Heart failure is a debilitating condition that affects millions of people worldwide and has a significant impact on their quality of life and mortality rates. An objective assessment of cardiac pressures remains an important method for the diagnosis and treatment prognostication for patients with heart failure. Although cardiac catheterization is the gold standard for estimating central hemodynamic pressures, it is an invasive procedure that carries inherent risks, making it a potentially dangerous procedure for some patients. Approaches that leverage non-invasive signals - such as electrocardiogram (ECG) - have the promise to make the routine estimation of cardiac pressures feasible in both inpatient and outpatient settings. Prior models trained to estimate intracardiac pressures (e.g., mean pulmonary capillary wedge pressure (mPCWP)) in a supervised fashion have shown good discriminatory ability but have been limited to the labeled dataset from the heart failure cohort. To address this issue and build a robust representation, we apply deep metric learning (DML) and propose a novel self-supervised DML with distance-based mining that improves the performance of a model with limited labels. We use a dataset that contains over 5.4 million ECGs without concomitant central pressure labels to pre-train a self-supervised DML model which showed improved classification of elevated mPCWP compared to self-supervised contrastive baselines. Additionally, the supervised DML model that is using ECGs with access to 8,172 mPCWP labels demonstrated significantly better performance on the mPCWP regression task compared to the supervised baseline. Moreover, our data suggest that DML yields models that are performant across patient subgroups, even when some patient subgroups are under-represented in the dataset. Our code is available at https://github.com/mandiehyewon/ssldml

翻译：心力衰竭是一种严重影响全球数百万人生活质量和死亡率的衰弱性疾病。客观评估心脏压力仍是心力衰竭患者诊断和治疗预后的重要方法。尽管心导管检查是评估中央血流动力学压力的金标准，但作为一种侵入性操作，其固有风险对部分患者而言可能带来危险。利用非侵入性信号（如心电图）的方法有望使住院及门诊患者的常规心脏压力评估成为可能。此前以监督方式训练用于估算心内压力（如平均肺毛细血管楔压，mPCWP）的模型已展现出良好的区分能力，但其应用局限于心力衰竭队列中的标注数据集。为解决这一问题并构建稳健的表征，我们应用深度度量学习（DML），并提出一种新颖的基于距离挖掘的自监督DML方法，从而提升有限标签模型的性能。我们使用包含超过540万份无同步中心压力标签的心电图数据集预训练自监督DML模型，该模型在识别升高的mPCWP方面优于自监督对比基线。此外，利用包含8172个mPCWP标签的心电图训练的监督DML模型在mPCWP回归任务中显著优于监督基线。同时，我们的数据表明，即使某些患者亚组在数据集中代表性不足，DML仍能为各亚组生成性能优秀的模型。我们的代码开源在https://github.com/mandiehyewon/ssldml