# Deep learning on ECGs to improve outcomes in patients on dialysis

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $716,516

## Abstract

ABSTRACT.
Intradialytic hypotension (IDH) and major adverse cardiovascular events (MACE) are common in patients on
maintenance hemodialysis (HD) and contribute significantly to morbidity and mortality in this vulnerable patient
population. Although strategies to decrease these adverse outcomes exist, the lack of accurate and actionable
predictive risk models has led to overall low and non-targeted utilization of these strategies.
Electrocardiography (ECG) is ubiquitous, cheap, simple to perform, and it provides an immediately accessible,
non-invasive insight into cardiovascular reflexes and health. The raw waveform data can be leveraged by
advanced deep learning for accurate determination of various cardiac features as well as prognostication of
key outcomes. In our prior published work, we demonstrated the utility of deep learning to determine both right
and left heart function and the utility of transfer learning to improve outcome prediction in patients on HD. In
recent preliminary analysis, we also show utility of waveform data to predict in hospital IDH and association
with 30-day mortality using retrospective data. However, prospective development and validation on IDH and
MACE are critical to clinical deployment. Thus, extending our prior work, we propose the largest prospective
study on utilizing ECGs for prediction of key outcomes in patients on HD. We will recruit 1000 diverse patients
on HD from dialysis units in New York City (derivation) and 150 patients from North Carolina (validation) and
obtain standard duration, 12-lead ECGs at baseline and 4 weeks after baseline. In addition, a subset of
participants will undergo continuous waveform monitoring during 3 consecutive HD sessions in an exploratory
sub-study. We will then use deep learning and transfer learning (using pre-trained models from our
approximately 11 million archival ECG database) and use this to predict IDH at the same session and within 30
days (Aim 1) and a composite outcome of MACE at 1 year of follow up (Aim 2). The results of this proposal
are of high clinical importance for the prediction of both short- and long-term cardiac outcomes. Positive results
will prompt studies testing deployment of our predictive models into HD units for detection and prevention of
IDH and MACE as well use of novel wearables for IDH and cardiac risk prediction.

## Key facts

- **NIH application ID:** 10889267
- **Project number:** 5R01HL167050-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** David M Charytan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $716,516
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10889267

## Citation

> US National Institutes of Health, RePORTER application 10889267, Deep learning on ECGs to improve outcomes in patients on dialysis (5R01HL167050-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10889267. Licensed CC0.

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