Long-term multisystemic efficacy of migalastat on Fabry-associated clinical events, including renal, cardiac and cerebrovascular outcomes
Bibliographic record
Abstract
Background Fabry disease is a rare, multisystemic disorder caused by GLA gene variants that lead to alpha galactosidase A deficiency, resulting in accumulation of glycosphingolipids and cellular dysfunction. Fabry-associated clinical events (FACEs) cause significant morbidity and mortality, yet the long-term effect of Fabry therapies on FACE incidence remains unclear. Methods This posthoc analysis evaluated incidence of FACEs (as a composite outcome and separately for renal, cardiac and cerebrovascular events) in 97 enzyme replacement therapy (ERT)-naïve and ERT-experienced adults with Fabry disease and amenable GLA variants who were treated with migalastat for up to 8.6 years (median: 5 years) in Phase III clinical trials of migalastat. Associations between baseline characteristics and incidence of FACEs were also evaluated. Results During long-term migalastat treatment, 17 patients (17.5%) experienced 22 FACEs and there were no deaths. The incidence rate of FACEs was 48.3 events per 1000 patient-years overall. Numerically higher incidence rates were observed in men versus women, patients aged >40 years versus younger patients, ERT-naïve versus ERT-experienced patients and men with the classic phenotype versus men and women with all other phenotypes. There was no statistically significant difference in time to first FACE when analysed by patient sex, phenotype, prior treatment status or age. Lower baseline estimated glomerular filtration rate (eGFR) was associated with an increased risk of FACEs across patient populations. Conclusions The overall incidence of FACEs for patients during long-term treatment with migalastat compared favourably with historic reports involving ERT. Lower baseline eGFR was a significant predictor of FACEs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".