Digital Treatment Signatures: Cardiovascular Risk Prediction Using Antihypertensive Medication Fill History

Abstract

Objective: Taking a blood pressure medication is not a single fact but a trajectory of fills, gaps, and regimen changes. Dispensing records capture that trajectory, yet risk calculators reduce it to a yes/no treatment indicator and quality programs to proportion of days covered (PDC). We asked how much of that discarded signal is recoverable. Materials and Methods: We studied 7,625 adults treated for hypertension using linked electronic health record and pharmacy dispensing data. From a 1-year medication-history window, we predicted 3-year myocardial infarction, stroke, or all-cause death. Holding cohort, covariates, horizon, and validation fixed, we compared PDC, engineered temporal and regimen features, and the ordered fill sequence modeled with an attention-based bidirectional LSTM survival network. Factorial analyses separated representation from model class. Results: C-index increased from 0.7113 with clinical factors alone to 0.7131 with PDC, 0.7273 with engineered summaries, and 0.7396 with the ordered sequence ({Delta}C=0.0283; 95% CI, 0.0189-0.0382). The sequence exceeded engineered summaries by 0.0123 (0.0040-0.0202). Richer representations improved discrimination by 0.0181-0.0191, approximately three times the 0.0059-0.0078 from changing model class. Among patients with PDC [≥]0.80, PDC ranked risk poorly (C=0.4628), whereas sequence-model strata had 3-year event rates of 3.6%-22.8%. Discussion: A medication history's prognostic value lies in its temporal structure, which aggregate adherence measures discard, so the sequence advantage reflects preserved information rather than model complexity. External validation is needed. Conclusion: Traditional metrics of antihypertensive treatment conceal substantial risk heterogeneity, whereas a tokenized dispensing timeline recovers a prognostic treatment signature that improves cardiovascular risk prediction from existing records.