Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection
Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection
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DOI：
10.1093/ofid/ofz186
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发表时间：
2019-05-01
影响因子：
4.2
通讯作者：
Wiens, Jenna
中科院分区：
文献类型：
--
作者：
Li, Benjamin Y.;Oh, Jeeheh;Wiens, Jenna
背景。梭状芽胞杆菌（梭状芽胞杆菌）艰难梭菌感染（CDI）是一种与医疗保健相关的感染，可能导致严重的并发症。潜在的并发症包括重症监护病房（ICU）入院，有毒的巨型巨型巨龙的发展，结肠切除术的需求和死亡。但是，确定最有可能发展复杂的CDI的患者具有挑战性。为此，我们探讨了使用电子健康记录（EHR）数据的机器学习方法（ML）方法对并发症的患者风险分层的实用性。我们认为在2010年10月至2013年1月在密歇根大学医院之间被诊断出患有CDI的成年患者。如果感染导致ICU入院，结肠切除术或30天死亡率，则将病例标记为复杂。利用EHR数据，我们训练了一个模型，以预测诊断后3天的每一个中的随后并发症。我们将基于EHR的模型与基于一系列手动策划功能的模型进行了比较。我们使用固定数据集根据接收器操作特征曲线（AUROC）.RESULTS来评估模型性能。在1118例CDI病例中，有8％变得复杂。在诊断当天，该模型的AUROC为0.69（95％置信区间[CI]，0.55-0.83）。 Using data extracted 2 days after CDI diagnosis, performance increased (AUROC, 0.90; 95% CI, 0.83-0.95), outperforming a model based on a curated set of features (AUROC, 0.84; 95% CI, 0.75-0.91).Conclusions 。使用EHR数据，我们可以根据其发展并发症的风险准确地对CDI案例进行分层。这种方法可用于指导未来的临床研究，研究可能预防或减轻复杂CDI的干预措施。
Background. Clostridium (Clostridioides) difficile infection (CDI) is a health care-associated infection that can lead to serious complications. Potential complications include intensive care unit (ICU) admission, development of toxic megacolon, need for colectomy, and death. However, identifying the patients most likely to develop complicated CDI is challenging. To this end, we explored the utility of a machine learning (ML) approach for patient risk stratification for complications using electronic health record (EHR) data.Methods. We considered adult patients diagnosed with CDI between October 2010 and January 2013 at the University of Michigan hospitals. Cases were labeled complicated if the infection resulted in ICU admission, colectomy, or 30-day mortality. Leveraging EHR data, we trained a model to predict subsequent complications on each of the 3 days after diagnosis. We compared our EHR-based model to one based on a small set of manually curated features. We evaluated model performance using a held-out data set in terms of the area under the receiver operating characteristic curve (AUROC).Results. Of 1118 cases of CDI, 8% became complicated. On the day of diagnosis, the model achieved an AUROC of 0.69 (95% confidence interval [CI], 0.55-0.83). Using data extracted 2 days after CDI diagnosis, performance increased (AUROC, 0.90; 95% CI, 0.83-0.95), outperforming a model based on a curated set of features (AUROC, 0.84; 95% CI, 0.75-0.91).Conclusions. Using EHR data, we can accurately stratify CDI cases according to their risk of developing complications. Such an approach could be used to guide future clinical studies investigating interventions that could prevent or mitigate complicated CDI.