Developing a model to estimate the probability of bacteremia in women with community-onset febrile urinary tract infection
DOI:
https://doi.org/10.3855/jidc.7559Keywords:
urinary tract infection, pyelonephritis, bacteremia, decision support technique, sensitivity, specificityAbstract
Introduction: Among patients with urinary tract infection (UTI), bacteremic cases show higher mortality rates than do nonbacteremic cases. Early identification of bacteremic cases is crucial for severity assessment of patients with febrile UTI. This study aimed to identify predictors associated with bacteremia in women with community-onset febrile UTI and to develop a prediction model to estimate the probability of bacteremic cases.
Methodology: This cross-sectional study included women consecutively hospitalized with community-onset febrile UTI at 10 hospitals in Korea. Multiple logistic regression identified predictors associated with bacteremia among candidate variables chosen from univariate analysis. A prediction model was developed using all predictors weighted by their regression coefficients.
Results: From July to September 2014, 383 women with febrile UTI were included: 115 (30.0%) bacteremic and 268 (70.0%) nonbacteremic cases. A prediction model consisted of diabetes mellitus (1 point), urinary tract obstruction by stone (2), costovertebral angle tenderness (2), a fraction of segmented neutrophils of > 90% (2), thrombocytopenia (2), azotemia (2), and the fulfillment of all criteria for systemic inflammatory response syndrome (2). The c statistic for the model was 0.807 (95% confidence interval [CI], 0.757–0.856). At a cutoff value of ≥ 3, the model had a sensitivity of 86.1% (95% CI, 78.1–91.6%) and a specificity of 54.9% (95% CI, 48.7–91.6%).
Conclusions: Our model showed a good discriminatory power for early identification of bacteremic cases in women with community-onset febrile UTI. In addition, our model can be used to identify patients at low risk for bacteremia because of its relatively high sensitivity.
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