Predictive model of antimicrobial-resistant Gram-negative bacteremia at the ED

Wen Chu Chiang, Shey Ying Chen, Kuo Liong Chien, Grace Hui-Min, Amy Ming-Fang Yen, Chan Ping Su, Chien Chang Lee, Yee Chun Chen, Shan Chwen Chang, Shyr Chyr Chen, Wen Jone Chen, Tony Hsiu-Hsi Chen

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

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. Objectives: 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. Materials and 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 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. Conclusion: 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.

Original languageEnglish
Pages (from-to)597-607
Number of pages11
JournalAmerican Journal of Emergency Medicine
Volume25
Issue number6
DOIs
Publication statusPublished - Jul 2007
Externally publishedYes

Fingerprint

Bacteremia
Hospital Emergency Service
Cefazolin
ROC Curve
Area Under Curve
Hospitalization
Physicians
Ceftriaxone
Anti-Infective Agents
Microbiology
Taiwan
Comorbidity
Research Design
Demography
Prospective Studies
Confidence Intervals
Anti-Bacterial Agents
Delivery of Health Care
Infection
Pharmaceutical Preparations

ASJC Scopus subject areas

  • Emergency Medicine

Cite this

Predictive model of antimicrobial-resistant Gram-negative bacteremia at the ED. / Chiang, Wen Chu; Chen, Shey Ying; Chien, Kuo Liong; Hui-Min, Grace; Ming-Fang Yen, Amy; Su, Chan Ping; Lee, Chien Chang; Chen, Yee Chun; Chang, Shan Chwen; Chen, Shyr Chyr; Chen, Wen Jone; Hsiu-Hsi Chen, Tony.

In: American Journal of Emergency Medicine, Vol. 25, No. 6, 07.2007, p. 597-607.

Research output: Contribution to journalArticle

Chiang, WC, Chen, SY, Chien, KL, Hui-Min, G, Ming-Fang Yen, A, Su, CP, Lee, CC, Chen, YC, Chang, SC, Chen, SC, Chen, WJ & Hsiu-Hsi Chen, T 2007, 'Predictive model of antimicrobial-resistant Gram-negative bacteremia at the ED', American Journal of Emergency Medicine, vol. 25, no. 6, pp. 597-607. https://doi.org/10.1016/j.ajem.2006.11.024
Chiang, Wen Chu ; Chen, Shey Ying ; Chien, Kuo Liong ; Hui-Min, Grace ; Ming-Fang Yen, Amy ; Su, Chan Ping ; Lee, Chien Chang ; Chen, Yee Chun ; Chang, Shan Chwen ; Chen, Shyr Chyr ; Chen, Wen Jone ; Hsiu-Hsi Chen, Tony. / Predictive model of antimicrobial-resistant Gram-negative bacteremia at the ED. In: American Journal of Emergency Medicine. 2007 ; Vol. 25, No. 6. pp. 597-607.
@article{65d29babe55046f8b033ad7c73e5921c,
title = "Predictive model of antimicrobial-resistant Gram-negative bacteremia at the ED",
abstract = "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. Objectives: 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. Materials and 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 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. Conclusion: 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.",
author = "Chiang, {Wen Chu} and Chen, {Shey Ying} and Chien, {Kuo Liong} and Grace Hui-Min and {Ming-Fang Yen}, Amy and Su, {Chan Ping} and Lee, {Chien Chang} and Chen, {Yee Chun} and Chang, {Shan Chwen} and Chen, {Shyr Chyr} and Chen, {Wen Jone} and {Hsiu-Hsi Chen}, Tony",
year = "2007",
month = "7",
doi = "10.1016/j.ajem.2006.11.024",
language = "English",
volume = "25",
pages = "597--607",
journal = "American Journal of Emergency Medicine",
issn = "0735-6757",
publisher = "W.B. Saunders",
number = "6",

}

TY - JOUR

T1 - Predictive model of antimicrobial-resistant Gram-negative bacteremia at the ED

AU - Chiang, Wen Chu

AU - Chen, Shey Ying

AU - Chien, Kuo Liong

AU - Hui-Min, Grace

AU - Ming-Fang Yen, Amy

AU - Su, Chan Ping

AU - Lee, Chien Chang

AU - Chen, Yee Chun

AU - Chang, Shan Chwen

AU - Chen, Shyr Chyr

AU - Chen, Wen Jone

AU - Hsiu-Hsi Chen, Tony

PY - 2007/7

Y1 - 2007/7

N2 - 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. Objectives: 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. Materials and 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 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. Conclusion: 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.

AB - 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. Objectives: 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. Materials and 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 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. Conclusion: 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.

UR - http://www.scopus.com/inward/record.url?scp=34250874391&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34250874391&partnerID=8YFLogxK

U2 - 10.1016/j.ajem.2006.11.024

DO - 10.1016/j.ajem.2006.11.024

M3 - Article

C2 - 17606081

AN - SCOPUS:34250874391

VL - 25

SP - 597

EP - 607

JO - American Journal of Emergency Medicine

JF - American Journal of Emergency Medicine

SN - 0735-6757

IS - 6

ER -