Scholarlink

<p>Empirical antibiotic therapy in the emergency department (ED) is often initiated before susceptibility results are available, risking treatment failure in bloodstream infections caused by <i>extended-spectrum β-lactamase</i> (ESBL)-producing <i>Escherichia coli</i> and <i>Klebsiella</i> spp. We retrospectively analysed 3,138 adult sepsis episodes (training: <i>n</i> = 1,863, ESBL + = 419, ESBL− = 1,444; test: <i>n</i> = 1,275, ESBL + = 373, ESBL− = 902) treated at Inha University Hospital between 2013 and 2022 and trained a machine-learning model to predict ESBL-producing organisms using routinely available clinical and laboratory data. We used stratified 5-fold cross-validation and computed SHapley Additive exPlanations (SHAP) values within folds to avoid information leakage. We performed SHAP-guided stepwise feature elimination based on training performance. The final model, trained on the full training cohort (2013–2019), achieved an AUROC of 0.78 on an independent test set (2020–2022). This interpretable pipeline may support empirical antibiotic selection and antimicrobial stewardship in the ED.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An interpretable machine learning model for early prediction of ESBL-producing bacteraemia in the emergency department

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月14日

查看全文
  • Ye-Chan Kim,
  • Jong-Bub Lee,
  • Areum Durey,
  • Hyun-Gyu Lee

摘要

Empirical antibiotic therapy in the emergency department (ED) is often initiated before susceptibility results are available, risking treatment failure in bloodstream infections caused by extended-spectrum β-lactamase (ESBL)-producing Escherichia coli and Klebsiella spp. We retrospectively analysed 3,138 adult sepsis episodes (training: n = 1,863, ESBL + = 419, ESBL− = 1,444; test: n = 1,275, ESBL + = 373, ESBL− = 902) treated at Inha University Hospital between 2013 and 2022 and trained a machine-learning model to predict ESBL-producing organisms using routinely available clinical and laboratory data. We used stratified 5-fold cross-validation and computed SHapley Additive exPlanations (SHAP) values within folds to avoid information leakage. We performed SHAP-guided stepwise feature elimination based on training performance. The final model, trained on the full training cohort (2013–2019), achieved an AUROC of 0.78 on an independent test set (2020–2022). This interpretable pipeline may support empirical antibiotic selection and antimicrobial stewardship in the ED.