A new artificial intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, “has the potential to transform clinical decision-making”, according to the study, published in Nature Cardiovascular Research.
“Sudden cardiac death caused by arrhythmia accounts for as many as 20% of all deaths worldwide and we know little about why it’s happening or how to tell who’s at risk,” said senior author Natalia Trayanova, the Murray B. Sachs professor of Biomedical Engineering and Medicine at Johns Hopkins University, which led the study.
“There are patients who may be at low risk of sudden cardiac death getting defibrillators they might not need and then there are high-risk patients who aren’t getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done.”
The team is the first to use neural networks to build a personalised survival assessment for each patient with heart disease. These risk measures provide, with high accuracy, the chance for a sudden cardiac death over 10 years, and when it’s most likely to happen.
The deep learning technology is called Survival Study of Cardiac Arrhythmia Risk (SSCAR). The name alludes to cardiac scarring caused by heart disease that often results in lethal arrhythmias, and the key to the algorithm’s predictions.
The team used contrast-enhanced cardiac images that visualise scar distribution from hundreds of real patients at Johns Hopkins Hospital with cardiac scarring to train an algorithm to detect patterns and relationships not visible to the naked eye.
Current clinical cardiac image analysis extracts only simple scar features like volume and mass, severely underutilising what’s demonstrated in this work to be critical data.
“The images carry critical information that doctors haven’t been able to access,” said first author Dan Popescu, a former Johns Hopkins doctoral student. “This scarring can be distributed in different ways and it says something about a patient’s chance for survival. There is information hidden in it.”
The team trained a second neural network to learn from 10 years of standard clinical patient data, 22 factors such as patients’ age, weight, race and prescription drug use.
The algorithms’ predictions were not only significantly more accurate on every measure than doctors, they were validated in tests with an independent patient cohort from 60 health centres across the United States, with different cardiac histories and different imaging data, suggesting the platform could be adopted anywhere.
“This has the potential to significantly shape clinical decision-making regarding arrhythmia risk and represents an essential step towards bringing patient trajectory prognostication into the age of artificial intelligence,” said Trayanova, co-director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation. “It epitomises the trend of merging artificial intelligence, engineering, and medicine as the future of healthcare.”
The team is now working to build algorithms now to detect other cardiac diseases.
According to Trayanova, the deep-learning concept could be developed for other fields of medicine that rely on visual diagnosis.
Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart
Dan Popescu, Julie Shade, Changxin Lai, Konstantinos Aronis, David Ouyang, M. Vinayaga Moorthy, Nancy Cook, Daniel Lee, Alan Kadish, Christine Albert, Katherine Wu, Mauro Maggioni, Natalia Trayanova.
Published in Nature Cardiovascular Research on 2022.
Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. In this study, we developed a novel deep learning (DL) approach that blends neural networks and survival analysis to predict patient-specific survival curves from contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischaemic heart disease. The DL-predicted survival curves offer accurate predictions at times up to 10 years and allow for estimation of uncertainty in predictions. The performance of this learning architecture was evaluated on multi-centre internal validation data and tested on an independent test set, achieving concordance indexes of 0.83 and 0.74 and 10-year integrated Brier scores of 0.12 and 0.14. We demonstrate that our DL approach, with only raw cardiac images as input, outperforms standard survival models constructed using clinical covariates. This technology has the potential to transform clinical decision-making by offering accurate and generalisable predictions of patient-specific survival probabilities of arrhythmic death over time.
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