Efficacy of eculizumab discontinuation in atypical hemolytic uremic syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT: Atypical hemolytic uremic syndrome (aHUS), a life-threatening complement-mediated disorder, is now treatable with terminal complement inhibitors like eculizumab. Although effective, these therapies are costly, and increase susceptibility to infections, notably meningococcal disease, raising concerns about long-term use. The optimal duration of complement inhibition remains unclear, prompting efforts to explore the possibility of treatment discontinuation. We conducted a systematic review and meta-analysis to evaluate the benefits and risks of stopping terminal complement inhibitor therapy in aHUS. We searched PubMed, Scopus, and CINAHL for studies of continuing vs stopping anticomplement treatment in aHUS. Of 3303 identified studies, 13 observational studies (3 case control and 10 cohort) comprising 584 patients were included. Overall, continuing treatment was associated with an ∼76% reduction in the odds of relapse (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.09-0.62; P = .01). Study design influenced results: cohort studies showed a more modest effect (OR, 0.40 [95% CI, 0.15-1.09]), whereas case-control studies reported inflated estimates (OR, 0.04; 95% CI, 0.02-0.08; subgroup interaction P = .03). When the analysis was restricted to cohort studies, the effects became uncertain (statistically nonsignificant with large CIs, indicating the possibility that outcomes with continued treatment could be either superior or inferior to those observed after treatment withdrawal, or that there may be no true difference in relapse rates between the 2 therapies). Although current evidence is insufficient to provide personalized guidance on which patients with aHUS can safely discontinue anticomplement therapy, findings from higher-quality studies, which show no statistical difference between continued and discontinued treatment, suggest that discontinuation may be possible for at least some patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".