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Evaluating the health economic impact of misclassification during the cardiovascular risk assessment due to HDL-c measurement bias

Seydel, T.J. (2017) Evaluating the health economic impact of misclassification during the cardiovascular risk assessment due to HDL-c measurement bias.

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Abstract:Background: Current practice for cardiovascular risk management involves the determination of a lipid profile. Among other things, these lipid profiles consist of high density lipoprotein cholesterol (HDL-c) and triglycerides (TG). Recent studies have shown that discrepancies are present in the results of the currently used HDL-c assays when high triglyceride (TG) values are present. Objective: This study aims to estimate the impact of the observed bias in HDL-c measurements on the cardiovascular risk classification and the health economic impact of misclassification in a real-world population. Methods: The study population consists of patients with an increased probability of hypertriglyceridemia. The HDL-c bias distributions are derived from literature. A health economic decision model is constructed to estimate the cost- effectiveness, which is determined by the differences in health outcomes and costs of the different assays compared to a non-biased HDL-c measurement. The input parameters for the model are obtained through a systematic literature review of the available literature in the Scopus database. The Dutch College of General Practitioners (Nederlands Huisartsen Genootschap; NHG) guidelines for cardiovascular risk management are used to estimate the differences in risk classifications, and with it the cost-effectiveness of the assays. Further sub analyses are performed on the sub population of patients with high TG values using a resampling method called bootstrapping. Results: Overall differences between the biased HDL-c assays and the non-biased measurements were minimal. On average, 2.7% of the original population were falsely reclassified to a lower or higher risk group, which resulted in minor differences in health outcomes (-0.02 to 0.01 QALYs per patient) and costs (-€36.18 to €59.22 per patient). The sub analyses showed that the presence of high TG values or a higher age, has a negative effect on the certainty of both the health outcomes as well as the incurred costs. In the subgroup of high TG patients, the mean difference in costs per patient was -€2.99 (95%-CI: -€123.80 – €117.82) and the mean difference in QALYs per patients was 0.011 (95%-CI: -0.018 – 0.040). In the subgroup of older patients, the mean difference in costs per patient was €4.21 (95%-CI: -€48.21 – €56.63) and the mean difference in QALYs per patient was -0.001 (95%-CI: -0.010 – 0.007). The differences in health outcomes became bigger when a worst case scenario was applied (95%-CI: -0.087 to 0.021), though the uncertainty in costs remained the same as in the sub analyses. Conclusion: The overall impact of the observed HDL-c measurement biases in a high TG population is minimal. Because this study tried to incorporate statin-associated disutility’s that were based on assumptions, results have to be interpreted cautiously. Therefore we recommend that additional future research should be dedicated to the influence of adverse effects caused by statin therapy on the quality of life of a patient.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:44 medicine
Programme:Health Sciences MSc (66851)
Link to this item:http://purl.utwente.nl/essays/72377
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