Cost-based optimisation of chronic heart disease interventions

Abstract

Coronary heart disease (CHD) is the leading cause of death by non-communicable diseases. The severity of CHD places a large economic strain on the individual, thus the need for preventative strategies. Personalising such strategies is beneficial for the patient and would prevent generalised treatment with a low cost-effectiveness. Personalised cost-effective interventions were identified for two case studies. Blood tests were analysed to identify biomarkers indicating a high risk for CHD. Interventions affecting the biomarkers were identified and analysed using a Markov model. The model utilised four states to simulate the survivability of the patient. Cost parameters were added to the simulation to calculate the financial consequences of the interventions. Cost-effective interventions were identified based on the International dollar quality adjusted life year (QALY) value at completion of the simulation. Analysis of case study 1 and 2, identified thirteen and four interventions possibilities respectively. Of these, α-glucosidase inhibitors (6.80 QALY) and antidepressants (5.06 QALY) were found to be the most effective interventions for the respective case studies. The probability of remaining healthy in the case studies, after five years, increased with the use of these interventions (8% and 6%). Current and previous CHD state of living contributes the most to the cost distribution of the model (89% and 86%). Costs for β-blockers (Int dollar 33 663; case study 1) and biguanides (Int dollar 23 254; case study 2) were the lowest at the end of the simulation. These interventions were also found to be the most cost-effective for the respective case studies. It was recommended that β-blockers, diuretics or biguanides be considered as the most cost-effective interventions for case study 1. For case study 2, biguanides, antidepressants and statins were recommended as the most cost-effective preventative options.