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
this https URL

心脏衰竭是一种影响数百万人的严重疾病，对他们的生活质量和死亡率产生重要影响。本研究使用电生理信号等非侵入性方法来评估心脏压力，提出了一种基于深度度量学习的模型，该模型在有限标签数据情况下表现良好，并具有患者亚组之间的高度性能表现。