Severe Maternal Morbidity and Maternal Mortality Associated with Pregnancies Among Women With Solid Organ Transplants
Bibliographic record
Abstract
OBJECTIVES: Solid organ transplants (SOT), including kidney, liver, heart, lung, and pancreas transplants, have become more common in the last few decades. However, few population-based studies have attempted to comprehensively quantify severe maternal morbidity (SMM) and maternal mortality associated with pregnancies among recipients of SOTs. This study aimed to determine the proportion of women with SOTs among all delivering women, and to quantify the rates of SMM and maternal mortality associated with SOT. METHODS: weeks gestation in Canada (excluding Québec) from April 2003 to March 2018. SMM was defined using a standard list of SMM conditions. Logistic regression provided adjusted ORs (AORs) for associations between SOT and SMM/maternal mortality, adjusting for age, parity, plurality, and other confounders. RESULTS: Among the 4, 157 547 women who delivered during the study period, 342 had an SOT (63% kidney, 25% liver transplant). The composite SMM rate was significantly higher in the SOT group than in the non-SOT group (134.5 vs. 9.97 per 1000 deliveries; unadjusted odds ratio 13.5; 95% CI 10.1-18.0, AOR 10.8; 95% CI 7.60-15.3). Women with SOT had higher rates of specific SMM types, including severe preeclampsia (AOR 8.29; 95% CI 2.99-23.0), eclampsia (AOR 10.8; 95% CI 3.42-34.1), acute renal failure (AOR 93.9; 95% CI 58.3-151.2), acute fatty liver with transfusion (AOR 21.3; 95% CI 2.89-157.8), hepatic failure (AOR 91.4; 95% CI 11.6-722.4), cardiac conditions (AOR 7.52; 95% CI 2.72-20.8), and other SMM. The median hospital stay was 8 versus 3 days for women with and without SOT, respectively; the proportion with prolonged stay (≥7 days) was 22.3% versus 1.78% (rate ratio 12.5; 95% CI 9.84-16.0). CONCLUSIONS: Prolonged hospitalization and elevated severe morbidity rates highlight the substantial clinical risk associated with deliveries to women who have had an SOT.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".