The Correlative Study Between Prognosis, Serum Cytokine and Chinese Medical Syndrome in Septic Patients

Autor: Yung-Yen Cheng, 鄭勇彥
Rok vydání: 2006
Druh dokumentu: 學位論文 ; thesis
Popis: 94
The Correlative Study Between Prognosis,Serum Cytokine and Chinese Medical Syndrome in Septic Patients Yung Yen Cheng Major professor: Yi Chang Su Institute of Chinese Medical Science China Medical University The high mortality rate of sepsis is always a difficult problem to overcome in clinic. Because the course of sepsis is developed fast and the pathophisiology is complicated, it is difficult to control in treatment. Great amount of cytokine was produced after sepsis developed and it has been proved that the level of IL-6 and IL-10 is significantly correlated to the prognosis of sepsis. The purpose of this study is to research the correlation between IL-6, IL-10, cardiovascular parameter and prognosis, and combine the APACHE II score system to find out the prediction regressive equation of septic patient''s outcome. In the meantime, the correlation between Chinese medical syndrome and prognosis & cytokine in septic patients was analyzed. The subjects of this study are the patients who meet the diagnostic criteria of severe sepsis and septic shock. If the subject lives over 28 days after sepsis, it will be defined into the survival group. The total number of the evaluated subjects is 40, the number of survival group is 23, and non-survival group is 17.The result reveals that the regressive equation of septic patient''s prognosis could be conducted by three parameters including logIL6+logIL10, cardiovascular parameter and the ordinal variable of APACHE II score. The prediction accuracy of the death at the 3 days, 7 days, 28 days are 92.5%, 92.5%, and 85%. The survival time of the non-survival group could be forecasted by the multi-regressive equation conducted by four parameters including logIL-6, 1/IL-10, cardiovascular parameter, and the APACHE II score. The coefficient of determination (R2) is：0.952 (p
Databáze: Networked Digital Library of Theses & Dissertations
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