We used a retrospective case-control study to build a predictive model for FQ resistance in patients with HA-GNB-UTI. We subsequently developed a scoring system by simplifying coefficients of each independently predictive factor. We then validated our clinical prediction rule in a hypothetical cohort that was derived from the case-control population. The University of Pennsylvania Institutional Review Board reviewed and approved this study.
We enrolled all patients who were admitted to the hospital from January 2003 through March 2005 at 2 medical centers within the University of Pennsylvania Health System (UPHS): the Hospital of University of Pennsylvania (HUP), a 725-bed academic tertiary and quaternary medical center, and Penn Presbyterian Medical Center (PPMC), a 324-bed urban community hospital center. Both centers are located in Philadelphia.
To determine the case-control population (derivation population), all patients with urine culture results positive for GNB who met the Centers for Disease Control and Prevention (CDC) definition for HA-UTI3
were prospectively identified from the clinical microbiology laboratory database. From this population, all patients who had FQ-resistant GNB-UTI (case patients) were included, whereas patients with FQ-susceptible GNB-UTI were randomly selected to equal the number of case patients. Frequency matching on month of isolation and species of infecting organism was used to sample control subjects. Frequency matching was separately performed within each medical center. Only the first episode of infection in a given patient was included.
The hypothetical cohort population (validation population) was derived from the case-control population. All case patients were included, whereas control subjects were randomly sampled on the basis of a frequency weighting scheme. The probability for a control subject to be sampled was equal to a ratio of the number of FQ-susceptible UTIs to FQ-resistant UTIs for each particular organism in the source population. The frequency weighting scheme was performed to make the causative pathogen distribution and prevalence of FQ resistance for each particular organism similar to that for the source population.
We performed chart review to obtain data, including baseline demographic characteristics, hospital service, hospital location, number of hospital-days (before the onset of UTI), comorbidities, the presence of a urinary catheter, and receipt of inpatient antimicrobial therapy within the preceding 30 days.
An isolate was considered to be resistant if it demonstrated a minimum inhibitory concentration (MIC) of greater than or equal to 8 μ
g/mL to levofloxacin. Susceptibilities to levofloxacin were determined according to existing criteria established by the Clinical and Laboratory Standards Institute.4
Bivariable analysis was performed to determine the unadjusted association between FQ resistance and potential risk factors. Categorical variables were compared using the χ2 test. Continuous variables were compared using the Student’s t test. Multiple logistic regression analysis was subsequently performed by the forward stepwise method to build a final model. We included all variables with P less than .05 in the final model. A simplified scoring system was developed by simplifying coefficients of each independent predictor variable. The simplified model was built by including a summation of scores from the presence of all independent predictor variables as a continuous variable.
The Hosmer-Lemeshow χ2 goodness-of-fit test was performed to determine the accuracy of the models. The C-statistic or area under the receiver operating characteristic (ROC) curve was used to evaluate the discrimination ability (sensitivity and specificity).
Both models (the final model and the simplified model) were internally validated by the optimism adjustment method.5
Our models may not be fitted to other populations, and the sensitivity and specificity obtained from our study may be overly optimistic. Therefore, we calculated the adjusted sensitivity and specificity of the clinical prediction rule from 10,000 bootstrap data sets.
A 2-tailed P value of less than .05 was considered significant. All statistical calculations were performed using STATA software, version 10 (StataCorp).