Assessing patient risk of central line-associated bacteremia via machine learning
Assessing patient risk of central line-associated bacteremia via machine learning
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DOI：
10.1016/j.ajic.2018.02.021
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发表时间：
2018-09-01
影响因子：
4.9
通讯作者：
Azar, Jose
中科院分区：
文献类型：
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作者：
Beeler, Cole;Dbeibo, Lana;Azar, Jose
背景：中心静脉导管相关血流感染（CLABSI）导致发病率、住院时间和费用增加。尽管在了解风险因素方面取得了进展。仍然需要准确地预测CLABSI的风险。在真实的时间里。方法：使用来自大型学术医疗保健系统的回顾性数据开发预测模型。通过使用验证的输入变量构建随机森林，使用机器学习开发模型。结果：基于2013年1月1日至2016年5月31日期间70，218例独特患者的回顾性研究，15个变量对CLABSI预测的影响最为显著。在生产中，表现最好的模型的受试者工作特征曲线下的面积为0.82。讨论：该模型在CLABSI预防的资源分配方面有多种应用，包括作为一种工具，针对风险最高的患者进行潜在的成本效益，但在其他方面有时间限制的干预。机器学习可用于开发准确的模型，以在感染发展之前真实的预测CLABSI的风险。(C)2018年协会在感染控制和流行病学专业人员。Inc.爱思唯尔公司出版All rights reserved.
Background: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors. there remains a need to accurately predict the risk of CLABSIs and. in real time. prevent them from occurring.Methods: A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables.Results: Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production.Discussion: This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions.Conclusions: Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection. (C) 2018 Association for Professionals in Infection Control and Epidemiology. Inc. Published by Elsevier Inc. All rights reserved.