Pregnancy outcomes among liver transplant recipients in the United States: A nationwide case-control analysis
Bibliographic record
Abstract
Liver transplant recipients and their infants may have an increased risk of obstetric complications. Our objective was to describe pregnancy outcomes in women with a prior transplant from a population-based perspective. We analyzed the 1993-2005 US Nationwide Inpatient Sample database to identify obstetric hospitalizations among transplant recipients (n = 206) and controls matched by age, hospital, and year (n = 4060). The effect of prior transplantation on maternal and fetal outcomes was evaluated with regression models with adjustments for patient and hospital factors, including admission to a transplant center. Between 1993 and 2005, 146 delivery admissions among liver transplant recipients were identified. Cesarean deliveries were more common among transplant recipients (38% versus 24%; P = 0.0001); however, this difference was not significant after multivariate adjustment [OR (odds ratio) = 0.87; 95% confidence interval (CI) = 0.60-1.27]. Maternal mortality was similar among cases and controls (0% versus 0.02%; P = 1.00), but transplant patients had higher rates of fetal mortality (6.3% versus 2.0%; P = 0.0006), antepartum admission (OR = 2.27; 95% CI = 1.59-3.25), and maternal (OR = 2.63; 95% CI = 1.82-3.80) and fetal complications (OR = 2.49; 95% CI = 1.68-3.70). Gestational hypertension (30% versus 9%; P < 0.0001) and postpartum hemorrhage (8% versus 3%; P = 0.009) were more common among transplant recipients; their infants had higher rates of prematurity (27% versus 11%; P < 0.0001), distress (10% versus 5%; P = 0.005), and growth restriction (5% versus 2%; P = 0.05) but not congenital anomalies. Hospitalization in a transplant center ( approximately 50%) was associated with similar obstetric outcomes. In conclusion, although most pregnancy outcomes are favorable, liver transplant recipients and their infants have an increased risk of obstetric complications. Additional studies evaluating mechanisms aimed at reducing these complications are necessary.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| 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".