An objective assessment of intrathoracic pressures remains an important diagnostic method for patients with heart failure. Although cardiac catheterization is the gold standard for estimating central hemodynamic pressures, it is an invasive procedure where a pressure transducer is inserted into a great vessel and threaded into the right heart chambers. Approaches that leverage non-invasive signals – such as the electrocardiogram (ECG) – have the promise to make the routine estimation of cardiac pressures feasible in both inpatient and outpatient settings. Prior models that were trained in a supervised fashion to estimate central pressures have shown good discriminatory ability over a heterogeneous cohort when the number of training examples is large. As obtaining central pressures (the labels) requires an invasive procedure that can only be performed in an inpatient setting, acquiring large labeled datasets for different patient cohorts is challenging. In this work, we leverage a dataset that contains over 5.4 million ECGs, without concomitant central pressure labels, to improve the performance of models trained with sparsely labeled datasets. Using a deep metric learning (DML) objective function, we develop a procedure for building latent 12-lead ECG representations and demonstrate that these latent representations can be used to improve the discriminatory performance of a model trained in a supervised fashion on a smaller labeled dataset. More generally, our results show that training with DML objectives with both labeled and unlabeled ECGs showed the downstream performance on par with the supervised baseline.