Electronic health record phenotype choice and real-world outcome estimates: A propensity score–matched comparison of three invasive breast cancer definitions in TriNetX

Electronic health record-based oncology studies depend on computable case definitions, but different operational definitions may identify different patient populations and change downstream estimates. This retrospective benchmarking cohort study evaluated three definitions of incident invasive breast cancer in the TriNetX Global Collaborative Network: at least one malignant breast cancer diagnosis code (Dx1), a repeated diagnosis-code definition within 90 days (Dx2), and a diagnosis code plus breast cancer-directed treatment evidence (DxTx). Female patients aged 20 years or older were identified after a 12-month washout period and exclusion of patients with carcinoma in situ only. Pairwise one-to-one propensity-score matching was performed using 13 baseline covariates. Outcomes were all-cause mortality and time to the first emergency department visits, assessed from day 180 after the index date to 1, 3, and 5 years after the original index date. Appendicitis and femoral neck fracture were included as negative-control outcomes. Dx1, Dx2, and DxTx identified 1,450,233, 678,739, and 578,576 patients, respectively. After matching, more restrictive definitions were consistently associated with higher hazards of mortality and emergency department visits. For mortality, hazard ratios ranged from 1.26 to 1.32 for DxTx versus Dx1, 1.13 to 1.14 for Dx2 versus Dx1, and 1.13 to 1.17 for DxTx versus Dx2 across assessment windows. Emergency department visit estimates showed a similar but attenuating pattern. These associations should be interpreted as differences between phenotype-defined populations arising from selection and ascertainment mechanisms rather than as treatment effects. Negative-control outcomes were generally close to the null; small appendicitis associations observed in comparisons involving Dx1 did not remain statistically significant after correction for multiple testing. These findings indicate that plausible electronic health record-based breast cancer definitions are not analytically interchangeable. Phenotype specification should therefore be treated as a core analytic decision and evaluated through transparent reporting and sensitivity analyses.