Predicting antibiotic resistance in Enterobacterales to support optimal empiric treatment of urinary tract infections in outpatient veterans.

Autor:	Brintz BJ; Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.; IDEAS Center, VA Salt Lake City Healthcare System, Salt Lake City, UT, USA., Madaras-Kelly K; College of Pharmacy, Idaho State University, Meridian, ID, USA.; Pharmacy Service, Boise VA Medical Center, Boise, ID, USA., Nevers M; Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.; IDEAS Center, VA Salt Lake City Healthcare System, Salt Lake City, UT, USA., Echevarria KL; Pharmacy Benefits Management Program, U.S. Department of Veterans Affairs, Hines, IL, USA., Goetz MB; David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.; VA Greater Los Angeles Healthcare System, Los Angeles, California, USA., Samore MH; Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.; IDEAS Center, VA Salt Lake City Healthcare System, Salt Lake City, UT, USA.
Jazyk:	angličtina
Zdroj:	Antimicrobial stewardship & healthcare epidemiology : ASHE [Antimicrob Steward Healthc Epidemiol] 2024 Sep 09; Vol. 4 (1), pp. e118. Date of Electronic Publication: 2024 Sep 09 (Print Publication: 2024).
DOI:	10.1017/ash.2024.377
Abstrakt:	Objective: Bacterial resistance is known to diminish the effectiveness of antibiotics for treatment of urinary tract infections. Review of recent healthcare and antibiotic exposures, as well as prior culture results is recommended to aid in selection of empirical treatment. However, the optimal approach for assessing these data is unclear. We utilized data from the Veterans Health Administration to evaluate relationships between culture and treatment history and the subsequent probability of antibiotic-resistant bacteria identified in urine cultures to further guide clinicians in understanding these risk factors. Methods: Using the XGBoost algorithm, a retrospective cohort of outpatients with urine culture results and antibiotic prescriptions from 2017 to 2022 was used to develop models for predicting antibiotic resistance for three classes of antibiotics: cephalosporins, fluoroquinolones, and trimethoprim/sulfamethoxazole (TMP/SMX) obtained from urine cultures. Model performance was assessed using Area Under the Receiver Operating Characteristic curve (AUC) and Precision-Recall AUC (PRAUC). Results: There were 392,647 prior urine cultures identified in 214,656 patients. A history of bacterial resistance to the specific treatment was the most important predictor of subsequent resistance for positive cultures, followed by a history of specific antibiotic exposure. The models performed better than previously established risk factors alone, especially for fluoroquinolone resistance, with an AUC of .84 and PRAUC of .70. Notably, the models' performance improved markedly (AUC = .90, PRAUC = .87) when applied to cultures from patients with a known history of resistance to any of the antibiotic classes. Conclusion: These predictive models demonstrate potential in guiding antibiotic prescription and improving infection management. Competing Interests: The authors report no relevant conflicts of interest. (© The Author(s) 2024.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu