Hearing diabetes in a one-minute electrocardiogram: Why phenotype-stratified machine learning may outperform one-size-fits-all screening

Table 1 Translational considerations for phenotype-aware single-lead electrocardiogram screening of diabetes mellitus

Domain	Key question	Practical recommendation
Cohort design	Is the training cohort representative of the target screening population?	Include multi-site data; report phenotype distributions; avoid convenience-only sampling
Phenotype heterogeneity	Does performance vary across cardiovascular phenotypes and comorbidity burden?	Use phenotype clustering or stratified analyses; consider phenotype-specific thresholds
Device generalizability	Will the model transport across single-lead devices and preprocessing pipelines?	Validate across hardware/software stacks; quantify performance drift; standardize signal processing when feasible
Model validity	Is discrimination and calibration adequate for case-finding?	Report area under the receiver operating characteristic curve plus calibration; provide decision curves; justify threshold selection by clinical workflow
Equity and bias	Are error rates consistent across sex, age, and racial/ethnic subgroups?	Pre-specify subgroup analyses; rebalance training; assesses fairness metrics; monitor post-deployment
Clinical workflow	How will predictions trigger confirmatory testing and follow-up?	Define a triage pathway (electrocardiogram flag → glycated hemoglobin or fasting glucose → counseling/treatment)
Reporting and quality	Is the work transparent and reproducible?	Follow TRIPOD + AI; assess with PROBAST/PROBAST + AI; share code, model cards, and validation plans when possible

AI: Artificial intelligence; TRIPOD: Transparent Reporting of a multivariable prediction model of Individual Prognosis or Diagnosis; PROBAST: Prediction model Risk of Bias Assessment Tool.