用人工智能预测尿路感染的抗生素敏感性--多中心队列中的模型表现。

IF 3.7 Q2 INFECTIOUS DISEASES JAC-Antimicrobial Resistance Pub Date : 2024-08-07 eCollection Date: 2024-08-01 DOI:10.1093/jacamr/dlae121
Alfred Lok Hang Lee, Curtis Chun Kit To, Ronald Cheong Kin Chan, Janus Siu Him Wong, Grace Chung Yan Lui, Ingrid Yu Ying Cheung, Viola Chi Ying Chow, Christopher Koon Chi Lai, Margaret Ip, Raymond Wai Man Lai
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引用次数: 0

摘要

摘要开发一种人工智能模型,用于预测尿路感染(UTI)患者的抗菌药敏感性模式。材料与方法:纳入 2015-2020 年间香港一所大学教学医院和三所社区医院的 26 087 例经培养证实的UTI成人患者。排除了无症状菌尿(无UTI诊断代码或尿液镜检无白细胞)的病例。训练集包括 2015 年至 2019 年的患者,测试集包括 2020 年的患者。为预测UTI细菌分离株的药敏性,选择了三种一线抗生素:硝基呋喃妥因、环丙沙星和阿莫西林-克拉维酸。基线流行病学因素、既往抗菌药物使用情况、病史和既往培养结果均被列为特征。数据集采用了逻辑回归和随机森林方法。通过 F1 分数和曲线下面积-接收器操作特征(AUC-ROC)对模型进行评估:随机森林是预测三种抗生素(硝基呋喃妥因、阿莫西林-克拉维酸和环丙沙星)药敏性的最佳算法。AUC-ROC 值分别为 0.941、0.939 和 0.937。F1 分数分别为 0.938、0.928 和 0.906:随机森林模型可帮助UTI患者合理使用抗生素。鉴于其合理的性能和准确性,这些精确的模型可帮助临床医生在UTI治疗中选择不同的一线抗生素。
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Predicting antibiotic susceptibility in urinary tract infection with artificial intelligence-model performance in a multi-centre cohort.

Objective: To develop an artificial intelligence model to predict an antimicrobial susceptibility pattern in patients with urinary tract infection (UTI).

Materials and methods: 26 087 adult patients with culture-proven UTI during 2015-2020 from a university teaching hospital and three community hospitals in Hong Kong were included. Cases with asymptomatic bacteriuria (absence of diagnosis code of UTI, or absence of leucocytes in urine microscopy) were excluded. Patients from 2015 to 2019 were included in the training set, while patients from the year 2020 were included as the test set.Three first-line antibiotics were chosen for prediction of susceptibility in the bacterial isolates causing UTI: namely nitrofurantoin, ciprofloxacin and amoxicillin-clavulanate. Baseline epidemiological factors, previous antimicrobial consumption, medical history and previous culture results were included as features. Logistic regression and random forest were applied to the dataset. Models were evaluated by F1-score and area under the curve-receiver operating characteristic (AUC-ROC).

Results: Random forest was the best algorithm in predicting susceptibility of the three antibiotics (nitrofurantoin, amoxicillin-clavulanate and ciprofloxacin). The AUC-ROC values were 0.941, 0.939 and 0.937, respectively. The F1 scores were 0.938, 0.928 and 0.906 respectively.

Conclusions: Random forest model may aid judicious empirical antibiotics use in UTI. Given the reasonable performance and accuracy, these accurate models may aid clinicians in choosing between different first-line antibiotics for UTI.

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