Short‐term efficacy of biologics in moderate‐to‐severe hidradenitis suppurativa: A systematic review and <scp>NMA</scp>
Bibliographic record
Abstract
BACKGROUND: Given the lack of head-to-head studies of approved biologic therapies in hidradenitis suppurativa (HS), a chronic, recurrent inflammatory skin disease, a systematic literature review (SLR) and network meta-analysis (NMA) were conducted to provide insight into their comparative short-term efficacy. OBJECTIVES: To assess the relative efficacy of approved biologic therapies (bimekizumab 320 mg every 2 weeks [Q2W], secukinumab 300 mg Q2W and every 4 weeks [Q4W] and adalimumab 40 mg every week [QW]) at Week 12-16 in moderate-to-severe HS. METHODS: July 2024 for inclusion in Bayesian NMAs. Two evidence networks (predominantly biologic-naïve and biologic-experienced) were constructed to represent populations with differing biologic treatment histories. Outcomes of interest were improved HS Clinical Response (HiSCR; ≥50%/≥75%/≥90%/100%), change from baseline (CFB) in International Hidradenitis Suppurativa Severity Score System (IHS4) and improvement from baseline of ≥55% (IHS4-55). Percentage CFB in abscess and inflammatory nodule (AN) count and CFB in absolute draining tunnels (DT) count were also assessed. RESULTS: The NMA included nine trials. Bimekizumab ranked as the most efficacious treatment across all predefined efficacy outcomes in both predominantly biologic-naïve and biologic-experienced networks, showing consistent response levels. In the predominantly biologic-naïve network, bimekizumab Q2W compared with secukinumab (Q4W) demonstrated significantly higher odds of response for all HiSCR outcomes (odds ratio [OR]: HiSCR50 = 1.69; HiSCR75 = 1.85; HiSCR90 = 1.62; HiSCR100 = 1.88) and IHS4-55 (OR = 1.91), and for HiSCR75 (OR = 1.60) and HiSCR90 (OR = 1.56) compared with adalimumab QW. Similar results were observed for secukinumab Q2W and both secukinumab dosing regimens in the biologic-experienced network. Note, adalimumab studies did not report the proportion of biologic-experienced patients. Systematic review of safety data is required for full benefit-risk assessment and decision-making. CONCLUSIONS: This NMA, the first to adjust for intercurrent events in moderate-to-severe HS across multiple efficacy outcomes, assessed up-to-date data, with estimates demonstrating bimekizumab's favourable efficacy among approved biologics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".