Predictive model of antimicrobial-resistant gram-negative bacteremia at the ED.
BACKGROUND
Despite numerous studies identifying the risk factors related to gram-negative antimicrobial resistance, an epidemiological model to reliably predict antimicrobial gram-negative resistance in clinics, before the bacterial culture result is available, has not yet been developed.


OBJECTIVE
The aim of this study was to develop a predictive model to assist physicians in selecting appropriate antimicrobial agents before the details of the microbiology and drug susceptibility are known.


METHODS
A prospective study was conducted between June 1, 2001, and May 31, 2002, at the emergency department (ED) of National Taiwan University Hospital. Enrollees were patients with gram-negative bacteremia (GNB) at ED. Other information collected included demographic characteristics, underlying comorbidities, hospital exposure and health care-associated factors, and details of initial presentation. Two primary outcomes were defined, including cefazolin-resistant (CZ-RES) GNB and ceftriaxone-resistant (CTX-RES) GNB. Two thirds of the data was randomly allocated to a derivation data set (for developing predictive models), and the rest, to a validation data set (for testing model validity). Simplified models, using a coefficient-based scoring method, were also developed for clinical applications.


RESULTS
Based on 695 episodes of GNB, predictors of CZ-RES GNB were time since last hospitalization (increased risk for durations <1 month), prior infection with a CTX-RES strain, post-transplantation immunosuppressant use, residence in a nursing home or history of stroke with repeated choking, and poor oxygen saturation (<95%) at admission to ED. Cirrhosis showed a protective effect by reducing the odds of antimicrobial-resistant GNB. The area under receiver operating characteristic (ROC) curve for the CZ-RES model was 0.76 (95% confidence interval, 0.71-0.81). The CTX-RES model included all the variables that were in the CZ-RES model plus abnormal leukocyte count (<1000 or >15,000 /mm3) at entry to ED. In this case, however, previous hospitalization within the last 2 weeks was a key factor. The area under this ROC curve was 0.82 (95% confidence interval, 0.76-0.88). There was lacking of difference in the area under the ROC curve between the 2 final (simplified) models either based on the derivation or validation data sets.


CONCLUSIONS
We have developed 2 models for predicting risk of antimicrobial gram-negative infection by identifying and quantifying associated risk factors. These models could be used by physicians to determine the most appropriate choice of antibiotic for first-line therapy, particularly in situations where the culture result is not yet known.
Chiang
Wen-Chu
WC
Department of Emergency Medicine, National Taiwan University Hospital, Taipei 100, Taiwan. drchiang@ha.mc.ntu.edu.tw
Chen
Shey-Ying
SY
Chien
Kuo-Liong
KL
Wu
Grace Hui-Min
GH
Yen
Amy Ming-Fang
AM
Su
Chan-Ping
CP
Lee
Chien-Chang
CC
Chen
Yee-Chun
YC
Chang
Shan-Chwen
SC
Chen
Shyr-Chyr
SC
Chen
Wen-Jone
WJ
Chen
Tony Hsiu-Hsi
TH
D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D000890:Anti-Infective Agents / Q000627:therapeutic use*; D016470:Bacteremia; D002511:Cephalosporins / Q000627:therapeutic use; D003661:Decision Support Techniques; D004352:Drug Resistance, Microbial; D004636:Emergency Service, Hospital; D005260:Female; D006090:Gram-Negative Bacteria / Q000187:drug effects*; D016905:Gram-Negative Bacterial Infections / Q000188:drug therapy / Q000382:microbiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012372:ROC Curve; D012307:Risk Factors
D000890:Anti-Infective Agents; D002511:Cephalosporins