Achieving Minimal Disease Activity and Predictors of clinical response in Patients with Psoriatic Arthritis treated with Golimumab in clinical practice: a Multicenter Observational Study (MK-8259-039)

Study type

• Study topic:
  Disease /health condition
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology
• Data collection methods:
  Primary data collection

Non-interventional study

• Non-interventional study design:
  Other
• Non-interventional study design, other:
  Multicenter, prospective, observational study

Study drug and medical condition

• Medical condition to be studied:
  Psoriatic arthropathy

Population studied

• Short description of the study population:
  The study population included all patients referred to participating clinics and diagnosed with Psoriatic arthritis (PsA) that fulfilled the study inclusion and exclusion criteria.; A subject had to meet all the criteria listed below to participate in the study.; 1. Adult ≥ 18 years of age with PsA predominantly characterized by peripheral synovitis (as per CASPAR [Classification Criteria for Psoriatic Arthritis] criteria);; 2. Subject non-responder or insufficient responder to the conventional therapies according to physician’s decision;; 3. Subject newly prescribed golimumab as indicated by the treating physician according to usual clinical practice;; 4. Patient was informed of the potential benefits and risks of golimumab as per normal practice using the patient alert card and the product leaflet; 5. Concomitant treatment with traditional DMARDs was allowed according to investigators’ decision.; 6. Naive to anti-TNFs or other biologic agents prior to initiation of golimumab as indicated by the patient’s medical records. In addition, this was evaluated by the investigator and patient interview during screening.; 7. Data on the following parameters (a set of core variables) had to be available at enrolment (prior to the first injection of golimumab): age, gender, BMI, diagnosis duration (duration since date of diagnosis), information on presence/absence of polyarthritis, ESR/CRP, concurrent DMARD, functional disability measure (e.g. HAQ), disease activity measure (DAPSA). The associated timeframe for the acute phase reactants as well as clinical and functional outcome measures availability were ± 4 weeks of golimumab prescribed;; 8. Signed informed consent form. The informed consent included a section relating to the request for consent for evaluation of biomarkers in serum from blood sample as per the Italian Ministry of Health regulation (AIFA Determination of March 2008).; 9. Each female subject had to agree to use a medically accepted method of contraception while partic
• 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)
• Special population of interest:
  Immunocompromised
• Estimated number of subjects:
  140

Study design details

• Main study objective:
  To develop a clinical prediction model, using a combination of baseline (pre-antiTNF treatment) clinical variables, for the achievement of 6-month MDA in PsA patients starting golimumab.
• Outcomes:
  Response rate to treatment defined as proportion of patients achieving MDA at 6 months. 1. Proportion of patients achieving MDA at 3 months.2. Evaluation of clinical outcomes at 3 and 6 months (change vs. baseline)o Change in Leeds Enthesitis Indexo Change in dactilytis scoreo Changes in functional score (by Health Assessment Questionnaire)
• Data analysis plan:
  For developing of the clinical prediction model of MDA a backward stepwise selection strategy will be applied to multivariable logistic model. Statistical interactions will be systematically fitted between statistically significant variables. The overall performance of the model will be evaluated by the Brier score. Discriminatory ability will be evaluating estimating the c-statistics, and calibration evaluated both statistically and graphically by the Hosmer-Lemeshow goodness-of-fit test and the calibration plot. Further logistic models will be fitted including biomarker levels as additional candidate predictors. The improvement of the performance of the prediction models will be evaluated by estimating the integrated discrimination improvement (IDI) index.