Dataset Information

Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.

ABSTRACT: Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. Here, we develop 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 ischemic 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-center internal validation data and tested on an independent test set, achieving concordance index of 0.83 and 0.74, and 10-year integrated Brier score 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 generalizable predictions of patient-specific survival probabilities of arrhythmic death over time.

SUBMITTER: Popescu DM

PROVIDER: S-EPMC9022904 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.

Popescu Dan M DM Shade Julie K JK Lai Changxin C Aronis Konstantinos N KN Ouyang David D Moorthy M Vinayaga MV Cook Nancy R NR Lee Daniel C DC Kadish Alan A Albert Christine M CM Wu Katherine C KC Maggioni Mauro M Trayanova Natalia A NA

Nature cardiovascular research 20220407 4

Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. Here, we develop 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 ischemic 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 performa ...[more]

PMID: 35464150

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Project description:BackgroundPreviously, we showed that a mouse model (ACE8/8) of cardiac renin-angiotensin system activation has a high rate of spontaneous ventricular tachycardia and sudden cardiac death secondary to a reduction in connexin43 level. Angiotensin-II activation increases reactive oxygen species (ROS) production, and ACE8/8 mice show increased cardiac ROS. We sought to determine the source of ROS and whether ROS played a role in the arrhythmogenesis.Methods and resultsWild-type and ACE8/8 mice with and without 2 weeks of treatment with L-NIO (NO synthase inhibitor), sepiapterin (precursor of tetrahydrobiopterin), MitoTEMPO (mitochondria-targeted antioxidant), TEMPOL (a general antioxidant), apocynin (nicotinamide adenine dinucleotide phosphate oxidase inhibitor), allopurinol (xanthine oxidase inhibitor), and ACE8/8 crossed with P67 dominant negative mice to inhibit the nicotinamide adenine dinucleotide phosphate oxidase were studied. Western blotting, detection of mitochondrial ROS by MitoSOX Red, electron microscopy, immunohistochemistry, fluorescent dye diffusion technique for functional assessment of connexin43, telemetry monitoring, and in vivo electrophysiology studies were performed. Treatment with MitoTEMPO reduced sudden cardiac death in ACE8/8 mice (from 74% to 18%; P<0.005), decreased spontaneous ventricular premature beats, decreased ventricular tachycardia inducibility (from 90% to 17%; P<0.05), diminished elevated mitochondrial ROS to the control level, prevented structural damage to mitochondria, resulted in 2.6-fold increase in connexin43 level at the gap junctions, and corrected gap junction conduction. None of the other antioxidant therapies prevented ventricular tachycardia and sudden cardiac death in ACE8/8 mice.ConclusionsMitochondrial oxidative stress plays a central role in angiotensin II-induced gap junction remodeling and arrhythmia. Mitochondria-targeted antioxidants may be effective antiarrhythmic drugs in cases of renin-angiotensin system activation.

Project description:Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).

Dataset Information

Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.

Publications

Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.

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