Abstract:
Hypertension is a preventable and treatable disease however; blood pressure control remains insufficient in many countries. An incomplete understanding of the highly complex mechanism of blood pressure regulation is one of the reasons for poor hypertension management. Physiological differentiation in individuals causes optimal antihypertensive treatment strategies to vary for patients. Consequently, instead of one fits all treatments, personalized treatments can offer improved results on therapeutic goals. Due to the sheer number of feedback loops and interconnected mechanisms, we developed and validated a system dynamics model for understanding and interpreting relationships within hypertension treatment. We aim to demonstrate the use of simulation modeling to study inter-patient physiological variations and their effects on hypertension treatment outcomes. The model includes the central nervous system and renal control mechanisms, vascular elasticity, and kidney function, capturing long-term dynamics of blood pressure. Three pathogenesis mechanisms of primary hypertension (the over-active nervous system, the over-active renin-angiotensin-aldosterone system, and low sodium excretion) are examined to represent different patient profiles. Later, treatment outcomes are analyzed on the identified patient profiles by comparing the decline in kidney and vascular functions under various treatment options. This study presented the potential benefit of using dynamic simulation models to compare treatment options for patients with physiological differences. Even though obtaining granular empirical data is challenging, they can be used for calibrating simulation models such as these to get closer to individualized treatments in clinical settings. As a future study, cost-effectiveness and value of information aspects can be incorporated to assess the benefit of personalized treatments.
