Abstract：
BACKGROUND: Prediction of antibiotic non-susceptibility based on patient characteristics and clinical status may support selection of empiric antibiotics for suspected hospital-acquired urinary tract infections (HA-UTIs).
METHODS: Prediction models were developed to predict non-susceptible results of eight antibiotic susceptibility tests ordered for suspected HA-UTI. Eligible patients were those with urine culture and susceptibility test results after 48 hours of admission between 2010-2021. Patient demographics, diagnosis, prescriptions, exposure to multidrug-resistant organisms, transfer history, and a daily calculated antibiogram were used as predictors. Lasso logistic regression (LLR), extreme gradient boosting (XGB), random forest, and stacked ensemble methods were used for development. Parsimonious models were also developed for clinical utility. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC).
RESULTS: In 10 474 suspected HA-UTI cases, the mean age was 62.1 ± 16.2 years and 48.1% were male. Non-susceptibility prediction for ampicillin/sulbactam, cefepime, ciprofloxacin, imipenem, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole performed best using the stacked ensemble (AUROC 76.9, 76.1, 77.0, 80.6, 76.1, and 76.5, respectively). The model for ampicillin performed best with LLR (AUROC 73.4). Extreme gradient boosting only performed best for gentamicin (AUROC 66.9). In the parsimonious models, the LLR yielded the highest AUROC for ampicillin, ampicillin/sulbactam, cefepime, gentamicin, and trimethoprim/sulfamethoxazole (AUROC 70.6, 71.8, 73.0, 65.9, and 73.0, respectively). The model for ciprofloxacin performed best with XGB (AUROC 70.3), while the model for imipenem performed best in the stacked ensemble (AUROC 71.3). A personalised application using the parsimonious models was publicly released.
CONCLUSIONS: Prediction models for antibiotic non-susceptibility were developed to support empiric antibiotic selection for HA-UTI.
METHODS: Prediction models were developed to predict non-susceptible results of eight antibiotic susceptibility tests ordered for suspected HA-UTI. Eligible patients were those with urine culture and susceptibility test results after 48 hours of admission between 2010-2021. Patient demographics, diagnosis, prescriptions, exposure to multidrug-resistant organisms, transfer history, and a daily calculated antibiogram were used as predictors. Lasso logistic regression (LLR), extreme gradient boosting (XGB), random forest, and stacked ensemble methods were used for development. Parsimonious models were also developed for clinical utility. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC).
RESULTS: In 10 474 suspected HA-UTI cases, the mean age was 62.1 ± 16.2 years and 48.1% were male. Non-susceptibility prediction for ampicillin/sulbactam, cefepime, ciprofloxacin, imipenem, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole performed best using the stacked ensemble (AUROC 76.9, 76.1, 77.0, 80.6, 76.1, and 76.5, respectively). The model for ampicillin performed best with LLR (AUROC 73.4). Extreme gradient boosting only performed best for gentamicin (AUROC 66.9). In the parsimonious models, the LLR yielded the highest AUROC for ampicillin, ampicillin/sulbactam, cefepime, gentamicin, and trimethoprim/sulfamethoxazole (AUROC 70.6, 71.8, 73.0, 65.9, and 73.0, respectively). The model for ciprofloxacin performed best with XGB (AUROC 70.3), while the model for imipenem performed best in the stacked ensemble (AUROC 71.3). A personalised application using the parsimonious models was publicly released.
CONCLUSIONS: Prediction models for antibiotic non-susceptibility were developed to support empiric antibiotic selection for HA-UTI.
摘要：
背景：基于患者特征和临床状态的抗生素非敏感性预测可能支持选择经验性抗生素治疗疑似医院获得性尿路感染（HA-UTIs）。
方法：建立了预测模型来预测针对疑似HA-UTI订购的8种抗生素药敏试验的非敏感结果。符合条件的患者是2010年至2021年入院48小时后尿培养和药敏试验结果的患者。我们利用了病人的人口统计,诊断,处方,暴露于多重耐药生物，转移历史，和每日计算的抗生素图作为预测因子。我们使用Lasso逻辑回归(LLR)，极端梯度增强（XGB），随机森林(RF)，以及用于开发的堆叠集成方法。还开发了用于临床用途的简约模型。使用接受者工作特征曲线下面积(AUROC)评估辨别。
结果：在10474例疑似HA-UTI病例中，平均年龄为62.1±16.2岁，男性占48.1%。氨苄西林/舒巴坦的非敏感性预测，头孢吡肟,环丙沙星,亚胺培南,哌拉西林/他唑巴坦,和甲氧苄啶/磺胺甲恶唑使用堆叠的集合表现最好（AUROC分别为76.9、76.1、77.0、80.6、76.1和76.5）。使用LLR的氨苄青霉素模型表现最好(AUROC=73.4)。仅对于庆大霉素，XGB表现最佳（AUROC=66.9）。在简约的模型中，LLR对氨苄青霉素的AUROC最高，氨苄西林/舒巴坦,头孢吡肟,庆大霉素,和甲氧苄啶/磺胺甲恶唑（AUROC分别为70.6、71.8、73.0、65.9和73.0）。环丙沙星的模型在XGB（AUROC=70.3）下表现最好，而亚胺培南模型在堆叠集合中表现最好(AUROC=71.3)。公开发布了使用简约模型的个性化应用程序。
结论：我们开发了抗生素非敏感性预测模型，以支持HA-UTI的经验性抗生素选择。
方法：建立了预测模型来预测针对疑似HA-UTI订购的8种抗生素药敏试验的非敏感结果。符合条件的患者是2010年至2021年入院48小时后尿培养和药敏试验结果的患者。我们利用了病人的人口统计,诊断,处方,暴露于多重耐药生物，转移历史，和每日计算的抗生素图作为预测因子。我们使用Lasso逻辑回归(LLR)，极端梯度增强（XGB），随机森林(RF)，以及用于开发的堆叠集成方法。还开发了用于临床用途的简约模型。使用接受者工作特征曲线下面积(AUROC)评估辨别。
结果：在10474例疑似HA-UTI病例中，平均年龄为62.1±16.2岁，男性占48.1%。氨苄西林/舒巴坦的非敏感性预测，头孢吡肟,环丙沙星,亚胺培南,哌拉西林/他唑巴坦,和甲氧苄啶/磺胺甲恶唑使用堆叠的集合表现最好（AUROC分别为76.9、76.1、77.0、80.6、76.1和76.5）。使用LLR的氨苄青霉素模型表现最好(AUROC=73.4)。仅对于庆大霉素，XGB表现最佳（AUROC=66.9）。在简约的模型中，LLR对氨苄青霉素的AUROC最高，氨苄西林/舒巴坦,头孢吡肟,庆大霉素,和甲氧苄啶/磺胺甲恶唑（AUROC分别为70.6、71.8、73.0、65.9和73.0）。环丙沙星的模型在XGB（AUROC=70.3）下表现最好，而亚胺培南模型在堆叠集合中表现最好(AUROC=71.3)。公开发布了使用简约模型的个性化应用程序。
结论：我们开发了抗生素非敏感性预测模型，以支持HA-UTI的经验性抗生素选择。
