PB0647 Life Expectancy and Mortality of Patients with Haemophilia A and Haemophilia B in Australia
Bibliographic record
Abstract
Background: Patients with severe haemophilia, characterised by baseline factor levels of less than 1%, represent approximately one-third of total haemophilia A (HA) and one-fifth of total haemophilia B (HB) population in Australia.Prescribing prophylaxis with regular infusions of the missing factor concentrate for such patients or more recently with non-factor replacement such as emcizumab is a well-established standard of care.However life expectancy and mortality remains unknown in this group.Aims: To assess impact of severity and prophylaxis on life expectancy, mortality in patients with HA and HB in Australia, develop survival models based on all-cause mortality and examine probability of survival by severity and treatment regimen.Methods: A national, retrospective study was conducted using data extracted from the Australian Bleeding Disorder Registry (ABDR).Data was obtained on severity, age, treatment regimen and mortality in patients with HA and HB.Patient demographics and results presented are based on ABDR data up until October 2022.Results: There are 2874 HA and 654 HB patients in the ABDR.All-cause mortality in HA accounted for 13.5%, 11.5% and 7.6% in severe, moderate and mild patients respectively.In HB, that was 11.7%, 5.4% and 9.8% in severe, moderate and mild patients respectively.Estimated median life expectancy is 79 years for HA and 82 years for HB patients, which is slightly lower than median life expectancy of general Australian population (83 years).Severity significantly impacts estimated median life expectancy, reducing to 72 years in severe HA and 68 years in severe HB.Prophylaxis favourably improved estimated median life expectancy to 78 years in severe HA, whereas reduced to 55 years in non-prophylaxis group.Conclusion(s): This study provides valuable insights of survival in patients with haemophilia in Australia.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".