Development of a Prediction Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health Records from Multiple Clinics in North-Central Florida.
Development of a Prediction Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health Records from Multiple Clinics in North-Central Florida.
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DOI：
10.1007/s40121-022-00677-x
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发表时间：
2022-10
影响因子：
5.4
通讯作者：
Prosperi, Mattia
中科院分区：
文献类型：
--
作者：
Rich, Shannan N.;Jun, Inyoung;Bian, Jiang;Boucher, Christina;Cherabuddi, Kartik;Morris, J. Glenn, Jr.;Prosperi, Mattia
关键词：
尿路感染(UTI)是一种常见的感染，最初的抗生素治疗决定是基于经验的，通常没有进行抗生素敏感性测试来评估耐药性，这增加了不适当治疗的风险。我们假设，基于电子健康记录(EHR)的模型可以帮助识别具有抗生素耐药性的尿路感染的高风险患者，并帮助指导医院和临床环境中抗菌药的选择。2011-2019年期间，从佛罗里达州中北部多个中心获得了9990名被诊断为尿路感染的患者的电子病历，包括患者人口统计数据、以前的诊断、处方和抗生素敏感性测试。建立决策树、增强Logistic回归(BLR)和随机森林模型预测UTI治疗中常用抗生素[磺胺甲恶唑-甲氧嘧啶(SXT)、呋喃妥因(NIT)、环丙沙星(CIP)]的耐药性和多药耐药(MDR)。有6307人(63.1%)的尿路感染是由耐药微生物引起的。总体而言，人口以女性、白人、非西班牙裔和老年人口为主(平均 = 为60.7岁)。根据受试者工作曲线下的袋外面积(AUROC)衡量，BLR模型对耐药结果的判别能力最高[AUROC = 0.58(SXT)、0.62(NIT)、0.64(CIP)和0.66(MDR)]。最佳模型中的变量包括性别、尿路感染病史、导尿术、肾病、痴呆症、偏瘫/截瘫和高血压。预测模型的判别能力为中等。尽管如此，这些仅基于EHR的模型在识别耐药感染风险较高的患者方面表现出了实用性。反过来，这些模型可能有助于指导临床对这些患者的尿液培养顺序和经验性治疗的决策。网上版载有补充材料，可在10.1007/s40121022-00677-x上查阅。
Urinary tract infections (UTIs) are common infections for which initial antibiotic treatment decisions are empirically based, often without antibiotic susceptibility testing to evaluate resistance, increasing the risk of inappropriate therapy. We hypothesized that models based on electronic health records (EHR) could assist in the identification of patients at higher risk for antibiotic-resistant UTIs and help guide the selection of antimicrobials in hospital and clinic settings. EHR from multiple centers in North-Central Florida, including patient demographics, previous diagnoses, prescriptions, and antibiotic susceptibility tests, were obtained for 9990 patients diagnosed with a UTI during 2011–2019. Decision trees, boosted logistic regression (BLR), and random forest models were developed to predict resistance to common antibiotics used for UTI management [sulfamethoxazole-trimethoprim (SXT), nitrofurantoin (NIT), ciprofloxacin (CIP)] and multidrug resistance (MDR). There were 6307 (63.1%) individuals with a UTI caused by a resistant microorganism. Overall, the population was majority female, white, non-Hispanic, and older aged (mean = 60.7 years). The BLR models yielded the highest discriminative ability, as measured by the out-of-bag area under the receiver-operating curve (AUROC), for the resistance outcomes [AUROC = 0.58 (SXT), 0.62 (NIT), 0.64 (CIP), and 0.66 (MDR)]. Variables in the best performing model were sex, history of UTIs, catheterization, renal disease, dementia, hemiplegia/paraplegia, and hypertension. The discriminative ability of the prediction models was moderate. Nonetheless, these models based solely on EHR demonstrate utility for the identification of patients at higher risk for resistant infections. These models, in turn, may help guide clinical decision-making on the ordering of urine cultures and decisions regarding empiric therapy for these patients. The online version contains supplementary material available at 10.1007/s40121-022-00677-x.
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影响因子：
4.4
作者：
Foxman, Betsy
通讯作者：
Foxman, Betsy
DOI：
10.1198/106186006x133933
发表时间：
2006-09-01
影响因子：
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作者：
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