Burden of Comorbid Diseases in Patients with Hemophilia: The Cross-Sectional Analysis of the Patient Reported Outcomes, Burden and Experiences (PROBE) Study
Bibliographic record
Abstract
Abstract Background: Health status in patients with hemophilia (PWH) are affected by bleeding related complications, for instance, intermittent joint bleeding, hemophilic arthropathy and chronic pain. Aged PWH are also found to have comorbid diseases which impact their health status. Optimal care for PWH requires an integrated and multidisciplinary approaches. The aim of this study is to evaluate the burden of comorbid diseases in PWH. Methods: We performed a cross-sectional analysis of respondents who participated to the phase 1 (17 countries) and 2b (6 countries) of the PROBE study. All included participants were asked to answer the 29-item PROBE questionnaire. We calculated the prevalence of comorbid diseases reported by the participants including, hepatitis, stroke, hypertension, angina, heart attack, liver cancer, other cancer, diabetes, seizure, arthritis, gingivitis and HIV infection. Aged adjusted odds ratios of the prevalence of comorbid diseases in PWH were calculated as compared to participants without bleeding disorders. Results: There were 1170 PWHs and 525 participants without bleeding disorders included in the analysis. Mean age of participants was lower in PWHs group (34.18±17.84 vs 44.80±13.77). Among PWHs, 84.27% were hemophilia A and 15.73% were hemophilia B. With regards to severity of hemophilia 13.93% were mild, 17.81% were moderate and 66.27% were severe. Table 1 demonstrates prevalence of comorbid diseases in participants. PWHs were associated with higher prevalence of hepatitis B (OR 6.2, 95%CI 2.2-17.7), hepatitis C (OR 263.0, 95%CI 36.5-1894.3), HIV (OR 24.2 95%CI 5.9-99.6), hypertension (OR 2.5, 95%CI 1.6-4.0), angina (OR 2.2, 95%CI 1.07-4.6), seizure (OR 8.6, 95%CI 1.1-66.9), arthritis (OR 6.5, 95%CI 4.0-10.6) and gingivitis (OR 3.2, 95%CI 2.0-5.2). Conclusion: When compared to participants without bleeding disorders, PWHs frequently reported hemophilia related diseases (hepatitis B, C and HIV infection and arthritis). Moreover, PWHs were associated with higher prevalence of hypertension and gingivitis across all disease severity. These findings suggested that selective comorbid diseases assessment in PWHs should be incorporated in usual hemophilia care. Download : Download high-res image (236KB) Download : Download full-size image Disclosures Skinner: Baxalta, now part of Shire; Bayer; Bioverativ; CSL; Novo Nordisk, Roche and Sobi with administrative support provided by the US National Hemophilia Foundation: Research Funding; US National Hemophilia Foundation: Other: non-financial support ; Baxalta, now part of Shire; Bayer; Bioverativ; CSL; Novo Nordisk, Roche and Sobi with administrative support provided by the US National Hemophilia Foundation: Research Funding; US National Hemophilia Foundation: Other: non-financial support . Curtis: Bayer: Research Funding, Speakers Bureau; Bioverativ: Research Funding; Genentech: Honoraria, Research Funding; Gilead: Honoraria; Pfizer: Research Funding; Novo Nordisk: Honoraria, Research Funding; Baxter: Research Funding; CSL Behring: Research Funding. Noone: Baxalta, now part of Shire; Bayer; Bioverativ; CSL; Novo Nordisk, Roche and Sobi with administrative support provided by the US National Hemophilia Foundation: Research Funding. O'Mahony: US National Hemophilia Foundation: Other: non-financial support.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".