多任务深度学习模型推进镰状细胞病死亡风险预测
Advancing mortality risk prediction in sickle cell disease through multi-task deep learning models
镰状细胞病（一种遗传性血红蛋白异常疾病）患者的病程差异很大，而造血干细胞移植和基因治疗等治愈手段风险较高，临床需要准确、可解释的工具来识别高危患者。研究者开发了Multi-Task DeepHit神经网络生存模型，在开发阶段学习3年生物标志物轨迹，训练完成后仅需基线数据即可预测。研究纳入598名成年患者并在一独立队列383人中外部验证，模型在区分度和5年预测误差上均优于传统两步法和仅做生存分析的模型，且打乱轨迹后性能下降，说明其获益来自真实的个体轨迹。预测中贡献最大的是网织红细胞百分比、碱性磷酸酶、右心房压力、三尖瓣反流速度和右心房面积。
为什么推荐给您：纵向生物标志物轨迹建模提升了死亡风险预测，属值得关注的方法学进展，且有外部验证。
不需要生物学背景，多打比方
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摘要Abstract
Patients with sickle cell disease (SCD) have highly variable clinical courses that become more heterogeneous over time. As curative therapies such as hematopoietic cell transplantation and gene therapy become more available but remain risky, clinicians need accurate, interpretable tools to identify patients at increased mortality risk and the organ-system complications driving it. We developed Multi-Task DeepHit, a neural-network survival model that uses baseline clinical data and learns from 3-year biomarker trajectories during model development. Once trained, it required only baseline data for mortality prediction. We studied 598 adults with SCD (median age 34 years; 51% female, 2006 - 2025) in one NHLBI cohort and externally validated the model in a separate, non-overlapping NHLBI cohort of 383 adults. Baseline inputs included 68 demographic, laboratory, vital-sign, and echocardiographic covariates. Twelve longitudinal biomarkers captured renal, hepatic, cardiopulmonary, and systemic function. Multi-Task DeepHit outperformed both a two-step statistical/machine-learning model and a survival-only DeepHit model, with better discrimination and lower 5-year prediction error in both cohorts. This improvement reflected true patient-specific trajectories, not added model complexity, as performance fell when trajectories were shuffled. The strongest contributors to predicted mortality were reticulocyte percentage, alkaline phosphatase, right atrial pressure, tricuspid regurgitation velocity, and right atrial area. Lower reticulocyte percentage contributed to higher predicted risk, although this pattern depended on other biomarkers. By leveraging longitudinal biomarker trajectories, Multi-Task DeepHit improves mortality prediction in adults with SCD, supports individualized risk assessment, and identifies patients who may benefit from closer surveillance or more intensive disease-modifying or curative therapies.