基于电子健康记录数据的围手术期结局预测领域结构化集成框架
A Domain-Structured Ensemble Framework for Perioperative Outcome Prediction Using Electronic Health Record Data
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中文总结 AI 辅助
该研究针对围手术期风险预测模型的局限，提出领域结构化集成框架，以术后谵妄为案例，在5386例样本中验证其预测性能优于单阶段模型，为围手术期临床决策提供可扩展基础。
中文摘要 AI 辅助
围手术期风险预测模型常受限于手术人群范围狭窄、术中数据不完整、校准效果差、可解释性有限等问题。我们提出一种基于常规收集的电子健康记录（EHR）数据的围手术期结局预测领域结构化集成框架，将预测因子划分为患者相关、手术相关、麻醉相关三个领域，各领域的梯度提升模型生成独立风险估计值，通过逻辑回归元学习器进行整合。我们以术后谵妄（POD）为例，在来自全州卫生信息交换的5386例手术 encounters（2693例病例、2693例对照）的病例-对照样本中验证该框架，POD的判定需同时满足谵妄相关ICD编码及术后7天内阳性的意识模糊评估法筛查结果，排除术前已存在痴呆的患者。堆叠元学习器的AUROC达0.899（95%置信区间：0.891-0.906）、精确率-召回率AUC为0.881、Brier评分为0.126，而最佳单阶段模型的AUROC为0.849；领域消融实验显示，与仅基于手术的模型（AUROC 0.879、Brier 0.140）相比，本框架的判别能力和校准效果更优。对2017年后留存数据的时间验证显示AUROC达0.915，校准效果极佳，截距为-0.006（95%置信区间：-0.083至0.070）、斜率为1.035（95%置信区间：0.982至1.088）；经病例-对照抽样校正的决策曲线分析显示，在临床合理阈值范围内存在正净获益。该模块化框架支持替代结局、预测因子领域扩展及动态风险更新，为可解释、校准感知的围手术期临床决策支持提供了可扩展基础。
英文摘要
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.
