BACKGROUND: Thrombosis inhibitors can be used to treat acute coronary syndromes (ACS). However, there are various alternative treatment strategies, of which some have been compared using health economic decision models. OBJECTIVE: To assess the quality of health economic decision models comparing thrombosis inhibitors in patients with ACS undergoing percutaneous coronary intervention, and to identify areas for quality improvement. DATA SOURCES: The literature databases MEDLINE, EMBASE, EconLit, National Health Service Economic Evaluation Database (NHS EED), Database of Abstracts of Reviews of Effects (DARE) and Health Technology Assessment (HTA). STUDY APPRAISAL AND SYNTHESIS METHODS: A review of the quality of health economic decision models was conducted by two independent reviewers, using the Philips checklist. RESULTS: Twenty-one relevant studies were identified. Differences were apparent regarding the model type (six decision trees, four Markov models, eight combinations, three undefined models), the model structure (types of events, Markov states) and the incorporation of data (efficacy, cost and utility data). Critical issues were the absence of particular events (e.g. thrombocytopenia, stroke) and questionable usage of utility values within some studies. LIMITATIONS: As we restricted our search to health economic decision models comparing thrombosis inhibitors, interesting aspects related to the quality of studies of adjacent medical areas that compared stents or procedures could have been missed. CONCLUSIONS: This review identified areas where recommendations are indicated regarding the quality of future ACS decision models. For example, all critical events and relevant treatment options should be included. Models also need to allow for changing event probabilities to correctly reflect ACS and to incorporate appropriate, age-specific utility values and decrements when conducting cost-utility analyses.
BACKGROUND:Thrombosis inhibitors can be used to treat acute coronary syndromes (ACS). However, there are various alternative treatment strategies, of which some have been compared using health economic decision models. OBJECTIVE: To assess the quality of health economic decision models comparing thrombosis inhibitors in patients with ACS undergoing percutaneous coronary intervention, and to identify areas for quality improvement. DATA SOURCES: The literature databases MEDLINE, EMBASE, EconLit, National Health Service Economic Evaluation Database (NHS EED), Database of Abstracts of Reviews of Effects (DARE) and Health Technology Assessment (HTA). STUDY APPRAISAL AND SYNTHESIS METHODS: A review of the quality of health economic decision models was conducted by two independent reviewers, using the Philips checklist. RESULTS: Twenty-one relevant studies were identified. Differences were apparent regarding the model type (six decision trees, four Markov models, eight combinations, three undefined models), the model structure (types of events, Markov states) and the incorporation of data (efficacy, cost and utility data). Critical issues were the absence of particular events (e.g. thrombocytopenia, stroke) and questionable usage of utility values within some studies. LIMITATIONS: As we restricted our search to health economic decision models comparing thrombosis inhibitors, interesting aspects related to the quality of studies of adjacent medical areas that compared stents or procedures could have been missed. CONCLUSIONS: This review identified areas where recommendations are indicated regarding the quality of future ACS decision models. For example, all critical events and relevant treatment options should be included. Models also need to allow for changing event probabilities to correctly reflect ACS and to incorporate appropriate, age-specific utility values and decrements when conducting cost-utility analyses.
Authors: Josephine A Mauskopf; Jonathan B Graham; Jay P Bae; Krishnan Ramaswamy; Anthony J Zagar; Elizabeth A Magnuson; David J Cohen; Eric S Meadows Journal: J Med Econ Date: 2011-11-18 Impact factor: 2.448
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Authors: Sarah Dewilde; Bernd Brüggenjürgen; Christoph Nienaber; Jochen Senges; Robert Welte; Stefan N Willich Journal: Eur J Health Econ Date: 2011-04-12
Authors: David J Cohen; A Michael Lincoff; Tara A Lavelle; Huei-Ling Chen; Ameet Bakhai; Ronna H Berezin; Daniel Jackman; Ian J Sarembock; Eric J Topol Journal: J Am Coll Cardiol Date: 2004-11-02 Impact factor: 24.094
Authors: David M Eddy; William Hollingworth; J Jaime Caro; Joel Tsevat; Kathryn M McDonald; John B Wong Journal: Med Decis Making Date: 2012 Sep-Oct Impact factor: 2.583