Development and initial validation of the HS-IGA: a novel hidradenitis suppurativa-specific investigator global assessment for use in interventional trials.

BACKGROUND

Few validated instruments exist for use in hidradenitis suppurativa (HS) trials.

OBJECTIVES

To develop a novel HS Investigator Global Assessment (HS-IGA) and to validate its psychometric properties.

METHODS

Development of HS-IGA involved discussion among stakeholders, including patients, within HISTORIC. Data from replicate phase III randomized controlled trials evaluating HS treatment were utilized. Multivariate models identified lesion type and body region as variables of importance. Classification and regression trees for ordinal responses were built. Validation included assessment of test-retest reliability, predictive validity, responsiveness and clinical meaningfulness.

RESULTS

There were 3024 unique measurements available in PIONEER I. Mean and median lesion counts by region were largely <10 and were highest in axillary and inguinal regions. The mean and median number of regions involved were ≤ 3 for individual lesions and combinations. Regardless of lesion type, axillary and inguinal regions most influenced the HS-IGA score. Accordingly, regions were combined into a six-point IGA based on the maximum lesion number in either upper or lower body regions with a score of 0 (0-1 lesions), 1 (2-5), 2 (6-10), 3 (11-15), 4 (16-20) and 5 (≥ 20 lesions). The intraclass correlation coefficient for test-retest reliability was 0·91 (95% confidence interval 0·87-0·94). Spearman's rank order correlations (SROCs) with HS-PGA and Hidradenitis Suppurativa Clinical Response (HiSCR) were 0·73 and 0·51, respectively (P < 0·001 for both comparisons). SROCs with Dermatology Life Quality Index (DLQI), pain numerical rating scale and HS-QoL were 0·42, 0·34 and -0·25, respectively (P < 0·001 for all comparisons). HS-IGA was responsive at weeks 12 and 36. Predictive convergent validity was very good with HS-PGA (area under the curve = 0·89) and with HiSCR (area under the curve = 0·82). Predictive divergent validity was low with DLQI and HS-QoL.

CONCLUSIONS

HS-IGA has moderate-to-strong psychometric properties and is simple to calculate.