title: Identifying the biologic closest to the ideal to treat chronic plaque psoriasis in different clinical scenarios: using a pilot multi-attribute decision model as a decision-support aid.
abstract: OBJECTIVE
  Multi-attribute decision-making (MADM) models evaluate competing solutions for complex problems to identify the closest fit to the ideal solution. MADM models may assist dermatologists when selecting between biologics for plaque psoriasis. Here, is described the development of a pilot model to identify the preferred biologic from the dermatologist's perspective.
  
  
  METHODS
  A group of European dermatologists were surveyed to identify treatment attributes they consider when prescribing a biologic. The relative importance of each was determined by allocation of 100 importance points in the context of seven case vignettes, reflecting the breadth of disease encountered in dermatological practice. Biologic performance was rated anonymously on a scale of 1-10, scores entered into a MADM matrix, and TOPSIS (Technique for Ordered Preference by Similarity to the Ideal Solution) analysis applied to identify the biologic closest to the hypothetical ideal.
  
  
  RESULTS
  Long-term efficacy and safety were the most important attributes considered by dermatologists when selecting a biologic. For one case vignette (chronic stable psoriasis), TOPSIS scores showed that etanercept was closest to the ideal for 63% of respondents, with adalimumab closest to the ideal for 32% of respondents. Differences among the biologics were highly significant (p < 0.0001). For severe unstable psoriasis, infliximab and adalimumab were preferred.
  
  
  CONCLUSIONS
  This study was conducted with a group of dermatologists attending a Wyeth-sponsored advisory board meeting.
  
  
  CONCLUSIONS
  Based on responses from this expert group, etanercept was the preferred choice for stable chronic plaque psoriasis for the majority, with infliximab preferred for more severe disease. However, there are several limitations to this pilot model, most notably the non-random selection of the expert group. Further development of the model encompassing a random survey of dermatologists and inclusion of other treatment alternatives and the latest clinical data, will add to the clinical utility of the tool.
authors.affiliation: Hospital St Louis, Paris, France.
authors.forename: Fabien
authors.initials: F
authors.lastname: Guibal
authors.forename: Lars
authors.initials: L
authors.lastname: Iversen
authors.forename: Lluís
authors.initials: L
authors.lastname: Puig
authors.forename: Robert
authors.initials: R
authors.lastname: Strohal
authors.forename: Paul
authors.initials: P
authors.lastname: Williams
mesh_terms: D000328:Adult; D000465:Algorithms; D001688:Biological Products / Q000627:therapeutic use*; D016022:Case-Control Studies; D002908:Chronic Disease; D003661:Decision Support Techniques; D003663:Decision Trees; D003879:Dermatologic Agents / Q000627:therapeutic use; D019317:Evidence-Based Medicine / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010865:Pilot Projects; D011565:Psoriasis / Q000188:drug therapy*; D016896:Treatment Outcome
chemical_list: D001688:Biological Products; D003879:Dermatologic Agents