Electrocardiography score based on the Minnesota code classification system predicts cardiovascular mortality in an asymptomatic low-risk population

Abstract

Background

The use of a single abnormal finding on electrocardiography (ECG) is not recommended for stratifying the risk of cardiovascular (CV) events in low-risk general populations because of its low discriminative power. However, the value of a scoring system containing multiple abnormal ECG findings for predicting CV death has not been sufficiently evaluated.

Methods

In a prospective community-based cohort study, 8417 participants without atherosclerotic CV diseases (ASCVDs) and any related symptoms were followed for 18 years. The standard 12-lead ECGs were recorded at baseline and the ECG findings were categorized using the Minnesota code classification. CV deaths were defined as death from myocardial infarction (MI), chronic ischemic heart disease, heart failure, fatal arrhythmia, cerebrovascular event, pulmonary thromboembolism, peripheral vascular disease and sudden cardiac arrest and identified using the Korean National Statistical Office (KOSTAT) database.

Results

In a multivariate Cox proportional hazard (CPH) model, major and minor ST-T wave abnormalities, atrial fibrillation (AF), Q waves in the anterior leads, the lack of Q waves in the posterior leads, high amplitudes of the left and right precordial leads, left axis deviation and sinus tachycardia were associated with higher risks of CV deaths. The ECG score consisted of these findings showed modest predictive values represented by C-statistics that ranged from 0.632 to 760 during the follow-up and performed better in the early follow-up period. The ECG score independently predicted CV death after adjustment for relevant covariates in a multivariate model, and improved the predictive performance of the 10-year ASCVD risk estimator and a model of conventional risk factors including age, diabetes and current smoking. The combined ECG score (Harrell’s C-index: 0.852, 95% confidence interval [CI], 0.828–0.876) composed of the ECG score and the conventional risk factors outperformed the 10-year ASCVD risk estimator (Harrell’s C-index: 0.806; 95% CI, 0.780–0.833) and the model of the conventional risk factors (Harrell’s C-index: 0.841, 95% CI, 0.817–0.865) and exhibited an excellent goodness of fit between the predicted and observed probabilities of CV death.

Conclusions

The ECG score could be useful to predict CV death independently and may add value to the conventional CV risk estimators regarding the risk stratification of CV death in asymptomatic low-risk general populations.

KEY MESSAGES

• The ECG score based on the Minnesota code classification can independently predict CV death and significantly improve the predictive power of the conventional CV risk estimators in asymptomatic low-risk general population.

• The combined ECG score comprised the ECG score, age and the presence of diabetes and current smoking predicted CV mortality more accurately than the conventional SV risk estimators.

• ECG may still be a viable CV risk stratification tool for population-based health screening projects.

Keywords:

• Electrocardiography
• Minnesota code classification
• cardiovascular mortality
• low-risk population

Acknowledgments

The authors would like to thank all study participants and the research staff of the Institute of Human Genomic Study at Ansan Hospital of Korea University. We would also like to thank the Biostatistical Consulting and Research Lab, Hanyang University, for assistance with statistical analyses.

Disclosure statement

The authors declare that there are no conflicts of interest.

Authors’ contributions

Conceptualization by Yonggu Lee and Jin-Kyu Park; Data curation by Yonggu Lee and Byung Sik Kim; Formal analysis by Yonggu Lee and Wook-Dong Kim; Investigation by Yonggu Lee, Hyun-Jin Kim and Byung Sik Kim; Methodology by Yonggu Lee; Project administration by Jinho Shin and Jeong-Hun Shin; Software by Yonggu Lee and Byung Sik Kim; Supervision by Jeong-Hun Shin and Young-Hyo Lim; Validation by Byung Sik Kim and Yonggu Lee; Visualization by Wook-Dong Kim and Yonggu Lee; Writing original draft by Wook-Dong Kim and Yonggu Lee; Editing by Hyun-Jin Kim, Jeong-Hun Shin, Young-Hyo Lim, Jinho Shin, Hwan-Cheol Park and Jin-Kyu Park. All authors reviewed and approved the final submitted version of the manuscript.

Data availability statement

The KoGES data are available on reasonable request from the Korea Center for Disease Control and Prevention website after proper review procedures (http://nih.go.kr/contents.es?mid=a40504060100).

Additional information

Funding

None to declare.