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Performance of an Easy and Simple New Scoring Model in Predicting Multidrug-Resistant Enterobacteriaceae in
Houda Ben Ayed1, Makram Koubaa2, Fatma Hammami2
1Community Health and Epidemiology Department, Hedi Chaker University Hospital, University of Sfax, Sfax, Tunisia.
Background:
Multidrug resistance (MDR) is a growing global problem in bacterial community-acquired urinary tract infections (CUTIs). We aimed to propose an easy-to-use clinical prediction model to identify patients with MDR in CUTI.
Methods:
We conducted a retrospective study including 770 patients with documented CUTI diagnosed during 2010-2017. Logistic regression-based prediction scores were calculated based on variables independently associated with MDR. Sensitivities and specificities at various cutoff points were determined, and the area under the receiver operating characteristic curve (AUROC) was computed.
Results:
We found MDR Enterobacteriaceae in 372 cases (45.1%). Multivariate analysis showed that age ≥70 years (adjusted odds ratio [aOR], 2.5; 95% confidence interval [CI], 1.8-3.5), diabetes mellitus (aOR, 1.65; 95% CI, 1.19-2.3), history of urinary tract surgery in the last 12 months (aOR, 4.5; 95% CI, 1.22-17), and previous antimicrobial therapy in the last 3 months (aOR, 4.6; 95% CI, 3-7) were independent risk factors of MDR in CUTI. The results of Hosmer-Lemshow chi-square testing were indicative of good calibration of the model (χ2 = 3.4; P = .49). At a cutoff of ≥2, the score had an AUROC of 0.71, a sensitivity of 70.5%, a specificity of 60%, a positive predictive value of 60%, a negative predictive value of 70%, and an overall diagnostic accuracy of 65%. When the cutoff was raised to 6, the sensitivity dropped (43%), and the specificity increased appreciably (85%).
Conclusions:
We developed a novel scoring system that can reliably identify patients likely to be harboring MDR in CUTI.
Insights
A new scoring system helps identify patients with multidrug-resistant (MDR) community-acquired urinary tract infections (CUTIs). Key risk factors include advanced age, diabetes, recent urinary surgery, and prior antibiotic use.
Area of Science:
- Infectious Diseases
- Clinical Microbiology
- Epidemiology
Background:
- Multidrug resistance (MDR) poses a significant challenge in treating bacterial community-acquired urinary tract infections (CUTIs).
- Effective identification of patients with MDR-CUTIs is crucial for appropriate antimicrobial selection and improved patient outcomes.
Purpose of the Study:
- To develop and validate a simple clinical prediction model for identifying patients with MDR in CUTI.
- To establish a scoring system based on independent risk factors associated with MDR in this patient population.
Main Methods:
- A retrospective study of 770 patients with diagnosed CUTI between 2010-2017.
- Logistic regression was used to identify independent risk factors and calculate prediction scores.
- Model performance was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC).
Main Results:
- Multidrug-resistant Enterobacteriaceae were identified in 45.1% of cases.
- Independent risk factors for MDR-CUTI included age ≥70 years, diabetes mellitus, recent urinary tract surgery, and recent antimicrobial therapy.
- The developed scoring system demonstrated good calibration (P = .49) with an AUROC of 0.71 at a cutoff of ≥2, achieving 70.5% sensitivity and 60% specificity.
Conclusions:
- A novel, easy-to-use scoring system was developed to reliably identify patients at risk for MDR in CUTI.
- This tool can aid clinicians in making informed treatment decisions for patients with urinary tract infections.
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