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Updated: Feb 10, 2026

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在败血症中预测死亡率的S-S.M.A.R.T得分:用qSOFA和一种新的SSMART-MC模型进行比较分析
Serdar Özdemir1, İbrahim Altunok1, Merve Osoydan Satıcı1
1Department of Emergency Medicine, Ümraniye Training and Research Hospital, Istanbul, Turkey.
Heart & lung : the journal of critical care
|February 8, 2026
概括
在S-S.M.A.R.T得分显示类似的败血症死亡率预测到SOFA. 在SSMART-MC模型中添加恶性瘤和CRP数据显著改善了30天死亡率预测的准确性.
科学领域:
- 紧急医疗 紧急医疗
- 关键护理医学 关键护理医学
- 临床预后 临床预后
背景情况:
- 败血症早期风险分层对于及时的急救部门 (ED) 决策至关重要.
- 现有的预后得分往往需要实验室结果,这些实验室结果在分选时无法获得.
- 败血症-3标准定义了败血症,以准确识别患者队列.
研究的目的:
- 评估S-S.M.A.R.T得分对30天败血症死亡率的预后表现.
- 评估是否并发症和生物标志物数据可以提高S-S.M.A.R.T得分的预测准确性.
主要方法:
- 在ED中成人败血症患者的前性观察研究.
- 收集的人口,临床和实验室数据.
- 计算了S-S.M.A.R.T,SOFA和SSMART-MC (S-S.M.A.R.T + 恶性病 + CRP) 的得分.
- 使用后勤回归和ROC分析来评估预后性能.
主要成果:
- 包括180名败血症患者;57.8%的人经历了30天死亡率.
- 在非幸存者中,S-S.M.A.R.T和SOFA分数更高.
- S-S.M.A.R.T (AUC 0.718) 和SOFA (AUC 0.735) 的死亡率预测结果相似.
- SSMART-MC模型实现了更高的AUC (0.867) 与87.3%的灵敏度和80.8%的整体准确度.
结论:
- 在预测败血症死亡率方面,S-S.M.A.R.T得分的表现与SOFA相似.
- 结合恶性瘤和CRP (SSMART-MC模型) 改善了预后准确性.
- SSMART-MC是一个有前途的工具,但需要在更大的研究中进行外部验证,以便在临床中使用.
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