面向早期术中急性肾损伤预测的泄漏感知多模态评估框架
A Leakage-Aware Multimodal Evaluation Framework for Early Intraoperative Acute Kidney Injury Prediction
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中文总结 AI 辅助
针对术后AKI早期预测，提出SynerT系列模型，结合波形与临床上下文，在严格泄漏感知框架下评估，堆叠集成SynerT-Stack取得最佳性能。
中文摘要 AI 辅助
重大非心脏手术后的术后急性肾损伤（AKI）会带来显著的发病率，但早期术中风险分层仍然困难。在这项回顾性队列研究中，我们提出了SynerT，一种仅基于波形的混合时间骨干网络，它将因果扩张TCN与分层扩张循环层相结合，以编码早期术中生理轨迹用于AKI风险预测。基于SynerT，我们进一步设计了两种模型变体，通过结构化临床上下文扩展骨干网络：SynerT-MM，一种晚期融合的多模态扩展，整合了血流动力学负担摘要和术前协变量；以及SynerTStack，一种泄漏安全的堆叠集成，在元学习阶段将SynerT-MM的交叉验证预测与强大的表格基线相结合。所有模型均在VitalDB（一个高保真围手术期数据库）上，在严格的泄漏感知框架下进行评估，预测仅限于术中前60分钟内可用的信息。在2，413例可用的波形病例中（180例AKI阳性；患病率7.46%），SynerT的表现远低于强大的结构化数据基线，表明在严格的早期约束下，仅基于波形的时序建模是不够的。SynerT-MM通过整合血流动力学负担摘要和术前协变量恢复了判别能力，而SynerT-Stack在AUROC、AUPRC和F1-max方面取得了最佳整体性能。交叉拟合的Platt重校准显著纠正了两种多模态变体的校准缺陷，决策曲线分析证实，重校准的堆叠模型在低至中等阈值范围内提供了最强的净临床获益。
英文摘要
Postoperative acute kidney injury (AKI) after major non-cardiac surgery carries substantial morbidity, yet early intraoperative risk stratification remains difficult. In this retrospective cohort study, we propose SynerT, a waveform-only hybrid temporal backbone that combines a causal dilated TCN with a hierarchy of dilated recurrent layers to encode early intraoperative physiologic trajectories for AKI risk prediction. Building on SynerT, we further design two model variants that extend the backbone with structured clinical context: SynerT-MM, a late-fusion multimodal extension that integrates hemodynamic burden summaries and preoperative covariates, and SynerTStack, a leakage-safe stacked ensemble that combines cross-validated predictions from SynerT-MM with strong tabular baselines at the meta-learning stage. All models are evaluated under a strict leakage-aware framework on VitalDB, a high-fidelity perioperative database, with prediction restricted to information available within the first 60 intraoperative minutes. Among 2,413 waveform-usable cases (180 AKI-positive; 7.46% prevalence), SynerT fell well below strong structured-data baselines, demonstrating that waveform-only temporal modeling is insufficient under strict early constraints. SynerTMM recovered discrimination by incorporating hemodynamic burden summaries and preoperative covariates, and SynerT-Stack achieved the best overall performance across AUROC, AUPRC, and F1-max. Cross-fitted Platt recalibration substantially corrected calibration defects in both multimodal variants, and decision-curve analysis confirmed the recalibrated stacked model delivered the strongest net clinical benefit across low-to-intermediate thresholds.
发表机构
- National Economics University（国民经济大学）
机构由 AI 辅助整理，请以论文原文为准。
