OBJECTIVES:

Hypertension is a chronic condition that if left unmanaged, will result in significant clinical, humanistic, social, and economic burdens for patients. These outcomes are closely associated with various socio-demographic variables, risk factors, comorbidities, and disease management/intervention strategies. Predicting these outcomes via predictive modelling can provide a critical support for hypertension management and downstream event prevention but requires a deep understanding of the progression pathway as well as the association of risk factors with outcomes. A targeted literature review (TLR) was thus conducted to study potential predictive variables and outcomes of interests as well as existing models to support the development of such a model based on patient characteristics.

METHODS:

Literature published between January 2017 to July 2022 was identified by searching PubMed. The search strategy combined terms for hypertension with terms for cardiovascular disease, obesity, machine learning, risk analysis, and predictive modelling.

RESULTS:

Out of an initial 4,494 articles, 42 publications were included in the analysis. Generally, the predictive models followed a cohort population and applied regression analysis onto patient characteristics and risk factors in determining the outcome of interest. The cohorts analyzed followed patients in both general and diseased populations, but no model specifically focused on hypertension. Risk factors presented in the models assessed included age, sex, body mass index, cigarette smoking, physical inactivity, parental history, ethnicity, diabetes, dyslipidemia, and poor diet. If left unmanaged, these risk factors led to outcomes including but not limited to stroke, heart failure, kidney disease, and death.

CONCLUSIONS: Hypertension and its downstream outcomes have major global implications towards population health and economics. Literature suggests that there is a capability gap in predicting the hypertension progression from patient characteristics and risk factors. The development of comprehensive and robust models to accomplish this task would be invaluable towards shaping hypertension prevention, care, and management.