Impact of Inherited Bleeding Disorders on Maternal Bleeding and Other Pregnancy Outcomes: A Population-Based Cohort Study
Notice bibliographique
Résumé
Abstract Introduction: Increasing rate of postpartum hemorrhage (PPH) has been observed between 2003 and 2010 in Canada. Given that bleeding disorders contribute to the risk of PPH, it is important to identify the current trend in PPH in the last decade and assess the impact of inherited bleeding disorders on maternal bleeding and other pregnancy outcomes. Methods: This is a retrospective population-based cohort study using the Alberta Pregnancy Birth Cohort. The creation of this cohort using multiple linked administrative databases has been previously described. Number of deliveries per year in Alberta, Canada was determined by Vital Statistics birth registry from 2010 to 2018 and was linked with Discharge Abstract Database (DAD) to identify cases of PPH and other pregnancy outcomes. PPH was defined as a blood loss of ≥500 ml following vaginal delivery or ≥1000 ml following Caesarean section, or as a diagnosis noted by a health care provider. All diagnoses and procedures were identified by International Classification of Diseases (ICD)-10 codes and Canadian Classification of Interventions (CCI) codes, respectively. Previous validation study of diagnostic code for PPH in DAD showed high sensitivity and specificity. Inherited bleeding disorders including von Willebrand disease, hemophilia carriers, platelet function disorder, and hereditary deficiencies of other coagulation factors were identified by presence of at least two ICD codes. All analyses were restricted to hospitalized deliveries with live births. Temporal trend of PPH rate was assessed by Mann-Kendall test. Univariate logistic regression analyses were used to compute odds of pregnancy outcomes among women with inherited bleeding disorders compared with those without at their index pregnancies during the study period. Results: Between 2010 to 2018, 311,657 women had a total of 452,846 pregnancies with live births. The mean age of the study cohort was 29 years. Most (90%) of them reached term pregnancies. The total number of PPH was 47,602 (10.5 per 100 deliveries). The rate of PPH did not have any significant change from 10.3 in 2010 (95% confidence interval [CI] 10.0-10.6) to 10.8 (95% CI 10.6 -11.1) in 2018 (P for trend =0.28) [Figure 1]. Among 311,657 women, 345 (0.1%) had a diagnosis of inherited bleeding disorders [Table 1]. Women with bleeding disorders were more likely to experience PPH (odds ratio [OR] 1.4; 95% CI 1.1-1.9), antepartum hemorrhage (OR 4.3; 95% CI 2.9-6.4) and had a 3-fold increased risk of undergoing hysterectomy (OR 3.1; 95% CI 1.8-5.2). The bleeding cohort had 3.8 (95% CI: 2.4-6.0) times greater risk of being transfused with blood products. We observed a trend towards higher odds of caesarean delivery in women with bleeding disorders compared with those without (OR 1.2, 95% CI 0.9-1.5), albeit not statistically significant. However, there was no significant difference in prolonged labor, obstetric hematoma, low birth weight baby and induced labour. Conclusion: Despite a rise in the rate of PPH between 2003-2010, we observed no significant change in the rate of PPH in Alberta between 2010-2018. Women with inherited bleeding disorders are at an increased risk of bleeding events during pregnancy and childbirth. Further investigation into quality of care among this patient population is ongoing to identify areas for improvement. Figure 1 Figure 1. Disclosures Wu: BMS-Pfizer: Honoraria, Research Funding; Leo Pharma: Honoraria; Pfizer: Honoraria; Servier: Honoraria; Bayer: Research Funding; Daiichi-Sankyo: Research Funding. Sun: Pfizer: Consultancy; Novo Nordisk: Consultancy; Bayer: Consultancy; Octapharma: Consultancy, Research Funding; Shire: Consultancy.
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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 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 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 ».