在急性至慢性肝衰竭中基于炎症的动态预后:COSSH-CAR模型作为个性化风险分层的一步
Noura A A Ebrahim1, Thoraya A Farghaly2, Soliman M A Soliman3
1Department of Oncologic Pathology, National Cancer Institute, Cairo University, Cairo 11796, Al Qāhirah, Egypt. npathologist@gmail.com.
World journal of hepatology
|March 11, 2026
概括
动态C反应蛋白与白蛋白比率 (CAR) 的变化,特别是超过7天的变化,更好地预测急性至慢性肝衰竭 (ACLF) 患者的死亡率. 新的COSSH-CAR模型将这种动态的CAR与其他评分相结合,以改善风险评估.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 生物标志物研究 生物标志物研究
- 关键护理医学 关键护理医学
背景情况:
- 急性至慢性肝衰竭 (ACLF) 具有重大临床挑战,死亡率高.
- 现有的ACLF预测工具缺乏足够的准确性,无法及时分层风险.
- 对动态生物标志物的需求尚未得到满足,以评估ACLF患者的预后.
研究的目的:
- 评估ACLF中C-反应蛋白与白蛋白比率 (CAR) 的时间变化的预后值.
- 引入和评估COSSH-CAR模型的性能,用于预测ACLF的28天死亡率.
主要方法:
- 对C反应蛋白与白蛋白比率 (CAR) 的时间变化的分析,重点关注7天的变化.
- 通过整合7天CAR变异与MELD-Na和肝脑病变等级来开发COSSH-CAR模型.
- 对比COSSH-CAR模型的预测准确度与传统的预测得分 (查尔德-皮格,MELD,COSSH-ACLF).
主要成果:
- 与基线CAR测量相比,CAR的7天变化显示出28天死亡率的优异预测.
- 新的COSSH-CAR模型在预测ACLF死亡率方面表现优于已有的预测工具.
- CAR有效地反映了系统性炎症和患者的生理储备.
结论:
- 动态生物标志物轨迹,如7天CAR变化,对于准确的ACLF预后至关重要.
- 该COSSH-CAR模型提供了一个有前途的,数据驱动的方法来改进ACLF风险评估.
- 为了广泛采用基于CAR的模型,在不同的队列和前性研究中进一步验证是必不可少的.
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