Kim, Tae‐Hoon

Abstract Background and Aims Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk. Methods An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed. Results The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), −.1 (6.0), 4.7 (8.7), and −1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24–2.78), 1.89 (1.46–2.43), 1.90 (1.55–2.33), and 1.76 (1.67–1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47–3.37), 1.94 (1.39–2.70), 1.58 (1.06–2.35), and 1.79 (1.62–1.97) in these cohorts compared with the normal group. Conclusions The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.

Introduction. High BMI is a risk factor for upper body breast cancer-related lymphedema (BCRL) onset. Black cancer survivors are more likely to have high BMI than White cancer survivors. While observational analyses suggest up to 2.2 times increased risk of BCRL onset for Black breast cancer survivors, no studies have explored race or other social factors that may affect BCRL severity, operationalized by interlimb volume difference (ILD).Materials and Methods. ILD was measured by perometry for 296 overweight (25 > BMI < 50) Black (n=102) or White (n=194) breast cancer survivors (>6 months from treatment) in the WISER Survivor trial. Multivariable linear regression examined associations between social and physical factors and ILD.Results. Neither Black race (−0.26,p=0.89) nor BMI (0.22,p=0.10) was associated with ILD. Attending college (−4.89,p=0.03) was the strongest factor associated with ILD, followed by having more lymph nodes removed (4.75,p=0.01), >25% BCRL care adherence (4.10,p=0.01), and years since treatment (0.55,p<0.001).Discussion. Neither race nor BMI was associated with ILD among overweight cancer survivors. Education, a proxy for resource level, was the strongest factor associated with greater ILD. Tailoring physical activity and weight loss interventions designed to address BCRL severity by resource rather than race should be considered.