Predicting mortality in cardiovascular patients using electrocardiogram data and artificial intelligence. (14th October 2021)

• Record Type: Journal Article
• Authors: Wegener, S; Gruen, D; Prim, J; Gumpfer, N; Wolter, J S; Hamm, C W; Liebetrau, C; Hannig, J; Guckert, M; Keller, T
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Epidemiology, Prognosis, Outcome; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.1132
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background/Introduction: The electrocardiogram (ECG) is an ubiquitously used non-invasive tool for diagnosis and risk prediction in cardiology, granting deep extensive insights into the heart. Artificial intelligence (AI) is a modern resource allowing the processing of vast complex datasets in a way that is comparable to humans. Risk stratification in cardiovascular patients is mainly based on scoring systems, such as the ESC-SCORE, relying on traditional risk variables like cholesterol levels or arterial hypertension, rather than actual cardiac structure and function. Goal of this project was to predict mortality using AI in patients with cardiovascular risk based on the current cardiac situation represented by a standard 12-lead ECG recording. Methods: The study population is based on an ongoing registry that started in 2010 and enrolled patients scheduled for an invasive coronary angiography due to suspected chronic coronary syndrome. Data of the following study patients were analysed: enrolment within the first two study years with available long-term follow-up data on the outcome measure overall mortality, availability of an ECG at admission without pacemaker stimulation and availability of all variables needed to calculate the ESC-SCORE (in the version weighed for a German population) as comparison. This led to a cohort of 720 patients, of whom 70 died within the follow-up period. Information on presence of a relevant coronary artery disease (CAD) was available for all patients, to differentiate between primary and secondary prevention. A deep learning architecture that was previously developed to detect myocardial scar in raw ECG time-series data was used. This model was trained with 1400 ECG recordings, from the publicly available PTB-XL dataset with 700 of those ECGs labelled for acute, recent or old myocardial infarction while 700 were labelled as healthy. This pre-trained model was then applied to our study cohort to predict long-term mortality based on a single 12-lead ECG obtained at admission. Results: For mortality prediction in patients without CAD (primary prevention) the AI model compares to the ESC-SCORE with an AUROC of 0.606 vs 0.584. For CAD patients (secondary prevention) the AI model compares with an AUROC of 0.612 vs 0.658. Detailed results are presented in Table 1. Conclusion(s): Our data underlines the potential of an AI based approach, predicting mortality in cardiovascular patients using only single 12-lead ECG recordings. Additionally, our model achieved similar predictive information to established risk classification systems, such as the ESC-SCORE. Since data acquisition is still ongoing, we will continue to improve our model. In future work training AI to specifically predict mortality while also exploring explainable AI could lead to breakthrough findings in ECG interpretation. FUNDunding Acknowledgement: Type of funding sources: Public Institution(s). Main funding source(s): FlexiFunds by Forschungscampus Mittelhessen