Long-Term Outcomes in Eculizumab-Treated Patients Enrolled in the Global aHUS Registry
Bibliographic record
Abstract
Background: The global atypical hemolytic uremic syndrome (aHUS) Registry (NCT01522183) is the largest repository of real-world data on patients with aHUS. We report long-term (LT) outcomes with eculizumab (ecu) treatment in two patient cohorts: those with LT follow-up (FU) without progression to end-stage kidney disease (ESKD) and those who developed ESKD during treatment. Our aim was to determine whether LT ecu preserves kidney function. Methods: The analysis included registry patients enrolled between Apr 2012-Nov 2021 and treated with ecu for ≥90 days. Cohort A included patients with ≥3-year FU, no ESKD, and two creatinine values; near treatment start and after ≥3-year observation. Cohort B included patients who developed ESKD after treatment with no FU duration specified. Results: Demographics and laboratory parameters (baseline [BL], last FU [LFU]) are shown in the Table. For Cohort A, 249 patients met inclusion criteria; at LFU, eGFR improved, 7% received acute dialysis (9% of adults, 6% of children) and two adults died. For Cohort B, 56 patients met inclusion criteria; at LFU, 48% received a kidney transplant (38% of adults, 68% of children), 89% received dialysis (95% of adults, 79% of children) and eight died (six adults, two children). BL eGFR was lower in patients who progressed to ESKD (Cohort B, N=56) than in patients who did not progress (Cohort A, N=249). Platelet and LDH levels improved in both cohorts at LFU. Conclusions: In a real-world setting, eGFR was stable in most patients receiving ecu. Some patients demonstrated hematologic benefits but no renal response; they had lower BL eGFR and initiated treatment late. Funding: Commercial Support - Alexion, AstraZeneca Rare Disease
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".