Characterization of Patients With Thrombotic Microangiopathy and Triggering/Associated Events: A Global aHUS Registry Analysis
Bibliographic record
Abstract
Background: Atypical hemolytic uremic syndrome (aHUS), a rare disease of complement dysregulation, leads to thrombotic microangiopathy (TMA). In this study, we characterize patients (pts) with triggering/associated events occurring prior/up to aHUS onset from the Global aHUS Registry (NCT01522183). Methods: All pts with triggering/associated events prior/up to aHUS onset and enrolled in the Registry from 2011 to July 2021 were included. Pts with an alternative clinical diagnosis after enrollment were excluded. Results: In total, 349 pts with triggering/associated events were included, of which 75% were adults (≥ 18 years) (n=229) and 25% (n=78) were pediatric pts (<18 years). Most pts (n=307, 88%) had only one triggering/associated event, and in those, the most frequent triggering/associated events were malignancy in adults (n=51; 22%) and acute infection (n=28; 36%) in pediatric pts. The mean time from event to aHUS onset in these categories were 22 and 0.03 months, respectively. The least frequent were bone marrow transplant (1.7%) and C3 glomerulopathy (0.9%) in adults, and C3 glomerulopathy (0%), and drug-induced aHUS (0%) in pediatric pts. Autoimmune disorders (n=36) had the longest time from event to aHUS onset (41 months), while acute infection (n=53) and malignant hypertension (n=26) had the shortest (0.03 months). The most common triggering and associated events combinations were drug-induced aHUS and malignancy (n=7). Conclusions: This study suggests that triggering events can be organized into clinically relevant categories. The frequency of triggering/associated events occurring prior/up to aHUS differ in pediatric and adult populations. The interval between triggering/associated events and the eventual diagnosis of aHUS differ by type of event. Funding: Commercial Support - Alexion, AstraZeneca Rare Disease
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".