Characterising patients at risk of failed Diskus use in primary care

Study type

• Study topic:
  Disease /health condition

  Medical device
• Study type:
  Non-interventional study
• Scope of the study:
  Other
• If ‘other’, further details on the scope of the study:
  Device utilisation study
• Data collection methods:
  Secondary data collection

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Medical condition to be studied:
  Asthma

Population studied

• Short description of the study population:
  Adults aged ≥ 18 years with current diagnosis of asthma (Step 3 or 4 of Global Initiative for Asthma [GINA] guidelines) and receiving current asthma therapy as fixed dose combination (FDC) inhalation corticosteroids (ICS) in combination with long-acting beta agonist (LABA) by using a Diskus device.; Patients should be prescribed Diskus for regular/preventer asthma therapy.
• Age groups:
  Adults (18 to < 46 years)

  Adults (46 to < 65 years)

  Adults (65 to < 75 years)

  Adults (75 to < 85 years)

  Adults (85 years and over)
• Special population of interest:
  Other
• Special population of interest, other:
  Asthma patients
• Estimated number of subjects:
  623

Study design details

• Main study objective:
  1. Define the serious errors commonly performed by patients with asthma using Diskus 2. Characterise patients who perform serious errors using Diskus and those that do not3. Examine patient reported outcomes with Diskus usageThe above objectives will enable the relationship between inhalation technique and clinical outcomes to be investigated.
• Data analysis plan:
  Patients will be separated into two groups: those performing serious error(s) and those not. Patient demographics and clinical characteristics analysed as follows:Summary statistics were produced for all variables, as a complete dataset and by error categories analysed. Statistically significant results will be defined as p<0.05 and trends as 0.05≤p<0.10.Univariable logistic regression models, with a dichotomous indicator variable for serious errors made (yes/no) as the dependent variable and each patient characteristic as an explanatory variable, were first used to identify characteristics associated with making serious errors. Demographic and clinical characteristics associated with making ≥1 serious errors in the univariable model (P<0.05) were entered into a multivariable model, which was stepwise reduced to produce a final list of non-collinear independently associated variables.