术后新发心房颤动预测模型研究进展： 从静态评分到多模态智能矩阵

Research Progress on Prediction Models for Postoperative Atrial Fibrillation: From Static Scoring Systems to Multimodal Intelligent Matrices

摘要: 术后新发心房颤动(POAF)是围手术期极具危害的并发症，直接影响患者的远期预后。随着人工智能技术的迅猛发展，POAF的风险预警已实现从传统回归评分向集成机器学习(ML)与多模态深度学习(DL)架构的跨越。本文系统性地综述了2021年至2026年期间的关键技术演进，论证了基于电生理异质性(如心房传导阻滞与非同步性)的特征选择逻辑，深入剖析了Stacking集成架构与Transformer注意力机制在提升预测精度(AUC > 0.9)方面的数学优势。同时，文章探讨了SHAP归因分析在重构临床信任、指导闭环干预路径中的转化价值。最后，针对算法漂移及跨中心验证等临床转化瓶颈，提出了持续学习与穿戴式实时监测的未来愿景，旨在为构建高精度、可解释的围术期智慧决策平台提供理论支撑。

Abstract: Postoperative atrial fibrillation (POAF) is a critical perioperative complication that significantly impacts long-term patient prognosis. With the rapid evolution of artificial intelligence, POAF risk stratification is undergoing a fundamental paradigm shift from traditional regression-based scores to sophisticated ensemble machine learning (ML) and multimodal deep learning (DL) architectures. This review systematically evaluates key technological advancements from 2021 to 2026, elucidating the feature selection logic grounded in electrophysiological heterogeneity (e.g., conduction block and atrial asynchrony). We analyze the mathematical underpinnings of Stacking ensembles and Transformer-based Attention mechanisms in achieving superior predictive accuracy (AUC > 0.9). Furthermore, the clinical utility of SHAP-based explainability in reconstructing medical trust and guiding closed-loop intervention pathways is discussed. Finally, addressing the critical bottlenecks of clinical translation such as algorithm drift and multi-center validation, we propose future directions involving continual learning and wearable real-time monitoring to establish highly accurate, interpretable perioperative decision-support platforms.