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

Aniruddh Raghu; Daphne Schlesinger; Eugene Pomerantsev; Srikanth Devireddy; Pinak Shah; Joseph Garasic; John Guttag; Collin M Stultz

doi:10.1038/s41598-023-30900-9

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

Sci Rep. 2023 Mar 9;13(1):3923. doi: 10.1038/s41598-023-30900-9.

Authors

Aniruddh Raghu^{1

2

3}, Daphne Schlesinger^{1

2

3

4}, Eugene Pomerantsev^{5

6}, Srikanth Devireddy⁷, Pinak Shah^{5

7}, Joseph Garasic^{5

6}, John Guttag^{1

2}, Collin M Stultz^{8

9

10

11

12

13}

Affiliations

¹ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Building 36-796, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
² Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA, 02139, USA.
³ Research Laboratory of Electronics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
⁴ Institute of Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
⁵ Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA.
⁶ Division of Cardiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.
⁷ Division of Cardiovascular Medicine, Brigham and Women's Hospital, 75 Francis St., Boston, MA, 02115, USA.
⁸ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Building 36-796, 77 Massachusetts Ave., Cambridge, MA, 02139, USA. cmstultz@mit.edu.
⁹ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA, 02139, USA. cmstultz@mit.edu.
¹⁰ Research Laboratory of Electronics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA. cmstultz@mit.edu.
¹¹ Institute of Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA. cmstultz@mit.edu.
¹² Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. cmstultz@mit.edu.
¹³ Division of Cardiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA. cmstultz@mit.edu.

Abstract

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 0.8 [Formula: see text] 0.01 and 0.[Formula: see text] 0.01 on the internal and external datasets, respectively. The AUROC on predictions with the highest uncertainty are 0.50 [Formula: see text] 0.02 (internal) and 0.[Formula: see text] 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.[Formula: see text] 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.[Formula: see text] 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electrocardiography
Heart Failure* / complications
Heart Failure* / diagnosis
Hemodynamics
Humans
Lung
Retrospective Studies