Long‐term effectiveness of eculizumab: Data from the International <scp>PNH</scp> Registry
Bibliographic record
Abstract
Abstract Objectives Data from the International PNH Registry (NCT01374360) were used to estimate the overall survival and first occurrence of thromboembolic events/major adverse vascular events (TEs/MAVEs) for eculizumab‐treated patients with paroxysmal nocturnal hemoglobinuria (PNH) compared with a contemporaneous untreated cohort. Methods Patients enrolled in the Registry from March 16, 2007, to February 14, 2022, were included. Treated patients received eculizumab for >35 days; untreated patients did not receive eculizumab at any time. Univariable and multivariable analyses were performed using a Cox proportional hazards regression model comparing eculizumab treatment periods to untreated periods and were adjusted for baseline covariates (e.g., high disease activity [HDA], transfusion dependency, and eculizumab treatment status). Results The analysis included 4118 patients. The univariable hazard ratio (HR) (95% CI) for mortality in eculizumab‐treated time versus untreated time was 0.51 (0.41–0.64; p < 0.0001). Significant baseline covariates included age, sex, history of bone marrow failure, ≥4 erythrocyte transfusions within 12 months before baseline, and an estimated glomerular filtration rate ≤ 60 mL/min/1.73 m 2 (all p < 0.0001). In the adjusted analysis, patients with baseline HDA had the greatest reduction in mortality risk (HR [95% CI], 0.51 [0.36–0.72]). Treated patients had approximately 60% reduction in TE/MAVE risk during treated versus untreated time (HR [95% CI]: TE: 0.40 [0.26–0.62], MAVE: 0.37 [0.26–0.54]; p < 0.0001). Conclusion Using data from the largest Registry of patients with PNH, with ≥14 years of overall follow‐up, we demonstrate that treatment with eculizumab conferred a 49% relative benefit in survival and an approximately 60% reduction in TE/MAVE risk.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".