ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure

Quantifying hemodynamic severity in patients with heart failure (HF) is an integral part of clinical care. A key indicator of hemodynamic severity is the mean Pulmonary Capillary Wedge Pressure (mPCWP), which is ideally measured invasively. Accurate non-invasive estimates of the mPCWP in patients with heart failure would help identify individuals at the greatest risk of a HF exacerbation. We developed a deep learning model, HFNet, that uses the 12-lead electrocardiogram (ECG) together with age and sex to identify when the mPCWP > 18 mmHg in patients who have a prior diagnosis of HF. The model was developed using retrospective data from the Massachusetts General Hospital and evaluated on both an internal test set and an independent external validation set, from another institution. We developed an uncertainty score that identifies when model performance is likely to be poor, thereby helping clinicians gauge when to trust a given model prediction. HFNet AUROC for the task of estimating mPCWP > 18 mmHg was 0.82 +/- 0.01 and 0. 81 +/- 0.01 on the internal and external datasets, respectively. The AUROC on predictions with the highest uncertainty are 0.50 +/- 0.02 (internal) and 0.56 +/- 0.04 (external), while the AUROC on predictions with the lowest uncertainty were 0.86 +/- 0.01 (internal) and 0.82 +/- 0.01 (external). Using estimates of the prevalence of mPCWP > 18 mmHg in patients with reduced ventricular function, and a decision threshold corresponding to an 80% sensitivity, the calculated positive predictive value (PPV) is 0. 89 +/- 0.01when the corresponding chest x-ray (CXR) is consistent with interstitial edema HF. When the CXR is not consistent with interstitial edema, the estimated PPV is 0. 78 +/- 0.02, again at an 80% sensitivity threshold. HFNet can accurately predict elevated mPCWP in patients with HF using the 12-lead ECG and age/sex. The method also identifies cohorts in which the model is more/less likely to produce accurate outputs.

Automated summary

Measuring hemodynamic severity in patients with heart failure is crucial for clinical care. Invasive measurement of mean Pulmonary Capillary Wedge Pressure (mPCWP) is a key indicator, but accurate non-invasive estimation would be beneficial. We developed a deep learning model, HFNet, that uses ECG and demographic information to identify elevated mPCWP in HF patients. The model was trained and evaluated on retrospective data, and an uncertainty score was developed to help clinicians assess model performance. HFNet achieved high AUROC for estimating mPCWP and can identify when the model is likely to produce accurate outputs.