Development of Predictive Models to Inform a Novel Risk Categorization Framework for Antibiotic Resistance in Escherichia coli–Caused Uncomplicated Urinary Tract Infection.
| Autor: | Shields, Ryan K, Cheng, Wendy Y, Kponee-Shovein, Kalé, Indacochea, Daniel, Gao, Chi, Kuwer, Fernando, Joshi, Ashish V, Mitrani-Gold, Fanny S, Schwab, Patrick, Ferrinho, Diogo, Mahendran, Malena, Pinheiro, Lisa, Royer, Jimmy, Preib, Madison T, Han, Jennifer, Colgan, Richard |
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| Předmět: |
ANTIBIOTICS
RISK assessment URINARY tract infections NITROFURANTOIN RANDOM forest algorithms PREDICTION models RESEARCH funding FLUOROQUINOLONES MICROBIAL sensitivity tests RECEIVER operating characteristic curves DRUG resistance in microorganisms BETA lactam antibiotics RETROSPECTIVE studies DESCRIPTIVE statistics ODDS ratio ESCHERICHIA coli diseases MEDICAL records ACQUISITION of data CO-trimoxazole THEORY DATA analysis software CONFIDENCE intervals NOSOLOGY ALGORITHMS |
| Zdroj: | Clinical Infectious Diseases; 8/15/2024, Vol. 79 Issue 2, p295-304, 10p |
| Abstrakt: | Background In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic nonsusceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of nonsusceptibility to 4 commonly prescribed antibiotic classes for uUTI, identify predictors of nonsusceptibility to each class, and construct a corresponding risk categorization framework for nonsusceptibility. Methods Eligible females aged ≥12 years with Escherichia coli– caused uUTI were identified from Optum's de-identified Electronic Health Record dataset (1 October 2015–29 February 2020). Four predictive models were developed to predict nonsusceptibility to each antibiotic class and a risk categorization framework was developed to classify patients' isolates as low, moderate, and high risk of nonsusceptibility to each antibiotic class. Results Predictive models were developed among 87 487 patients. Key predictors of having a nonsusceptible isolate to ≥3 antibiotic classes included number of previous UTI episodes, prior β-lactam nonsusceptibility, prior fluoroquinolone treatment, Census Bureau region, and race. The risk categorization framework classified 8.1%, 14.4%, 17.4%, and 6.3% of patients as having isolates at high risk of nonsusceptibility to nitrofurantoin, trimethoprim-sulfamethoxazole, β-lactams, and fluoroquinolones, respectively. Across classes, the proportion of patients categorized as having high-risk isolates was 3- to 12-fold higher among patients with nonsusceptible isolates versus susceptible isolates. Conclusions Our predictive models highlight factors that increase risk of nonsusceptibility to antibiotics for uUTIs, while the risk categorization framework contextualizes risk of nonsusceptibility to these treatments. Our findings provide valuable insight to clinicians treating uUTIs and may help inform empiric prescribing in this population. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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