Hospital readmission among patients with chronic heart failure (HF) is a major clinical and economic burden. Dynamic prediction models that leverage longitudinal biomarkers may improve risk stratification over traditional static models. This study aims to develop and validate a joint model using longitudinal N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurements to predict the risk of rehospitalization or death in HF patients. We analyzed real-world data from the TriNetX database, including patients with an incident HF diagnosis between 2016 and 2022. The final selected cohort included 1,804 patients. A Bayesian joint modeling framework was developed to link patient-specific NT-proBNP trajectories to the risk of a composite endpoint (HF rehospitalization or all-cause mortality) within a 180-day window following hospital discharge. The model's performance was evaluated using 5-fold cross-validation and assessed with the Integrated Brier Score and Integrated Calibration Index. The joint model demonstrated a strong predictive advantage over a benchmark static model, particularly when making updated predictions at later time points (180-360 days). A joint model trained on patients with more frequent NT-proBNP measurements achieved the highest accuracy. The main joint model showed excellent calibration, suggesting its risk estimates are reliable. Our findings suggest that modeling the full trajectory of NT-proBNP with a joint modeling framework enables more accurate and dynamic risk assessment compared to static, single-timepoint methods. This approach supports the development of adaptive clinical decision-support tools for personalized HF management.

翻译：慢性心力衰竭（HF）患者再入院是临床和经济上的重大负担。利用纵向生物标志物的动态预测模型可能比传统静态模型改善风险分层。本研究旨在开发并验证一种联合模型，该模型使用纵向N末端B型利钠肽原（NT-proBNP）测量值来预测HF患者再住院或死亡的风险。我们分析了来自TriNetX数据库的真实世界数据，包括2016年至2022年间新诊断为HF的患者。最终选定的队列包括1,804名患者。我们开发了一个贝叶斯联合建模框架，将患者特定的NT-proBNP轨迹与出院后180天窗口期内复合终点（HF再住院或全因死亡）的风险联系起来。模型的性能通过5折交叉验证进行评估，并使用综合Brier评分和综合校准指数进行评估。联合模型显示出相对于基准静态模型的强大预测优势，特别是在较晚时间点（180-360天）进行更新预测时。在NT-proBNP测量更频繁的患者上训练的联合模型达到了最高的准确性。主要联合模型显示出极好的校准，表明其风险估计是可靠的。我们的研究结果表明，与静态、单时间点方法相比，使用联合建模框架对NT-proBNP的完整轨迹进行建模能够实现更准确和动态的风险评估。这种方法支持开发用于个性化HF管理的自适应临床决策支持工具。