Biomarkers of Kidney Injury: Statistical Methods for Risk Model Development and Evaluation

 描述（由申请方提供）：急性肾损伤（阿基）是心脏手术的常见并发症，对长期健康有潜在严重影响，包括需要透析以及心血管事件和死亡风险增加。目前在阿基的诊断、预防和治疗方面存在差距，人们对开发阿基风险预测模型来解决这些问题非常感兴趣。传统的临床和人口统计学变量对阿基的预测能力有限。因此，人们越来越关注使用生物标志物和生物标志物组合来预测阿基。虽然生物标志物有可能识别阿基高风险患者，但问题仍然存在。拟议的研究将解决两个这样的问题：多水平阿基结果的使用和多中心研究中生物标志物组合的开发。首先，虽然定义了多水平阿基结局（如无、轻度和重度），但重度阿基通常是关注的结局，因为该结局与发病率和死亡率最密切相关。将使用多项式建模方法和专门的模型选择技术来利用无阿基和轻度阿基个体之间生物标志物水平的变化，以改善对重度阿基风险的预测。其次，虽然多中心生物标志物研究通常提供更大的功效和增加的结果的普遍性，但由于阿基患病率和/或生物标志物测量的差异，可能存在中心差异。如果忽略这些差异，可能导致生物标志物组合性能评估的偏倚。因此，拟议的研究将创建在多中心研究中开发生物标志物组合的方法，包括表征中心差异的工具和识别占中心的预测生物标志物组合的技术。解决这两个问题将提高阿基风险预测生物标志物的潜力。能够预测心脏手术中阿基风险的生物标志物可能是 用于通过以下方式减轻阿基的负担：（1）提供更准确的诊断;（2）更早诊断阿基，打开治疗窗口;（3）识别应实施预防措施的高风险个体;（4）丰富临床试验招募，帮助开发新的预防疗法和工具;以及（5）为临床医生和患者提供更好的信息以作为决策的基础。这些结果中的任何一个都将改变肾脏病学的临床护理，并改善接受心脏手术的患者的健康状况。

 DESCRIPTION (provided by applicant): Acute kidney injury (AKI) is a common complication of cardiac surgery with potentially serious effects on long- term health, including need for dialysis and increased risk of cardiovascular events and mortality. Current gaps exist in the diagnosis, prevention and treatment of AKI, and there is great interest in developing AKI risk prediction models to address these issues. Traditional clinical and demographic variables have limited predictive capacity for AKI. As a result, there is increasing interest in using biomarkers and biomarker combinations to predict AKI. While biomarkers have the potential to identify patients at high risk for AKI, issues remain. The proposed research will address two such issues: the use of multi-level AKI outcomes and the development of biomarker combinations in multi-center studies. First, while multi-level AKI outcomes (such as no, mild and severe) are defined, severe AKI is often the outcome of interest, as this outcome is most strongly associated with morbidity and mortality. Multinomial modeling methods and specialized model selection techniques will be used to leverage variation in biomarker levels between individuals with no AKI and those with mild AKI to improve prediction of severe AKI risk. Second, though multi-center biomarker studies typically offer greater power and increased generalizability of results, i is possible to have center differences due to varying AKI prevalence and/or differences in biomarker measurements. Such differences, if ignored, can lead to bias in the assessment of the performance of biomarker combinations. Thus, the proposed research will create methods for developing biomarker combinations in multi-center studies, including tools to characterize differences by center and techniques to identify predictive biomarker combinations that account for center. Addressing these two issues will advance the potential of biomarkers for AKI risk prediction. Biomarkers capable of predicting risk of AKI in the setting of cardiac surgery could be used to reduce the burden of AKI by (1) providing a more accurate diagnosis; (2) diagnosing AKI earlier, opening a therapeutic window; (3) identifying high risk individuals for whom preventative measures should be implemented; (4) enriching clinical trial enrollment, aiding in the development of novel therapies and tools for prevention; and (5) providing the clinician and patient with better information on which to base decisions. Any of these outcomes would transform clinical care in nephrology and improve the health of patients undergoing cardiac surgery.