Aims: Hypertension is the strongest modifiable risk factor for cardiovascular disease, affecting 80 million individuals in the US and responsible for ∼360,000 deaths, at total annual costs of $93.5 billion. Antihypertension therapies guided by single genotypes are clinically more effective and may avert more adverse events than the standard of care of layering anti-hypertensive drug therapies, thus potentially decreasing costs. This study aimed to determine the economic benefits of the implementation of multi-gene panel guided therapies for hypertension from the payer perspective within a 3-year time horizon.
Materials and methods: A simulation analysis was conducted for a panel of 10 million insured patients categorized clinically as untreated, treated but uncontrolled, and treated and controlled over a 3-year treatment period. Inputs included research data; empirical data from a 11-gene panel with known functional, heart, blood vessel, and kidney genotypes; and therapy efficacy and safety estimates from literature. Cost estimates were categorized as related to genetic testing, evaluation and management, medication, or adverse events.
Results: Multi-gene panel guided therapy yielding savings of $6,256,607,500 for evaluation and management, $908,160,000 for medications, and $37,467,508,716 for adverse events, after accounting for incremental genetic testing costs of $2,355,540,000. This represents total 3-year savings of $42,276,736,216, or a 47% reduction, and 3-year savings of $4,228 and annual savings of $1,409 per covered patient.
Conclusions: A precision medicine approach to genetically guided therapy for hypertension patients using a multi-gene panel reduced total 3-year costs by 47%, yielding savings exceeding $42.3 billion in an insured panel of 10 million patients. Importantly, 89% of these savings are generated by averting specific adverse events and, thus, optimizing choice of therapy in function of both safety and efficacy.
Keywords: C15; E17; Economics; Genetic testing; Hypertension; I11; I15; Pharmacogenomics; Precision medicine; Simulation.