The importance of terminal complement inhibition in paroxysmal nocturnal hemoglobinuria
Bibliographic record
Abstract
Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, chronic hematologic disorder associated with inappropriate terminal complement activity on blood cells that can result in intravascular hemolysis (IVH), thromboembolic events (TEs), and organ damage. Untreated individuals with PNH have an increased risk of morbidity and mortality. Patients with PNH experiencing IVH often present with an elevated lactate dehydrogenase (LDH; ⩾ 1.5 × the upper limit of normal) level which is associated with a significantly higher risk of TEs, one of the leading causes of death in PNH. LDH is therefore used as a biomarker for IVH in PNH. The main objective of PNH treatment should therefore be prevention of morbidity and mortality due to terminal complement activation, with the aim of improving patient outcomes. Approval of the first terminal complement inhibitor, eculizumab, greatly changed the treatment landscape of PNH by giving patients an effective therapy and demonstrated the critical role of terminal complement and the possibility of modulating it therapeutically. The current mainstays of treatment for PNH are the terminal complement component 5 (C5) inhibitors, eculizumab and ravulizumab, which have shown efficacy in controlling terminal complement-mediated IVH, reducing TEs and organ damage, and improving health-related quality of life in patients with PNH since their approval by the United States Food and Drug Administration in 2007 and 2018, respectively. Moreover, the use of eculizumab has been shown to reduce mortality due to PNH. More recently, interest has arisen in developing additional complement inhibitors with different modes of administration and therapeutics targeting other components of the complement cascade. This review focuses on the pathophysiology of clinical complications in PNH and explores why sustained inhibition of terminal complement activity through the use of complement inhibitors is essential for the management of patients with this chronic and debilitating disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".