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败血症病人死亡危险性预评估方法的探讨
引用本文:赵擎宇,贾清颜,黄子通,HJ BARDENHEUER,E Martin. 败血症病人死亡危险性预评估方法的探讨[J]. 岭南急诊医学杂志, 2001, 6(1): 6-9
作者姓名:赵擎宇  贾清颜  黄子通  HJ BARDENHEUER  E Martin
作者单位:1. 广州中山医科大学孙逸仙纪念医院急诊科,510120
2. 北京市安贞医院麻醉科
3. 德国海德堡大学医学院
摘    要:
目的:探索可早期预测严重败血症病人死亡的临床和实验室指标或系统模型。方法:对ICU连续收治的26例严重败血症病人行前瞻性观察28天,分析其死亡的相关临床和实验室指标。结果:共有14人(54%)死亡,多死于第1周内(74%)。根据循环中的细胞间粘附分子-1水平能早期预测其脏器衰竭和死亡。一些临床指标水平在死亡病人与生存病人之间差别较大,其中包括血总胆红素、pH值、红细胞压积、氧合指数、动静脉血氧差、四项SIRS指标和一些血流动力学指标等。结论:综合上述指标可以尝试建立起了一个能够早期评估病人死亡可能性的积分系统。

关 键 词:败血症  临床指标  炎性介质  死亡危险性

Investigation of Death Prediction-Related Clinical and Laboratory Factors in Patients with Sepsis
Zhao Qingyu,Jia Qingyan,Huang Zitong,HJ BARDENHEUER,E Martin. Investigation of Death Prediction-Related Clinical and Laboratory Factors in Patients with Sepsis[J]. Lingnan Journal of Emergency Medicine, 2001, 6(1): 6-9
Authors:Zhao Qingyu  Jia Qingyan  Huang Zitong  HJ BARDENHEUER  E Martin
Affiliation:Zhao Qingyu~1 Jia Qingyan~2 Huang Zitong~1 HJ Bardenheuer~3 E Martin~3 1. Dept. of Emergency,Sun Yat-sen Memorial Hospital,Sun Yat-sen University of Medical Sciences,Guangzhou,510120 2. Dept. of Anesthesia,Anzhen Hospital,Beijing, 3. Faculty of Medicine,University of Heidelberg,Germany
Abstract:
Aim: To investigate the mortality-related factors in clinical sepsis and try the probability to set up a predicting system. Method: To observe 26 consecutive ICU patients with severe sepsis during a 28-day interval. Results: 14 patients died(mortality rate 54%), most of them (74%) in the first week. Circulating ICAM-1 is found to be an outstanding factor in predicating mortality and organ failure. In addition, some clinical parameters such as blood total bilirubin, pH, hematocrit, PaO_2/FiO_2, AVDO_2,4 SIRS parameters and some hemodynamic variables, demonstrated great difference in values between the survivors and the non-survivors. Conclusion: Based on these parameters, a mortality prediction scoring system (MPSS)could be set up to predict the risk of death.
Keywords:sepsis  clinical parameters  inflammatory mediators  mortality prediction
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