Blood and Urine Biomarkers for Predicting Long-Term Adverse Kidney and Cardiovascular Outcomes after Cardiac Surgery

用于预测心脏手术后长期不良肾脏和心血管结果的血液和尿液生物标志物

• 批准号: 10161608
• 负责人: Amanda A Fox
• 金额: $ 78.79万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10161608
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Anesthesiology; Angiotensin Receptor; Angiotensin-Converting Enzyme Inhibitors; Applications Grants; Arrhythmia; Biological Markers; Biology; Biometry; Blood; Brain natriuretic peptide; Cardiac; Cardiac Surgery procedures; Cardiac rehabilitation; Cardiology; Cardiopulmonary Bypass; Cardiovascular system; Cessation of life; Chronic Kidney Failure; Clinical; Clinical Data; Clinical Informatics; Clinical Management; Clinical Trials; Cohort Studies; Computational Science; Coronary; Creatinine; Data; Databases; Development; Dialysis procedure; Dose; Enrollment; Event; Foundations; Foxes; Funding; Future; Goals; Grant; Health; Heart failure; Hospitalization; Hospitals; Hour; Incidence; Information Systems; Intensive Care Units; Intervention; Intervention Studies; Intraoperative Care; Kidney; Knowledge; Life; Literature; Longterm Follow-up; Machine Learning; Measurement; Measures; Medical; Medical center; Methods; Mission; Modeling; Myocardial Infarction; Nephrology; Operative Surgical Procedures; Outcome; Patients; Perioperative; Pharmaceutical Preparations; Plasma; Postoperative Period; Prospective Studies; Prospective cohort study; Public Health; Publishing; Questionnaires; Records; Registries; Regression Analysis; Renal function; Reporting; Research; Risk; Sampling; Sensitivity and Specificity; Serum; Source; Stroke; Time; Transfusion; United States; United States National Institutes of Health; Urine; base; biobank; clinical biomarkers; cohort; design; disability; fibroblast growth factor 23; health record; hemodynamics; high risk; innovation; mortality; multidisciplinary; novel; outcome prediction; post gamma-globulins; predictive marker; predictive modeling; prevent; prospective; rat KIM-1 protein

PROJECT SUMMARY In the United States over 500,000 cardiac surgeries are performed annually. Postoperative acute kidney injury (AKI) occurs in ~20% of cardiac surgical patients. Several cohort and registry studies have reported that AKI defined by rise in serum creatinine after cardiac surgery associates with development of chronic kidney disease and cardiovascular events. However, it is unclear which patients will develop long-term adverse kidney events, adverse cardiovascular events, or both. In recent years blood and urine AKI biomarkers have been identified that reflect different aspects of AKI biology and that detect serum creatinine defined AKI earlier than rise in serum creatinine or detect subclinical AKI that is not revealed by traditional serum creatinine assessment. Clinical outcomes and biomarker research has focused mainly on detecting cardiac surgery- associated AKI itself, but not on predicting which patients are at greatest risk for long-term adverse kidney and cardiovascular outcomes after AKI. The central hypothesis of this proposal is that perioperative blood and urine AKI biomarkers significantly associate with increased long-term (2 to 5 years) postoperative major adverse kidney events (MAKE) and major adverse cardiovascular events (MACE). MAKE is defined as the composite of dialysis, death, renal hospitalization, or ≥ 30 day postoperative eGFR decline >25% from preoperative baseline. MACE is defined as the composite of death or hospitalizations for heart failure, myocardial infarction, coronary revascularization, arrhythmia, or stroke. We propose a prospective observational cohort study of 610 patients undergoing cardiac surgery at UT Southwestern Medical Center who will be followed for a minimum of 2 years and up to 5 years following cardiac surgery. Based on preliminary data we will assess plasma NT-pro- B-type natriuretic peptide, plasma intact fibroblast growth factor 23, serum cystatin C, urine TIMP-2*IGFBP7, and urine Kidney Injury Molecule-1 AKI biomarkers preoperatively and at 5 postoperative time points. This proposal will address three specific aims: 1) To determine the association between in-hospital AKI biomarkers and occurrence of MAKE during long-term follow-up; 2) To determine the association between in-hospital AKI biomarkers and occurrence of MACE during long-term follow-up; and 3) To develop clinical prediction models for long-term MAKE and MACE after cardiac surgery. In addition to traditional regression modeling, we will use machine learning that leverages detailed perioperative data including time-varying intraoperative and intensive care unit clinical data and blood and urine AKI biomarker data to create high performing prediction models. Our proposal is significant because knowing what blood and urine AKI biomarkers and clinical parameters accurately predict long-term major adverse kidney and cardiovascular outcomes after cardiac surgery provides the foundation for clinical trials that will identify effective short and long-term interventions for patients at highest risk. Our proposal is innovative because clinical and biomarker data has not been leveraged in this way to predict long-term MAKE and MACE after cardiac surgery.

项目摘要 在美国，每年进行超过50万例心脏手术。术后急性肾损伤 (AKI)发生在约20%的心脏手术患者中。几项队列和登记研究报告称阿基 定义为心脏手术后血清肌酐升高与慢性肾脏病的发生有关 疾病和心血管事件。然而，目前尚不清楚哪些患者会出现长期不良肾功能， 不良心血管事件或两者。近年来，血液和尿液中的阿基标志物已被发现。 确定了反映阿基生物学的不同方面，并检测血清肌酐定义的阿基早于 血清肌酐升高或检测到传统血清肌酐未显示的亚临床阿基 考核临床结果和生物标志物研究主要集中在检测心脏手术- 与阿基本身相关，但不能预测哪些患者长期肾脏不良反应的风险最大， 阿基后的心血管结局。这项建议的中心假设是，围手术期的血液和尿液 阿基生物标志物与长期（2 - 5年）术后主要不良事件增加显著相关 肾脏事件（MAKE）和主要不良心血管事件（MACE）。MAKE定义为复合材料 透析、死亡、肾性住院或术后≥ 30天eGFR较术前下降>25% 基线。MACE定义为死亡或因心力衰竭、心肌梗死 冠状动脉血运重建心律失常或中风我们提出了一项前瞻性观察性队列研究， 在UT西南医学中心接受心脏手术的患者，将接受至少 心脏手术后2年至5年。根据初步数据，我们将评估血浆NT-pro- B型利钠肽、血浆完整成纤维细胞生长因子23、血清胱抑素C、尿TIMP-2* IGFBP 7、 以及术前和术后5个时间点的尿肾损伤分子-1阿基生物标志物。这 提案将解决三个具体目标：1）确定院内阿基生物标志物之间的关联 和长期随访期间MAKE的发生率; 2）确定院内阿基与 生物标志物和长期随访期间MACE的发生;和3）开发临床预测模型 心脏手术后的长期MAKE和MACE。除了传统的回归建模，我们还将使用 利用详细的围手术期数据的机器学习，包括随时间变化的术中和强化 护理单元临床数据以及血液和尿液阿基生物标志物数据，以创建高性能预测模型。我们 该提案意义重大，因为了解血液和尿液阿基标志物和临床参数 准确预测心脏手术后长期主要不良肾脏和心血管结局， 临床试验的基础，将确定有效的短期和长期干预措施，为患者在 最高风险我们的建议是创新的，因为临床和生物标志物数据尚未以这种方式利用 预测心脏手术后的长期MAKE和MACE。