The treatment of cardiovascular diseases often involves highly technical medical equipment. Demonstrating effectiveness with top end study designs can be challenging in this field. For surgical procedures, for example, double-blinded RCTs are often not possible. As economic evaluations require input from these effectiveness studies, creative statistical procedures are required to model cost-effectiveness.
Achievements in the field of heart disease
Heart disease costing study
iMTA used observational data to assess the hospital costs for acute myocardial infarct, most notably the cost of the first myocardial infarct. Also the costs of stroke. For stroke, the inpatient days are the most important cost-driver, around two-thirds of total costs. iMTA has also performed a cost-comparison of two implantable loop recorders.more info →
A particular challenge in estimating cost-effectiveness of medical devices is that the economic evaluation is often performed right before or after market access, while newer devices become available during, or right after the HTA study. This issue requires timely as well as fast assessments, without losing quality. iMTA worked on several high quality economic evaluations, some for NICE, some for industry. In most instances, open surgery was compared to endovascular procedures, for example for TAVI, EVAR, FEVAR and BEVAR procedures. QALY estimates as well as costs were often modelled from intermediate outcomes using Markov models. We also published a key article on modelling issues in cardiovascular diseases and potential solutions.more info →
iMTA performed early HTA studies into primary prevention in cardiovascular diseases, as well as biomarker tests. iMTA is also working on stroke, estimating the cost-effectiveness of non-invasive imaging technologies that are currently being developed to improve future stroke prediction to identify patients with a recent TIA or minor ischemic stroke for surgery. iMTA estimated the minimum performance (i.e., sensitivity and specificity) that a new test must have in order for it to be cost-effective versus currently available test strategies. Stroke, estimating cost-effectiveness of biomarkers which can predict the probability of a stroke event.
A good example of a high impact study was our early HTA model for a biomarker to assess if patients at intermediate risk for a cardiovascular incident would benefit from statins. While an important improvement, the biomarker test was not cost-effective for two reasons. First, statins have a high budget impact, but not a high price per unit. Second, statins do not have many negative side-effects that could be prevented by a better selection of patients caused by the biomarker.more info →
Late phase studies & real world evidence
iMTA has done CEAs of the real-world implementation of 9 integrated care programs for cardiovascular risk management.more info →
To populate cost-effectiveness models, iMTA performed systematic reviews of CEAs for Stents, ICDs, TAVI, EVAR and lifestyle interventions. Our main conclusion from these reviews: effectiveness is often well demonstrated but divergent methods for estimating cost-effectiveness cause a wide range of cost per QALY outcomes.more info →
Decision analytic modeling
As we often work with biomarker tests, we often make a decision-tree to identify false-positive and true-positive patient responders to the test, followed by a Markov model to assess costs and effects. A great example is our decision analytic model for CT scans versus heart catheterization. Both methods were used to identify heart problems in patients. While heart catheterization is more costly, invasive and involves an increased stroke risk, a CT scan involves radiation exposure. All these elements could be modelled at iMTA to identify the optimal strategy.more info →
Preference measurement / DCE & TTO
All economic valuations require information on patient health status. For cardiovascular conditions, we investigated patients who enter the hospital for Angina Pectoris (chest pain). For these patients, quality of life was assessed. It turned out that the most important factors predicting quality of life scores where the type of angina pectoris (stable/instable), comorbidities such as renal failure and diabetes as well as age. These factors were used to help doctors decide which disease elements should be given most attention during hospital visits.more info →