Title: Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection Show Chinese title

Title: 比较处理电子健康记录中缺失数据的方法以进行中央导管相关血流感染的动态风险预测

Abstract: Electronic health records (EHR) often contain varying levels of missing data. This study compared different imputation strategies to identify the most suitable approach for predicting central line-associated bloodstream infection (CLABSI) in the presence of competing risks using EHR data. We analyzed 30862 catheter episodes at University Hospitals Leuven (2012-2013) to predict 7-day CLABSI risk using a landmark cause-specific supermodel, accounting for competing risks of hospital discharge and death. Imputation methods included simple methods (median/mode, last observation carried forward), multiple imputation, regression-based and mixed-effects models leveraging longitudinal structure, and random forest imputation to capture interactions and non-linearities. Missing indicators were also assessed alone and in combination with other imputation methods. Model performance was evaluated dynamically at daily landmarks up to 14 days post-catheter placement. The missing indicator approach showed the highest discriminative ability, achieving a mean AUROC of up to 0.782 and superior overall performance based on the scaled Brier score. Combining missing indicators with other methods slightly improved performance, with the mixed model approach combined with missing indicators achieving the highest AUROC (0.783) at day 4, and the missForestPredict approach combined with missing indicators yielding the best scaled Brier scores at earlier landmarks. This suggests that in EHR data, the presence or absence of information may hold valuable insights for patient risk prediction. However, the use of missing indicators requires caution, as shifts in EHR data over time can alter missing data patterns, potentially impacting model transportability. Show Chinese abstract

Abstract: 电子健康记录（EHR）通常包含不同程度的缺失数据。 本研究比较了不同的填补策略，以确定在存在竞争风险的情况下，使用EHR数据预测中央导管相关血流感染（CLABSI）的最佳方法。 我们分析了鲁汶大学医院（2012-2013年）的30862个导管病例，使用地标特定原因超模型预测7天内CLABSI风险，同时考虑出院和死亡的竞争风险。 填补方法包括简单方法（中位数/众数、前向填补）、多重填补、基于回归和混合效应模型利用纵向结构，以及随机森林填补来捕捉交互作用和非线性。 缺失指示符单独评估以及与其他填补方法结合进行评估。 在导管置入后长达14天的日标点动态评估模型性能。 缺失指示符方法显示出最高的辨别能力，在14天后的地标处达到了高达0.782的平均AUROC，并且根据缩放Brier得分显示出了优越的整体性能。 与其他方法结合缺失指示符略微提高了性能，混合模型方法与缺失指示符结合在第4天实现了最高的AUROC（0.783），而missForestPredict方法与缺失指示符结合在较早的地标处产生了最佳的缩放Brier分数。 这表明在EHR数据中，信息是否存在可能对患者风险预测具有重要的价值。 然而，使用缺失指示符需要谨慎，因为EHR数据随时间的变化可能会改变缺失数据模式，从而可能影响模型的可移植性。