title: Reduce mortality risk above all else: a discrete-choice experiment in acute coronary syndrome patients.
abstract: OBJECTIVE
  Cardiovascular disease is the main cause of death in Germany and other industrialized countries. However, until now, little has been known about how people with acute coronary syndrome (ACS) value aspects of their medical treatment. The objective of this study was to evaluate patients' preferences regarding different antiplatelet medication options following an ACS.
  
  
  METHODS
  After identification of patient-relevant treatment attributes (a literature review and qualitative interviews), a discrete-choice experiment (DCE) including five patient-relevant attributes was conducted. The DCE used a forced-choice approach in which no "opt out" was present, as no treatment is not an option after ACS. The attribute and level combinations were created using a fractional-factorial NGene design with priors. Data analysis was performed using a random-effects logit model. An additional generalized linear latent and mixed models (GLLAMM) analysis was performed to evaluate subgroup differences.
  
  
  RESULTS
  ACS patients (N = 683) participated in computer-assisted personal interviews. Preference analysis showed a clear dominance of the attribute "mortality risk" (coefficient: 0.803). Ranked second was "side effect: dyspnea" (coefficient: 0.550) followed by "risk of a new myocardial infarction" (coefficient: 0.464) and "side effect: bleeding" (coefficient: 0.400). "Frequency of intake" was less important (coefficient: 0.025). Within the 3-class GLLAMM, the variables "marital status" (p = 0.008), "highest level of education" (p = 0.003), and "body-mass index" (according to World Health Organization cluster; p = 0.014) showed a significant impact on the estimated class probabilities.
  
  
  CONCLUSIONS
  Our study found "mortality risk" to be of the highest value for patients. Patient-centered care and decision making requires consideration of patient preferences; moreover, the information on preferences can be used to develop effective therapies after an ACS. The data generated will enable healthcare decision makers and stakeholders to understand patient preferences to promote patients' benefit.
authors.affiliation: IGM Institut Gesundheitsökonomie und Medizinmanagement, Hochschule Neubrandenburg, Brodaer Straße 2, 17033, Neubrandenburg, Germany, muehlbacher@hs-nb.de.
authors.forename: Axel C
authors.initials: AC
authors.lastname: Mühlbacher
authors.forename: Susanne
authors.initials: S
authors.lastname: Bethge
mesh_terms: D054058:Acute Coronary Syndrome / Q000188:drug therapy* / Q000401:mortality; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002755:Choice Behavior; D003657:Decision Making; D005260:Female; D005858:Germany; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D057240:Patient Preference; D018802:Patient-Centered Care / Q000379:methods; D010975:Platelet Aggregation Inhibitors / Q000009:adverse effects / Q000627:therapeutic use*; D012306:Risk; D055815:Young Adult
chemical_list: D010975:Platelet Aggregation Inhibitors