Medications used in the treatment of heart conditions are effective at improving the health of patients but may also present a risk in terms adverse drug events (ADEs). This is due to the chemical effects on the human body of blood pressure regulating drugs such as angiotensin converting enzyme inhibitors and diuretics and the toxic nature of certain medications, such as amiodarone used to treat irregular heart rhythms. ADEs can be very costly in terms of additional medical care and can have a major impact on the patient’s quality-of-life. Careful monitoring of the levels of potassium, sodium, thyroid hormone and other chemicals in the patient’s blood is used to prevent serious ADEs. However, little is known about the potential cost savings, improvements of quality-of-life and the value-for-money of monitoring during medication therapy in general practices in the UK. We aim to estimate the costs and quality-of-life losses caused by ADEs in a cohort of patients prescribed heart medications in general practices using Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) databases. This will help us to build a model to find the most cost-effective way of monitoring patients for each medication in the study.
Medication monitoring may improve the safety profile of certain cardiovascular medications, such as angiotensin-converting enzyme inhibitors (ACEI), loop diuretics and amiodarone. There is limited evidence of the effect of missed regular monitoring on the probability of occurrence of adverse drug events (ADEs), cost of treatment and patient quality-of-life in the medication safety literature. This study aims to estimate the cost-effectiveness of alternative strategies involving monitoring long-term therapy of ACEI, loop diuretics and amiodarone at different frequencies. This will be achieved using decision-analytic models built using the results of a previously conducted cohort study which combined the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) with evidence from literature. The main outcome of the analysis is mean cost per patient from the perspective of the NHS per quality-adjusted life-year (QALY) gained during the first 5 years of medication therapy. Both a deterministic analysis will be employed in order to rank different frequencies of monitoring in terms of incremental cost per QALY and a probabilistic sensitivity analysis in order to gauge the effect of uncertainty in the values of model parameters. The results of the analysis will inform evidence-based guidelines for regular monitoring in UK primary care.
Tony Avery - Chief Investigator - University of Nottingham
Vladislav Berdunov - Corresponding Applicant - University of Nottingham
Li-Chia Chen - Collaborator - University of Manchester
Rachel Elliott - Collaborator - University of Manchester
HES Admitted Patient Care