Artificial intelligence predicts all-cause and cardiovascular mortalities using 12-lead electrocardiography in sinus rhythm

FUNDING ACKNOWLEDGEMENTS: Type of funding sources: None. INTRODUCTION: Electrocardiography (ECG) can be easily obtained at a low cost and includes voltage and time interval representing heart conditions. We hypothesized that artificial intelligence (AI) detects a subtle abnormality in 12-lead ECG and may predict individual mortality. METHODS: Among 502,411 population in UK Biobank, 42,096 individuals had 12-lead ECG from 2013 to 2022. Among population with available ECG, 4,512 individuals were enrolled in this study adjusting the following inclusion criteria; age under 60 years, sinus rhythm, PR interval 120~200ms, QTc interval 350~460ms, and QRS duration 70~100ms. We developed and tested convolutional neural network (CNN) model to predict all cause death, cardiovascular (CV) death, or sudden cardiac arrest (SCA). The study population were divided into train (80%), validation (10%), and test (20%) set. RESULTS: Among 4,512 patients with median 3.7 years [IQR; 2.7-5.1] of follow-up, the rate of all-cause mortality was 11.6% (524). In overall study population, median age was 55.5 years and proportion of male sex was 42.2%. The patients with all-cause death were older (p<0.001) and had more comorbidities (p<0.001). In the train set, CNN model showed 0.93 in AUC for predicting all-cause death. In the test set, CNN model showed consistent good performance power (AUC 0.90) for all-cause death. In subgroup analysis, 102 of 4153 (2.46%) and 57 of 4065 (1.40%) patients experienced CV death and SCA, respectively. The performance power in test set were 0.90 in AUC for CV death and 0.87 in AUC for SCA. CONCLUSIONS: AI detects and predicts future all-cause death, CV death, and SCA in median of 2.6 years by analyzing standard 12-lead ECG in generally looking normal sinus rhythm. [Figure: see text]