Regular surveillance is an indispensable aspect of managing cardiovascular disorders. Patient recruitment for rare or specific diseases is often limited due to their small patient size and episodic observations, whereas prevalent cases accumulate longitudinal data easily due to regular follow-ups. These data, however, are notorious for their irregularity, temporality, sparsity, and absenteeism. In this study, we leveraged self-supervised learning (SSL) and transfer learning to overcome the above-mentioned barriers, transferring patient progress trends in cardiovascular laboratory parameters from prevalent cases to rare or specific cardiovascular events detection. We pretrained a general laboratory progress (GLP) pretrain model using hypertension patients (who were yet to be diabetic), and transferred their laboratory progress trend to assist in detecting target vessel revascularization (TVR) in percutaneous coronary intervention patients. GLP adopted a two-stage training process that utilized interpolated data, enhancing the performance of SSL. After pretraining GLP, we fine-tuned it for TVR prediction. The proposed two-stage training process outperformed SSL. Upon processing by GLP, the classification demonstrated a marked improvement, increasing from 0.63 to 0.90 in averaged accuracy. All metrics were significantly superior (p < 0.01) to the performance of prior GLP processing. The representation displayed distinct separability independent of algorithmic mechanisms, and diverse data distribution trend. Our approach effectively transferred the progression trends of cardiovascular laboratory parameters from prevalent cases to small-numbered cases, thereby demonstrating its efficacy in aiding the risk assessment of cardiovascular events without limiting to episodic observation. The potential for extending this approach to other laboratory tests and diseases is promising.

翻译：规律性监测是心血管疾病管理中不可或缺的环节。罕见或特定疾病的患者招募常因样本量小及偶发性观测而受限，而常见病例因定期随访易积累纵向数据。然而，这类数据存在不规则性、时效性、稀疏性和缺失性等显著问题。本研究利用自监督学习（SSL）和迁移学习突破上述障碍，将常见病例中心血管实验室参数的患者进展趋势迁移至罕见或特定心血管事件的检测。我们基于尚未发展为糖尿病的高血压患者数据，预训练了一个通用实验室进展（GLP）预训练模型，并将其实验室进展趋势迁移至经皮冠状动脉介入治疗患者的靶血管血运重建（TVR）检测。GLP采用两阶段训练流程，利用插值数据提升SSL性能。经预训练后，我们对GLP进行微调以预测TVR。所提出的两阶段训练流程优于标准SSL方法。经GLP处理后，分类任务的平均准确率从0.63显著提升至0.90。所有评估指标均显著优于（p < 0.01）未使用GLP处理时的性能。表征结果展现出独立于算法机制的可分离性及多样化的数据分布趋势。本方法有效将常见病例中心血管实验室参数的进展趋势迁移至小样本病例，在无需依赖偶发性观测的前提下，验证了其对辅助心血管事件风险评估的有效性。该方法有望扩展至其他实验室检测及疾病领域。