AI 中文总结
针对心脏性猝死预测工具无法直接告知治疗权衡的问题，提出双变量离散时间框架，将终末事件和纵向标志物联合建模，通过贝叶斯范式估计，能给出患者特定联合后验预测，展示了临床适用性。
AI 中文摘要
心脏性猝死（SCD）是美国主要死因之一。SCD风险升高的患者主要用植入式心脏复律除颤器（ICD）治疗，其虽能预防心血管死亡，但常引发严重副作用。预测工具专注单变量结果，无法直接告知权衡。为此，我们提出新通用框架，将终末事件和纵向标志物作为离散时间的双变量过程联合建模。离散研究时间可捕捉结果间动态相互作用，避免终端事件（如死亡）截断后的外推。该框架灵活适应应用场景现象，通过贝叶斯范式估计，给出患者特定联合后验预测。我们介绍此框架并提供相关指导，最后用心力衰竭试验（SCD-HeFT）数据展示其临床适用性。
英文摘要
Sudden cardiac death (SCD) is a leading cause of death in the U.S. Patients at elevated risk of SCD are primarily treated with an implantable cardioverter-defibrillator (ICD), which may prevent death from cardiovascular causes but may cause severe side effects, such as reduced quality of life from shock-induced pain. Decisions about ICD treatment therefore involve complex personal trade-offs across multiple health events, including mortality and quality of life. While prediction tools could help weigh these trade-offs, they commonly focus on univariate outcomes; at best, they treat other clinical endpoints as inputs, so trade-offs cannot be directly informed. To address this, we propose a novel general Bayesian framework that jointly models a terminal event and a longitudinal marker as a bivariate process over discrete time, for settings where prediction is the primary goal. Discretization of study time lets the framework capture the dynamic interplay between outcomes while avoiding implicit extrapolation beyond truncation by a terminal event. The framework flexibly accommodates phenomena arising in applied contexts, including global time-invariant and local time-dependent dependence structures between the terminal event and the longitudinal marker, and latent association via a shared frailty term. Estimation proceeds via the Bayesian paradigm, yielding patient-specific joint posterior predictions for the time to terminal event and the future marker trajectory. We introduce the framework with a focus on its modeling flexibility, provide guidance on discretization and on Bayesian model construction and selection, and discuss insights from the joint posterior predictions. Finally, we demonstrate the framework's clinical relevance and practicality for SCD and ICD therapy using data from the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), an important ICD-related benchmark trial.
CommentsStephanie Armbruster and Daniel Kramer and Rui Duan and Rajarshi Mukherjee and Sebastien Haneuse and Harrison Reeder
