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.

NARRATIVE. Intradialytic hypotension and cardiac events are common in patients on maintenance hemodialysis and accurate prediction remains a challenge; leading to under-utilization of therapies. Electrocardiograms have rich waveform data and including them in predictive models can improve accuracy and clinical actionability. In this proposal, we examine the utility of deep and transfer learning using electrocardiogram waveform data for prediction of key cardiac outcomes in patients on hemodialysis, which will lead to new paradigms in clinical management of this vulnerable population.