Novel Artificial Intelligence Model Accurately Predicts Near-Term Ventricular Arrhythmias

Results show model correctly identified ventricular arrhythmias in 88% of patients with sustained ventricular tachycardia

NEW ORLEANS, LA, May 19, 2023 – A novel artificial intelligence (AI) model correctly identified patients at near-term risk of life-threatening sustained ventricular tachycardia (VT) who could potentially benefit from preemptive interventions to prevent sudden cardiac death (SCD). The AI-model utilizes a single-lead electrocardiogram (ECG) screening tool that could offer physicians a new approach to SCD risk management. The findings were presented today as late-breaking clinical science during Heart Rhythm 2023.

Sudden cardiac death is the unexpected natural death from a cardiac cause within a short time period and accounts for half of all cardiovascular deaths world-wide¹. SCD typically occurs less than one hour from the onset of symptoms and sometimes among individuals without any known prior conditions². VT, which commonly occurs in patients with structural heart disease, can be associated with an increased risk of SCD³. Because traditional mechanisms for predicting and preventing mid- and long-term SCD are limited, this study sought to understand if artificial intelligence could be leveraged to better identify near-term occurrences of VT using data from Holter ECG recordings.

The authors of this study developed a deep learning-based model using the first 24 hours of extended Holter monitor recordings, a type of portable electrocardiogram, to predict the risk of sustained (≥30 sec) ventricular tachycardia (VT) over two weeks. The model used 78,294 unselected Holter recordings collected across the US, UK, France, Czech Republic, South Africa and India. Among 59,302 recordings used for validation, the mean age of patients were 61.3 ± 17.3 years and 40% were male. 222 recordings presented sustained VT with a mean rate of 157 ± 38 bpm, and median duration 62 seconds [IQR 42, 173]), with the vast majority (98%) being monomorphic.

"Current methods for predicting SCD are extremely limited. By leaning on artificial intelligence, we hope to revolutionize the way physicians monitor, prevent, and predict SCD, improving the lives of patients while generating cost savings for our healthcare system," said Laurent Fiorina, MD, Institut Cardiovasculaire Paris Sud, Ramsay, France. "For high-risk patients who suffer from multiple conditions including hypertension, obesity, older age and diabetes, this technology could be lifesaving to help more accurately predict sustained VT and offering physicians important insights to offer early SCD prevention interventions."

On the internal validation dataset, the model achieved an AUC of 0.939 with a sensitivity of 83.3% and a specificity of 88.7%. On the external validation dataset, the AUC was 0.911 with a sensitivity and specificity of 78.9% and 81.4%, respectively. The AI-model correctly predicted VT occurrence in 88% of patients with rapid VT (≥180 bpm). Lastly, the reference model revealed an internal validation AUC of 0.833.

The authors are currently looking to validate the model in future prospective clinical studies. They would also like to extend near-term prevention through ECG monitoring to hospital monitoring or wearable sensors with potential applicability to larger populations.

¹ Wong, CX, Brown, A, Lau, DH et al, Epidemiology of Sudden Cardiac Death: Global and Regional Perspectives. Heart Lung Circ. 2019 Jan; 28(1):6-14. Doi: 10.1016/j.hlc.2018.08.026. Epub 2018 Sep 24.

² Link MS, Wang PJ, Pandian NG, et al. An experimental model of sudden death due to low-energy chest wall impact (commotio cordis). N Engl J Med.1998; 338:1805–1811

³ Koplan, BA, Stevenson, WG, Ventricular Tachycardiac and Sudden Cardiac Death, Mayo Clin Proc. 2009 Mar; 84(3): 289-297

Session Details

"Late Breaking Clinical Trials: Late Breaking Science: Near-Term Prediction of Life-Threatening Ventricular Arrhythmias using Artificial Intelligence-Enabled Single Lead Ambulatory ECG" [Friday, May 19, 2023, at 3:30 pm CT]

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