Digital Treatment Signatures: Cardiovascular Risk Prediction Using Antihypertensive Medication Fill History
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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 (ΔC=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.
