PROJECT SUMMARY
Hypertension (HTN) is prevalent in nearly half of U.S. adults and treated with >3 million antihypertensive
prescription fills per day in the U.S. Although commonly-used antihypertensives are generally well-tolerated,
their ubiquitous use exposes millions of adults to potentially treatment-limiting adverse events (AEs), some of
which are well-known, but many are non-specific or indistinguishable from HTN-related symptoms and not
easily attributed to the offending antihypertensive. Failure to associate these AEs with the causative agent may
prompt additional therapy to treat the AE—known as a “prescribing cascade”—with potentially important
implications regarding polypharmacy, unnecessary costs, exposure to additional side effects, treatment
nonadherence, and reduced quality of life, especially in older adults. Most prescribing cascade studies to date
have been narrowly focused on drugs with a well-known AE that is highly specific to the drug, severely limiting
our understanding of prescribing cascades occurring due to less well-known or non-specific AEs. This
approach has resulted in slow knowledge generation and missed opportunities for comprehensively assessing
and discovering new prescribing cascades. In line with NHLBI Strategic Objective 7 to “leverage emerging
opportunities in data science to open new frontiers in research,” this proposal seeks to develop and utilize a
novel methodologic approach for high-throughput screening of prescribing cascades and discover novel
antihypertensive prescribing cascades using a nationally-representative administrative claims data source.
This goal will be achieved via the following Aims: 1) elucidate candidate antihypertensive-related prescribing
cascades using the SIDe Effect Resource, a collection of prescription labeling which include drug AEs and
drug indications; 2) identify prescribing cascade signals occurring during real world use of antihypertensives
using a Medicare database; and, 3) classify cascade signal detection and prioritize further research via an
expert panel. The proposed work is expected to 1) identify and characterize the magnitude of common
antihypertensive prescribing cascades, including those previously unknown; 2) develop an efficient framework
for wide-scale assessment of prescribing cascade detection; and, 3) establish the basis for a compendium of
known cascades. This proposal also builds logically towards future research applying this framework for
discovery of prescribing cascades with cardiovascular (and other) treatments, assessing downstream
consequences of prescribing cascades, and testing clinical decision support aids to prevent prescribing
cascades.