Population prevalence of ECG abnormalities and risk of incident CVD outcomes: 5-year follow-up of 25,000 Chinese adults

Abstract

Abstract Background and purpose Little is known about the utility of automated reporting of electrocardiograms (ECGs) for prediction of cardiovascular disease (CVD) types in low- and middle-income countries. We compared derived ECG phenotypes and a clinical risk score for prediction of incident major CVD types in a population study of Chinese adults. Methods CVD risk factors were collected and 12-lead ECGs performed with automated interpretation using Mortara’s VERITAS™ algorithm on 25,239 adults in the China Kadoorie Biobank resurvey in 2013-2014. Incident CVD outcomes were recorded prospectively over a median follow-up of 5 years. We examined the prevalence and prognosis of broad ECG phenotypes developed from automated outputs: (i) atrial fibrillation/flutter (AFib); (ii) ischaemia (possible/probable or definite); (iii) left ventricular hypertrophy (LVH); and (iv) normal ECG. Participants were stratified by modified CHA2DS2-VA score (<3 vs ≥3) and by age at resurvey. Kaplan-Meier curves were plotted to display cumulative CVD event rates during follow-up for ECG phenotypes. Results The mean (SD) age at resurvey was 59.5 (10.2) years, 62% were women and 57% were from rural areas. Overall, 44.3% had a normal ECG and 1.2% had AFib (0.4%, 0.9%, and 3.9% at ages <55, 55-69, and 70+). A total of 28.1% had ischaemia, and 13.6% had LVH (Table). The prevalence of all pathological ECG phenotypes increased with age and prior CVD. AFib was more common in individuals with CHA2DS2-VA ≥3 vs <3 (3.6% vs 0.8%). CHA2DS2-VA ≥3 was associated with higher HRs (95% CI) for stroke (1.91; 1.67-2.19), ischaemic heart disease (IHD) (1.79; 1.52-2.11), and heart failure (HF) (1.80; 1.23-2.62), respectively. Compared to CHA2DS2-VA ≥3, HRs for the AFib phenotype were greater for IHD (2.62; 1.97-3.50) and HF (3.79; 2.21-6.49) and similar for stroke (1.88; 1.44-2.77). LVH was most strongly associated with HF (1.83; 1.31-2.55) and positive associations with each CVD type were greater for definite vs probable/possible ischaemic phenotypes. After adjustment for age, 5-year cumulative incidence rates of CVD were 36.8% in individuals with AFib, 19.4% in those with ischaemia (all), 18.0% in those with LVH, and 14.1% in those with normal ECGs. Individuals with CHA2DS2-VA ≥3 had a 3-fold greater cumulative incidence of CVD than those with scores <3 (40.9% vs 13.9%). Conclusions All pathological ECG phenotypes were associated with higher risks of CVD outcomes, but AFib was most strongly predictive and comparable to moderate-high CHA2DS2-VA scores. However, absolute risks of subsequent CVD events were similar in those with ischaemia, LVH and a normal ECG. When combined with clinical risk scores, automated ECG interpretation is a useful adjunct in low-resource settings for CVD risk prediction in population studies and in clinical practice.