All Cause Mortality and Causes of Death in People with Hemophilia: A Systematic Review and Meta Analysis
Bibliographic record
Abstract
Background: There have been many improvements in hemophilia treatment over the last 30 years. Complications and resulting causes of death will be impacted by these changes. We aimed to conduct a systematic review and meta-analysis on all cause mortality and causes of death among people with hemophilia. Methods: Using a predefined search strategy, we systematically searched EMBASE, MEDLINE, Web of Science, CINAHL, Cochrane central register of controlled trials and Google Scholar from inception through November 6, 2019. Studies that reported standardized mortality ratio (SMR) of hemophilia patients compared with the general population and/or reported causes of death were included. Random-effects meta-analysis with inverse variance method was used to obtain pooled estimates. We further stratified the analysis by the year of cohort entry (before 2000 vs after 2000). Results: Of the 4072 studies identified , 16 studies from 10 different countries met the eligibility criteria. The pooled SMR for all-cause mortality among people with hemophilia, compared with the general population was 1.93 (95% CI 1.38-2·70; I²=97%) (Figure: 1). The pooled SMR before and after the year 2000 were 2.40 (95% CI 1.92-3·00; I²=87%) and 1.20 (95% CI 1.03-1·40; I²=62%) respectively. Causes of mortality were extracted for 5808 deaths. Before the year 2000, 34% deaths occurred due to AIDS followed by hemorrhage (28%) cardiovascular disease (20%) , liver disease (11%) and cancer (9%) (Table: 1). Fewer (18%) deaths were attributable to AIDS after the year 2000 without any notable difference due to other causes. Conclusion: : With advancement of treatments, mortality in hemophilia patients have declined over the last few decades. Particularly, effective prevention and treatment of HIV has made an impact. However, hemorrhage and cardiovascular disease still rank as leading causes of death. Disclosures Wu: Servier: Other: advisory board; BMS-pfizer: Honoraria, Other: advisory board; leo pharma: Other: advisory board; Pfizer: Honoraria. Sun:Octapharma: Research Funding; Sanofi: Other: Advisory board; Pfizer: Other: Advisory board; Octapharma: Other: Advisory board; Novo Nordisk: Other: Advisory board.
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.047 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".