Burden of Comorbid Diseases in Patients with Hemophilia: The Cross-Sectional Analysis of the Patient Reported Outcomes, Burden and Experiences (PROBE) Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».