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  3. Epidemiology and Preventive Medicine / 流行病學與預防醫學研究所
  4. PREDICTVE MODEL OF ANTIBIOTIC-RESISTANT GRAM-NEGATIVE BACTEREMIA AT EMERGENCY DEPARTMENT
 
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PREDICTVE MODEL OF ANTIBIOTIC-RESISTANT GRAM-NEGATIVE BACTEREMIA AT EMERGENCY DEPARTMENT

Date Issued
2005
Date
2005
Author(s)
Chiang, Wen-Chu
DOI
en-US
URI
http://ntur.lib.ntu.edu.tw//handle/246246/59193
Abstract
Background The increasing prevalence of antimicrobial resistance among gram-negative bacteria has increasingly gained attention. Despite numerous studies on risk factors related to gram-negative antimicrobial resistance, there was short of predictive model underpinning quantitative epidemiological findings, particularly in Taiwan, for the prediction of antimicrobial resistant gram-negative resistance before bacterial culture result is released. Objectives To find out the risk factors for gram-negative resistant bacteremia in Taiwan and to develop a predictive model to assist physician in appropriate selection of the empirical antimicrobial agent before the microbiologic idenditification and drug susceptibility known. Material and Methods A prospective study was conducted form June 1, 2001 to May 31, 2002 at emergency department (ED) in National Taiwan University Hospital. Enrollees were patients with gram-negative bacteremia sampled at ED. Collected exposures included demographic characteristics of patients, underlying comorbidities, hospital exposure and health-care associated factors, and initial presentation. Two primary outcomes were defined as cefazolin-resistant gram-negative bacteremia (CZ-RES) and ceftriaxone-resistant gram-negative bacteremia (CTX-RES). Two-third of data was randomly allocated to a derivation dataset for training parameters pertaining to predictive models and the others to a validation dataset for testing model validity. Simplified models by coefficient-based scoring method were also established for ease of clinical application. Results There were total 695 episodes of gram-negative bacteremia in final analysis. Predictors identified for CZ-RES gram-negative bacteremia included length from prior hospitalization to existent bacteremia (increasing risk within one month), prior infection by ceftriaxone resistant strain, post-transplantation patients with immunosuppressant in use, nursing home residence or history of cerebral vascular accidents with repeated chocking events, and poor oxygen saturation (<95%) at arrival at ED. Cirrhosis showed its protective effect in reducing the odd of antimicrobial resistant gram-negative bacteremia. As to CZ-RES models, the area under receiver operating characteristic curve (ROC curve) was 0.76 (95% C.I.: 0.71 ~ 0.81)(C.I.: confidence interval). The CTX-RES model included all predictors in CZ-RES model together with abnormal leukocyte count (<1000 or > 15,000 /mm3) at first blood sampling at ED. Besides, the risk temporal length form prior hospitalization is shorter (increasing risk within two weeks). The area ROC curve was 0.82 (95% C.I.: 0.76 ~ 0.88). Area under ROC curve of two simplified integral scoring models was very close to the models by derivation sets. Conclusion We developed two quantitative predictive models by the application of identification and quantification of risks factors associated with antimicrobial resistant gram-negative infection. Application of these predictive models provided in this study can help physician in choosing empirical antibiotic appropriately before the bacterial culture result available.
Subjects
抗生素抗藥性
抗藥性
格蘭氏陰性菌血症
預測模型
格蘭氏陰性菌
bacteremia
gram-negative bacteremia
infection
risk factor
antimicrobial resistance
antibiotic resistance
predictive model
Type
thesis
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ntu-94-R92846003-1.pdf

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