用电子病历数据预测急性肾损伤患者透析风险并评估药物影响。
Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records
- 基于时序医疗数据构建变压器因果模型，分析药物对透析风险的影响。
- 模型在测试集上AUC为0.694，F1得分为0.201，具备一定预测能力。
- 发现血管紧张素类药物可能降低风险，袢利尿剂可能加重病情，适合临床决策参考。
进展至透析或终末期肾病是罕见但重要的临床结局。医生需要了解药物暴露对后续风险的影响。我们构建了一个固定窗口的电子健康记录队列（90天观察期，730天预测期；N=81401；透析/终末期肾病患病率：1.1%），并利用肾脏实验室指标（肌酐、BUN、eGFR）与诊断、操作及用药序列进行建模。采用基于Transformer的因果多头模型，在完整用药史条件下通过反事实暴露移除与插入方法估计药物及成分水平的平均治疗效应（ATE）。测试集上预测性能达到AUC 0.694，PR-AUC 0.094。在选定决策阈值（0.883）下，模型获得F1分数0.201，Brier得分0.018。事后因果分析通过IPTW、AIPW、朴素和协变量调整的OLS方法评估实验室指标变化（eGFR、肌酐、BUN）的方向性，结果显示血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂（ACE/ARB）存在部分保护性信号，袢利尿剂则呈现恶化方向信号。
原文摘要 · Abstract (English)
Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures influence downstream risk. We constructed a fixed-window EHR cohort (90-day observation, 730-day prediction; N=81401; dialysis/ESRD prevalence: 1.1%) and modeled sequences of diagnoses, procedures, and medications with kidney laboratory trends (creatinine, BUN, eGFR). A transformer-based causal multi-head model was trained to estimate drug- and ingredient-level average treatment effects (ATEs) using counterfactual exposure removal and insertion under a full medication history setup. On test set, predictive performance reached an AUC of 0.694 and PR-AUC of 0.094. At the selected decision threshold (0.883), the model achieved an F1 score of 0.201 with a Brier score of 0.018. Post-hoc causal analyses of lab changes (eGFR, creatinine, BUN) using IPTW, AIPW, naive, and covariate-adjusted OLS methods assessed clinical directionality. Results showed partial protective-direction support for ACE/ARB exposures and worsening-direction signals for loop diuretics.
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