Population level risk stratification of hypertensive patients by predictive modeling
Bandhakavi, S.; Liu, Z.; Karigowda, S.; McCammon, J.; Rahmanian, F.; Lavoie, H. M.
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ObjectiveWe 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 Methods2.47 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. ResultsPrior 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. DiscussionIntegrating predictions and their "drivers" from above models supports early identification and targeted management of "at-risk" HTN patients. ConclusionWe have developed a predictive modeling based approach for risk stratification and management of HTN patients.
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