Background In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic non-susceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of non-susceptibility to four commonly prescribed antibiotic classes for uUTI, identify predictors of non-susceptibility to each class, and construct a corresponding risk categorization framework for non-susceptibility. Methods Eligible females aged ≥12 years with E. coli-caused uUTI were identified from Optum’s de-identified Electronic Health Record dataset (10/1/2015‒2/29/2020). Four predictive models were developed to predict non-susceptibility to each antibiotic class and a risk categorization framework was developed to classify patients’ isolates as low, moderate, and high risk of non-susceptibility to each antibiotic class. Results Predictive models were developed among 87487 patients. Key predictors of having a non-susceptible isolate to ≥3 antibiotic classes included number of previous UTI episodes, prior β-lactam non-susceptibility, 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 non-susceptibility to nitrofurantoin, trimethoprim-sulfamethoxazole, β-lactams, and fluoroquinolones, respectively. Across classes, the proportion of patients categorized as having high-risk isolates was 3–12 folds higher among patients with non-susceptible isolates versus susceptible isolates. Conclusions Our predictive models highlight factors that increase risk of non-susceptibility to antibiotics for uUTIs, while the risk categorization framework contextualizes risk of non-susceptibility to these treatments. Our findings provide valuable insight to clinicians treating uUTIs and may help inform empiric prescribing in this population.

中文翻译：

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开发预测模型，为引起单纯性尿路感染的大肠杆菌抗生素耐药性提供新的风险分类框架

背景 在临床实践中，识别具有抗生素不敏感性风险的单纯性尿路感染 (uUTI) 患者的挑战可能会导致不恰当的处方并导致抗生素耐药性。我们开发了预测模型来量化对四种常见的 uUTI 抗生素类别不敏感的风险，确定对每类抗生素不敏感的预测因素，并构建相应的不敏感风险分类框架。方法 从 Optum 去识别化的电子健康记录数据集（2015 年 10 月 1 日至 2020 年 2 月 29 日）中识别出年龄≥12 岁、患有大肠杆菌引起的 uUTI 的合格女性。开发了四种预测模型来预测对每种抗生素类别的不敏感性，并开发了风险分类框架来将患者的分离株分类为对每种抗生素类别不敏感的低风险、中风险和高风险。结果 在 87487 名患者中建立了预测模型。对 ≥ 3 类抗生素不敏感的关键预测因素包括既往 UTI 发作次数、既往对 β-内酰胺不敏感、既往氟喹诺酮治疗、人口普查局地区和种族。风险分类框架分别将 8.1%、14.4%、17.4% 和 6.3% 的患者分类为对呋喃妥因、甲氧苄啶-磺胺甲恶唑、β-内酰胺类和氟喹诺酮类不敏感的高风险分离株。在各个类别中，被归类为高风险分离株的患者中，非易感分离株患者的比例是易感分离株患者的 3-12 倍。结论 我们的预测模型强调了增加 uUTI 抗生素不敏感风险的因素，而风险分类框架则考虑了对这些治疗不敏感的风险。我们的研究结果为治疗 uUTI 的临床医生提供了宝贵的见解，并可能有助于为该人群制定经验性处方。