Determining elements of remission that predict long term outcomes (GLISTEN)

Study type

• Study topic:
  Disease /health condition
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Medicinal product name:
  CINQAERO

  DUPIXENT

  FASENRA

  NUCALA

  TEZSPIRE

  XOLAIR
• Medicinal product name, other:
  Cinqair
• Study drug International non-proprietary name (INN) or common name:
  BENRALIZUMAB

  DUPILUMAB

  MEPOLIZUMAB

  OMALIZUMAB

  RESLIZUMAB

  TEZEPELUMAB
• Additional medical condition(s):
  Severe asthma

Population studied

• Short description of the study population:
  Patients diagnosed with severe asthma from 30 countries
• Age groups:
  • Adults (18 to < 65 years)

    • Adults (18 to < 46 years)
    • Adults (46 to < 65 years)
• Estimated number of subjects:
  1.200

Study design details

• Study design:
  Registry-based longitudinal study
• Main study objective:
  To determine the relative strength of prediction between individual remission domains and long-term clinical outcomes ; ; To compare the discriminative and predictive performance of different remission definitions in predicting long-term outcomes;
• Setting:
  The study population will be extracted from the International Severe Asthma Registry database (data collected between 2017 and 2026). Eligible criteria include having initiated biologic therapy, being adult at initiation, and having follow-up data for at least 2 years after initiation.
• Comparators:
  Patients who met remission criteria in the first year of follow-up versus those who did not.
• Outcomes:
  Asthma exacerbations and lung function.
• Data analysis plan:
  A dominance analysis approach will be taken to estimate the relative importance of each remission domain (predictors) in relation to the outcomes of interest. Linear regressions will be used for modelling FEV1 measures and negative binomial regressions will be used for modelling annual exacerbation rates. The model fit metrics will be R² and the Kullback-Leibler-divergence-based R² for linear and negative binomial regressions, respectively.; ; Areas under the curve (AUCs) using the same eligible patients will be computed in order to compare their performance of predicting outcomes. Additionally, the net benefit of each definition from a clinical perspective will be assessed via decision curve analysis. ;
• Summary results:
  Not yet completed