Abstract

Objective

Central line–associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. In children in whom continued access is critical, salvage of infected central venous catheters (CVCs) with antimicrobial lock therapy is an alternative to removal and replacement of the CVC. However, the success of CVC salvage is uncertain, and when it fails the catheter has to be removed and replaced. We describe a machine learning approach to predict individual outcomes in CVC salvage that can aid the clinician in the decision to attempt salvage.

Materials and Methods

Over a 14-year period, 969 pediatric CLABSIs were identified in electronic health records. We used 164 potential predictors to derive 4 types of machine learning models to predict 2 failed salvage outcomes, infection recurrence and CVC removal, at 10 time points between 7 days and 1 year from infection onset.

Results

The area under the receiver-operating characteristic curve varied from 0.56 to 0.83, and key predictors varied over time. The infection recurrence model performed better than the CVC removal model did.

Conclusions

Machine learning–based outcome prediction can inform clinical decision making for children. We developed and evaluated several models to predict clinically relevant outcomes in the context of CVC salvage in pediatric CLABSI and illustrate the variability of predictors over time.

中文翻译：

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预测小儿中心静脉导管相关血流感染的中心静脉导管抢救结果

摘要

客观的

中线相关血流感染 (CLABSIs) 是儿童常见的、昂贵且危险的医疗保健相关感染。对于持续通路至关重要的儿童，采用抗菌药物锁定疗法抢救受感染的中心静脉导管 (CVC) 是移除和更换 CVC 的替代方法。然而，CVC 抢救的成功是不确定的，当失败时，导管必须被移除和更换。我们描述了一种机器学习方法来预测 CVC 抢救中的个体结果，可以帮助临床医生决定是否尝试抢救。

材料和方法

在 14 年的时间里，在电子健康记录中发现了 969 例儿科 CLABSI。我们使用 164 个潜在预测因子推导出 4 种类型的机器学习模型，以预测感染发作后 7 天到 1 年之间的 10 个时间点的 2 种失败的挽救结果、感染复发和 CVC 切除。

结果

接受者操作特征曲线下的面积从 0.56 到 0.83 不等，关键预测因子随时间而变化。感染复发模型比 CVC 去除模型表现更好。

结论

基于机器学习的结果预测可以为儿童的临床决策提供信息。我们开发并评估了几种模型来预测儿科 CLABSI 中 CVC 抢救情况下的临床相关结果，并说明预测因子随时间的变化。