The road to biologics in patients with hidradenitis suppurativa: a nationwide drug utilization study
Bibliographic record
Abstract
BACKGROUND: Prolonged systemic antibiotic treatment is often a part of management of hidradenitis suppurativa (HS). Although biologic therapies are now available, the patient's treatment journey leading to biologic therapy is unclear. OBJECTIVES: To examine treatment patterns and duration of systemic treatment use in patients with HS preceding biologic therapy. METHODS: We identified all patients with HS receiving treatment with biologics in the Danish National Patient Registry from 2010 to 2018 and extracted their entire prescription history of specific systemic treatments from the Danish National Prescription Registry since its inception in 1995. The patients' treatment journeys are graphically displayed through Sankey diagrams and box plots generated to show temporal distributions. Descriptive patient characteristics were presented as frequencies with percentages for categorical variables and as means with SDs or medians with interquartile ranges (IQRs) for continuous variables. RESULTS: A total of 225 patients with HS were included. Patients had most frequently been treated with penicillin (n = 214; 95·1%), dicloxacillin (n = 194; 86·2%), tetracycline (n = 145; 64·4%) and rifampicin/clindamycin (n = 111; 49·3%), as well as the retinoids isotretinoin and acitretin, and dapsone. Prior to biologic therapy, patients received a mean of 4·0 (SD 1·3) different systemic therapies, across a mean of 16·9 (SD 11·3) different treatment series. The mean time from first systemic therapy until biologic therapy was initiated was 15·3 (SD 5·1) years [8·2 (SD 5·9) years when excluding penicillin and dicloxacillin]. CONCLUSIONS: Patients with HS who receive biologic therapy have long preceding treatment histories with multiple drug classes and treatment series, many of which are supported by relatively weak evidence in HS. Delay in the initiation of biologic therapy may represent a missed opportunity to prevent disease progression. What is already known about this topic? The treatment journey leading to biologic therapy in patients with HS has not previously been investigated. What does this study add? Our data from 225 patients with HS illustrate that patients who receive biologic therapy have long preceding treatment histories with multiple drug classes and treatment series, many of which are supported by relatively weak evidence in HS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".