifying patients that can be sent home early are lacking. Improving the physician’s ability to identify and safely manage low-risk patients is essential to avoiding unnecessary use of hospital beds. Two studies (STRATIFY and DECIDE) have been funded by the National Heart Lung and Blood Institute with the goal of developing prediction rules to facilitate early decision making in AHF. Using prospectively gathered evaluation and treatment data from the acute setting (STRATIFY) and early inpatient stay (DECIDE), rules will be generated to predict risk for death and serious complications. Subsequent studies will be designed to test the external validity, utility, generalizability and cost-effectiveness of these prediction rules in different acute care environments representing racially and socioeconomically diverse patient populations. A major innovation is prediction of 5-day as well as 30-day outcomes, overcoming the limitation that 30-day outcomes are highly dependent on unpredictable, post-visit patient and provider behavior. A novel aspect of the proposed project is the use of a comprehensive cardiology review to correctly assign post-treatment outcomes to the acute presentation. Finally, a rigorous analysis plan has been developed to construct the prediction rules that will maximally extract both the statistical and clinical properties of every data element. Upon completion of this study we will subsequently externally test the prediction rules in a heterogeneous patient cohort.
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