Characterization of patients with <scp>aHUS</scp> and associated triggers or clinical conditions: A Global <scp>aHUS</scp> Registry analysis
Bibliographic record
Abstract
INTRODUCTION: Atypical haemolytic uremic syndrome (aHUS) is a rare form of thrombotic microangiopathy (TMA) associated with complement dysregulation; aHUS may be associated with other 'triggers' or 'clinical conditions'. This study aimed to characterize this patient population using data from the Global aHUS Registry, the largest collection of real-world data on patients with aHUS. METHODS: Patients enrolled in the Global aHUS Registry between April 2012 and June 2021 and with recorded aHUS-associated triggers or clinical conditions prior/up to aHUS onset were analysed. aHUS was diagnosed by the treating physician. Data were classified by age at onset of aHUS (< or ≥18 years) and additionally by the presence/absence of identified pathogenic complement genetic variant(s) and/or anti-complement factor H (CFH) antibodies. Genetically/immunologically untested patients were excluded. RESULTS: 1947 patients were enrolled in the Global aHUS Registry by June 2021, and 349 (17.9%) met inclusion criteria. 307/349 patients (88.0%) had a single associated trigger or clinical condition and were included in the primary analysis. Malignancy was most common (58/307, 18.9%), followed by pregnancy and acute infections (both 53/307, 17.3%). Patients with an associated trigger or clinical condition were generally more likely to be adults at aHUS onset. CONCLUSION: Our analysis suggests that aHUS-associated triggers or clinical conditions may be organized into clinically relevant categories, and their presence does not exclude the concurrent presence of pathogenic complement genetic variants and/or anti-CFH antibodies. Considering a diagnosis of aHUS with associated triggers or clinical conditions in patients presenting with TMA may allow faster and more appropriate treatment.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".