2114. How to Predict Multi-drug Resistance in Community-acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model
Session: Poster Abstract Session: Healthcare Epidemiology: Device-associated HAIs
Saturday, October 6, 2018
Room: S Poster Hall
Background:

Antibiotic resistance is a growing problem in community-acquired urinary tract infections (CAUTI) leading to significant challenges and costs in the health care system. We aimed to propose a reliable and an easy-to-use clinical prediction model to identify patients with multidrug resistant (MDR) uro-pathogens.

Methods:

We conducted a retrospective study including 824 patients with documented CAUTI diagnosed at an infectious diseases department during 2010-2017. Logistic-regression-based prediction scores were calculated based on variables independently associated with MDR. Sensitivities and specificities at various point cutoffs were studied and the determination of area under the receiver operating characteristic curve (AUROC) was performed.

Results:

The median age of 824 patients with documented CAUTI was 54 years (IQR= [33-72 years]) and 542 cases (65.8%) were females. MDR germs were found in 372 cases (45.1%). Multivariate analysis showed that age ≥ 70 years (Adjusted OR=2.5; 95%CI=[1.8-3.5]), diabetes (Adjusted OR=1.65; 95%CI=[1.19-2.3]), history of urinary tract surgery in the last past 12 months (Adjusted OR=4.5; 95%CI=[1.22-17]) and previous antimicrobial therapy in the last past 3 months (Adjusted OR=4.6; 95%CI=[3-7]) were the independent risk factors of MDR in CAUTI. The results of Hosmer-Lemshow chi-squared testing (ᵪ2=3.4; p =0.49) were indicative of good calibration of the model. At a cut-off of ≥ 2, the score had an AUROC of 0.71, a good sensitivity (70.5%) but a lower specificity (60%), a PPV of 60%, a NPV of 70% and an overall diagnostic accuracy of 65%. When the cutoff was raised to 6, the sensitivity dropped to 43% and the specificity increased to 85%.

Conclusion:

Our study provided an insight into the clinical predictors of MDR in CAUTI. We developed a novel scoring system that can reliably identify patients likely to be harboring MDR uro-pathogens on hospital admission.

Houda Ben Ayed, MD1, Makram Koubaa, MD2, Fatma Hammami, MD2, Chakib Marrakchi, MD2, Tarak Ben Jemaa, MD2, Imed Maaloul, MD2, Jamel Dammak, MD1 and Mounir Ben Jemaa, MD2, (1)Department of Community Health and Epidemiology, Hedi Chaker University Hospital, Sfax, Tunisia, (2)Department of Infectious Diseases, Hedi Chaker University Hospital, Sfax, Tunisia

Disclosures:

H. Ben Ayed, None

M. Koubaa, None

F. Hammami, None

C. Marrakchi, None

T. Ben Jemaa, None

I. Maaloul, None

J. Dammak, None

M. Ben Jemaa, None

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