用电子病历数据构建分域集成模型，精准预测手术后谵妄风险。
A Domain-Structured Ensemble Framework for Perioperative Outcome Prediction Using Electronic Health Record Data
- 将患者、手术、麻醉三类特征分域建模，再用逻辑回归融合结果。
- 在5386例手术中预测准确率达AUROC 0.899，优于单一模型。
- 模型可解释性强，适合临床决策支持系统部署。
围术期风险预测常受限于狭窄的手术人群、不完整的术中数据、校准不佳和可解释性差。本文提出一种基于领域结构的集成框架，利用常规电子健康记录（EHR）数据进行围术期结局预测。特征按患者、手术、麻醉三类领域组织，各领域分别训练梯度提升模型生成独立风险评分，再通过逻辑回归元学习器集成。以全州健康信息交换数据中5,386例手术为例（2,693例谵妄病例，2,693例对照），筛选出术后7天内有谵妄相关ICD编码且混淆评估法阳性者，排除既往痴呆患者。集成模型达到AUROC 0.899（95%置信区间：0.891–0.906），精确率-召回率曲线下面积0.881，布里尔分数0.126，显著优于最优单阶段模型（AUROC 0.849）。领域消融实验显示，相较仅含手术特征的模型（AUROC 0.879，布里尔分数0.140），本模型判别力与校准性均更优。对2017年后数据的时序验证获得AUROC 0.915。校准表现优异，截距-0.006（95%置信区间：-0.083至0.070），斜率1.035（95%置信区间：0.982至1.088）。经病例对照采样校正的决策曲线分析显示，在临床合理阈值范围内均有净获益。该模块化框架可拓展至其他结局，支持新增预测域与动态风险更新，为可解释、校准感知的围术期临床决策支持提供可扩展基础。
原文摘要 · Abstract (English)
Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited interpretability. We present a domain-structured ensemble framework for perioperative outcome prediction using routinely collected electronic health record (EHR) data. Predictors are organized into patient-related, surgery-related, and anesthetics-related domains. Domain-specific gradient boosting models generate independent risk estimates that are integrated through a logistic regression meta-learner. We demonstrate the framework using postoperative delirium (POD) in a case-control sample of 5,386 surgical encounters (2,693 cases, 2,693 controls) from a statewide health information exchange. POD required both delirium-related ICD codes and a positive Confusion Assessment Method screening within seven postoperative days; patients with preexisting dementia were excluded. The stacked meta-learner achieved AUROC 0.899 (95% CI: 0.891-0.906), precision-recall AUC 0.881, and Brier score 0.126, compared with AUROC 0.849 for the best single-stage model. Domain ablation showed improved discrimination and calibration over a surgery-only model (AUROC 0.879, Brier 0.140). Temporal validation on held-out post-2017 data yielded AUROC 0.915. Calibration was excellent, with intercept -0.006 (95% CI: -0.083 to 0.070) and slope 1.035 (95% CI: 0.982 to 1.088). Decision curve analysis, corrected for case-control sampling, showed positive net benefit across clinically plausible thresholds. The modular framework supports alternative outcomes, extension of predictor domains, and dynamic risk updating, providing a scalable foundation for interpretable, calibration-aware perioperative clinical decision support.
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