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American Heart Association, Hypertension, Suppl_1(80), 2023

DOI: 10.1161/hyp.80.suppl_1.p106

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Abstract P106: High-throughput Sequence Symmetry Analysis To Detect Angiotensin-receptor Blockers (arb) Induced Prescribing Cascades.

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

Introduction: ARBs are considered first-line antiHTN therapy, but side effects may prompt additional therapy resulting in prescribing cascades. We sought to screen for potential ARB-induced prescribing cascades during real-world use via high-throughput sequence symmetry analysis. Methods: Using claims from 5% (2011-15) and 15% (2016-20) samples of Medicare fee-for-service beneficiaries we identified new ARB users aged ≥66 years who had continuous enrollment for ≥360 days pre- and ≥180 days post-ARB initiation. We screened for initiation of 446 other ‘marker’ drug classes (based on WHO Anatomical Therapeutic Classification level 4 codes) within ±90 days of ARB initiation, generating sequence ratios (SRs) representing proportions of ARB initiators starting the marker class after, compared to before, the ARB. Adjusted SRs (aSRs), accounting for prescribing trends over time, were calculated with 95% CIs >1 considered statistically significant. Results: We identified 278,586 ARB initiators (mean ± SD age, 76.3 ± 7.3 years; 63% women, 92% hypertension). Of 446 ‘marker’ classes studied, and excluding other antihypertensive classes, we discovered 49 statistically significant signals indicative of potential prescribing cascades. The 30 strongest prescribing cascade signals by aSR are summarized (Figure), and included iron trivalent medications and H. pylori treatments. Conclusion: We identified potential prescribing cascade signals reflecting known as well as potentially underrecognized ARB adverse events in this Medicare cohort. Future work is needed to assess biologic plausibility and determine impact of these cascades on patient outcomes.