用术前细胞衰老生物标志物特征预测心脏手术后心脏和肾脏不良事件的风险

Cardiology and cardiovascular medicine Pub Date : 2024-01-01 Epub Date: 2024-06-26 DOI:10.26502/fccm.92920387
Amy Entwistle, Susan Walker, Anne Knecht, Susan L Strum, Asad A Shah, Aliaksei Pustavoitau, Natalia Mitin, Judson B Williams
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引用次数: 0

摘要

重要性:需要改进术前风险分层方法,以便有针对性地降低心脏病患者的风险并优化护理路径。这是第一份证明术前与衰老相关的细胞衰老和免疫系统功能生物标志物可预测常见和严重心脏手术相关不良事件风险的报告:多中心 331 例患者队列研究:对接受冠状动脉搭桥术(CABG)的患者进行为期 30 天的随访。包括一家四级医疗中心和两家社区医院。主要结果为 KDIGO 定义的急性肾损伤 (AKI)。次要结果:30 天时 eGFR 下降≥25%,以及 30 天时主要心脏和肾脏不良事件的复合结果(MACKE30)。对手术前采集的血液样本中的生物标志物进行评估:六个衰老生物标志物(p16、p14、LAG3、CD244、CD28 和 suPAR)的多变量回归模型确定了患者的 AKI 风险(NPV 86.6%,准确率 78.6%)、eGFR 下降风险(NPV 93.5%,准确率 85.2%)和 MACKE30 风险(NPV 91.4%,准确率 79.9%)。与风险最高的三分位数患者相比,风险最低的三分位数患者发生 AKI 的几率要高 7.8(3.3-18.4),随访 30 天时肾功能下降的几率要高 4.5(1.6-12.6),发生 MACKE30 的几率要高 5.7(2.1-15.6)。在对临床变量进行调整后,所有模型仍具有显著性:衰老生物标志物网络是衰老的一个基本机制,术前测量衰老生物标志物网络可识别有不良肾脏和心脏事件风险的患者。这些发现为利用能捕捉衰老异质性的测量方法改进术前风险评估奠定了基础,从而改善心脏手术的临床效果和资源利用率。
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A Signature of Pre-Operative Biomarkers of Cellular Senescence to Predict Risk of Cardiac and Kidney Adverse Events after Cardiac Surgery.

Importance: Improved pre-operative risk stratification methods are needed for targeted risk mitigation and optimization of care pathways for cardiac patients. This is the first report demonstrating pre-operative, aging-related biomarkers of cellular senescence and immune system function can predict risk of common and serious cardiac surgery-related adverse events.

Design: Multi-center 331-patient cohort study that enrolled patients undergoing coronary artery bypass grafing (CABG) surgery with 30-day follow-up. Included a quaternary care center and two community-based hospitals. Primary outcome was KDIGO-defined acute kidney injury (AKI). Secondary outcomes: decline in eGFR ≥25% at 30d and a composite of major adverse cardiac and kidney events at 30d (MACKE30). Biomarkers were assessed in blood samples collected prior to surgery.

Results: A multivariate regression model of six senescence biomarkers (p16, p14, LAG3, CD244, CD28 and suPAR) identified patients at risk for AKI (NPV 86.6%, accuracy 78.6%), decline in eGFR (NPV 93.5%, accuracy 85.2%), and MACKE30 (NPV 91.4%, accuracy 79.9%). Patients in the top risk tertile had 7.8 (3.3-18.4) higher odds of developing AKI, 4.5 (1.6-12.6) higher odds of developing renal decline at 30d follow-up, and 5.7 (2.1-15.6) higher odds of developing MACKE30 versus patients in the bottom tertile. All models remained significant when adjusted for clinical variables.

Conclusions: A network of senescence biomarkers, a fundamental mechanism of aging, can identify patients at risk for adverse kidney and cardiac events when measured pre-operatively. These findings lay the foundation to improve pre-surgical risk assessment with measures that capture heterogeneity of aging, thereby improving clinical outcomes and resource utilization in cardiac surgery.

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