Dabigatran study in the early phase of stroke. New neuroimaging markers and biomarkers study (SEDMAN STUDY)

Study type

• Study type:
  Non-interventional study
• Scope of the study:
  Other
• If ‘other’, further details on the scope of the study:
  Pharmacogenomics

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Anatomical Therapeutic Chemical (ATC) code:
  (B01AE07) dabigatran etexilate

  dabigatran etexilate

  (B01AA07) acenocoumarol

  acenocoumarol
• Medical condition to be studied:
  Embolic stroke

Population studied

• Age groups:
  • Adult and elderly population (≥18 years)

    • Adults (18 to < 65 years)

      • Adults (18 to < 46 years)
      • Adults (46 to < 65 years)
    • Elderly (≥ 65 years)

      • Adults (65 to < 75 years)
      • Adults (75 to < 85 years)
      • Adults (85 years and over)
• Estimated number of subjects:
  500

Study design details

• Main study objective:
  The main objective is found genetic risk factors associated with the occurrence of any bleeding or vascular event in a year follow-up in patients treated with dabigatran or acenocoumarol.
• Outcomes:
  Recurrence of vascular events and any bleeding
• Data analysis plan:
  Data will be analyzed through GWAS analysis. The results will be analyzed by experienced genotypic bioinformatics: Plink, Haploview, STATA, IMPUTE2, SNPtest, GTOOL, Galaxy managment. In addition, we have support of Spanish Consortium for Genetics of Stroke (Genestroke). The PLINK software is the main program for the analysis of GWAS. Using this software we can clean the raw results of genotyping platforms and determine the presence of risk alleles associated with resistance to the study drugs. Haploview software will be used for data management and graphics for significance analysis. STATA statistical software is a data management and very useful for the creation of Q_Q plots graphs that assist in the interpretation of genotypic results. GTOOL, SNPtest and IMPUTE2 are software for data imputation and analysis of imputed data through the information available in the 1000 Genomes Project.