OBJECTIVES: To estimate, using probabilistic decision-analytic modelling techniques, the cost effectiveness of treating familial hypercholesterolaemia (FH) patients with high-intensity statins compared to treatment with low-intensity statins. For the purpose of this economic analysis, and based on their known differences, statins were categorised as high intensity if they produce greater LDL-cholesterol reductions than simvastatin 40 mg (e.g., simvastatin 80 mg and appropriate doses of atorvastatin and rosuvastatin or combination of statins + ezetimibe). METHODS: A lifetime Markov model was developed to estimate the incremental cost per quality adjusted life year (QALY) of treating a hypothetical cohort of 1000 FH patients aged between 20 and 70 years. Baseline coronary heart disease risks reported in the NICE TA 94 on statins, and age-adjusted risk of cardiovascular disease reported in the FH population, were used to populate the model. A meta-analysis estimate of the reduction in cardiovascular events from using high-intensity compared with low-intensity statins was obtained from published trials. Results were interpreted using a cost-effectiveness threshold of pound20 000/QALY. RESULTS: Fewer cardiovascular events and deaths were predicted to occur in the group treated with higher-intensity statins, and the incremental cost-effectiveness ratio (ICER) was estimated at pound11 103/QALY. The ICER remained below the pound20 000 threshold for 20-39-year-olds and 40-59-year-olds, but rose above this threshold in individuals aged over 60 years. One-way sensitivity analysis showed that results were most sensitive to variation in treatment effect on mortality and the cost of high-intensity statins. CONCLUSIONS: Modelling demonstrates that high-intensity statins are cost-effective for the treatment of younger FH patients. If, as is likely, the relative price of high-intensity statins fall in the future as they come off patent, then their cost effectiveness will improve further.
OBJECTIVES: To estimate, using probabilistic decision-analytic modelling techniques, the cost effectiveness of treating familial hypercholesterolaemia (FH) patients with high-intensity statins compared to treatment with low-intensity statins. For the purpose of this economic analysis, and based on their known differences, statins were categorised as high intensity if they produce greater LDL-cholesterol reductions than simvastatin 40 mg (e.g., simvastatin 80 mg and appropriate doses of atorvastatin and rosuvastatin or combination of statins + ezetimibe). METHODS: A lifetime Markov model was developed to estimate the incremental cost per quality adjusted life year (QALY) of treating a hypothetical cohort of 1000 FHpatients aged between 20 and 70 years. Baseline coronary heart disease risks reported in the NICE TA 94 on statins, and age-adjusted risk of cardiovascular disease reported in the FH population, were used to populate the model. A meta-analysis estimate of the reduction in cardiovascular events from using high-intensity compared with low-intensity statins was obtained from published trials. Results were interpreted using a cost-effectiveness threshold of pound20 000/QALY. RESULTS: Fewer cardiovascular events and deaths were predicted to occur in the group treated with higher-intensity statins, and the incremental cost-effectiveness ratio (ICER) was estimated at pound11 103/QALY. The ICER remained below the pound20 000 threshold for 20-39-year-olds and 40-59-year-olds, but rose above this threshold in individuals aged over 60 years. One-way sensitivity analysis showed that results were most sensitive to variation in treatment effect on mortality and the cost of high-intensity statins. CONCLUSIONS: Modelling demonstrates that high-intensity statins are cost-effective for the treatment of younger FHpatients. If, as is likely, the relative price of high-intensity statins fall in the future as they come off patent, then their cost effectiveness will improve further.
Authors: Ching-Yun Wei; Ruben G W Quek; Guillermo Villa; Shravanthi R Gandra; Carol A Forbes; Steve Ryder; Nigel Armstrong; Sohan Deshpande; Steven Duffy; Jos Kleijnen; Peter Lindgren Journal: Pharmacoeconomics Date: 2017-03 Impact factor: 4.981
Authors: Albert Wiegman; Samuel S Gidding; Gerald F Watts; M John Chapman; Henry N Ginsberg; Marina Cuchel; Leiv Ose; Maurizio Averna; Catherine Boileau; Jan Borén; Eric Bruckert; Alberico L Catapano; Joep C Defesche; Olivier S Descamps; Robert A Hegele; G Kees Hovingh; Steve E Humphries; Petri T Kovanen; Jan Albert Kuivenhoven; Luis Masana; Børge G Nordestgaard; Päivi Pajukanta; Klaus G Parhofer; Frederick J Raal; Kausik K Ray; Raul D Santos; Anton F H Stalenhoef; Elisabeth Steinhagen-Thiessen; Erik S Stroes; Marja-Riitta Taskinen; Anne Tybjærg-Hansen; Olov Wiklund Journal: Eur Heart J Date: 2015-05-25 Impact factor: 29.983
Authors: Børge G Nordestgaard; M John Chapman; Steve E Humphries; Henry N Ginsberg; Luis Masana; Olivier S Descamps; Olov Wiklund; Robert A Hegele; Frederick J Raal; Joep C Defesche; Albert Wiegman; Raul D Santos; Gerald F Watts; Klaus G Parhofer; G Kees Hovingh; Petri T Kovanen; Catherine Boileau; Maurizio Averna; Jan Borén; Eric Bruckert; Alberico L Catapano; Jan Albert Kuivenhoven; Päivi Pajukanta; Kausik Ray; Anton F H Stalenhoef; Erik Stroes; Marja-Riitta Taskinen; Anne Tybjærg-Hansen Journal: Eur Heart J Date: 2013-08-15 Impact factor: 29.983
Authors: Karen Broekhuizen; Marieke F van Wier; Lando L J Koppes; Johannes Brug; Willem van Mechelen; Judith E Bosmans; Mireille N M van Poppel Journal: BMC Res Notes Date: 2015-07-29