Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator

BACKGROUND

Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias.

METHODS

A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves.

FINDINGS

2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions.

INTERPRETATION

Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models.

FUNDING

This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients

Aims

Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variability metrics could be used to discriminate between sustained and spontaneously terminating VT.

Methods and results

In this single-centre retrospective study, we analysed data from 69 VT episodes stored on ICDs from 27 patients (36 spontaneously terminating VT, 33 sustained VT). Several VT CL parameters including heart rate variability metrics were calculated. Additionally, a first order auto-regression model was fitted using the first 10 CLs. Using features derived from the first 10 CLs, a random forest classifier was used to predict VT termination. Sustained VT episodes had more stable CLs. Using data from the first 10 CLs only, there was greater CL variability in the spontaneously terminating episodes (mean of standard deviation of first 10 CLs: 20.1 ± 8.9 vs. 11.5 ± 7.8 ms, P < 0.0001). The auto-regression coefficient was significantly greater in spontaneously terminating episodes (mean auto-regression coefficient 0.39 ± 0.32 vs. 0.14 ± 0.39, P < 0.005). A random forest classifier with six features yielded an accuracy of 0.77 (95% confidence interval 0.67 to 0.87) for prediction of VT termination.

Conclusion

Ventricular tachycardia CL variability and instability are associated with spontaneously terminating VT and can be used to predict spontaneous VT termination. Given the harmful effects of unnecessary ICD shocks, this machine learning model could be incorporated into ICD algorithms to defer therapies for episodes of VT that are likely to self-terminate.

The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action

Despite major advancements in cardiovascular medicine, sudden cardiac death (SCD) continues to be an enormous medical and societal challenge, claiming millions of lives every year. Efforts to prevent SCD are hampered by imperfect risk prediction and inadequate solutions to specifically address arrhythmogenesis. Although resuscitation strategies have witnessed substantial evolution, there is a need to strengthen the organisation of community interventions and emergency medical systems across varied locations and health-care structures. With all the technological and medical advances of the 21st century, the fact that survival from sudden cardiac arrest (SCA) remains lower than 10% in most parts of the world is unacceptable. Recognising this urgent need, the Lancet Commission on SCD was constituted, bringing together 30 international experts in varied disciplines. Consistent progress in tackling SCD will require a completely revamped approach to SCD prevention, with wide-sweeping policy changes that will empower the development of both governmental and community-based programmes to maximise survival from SCA, and to comprehensively attend to survivors and decedents' families after the event. International collaborative efforts that maximally leverage and connect the expertise of various research organisations will need to be prioritised to properly address identified gaps. The Commission places substantial emphasis on the need to develop a multidisciplinary strategy that encompasses all aspects of SCD prevention and treatment. The Commission provides a critical assessment of the current scientific efforts in the field, and puts forth key recommendations to challenge, activate, and intensify efforts by both the scientific and global community with new directions, research, and innovation to reduce the burden of SCD worldwide.

Remote monitoring of cardiac implantable electronic devices and disease management

This reviews the transition of remote monitoring of patients with cardiac electronic implantable devices from curiosity to standard of care. This has been delivered by technology evolution from patient-activated remote interrogations at appointed intervals to continuous monitoring that automatically flags clinically actionable information to the clinic for review. This model has facilitated follow-up and received professional society recommendations. Additionally, continuous monitoring has provided a new level of granularity of diagnostic data enabling extension of patient management from device to disease management. This ushers in an era of digital medicine with wider applications in cardiovascular medicine.