Accurate prediction of major adverse cardiac events (MACE) remains a central challenge in cardiovascular prognosis. We present PRISM (Prompt-guided Representation Integration for Survival Modeling), a self-supervised framework that integrates visual representations from non-contrast cardiac cine magnetic resonance imaging with structured electronic health records (EHRs) for survival analysis. PRISM extracts temporally synchronized imaging features through motion-aware multi-view distillation and modulates them using medically informed textual prompts to enable fine-grained risk prediction. Across four independent clinical cohorts, PRISM consistently surpasses classical survival prediction models and state-of-the-art (SOTA) deep learning baselines under internal and external validation. Further clinical findings demonstrate that the combined imaging and EHR representations derived from PRISM provide valuable insights into cardiac risk across diverse cohorts. Three distinct imaging signatures associated with elevated MACE risk are uncovered, including lateral wall dyssynchrony, inferior wall hypersensitivity, and anterior elevated focus during diastole. Prompt-guided attribution further identifies hypertension, diabetes, and smoking as dominant contributors among clinical and physiological EHR factors.

翻译：准确预测主要不良心脏事件（MACE）仍然是心血管预后领域的核心挑战。本文提出PRISM（基于提示引导表征整合的生存建模框架），一种自监督框架，该框架整合了来自非对比剂心脏电影磁共振成像的视觉表征与结构化电子健康记录（EHRs）以进行生存分析。PRISM通过运动感知多视角蒸馏提取时间同步的成像特征，并利用医学知识引导的文本提示对其进行调制，以实现细粒度风险预测。在四个独立的临床队列中，PRISM在内部和外部验证下持续超越经典生存预测模型以及最先进的深度学习基线。进一步的临床发现表明，PRISM衍生的成像与EHR组合表征为不同队列的心脏风险提供了有价值的见解。研究揭示了三种与MACE风险升高相关的独特成像特征，包括侧壁运动不同步、下壁高敏性以及舒张期前壁高聚焦。提示引导归因进一步识别出高血压、糖尿病和吸烟是临床与生理EHR因素中的主要风险贡献者。