Advancing the Phenotyping of Acute Kidney Injury for the Million Veterans Program

急性肾损伤(AKI)是一种复杂而致命的疾病，与进行性肾功能丧失密切相关。 肾功能、心血管疾病、生活质量差和死亡。AKI引起的最严重的形式 肾实质损害，表现为肾脏功能的持续性丧失。这种情况称为内在性 Aki(Iaki)具有最高的死亡率和长期肾功能丧失的风险。由于他们的年龄较大 与退伍军人相比，退伍军人经历Iaki的风险尤其高 普通人口。尽管投资了几十年，但还没有成功的治疗方法从 常规临床实践的临床前研究。后者促使人们呼吁更好地理解 负责定义人类Iaki风险的机制。 一个日益增长的研究领域是了解Iaki易感性的遗传基础。早期研究 受到样本量小、缺乏公正的方法(例如全基因组关联)的限制 (GWA)和预测的基因表达研究，最重要的是，没有 区分AKI的原因。由于Iaki是一种异质性疾病，这一关键缺陷可以冲淡。 大规模研究中的生物信号和治疗效果。最后，在AKI中很少有研究探索 鉴定新的表型或内表型，这些表型已显示出改进的希望 了解其他复杂和异质的情况。这项提案的首要目标是 A)推进最常见和最严重的内在AKI(Iaki)的临床表型，以及b) 利用这些表型来确定与Iaki相关的遗传变异。 在目标1中，我们将数据驱动的深度学习算法应用于密集的结构化数据和叙事文本，以 发现可能代表先前已识别和未识别的混合的数据模式 AKI的内表型。在目标2中，我们将通过生成概率表型来补充这一策略 在3个临床环境中使用手动图表审查来识别传统Iaki表型的算法 常见的疾病有：心血管手术、心导管术和脓毒症。在目标3中，我们将执行一系列 在这些背景下进行的GWA研究将我们在Aim 2中的Iaki表型算法识别的病例与 百万退伍军人计划中没有AKI的患者。我们还将使用 最有希望的目标1数据驱动的内表型。我们将使用以下工具评估排名靠前的相关地区 PrediXcan用于检测肾脏组织中预测的基因表达。 拟议的研究将在退伍军人百万伽玛计划内由 AKI表型鉴定、基于信息学的表型开发人员和遗传学方面的多学科专家团队 流行病学家。这项提案的成果将推进Iaki的计算表型， 扩大Iaki大规模基因-表型研究的严密性和规模，并提供重要的 有关Iaki病临床发病机制的信息。

Acute kidney injury (AKI) is a complex and deadly disease that is strongly associated with progressive loss of kidney function, cardiovascular disease, poor quality of life, and death. The most severe forms of AKI cause parenchymal damage, which manifests as a persistent loss of kidney function. This condition, termed intrinsic AKI (iAKI), carries the highest mortality and risk for long-term loss of kidney function. Due to their older age and high prevalence of risk factors, Veterans are at especially high risk for experiencing iAKI compared to the general population. Despite decades of investment, no successful treatments have been translated from preclinical studies into routine clinical practice. The latter has led to calls for greater understanding of the mechanisms responsible for defining risk in human iAKI. A growing area of investigation is understanding the genetic basis for susceptibility to iAKI. Early studies have been limited by small sample sizes, a lack of unbiased approaches (e.g. genome wide association (GWA)) and predicted gene expression studies, and most importantly, superficial phenotyping that does not distinguish between causes of AKI. As iAKI is a heterogeneous condition, this critical deficiency can dilute biological signals and treatment effects in large-scale studies. Lastly, few studies in AKI have explored identifying novel phenotypes, or endophenotypes of iAKI, which have shown promise for improving understanding of other complex and heterogeneous conditions. The overarching goals of this proposal are to a) advance the clinical phenotyping of the most common and severe forms of intrinsic AKI (iAKI), and b) leverage these phenotypes to identify genetic variants associated with iAKI. In Aim 1, we will apply a data-driven deep learning algorithm to dense structured data and narrative text to discover data patterns that will likely represent a mixture of previously recognized and unrecognized endophenotypes of AKI. In Aim 2, we will complement this strategy by generating probabilistic phenotype algorithms that use manual chart review to identify traditional iAKI phenotypes in 3 clinical settings where iAKI is common: cardiovascular surgery, cardiac catheterization, and sepsis. In Aim 3, we will perform a series of GWA studies within these settings comparing cases identified by our iAKI phenotyping algorithms in Aim 2 to patients without AKI within the Million Veteran Program. We will also conduct the same analyses using the most promising Aim 1 data-driven endophenotypes. We will evaluate the top associated regions using PrediXcan to examine predicted gene expression in kidney tissues. The proposed studies will be performed within the VA Million Veterans Gamma Program by a multidisciplinary team of experts in AKI phenotyping, informatics-based phenotype developers, and genetic epidemiologists. The deliverables from this proposal will advance the computational phenotyping of iAKI, expand the rigor and scale of large-scale genotype-phenotype studies in iAKI, and provide important information regarding clinical iAKI disease mechanisms.