Impact of Inherited Bleeding Disorders on Maternal Bleeding and Other Pregnancy Outcomes: A Population-Based Cohort Study
Bibliographic record
Abstract
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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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".