摘要：大型非心脏手术后的急性肾损伤（AKI）会带来显著的发病风险，但在术中早期进行风险分层仍然困难。在这项回顾性队列研究中，我们提出 SynerT：一种仅使用波形数据的混合时序骨干网络，结合因果扩张 TCN 与分层扩张循环层，对术中早期的生理轨迹进行编码，以预测 AKI 风险。在 SynerT 的基础上，我们进一步设计了两种通过结构化临床上下文扩展该骨干网络的模型变体：SynerT-MM 是一种后期融合的多模态扩展模型，整合血流动力学负担汇总指标与术前协变量；SynerTStack 是一种防止数据泄漏的堆叠集成模型，在元学习阶段将 SynerT-MM 的交叉验证预测结果与强大的表格数据基线相结合。所有模型均在高保真围术期数据库 VitalDB 上，采用严格的泄漏感知框架进行评估，预测所用信息严格限定于术中前 60 分钟内可获得的数据。在 2,413 例可使用波形的病例中（其中 180 例 AKI 阳性，患病率为 7.46%），SynerT 的表现显著低于强大的结构化数据基线，表明在严格的早期预测限制下，仅基于波形的时序建模并不足够。通过纳入血流动力学负担汇总指标和术前协变量，SynerTMM 恢复了区分能力；SynerT-Stack 则在 AUROC、AUPRC 和 F1-max 指标上实现了最佳整体性能。交叉拟合的 Platt 重校准显著修正了两种多模态变体的校准缺陷，决策曲线分析证实，经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。

Abstract: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.