Jones Noah C, Shih Ming-Chieh, Healey Elizabeth, Zhai Chen Wen, Advani Sonali D, Smith-McLallen Aaron, Sontag David, Kanjilal Sanjat
MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA.
College of Life Sciences and Medicine, National Tsing Hua University, Hsinchu, Taiwan.
medRxiv. 2024 Aug 19:2024.08.18.24312104. doi: 10.1101/2024.08.18.24312104.
Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published by the Infectious Diseases Society of America in 2011, however it is not fully known the extent to which they align with current practices, patient diversity, and pathogen biology, all of which have evolved significantly in the time since their publication.
We aimed to re-evaluate efficacy and adverse events for first-line antibiotics (nitrofurantoin, and trimethoprim-sulfamethoxazole), versus second-line antibiotics (fluoroquinolones) and versus alternative agents (oral β-lactams) for uncomplicated UTI in contemporary clinical practice by applying machine learning algorithms to a large claims database formatted into the Observational Medical Outcomes Partnership (OMOP) common data model.
Our primary outcome was a composite endpoint for treatment failure, defined as outpatient or inpatient re-visit within 30 days for UTI, pyelonephritis or sepsis. Secondary outcomes were the risk of 4 common antibiotic-associated adverse events: gastrointestinal symptoms, rash, kidney injury and infection.
We adjusted for covariate-dependent censoring and treatment indication using a broad set of domain-expert derived features. Sensitivity analyses were conducted using OMOP-learn, an automated feature engineering package for OMOP datasets.
Our study included 57,585 episodes of UTI from 49,037 patients. First-line antibiotics were prescribed in 35,018 (61%) episodes, second-line antibiotics were prescribed in 21,140 (37%) episodes and alternative antibiotics were prescribed in 1,427 (2%) episodes. After adjustment, patients receiving first-line therapies had an absolute risk difference of -2.1% [95% CI -2.9% to -1.6%] for having a revisit for UTI within 30 days of diagnosis relative to second-line antibiotics. First-line therapies had an absolute risk difference of -6.6% [95% CI -9.4% to -3.8%] for 30-day revisit compared to alternative β-lactam antibiotics. Differences in adverse events were clinically similar between first and second line agents, but lower for first-line agents relative to alternative antibiotics (-3.5% [95% CI -5.9% to -1.2%]). Results were similar for models built with OMOPlearn.
Our study provides support for the continued use of first-line antibiotics for the management of uncomplicated UTI. Our results also provide proof-of-principle that automated feature extraction methods for OMOP formatted data can emulate manually curated models, thereby promoting reproducibility and generalizability.
单纯性尿路感染（UTI）是门诊抗菌治疗的常见适应症。美国传染病学会于2011年发布了单纯性UTI管理的国家指南，然而，目前尚不完全清楚这些指南与当前实践、患者多样性和病原体生物学的契合程度，自发布以来，所有这些方面都有了显著发展。
我们旨在通过将机器学习算法应用于格式化为观察性医疗结果合作组织（OMOP）通用数据模型的大型索赔数据库，重新评估一线抗生素（呋喃妥因和甲氧苄啶-磺胺甲恶唑）、二线抗生素（氟喹诺酮类）和替代药物（口服β-内酰胺类）在当代临床实践中治疗单纯性UTI的疗效和不良事件。
我们的主要结局是治疗失败的复合终点，定义为UTI、肾盂肾炎或败血症在30天内门诊或住院复诊。次要结局是4种常见抗生素相关不良事件的风险：胃肠道症状、皮疹、肾损伤和感染。
我们使用广泛的领域专家衍生特征对协变量依赖的删失和治疗指征进行了调整。使用OMOP-learn进行敏感性分析，OMOP-learn是一个用于OMOP数据集的自动特征工程软件包。
我们的研究包括来自49037名患者的57585例UTI发作。35018例（61%）发作使用一线抗生素，21140例（37%）发作使用二线抗生素，1427例（2%）发作使用替代抗生素。调整后，接受一线治疗的患者在诊断后30天内因UTI复诊的绝对风险差异为-2.1%[95%CI -2.9%至-1.6%]，相对于二线抗生素。与替代β-内酰胺类抗生素相比，一线治疗30天复诊的绝对风险差异为-6.6%[95%CI -9.4%至-3.8%]。一线和二线药物之间不良事件的差异在临床上相似，但一线药物相对于替代抗生素更低（-3.5%[95%CI -5.9%至-1.2%]）。使用OMOPlearn构建的模型结果相似。
我们的研究为继续使用一线抗生素治疗单纯性UTI提供了支持。我们的结果还提供了原理证明，即针对OMOP格式数据的自动特征提取方法可以模拟手动策划的模型，从而提高可重复性和可推广性。
