You, Seng Chan

Abstract In comparative effectiveness research (CER), ensuring internal, construct, and external validity is crucial. Internal validity determines whether observed outcomes are causally linked to an intervention; construct validity assesses whether a study measures what it intends to; and external validity relates to generalizability in routine practice. Double-blind randomized trials optimize internal validity by minimizing bias and confounding, while construct validity is strengthened through pre-specified protocols and standardized data collection. However, controlled conditions limit external validity. Pragmatic RCTs improve generalizability but may compromise internal validity due to open-label designs. Observational CER studies—including observational studies following the target trial emulation framework—offer broader external validity and feasibility in less time and at lower cost. However, due to lack of random assignment, these studies are susceptible to measured and unmeasured confounding. Several techniques help mitigate these concerns, including a detailed pre-specified protocol, tools such as propensity score matching to balance measured confounders, falsification endpoint testing for assessing the presence of unmeasured confounders, and quasi-experimental designs (including instrumental variable analysis), which may be able to address both. Pre-specified sensitivity analyses and triangulation with complementary data sources further enhance robustness. Construct validity in observational CER depends on accurate patient profiling and validated computational phenotypes for identifying patients, exposures, and outcomes. Thoughtful study design and analytic rigor are essential for balancing these validity considerations. This brief review highlights these issues with examples from thrombosis research.

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.