Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL.
Department of Preventative Medicine Biostatistics Collaboration Center, Northwestern University Feinberg School of Medicine, Chicago, IL.
Urology. 2020 Mar;137:72-78. doi: 10.1016/j.urology.2019.11.009. Epub 2019 Nov 17.
To evaluate how previous antimicrobial resistance, prior prescription data, and patient place of residence (ZIP code) can guide empirical therapy for uncomplicated urinary tract infections (UTI). Guidelines recommend empirical antimicrobial selection for women with symptoms of uncomplicated UTIs, most commonly trimethoprim-sulfamethoxazole (SXT), nitrofurantoin (NIT), or ciprofloxacin (CIP). Previous antimicrobial resistance and prior prescription data are potential predictors of resistance in subsequent urine cultures for UTIs. Also, there is evidence of geographic clustering of antimicrobial resistance for UTIs.
Retrospective data from women (age ≥18) with an assigned diagnosis of UTI, submitting urine cultures as outpatients (2011-2018), were gathered. Univariate analyses and multivariable regression models were used to determine odds ratios for predicting resistance to SXT, NIT, and CIP on the 2011-2017 data. Antimicrobial choice algorithms were created using 2011-2017 results and tested on 2018 data.
In the training cohort, 9455 women had diagnoses of uncomplicated UTIs and positive urine cultures. Prevalence of resistance for SXT, NIT, and CIP was 19.4%, 12.1%, and 10.3%, respectively. A urine culture with previous resistance, prior antimicrobial prescription within 2 years and ZIP code were the strongest predictors of a subsequent resistant culture. An algorithm based on these data had a success rate of 92.2%, compared to provider's choice (87.5%, P <.001) or best theoretical outcomes with guidelines (90.0%, P = .048).
Previous resistance, prior prescriptions, and patient ZIP code are predictors of subsequent resistance in patients with uncomplicated UTIs. Algorithms using these data can outperform real-world outcomes and guidelines.
评估先前的抗生素耐药性、既往处方数据和患者居住地（邮政编码）如何指导单纯性尿路感染（UTI）的经验性治疗。指南建议对有单纯性 UTI 症状的女性选择经验性抗菌药物治疗，最常用的是复方磺胺甲噁唑（SXT）、呋喃妥因（NIT）或环丙沙星（CIP）。先前的抗生素耐药性和既往处方数据是随后尿液培养中 UTI 耐药的潜在预测因素。此外，UTI 的抗生素耐药性存在地理聚集的证据。
收集了 2011-2018 年年龄≥18 岁的女性门诊就诊并进行尿液培养的单纯性 UTI 患者的回顾性数据。使用单变量分析和多变量回归模型来确定预测 SXT、NIT 和 CIP 耐药的比值比。使用 2011-2017 年的结果创建抗生素选择算法，并在 2018 年的数据上进行测试。
在训练队列中，9455 名女性被诊断为单纯性 UTI 并进行了尿液培养。SXT、NIT 和 CIP 的耐药率分别为 19.4%、12.1%和 10.3%。具有先前耐药性的尿液培养、2 年内的先前抗菌药物处方和邮政编码是随后耐药培养的最强预测因素。基于这些数据的算法的成功率为 92.2%，而与医生的选择（87.5%，P<0.001）或指南的最佳理论结果（90.0%，P=0.048）相比。
先前的耐药性、既往处方和患者的邮政编码是单纯性 UTI 患者后续耐药的预测因素。使用这些数据的算法可以优于实际结果和指南。
