Improving Diagnostic Accuracy for Acute Heart Failure

项目摘要/摘要 急性心力衰竭(HF)是一种高致命性、致命性和昂贵的疾病。考虑到它的症状，很难做出诊断 体征与其他心脏和非心脏疾病重叠。在急诊科(ED)， 急性心力衰竭的误诊很常见，并与不良结局相关。生物标记物检测可以促进 准确的诊断；然而，利钠肽(NP)是唯一推荐的心衰生物标志物。 诊断性测试，对排除急性心力衰竭比排除急性心力衰竭更好。即使使用NP测试，在 当代临床实践中，急性心力衰竭的误诊仍然发生在10%到45%的患者出现在 埃德呼吸困难。包括多个生物标志物的临床预测模型有望改进 诊断准确率。为数不多的用于诊断急性心力衰竭的多生物标志物研究 受限于来自已知生物途径的高度相关的标记，相对较小的样本 大小，缺乏将所有先验选择的生物标志物纳入单一模型，以及缺乏验证 一群人。我们的研究设计解决了这些限制。“组学”的最新进展使新的生物标志物成为可能 更大规模的发现和调查不那么“偏颇”于现有知识。因此，我们最重要的是 假说是一种结合了新发现的蛋白质的多标记模型，血浆蛋白质组学的改进 对急性心力衰竭的诊断准确率。在前期工作中，我们利用血浆进行了概念验证研究 蛋白质组学将发现一个由21个生物标记物组成的多标记物小组，该小组提高了急性胰腺炎的诊断准确性 使用临床数据和NP水平，心力衰竭超出了目前的临床实践。我们充满希望的初步数据激励着 在更大的样本量中更广泛的发现，以及随后的多标记模型的推导和验证 在足够大的独立样本中诊断急性心力衰竭。我们的具体目标是：1)扩大 发现队列并完善21个生物标记物的多标记物小组，以提高对急性胰腺炎的诊断准确性 心力衰竭，2)建立包含21个生物标志物的急性心力衰竭诊断模型，3)测试性能 前瞻性验证队列中的多标记模型，以及4)评估多标记模型的增量价值 诊断急性心力衰竭的标志物模型。在目标1中，来自989名患者的现有血浆样本将用于 检测925蛋白以发现一组与判决的最强相关的新生物标记物 急性心力衰竭诊断。在目标2中，我们将利用现有的预期观测队列，EMROC-AHF，以 建立1000例急诊患者急性呼吸困难的多标志物模型。在目标3中，来自 密歇根州底特律和田纳西州纳什维尔的四名急诊室我们将前瞻性地招募1,000名患者的新样本 出现急性呼吸困难，并通过心脏病专家小组的审查来判定是否存在急性心力衰竭。在目标4中， 我们将比较我们的多标记物模型和目前诊断急性心力衰竭的临床方法。 目标2和目标3的队列。考虑到心力衰竭的负担，不准确诊断的频率及其不良反应 我们将通过提高对急性心力衰竭的诊断准确率来解决一个尚未得到满足的重大需求。

PROJECT SUMMARY/ABSTRACT Acute heart failure (HF) is highly morbid, lethal, and costly. It is a difficult diagnosis to make given its symptoms and signs overlap with other cardiac and non-cardiac conditions. In the emergency department (ED), misdiagnosis of acute HF is common and associated with adverse outcomes. Biomarker testing can facilitate accurate diagnosis; however, natriuretic peptides (NP) are the only guideline recommend biomarker of HF for diagnostic testing, and are better for ruling-out, rather than ruling-in, acute HF. Even with NP testing, in contemporary clinical practice misdiagnosis of acute HF still occurs in 10 to 45% of patients presenting to the ED with dyspnea. Clinical prediction models including multiple biomarkers hold promise for improving diagnostic accuracy. The few prior studies investigating a multiple biomarker approach for diagnosing acute HF were limited by constraint to highly correlated markers from known biologic pathways, relatively small sample sizes, lack of inclusion of all a priori selected biomarkers into a single model, and absence of validation cohorts. Our study design addresses these limitations. Recent advances in “omics” enable novel biomarker discovery on a larger scale and investigations less “biased” by existing knowledge. Thus, our overarching hypothesis is a multi-marker model incorporating novel proteins discovered with plasma proteomics improves diagnostic accuracy for acute HF. In preliminary work, we performed a proof of concept study utilizing plasma proteomics to discover a multi-marker panel of 21 biomarkers which improved diagnostic accuracy for acute HF beyond current clinical practice using clinical data and NP levels. Our promising preliminary data motivate broader discovery in a larger sample size with subsequent derivation and validation of a multi-marker model for diagnosing acute HF in independent samples of adequate size. Our specific aims are to: 1) expand the discovery cohort and refine the multi-marker panel of 21 biomarkers to improve diagnostic accuracy for acute HF, 2) derive a model for diagnosing acute HF incorporating the 21-biomarker panel, 3) test performance of the multi-marker model in a prospective validation cohort, and 4) assess the incremental value of the multi- marker model for diagnosing acute HF. In aim 1, existing plasma samples from 989 patients will be used to assay 925 proteins to discover a smaller set of novel biomarkers most strongly associated with an adjudicated acute HF diagnosis. In aim 2, we will utilize an existing prospective observational cohort, EMROC-AHF, to derive the multi-marker model in 1,000 patients who presented to the ED with acute dyspnea. In aim 3, from four EDs in Detroit, MI and Nashville, TN we will prospectively recruit a new sample of 1,000 patients presenting with acute dyspnea and adjudicate the presence of acute HF by cardiologist panel review. In aim 4, we will compare our multi-marker model against the current clinical approach for diagnosing acute HF using the cohorts for Aims 2 and 3. Given the burden of HF, the frequency of inaccurate diagnosis and its adverse consequences, we will address a significant unmet need by improving diagnostic accuracy for acute HF.