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

肾损伤的生物标志物：风险模型开发和评估的统计方法

• 批准号: 9147470
• 负责人: Allison Meisner
• 金额: $ 3.69万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-16 至 2017-06-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9147470
• 关键词: Accounting; Acute Renal Failure with Renal Papillary Necrosis; Address; Affect; Biological Markers; Cardiac; Cardiac Surgery procedures; Cardiovascular Diseases; Cardiovascular system; Chronic Kidney Failure; Clinical; Clinical Trials; Complication; Data; Development; Diagnosis; Diagnostic; Dialysis procedure; Disease; Enrollment; Evaluation; Event; Frequencies; Goals; Hand; Health; Hospitalization; Individual; Injury; Kidney; Lead; Length; Long-Term Effects; Measurement; Methods; Modeling; Morbidity - disease rate; Multicenter Studies; Nephrology; Outcome; Patients; Performance; Physicians; Prevalence; Prevention; Preventive measure; Public Health; Research; Research Personnel; Risk; Role; Severities; Staging; Statistical Methods; Statistical Models; Techniques; Therapeutic; Variant; Work; accurate diagnosis; base; biomarker development; biomarker identification; biomarker panel; cardiovascular risk factor; clinical care; high risk; improved; interest; model development; mortality; novel therapeutics; potential biomarker; predictive marker; predictive modeling; public health relevance; tool

 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.

 描述(申请人提供)：急性肾损伤(AKI)是心脏手术的常见并发症，对长期健康有潜在的严重影响，包括需要透析，增加心血管事件和死亡的风险。目前在AKI的诊断、预防和治疗方面存在差距，开发AKI风险预测模型来解决这些问题是非常有兴趣的。传统的临床和人口统计学变量对AKI的预测能力有限。因此，人们对使用生物标记物和生物标记物组合来预测AKI越来越感兴趣。虽然生物标记物有可能识别AKI的高危患者，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AKI结果的使用和多中心研究中生物标记物组合的发展。首先，虽然定义了多个级别的AKI结果(如无、轻度和严重)，但严重AKI往往是令人感兴趣的结果，因为这种结果与发病率和死亡率最密切相关。多项式建模方法和专门的模型选择技术将被用来利用无AKI患者和轻度AKI患者之间生物标记物水平的差异，以提高对严重AKI风险的预测。其次，尽管多中心生物标记物研究通常提供更大的力量和更高的结果普适性，但由于不同的AKI患病率和/或生物标记物测量的差异，I可能会有中心差异。这种差异如果被忽视，可能会导致对生物标志物组合性能的评估出现偏差。因此，拟议的研究将创建在多中心研究中开发生物标记物组合的方法，包括按中心表征差异的工具和识别占中心的预测性生物标记物组合的技术。解决这两个问题将促进生物标记物在AKI风险预测中的潜力。在心脏手术中能够预测急性心肌梗死风险的生物标记物可能是 用于通过以下方式减轻AKI的负担：(1)提供更准确的诊断；(2)更早地诊断AKI，打开治疗窗口；(3)识别应对其实施预防措施的高危个体；(4)丰富临床试验登记，帮助开发新的治疗方法和预防工具；以及(5)为临床医生和患者提供更好的信息作为决策的基础。任何这些结果都将改变肾脏病的临床护理，并改善心脏手术患者的健康。