Objective
We recently reported that hypertension (HTN) patients having at least three rounds of distinct treatment options (atl_three_roto) in a 12-month window have elevated risk of next-year complications. However, early identification of these challenge to treat patients is non-trivial and drivers of complications in these vs remaining HTN patients are not fully defined. To address these challenges/gaps, we present predictive models for preceding outcomes, delineate their drivers, and highlight value of their integration for population level risk stratification/management of HTN patients.
Materials and Methods
2.47 million HTN patients enrolled through 2015-2016 were selected from a nation-wide commercial claims database. Features associated with their treatment patterns, comedications, and comorbidities were extracted for 2015 and used to model/predict 2016 outcomes of atl_three_roto status and/or HTN complications. Logistic regression-derived odds-ratios were used to delineate drivers of each outcome.
Results
Prior year treatment patterns, specific hypertension drugs (anti-hypertensives, calcium channel blockers, beta blockers), and congestive heart failure most increased future odds of atl_three_roto status. Regardless of prior year atl_three_roto status, specific comorbidities (renal disease, congestive heart failure, myocardial infarction, vascular disease, diabetes with chronic complications) and comedications (beta blockers, cardiac agents, anti-lipidemics) most increased future odds of HTN complications. Proof-of-concept analysis with an independent dataset demonstrated that integrating these model predictions/drivers thereof can be leveraged for risk stratification/management of HTN patients.
Discussion
Integrating predictions and their drivers from above models supports early identification and targeted management of at-risk HTN patients.
Conclusion
We have developed a predictive modeling based approach for risk stratification and management of HTN patients.
Population level risk stratification of hypertensive patients by predictive modeling
