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

项目总结 在美国，每年进行的心脏手术超过50万例。术后急性肾损伤 (AKI)发生在约20%的心脏手术患者中。一些队列和注册研究报告称，AKI 心脏手术后血肌酐升高与慢性肾脏的发展有关 疾病和心血管事件。然而，目前还不清楚哪些患者会出现长期的肾脏不良反应。 事件、不良心血管事件或两者兼而有之。近年来，血液和尿液AKI生物标志物已被 已确定反映AKI生物学不同方面，且检测血清肌酐定义AKI早于 血清肌酐升高或检测到传统血肌酐未显示的亚临床AKI 评估。临床结果和生物标记物研究主要集中在检测心脏手术- 相关的AKI本身，但不是基于预测哪些患者患长期不良肾脏和 急性心肌梗死后的心血管结局。这一建议的中心假设是围手术期的血和尿 AKI生物标志物与术后长期(2-5年)主要不良反应的增加显著相关 肾脏事件(MAK)和主要心血管不良事件(MACE)。Make被定义为复合体 透析、死亡、肾脏住院或≥术后30天较术前下降25% 基线。MACE被定义为因心力衰竭、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610人的前瞻性观察队列研究 在德克萨斯大学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*IGFBP7、 术前、术后5个时间点尿肾损伤分子-1AKI生物标志物。这 该提案将解决三个具体目标：1)确定住院AKI生物标记物之间的关联 2)确定院内AKI与AKI的相关性 生物标志物与长期随访中MACE的发生；3)建立临床预测模型 用于心脏手术后的长期治疗。除了传统的回归建模之外，我们还将使用 机器学习利用详细的围手术期数据，包括时变的、术中的和密集的 护理病房临床数据和血液和尿液AKI生物标记物数据，以创建高性能预测模型。我们的 这一建议意义重大，因为了解血液和尿液AKI生物标志物和临床参数 准确预测心脏手术后长期主要不良肾脏和心血管后果 为临床试验奠定基础，为患者确定有效的短期和长期干预措施 风险最高。我们的建议是创新的，因为临床和生物标记物数据没有以这种方式利用 目的：预测心脏手术后的远期预后。

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.