Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We aimed to compare different dynamic regression modeling approaches to predict central line-associated bloodstream infections (CLABSI) in EHR while accounting for competing events precluding CLABSI. Materials and Methods We analyzed data from 30,862 catheter episodes at University Hospitals Leuven from 2012 and 2013 to predict 7-day risk of CLABSI. Competing events are discharge and death. Static models at catheter onset included logistic, multinomial logistic, Cox, cause-specific hazard, and Fine-Gray regression. Dynamic models updated predictions daily up to 30 days after catheter onset (i.e. landmarks 0 to 30 days), and included landmark supermodel extensions of the static models, separate Fine-Gray models per landmark time, and regularized multi-task learning (RMTL). Model performance was assessed using 100 random 2:1 train-test splits. Results The Cox model performed worst of all static models in terms of area under the receiver operating characteristic curve (AUC) and calibration. Dynamic landmark supermodels reached peak AUCs between 0.741-0.747 at landmark 5. The Cox landmark supermodel had the worst AUCs (<=0.731) and calibration up to landmark 7. Separate Fine-Gray models per landmark performed worst for later landmarks, when the number of patients at risk was low. Discussion and Conclusion Categorical and time-to-event approaches had similar performance in the static and dynamic settings, except Cox models. Ignoring competing risks caused problems for risk prediction in the time-to-event framework (Cox), but not in the categorical framework (logistic regression).

翻译：目的 医院在电子健康记录（EHR）中持续登记患者信息直至出院或死亡，因此住院结局不存在删失。本研究旨在比较不同动态回归建模方法，在考虑可能阻碍中心导管相关血流感染（CLABSI）发生的竞争事件的前提下，预测EHR中的CLABSI风险。材料与方法 我们分析了2012至2013年间鲁汶大学医院30,862例导管置入事件的数据，用于预测7天CLABSI风险。竞争事件为出院和死亡。导管置入时的静态模型包括逻辑回归、多项逻辑回归、Cox模型、病因特异性风险模型和Fine-Gray回归模型。动态模型每日更新预测（导管置入后至30天，即标记点0至30天），包含静态模型的标记点超模型扩展、各标记点独立的Fine-Gray模型以及正则化多任务学习（RMTL）。采用100次随机2:1训练-测试划分评估模型性能。结果 在受试者工作特征曲线下面积（AUC）和校准度方面，Cox模型在所有静态模型中表现最差。动态标记点超模型在标记点5达到峰值AUC（0.741-0.747）。Cox标记点超模型的AUC（≤0.731）和校准度在标记点7之前表现最差。在风险患者数量较少的后期标记点，各标记点独立的Fine-Gray模型性能最差。讨论与结论 除Cox模型外，分类方法与生存分析方法在静态和动态场景中性能相似。在生存分析框架（Cox模型）中忽略竞争风险会导致风险预测问题，但在分类框架（逻辑回归）中则不会出现此问题。