Pregnancy Outcomes in Living Kidney Donors: Protocol of a Population-Based Cohort Study in Three Canadian Provinces
Bibliographic record
Abstract
Background: A substantial proportion of living kidney donors are women of childbearing age. Some prior studies report a higher risk of gestational hypertension and pre-eclampsia in living kidney donors compared with nondonors. Further research is needed to better quantify the risk of adverse maternal, fetal/infant, and neonatal outcomes attributable to living kidney donation. Objective: To determine the risk of hypertensive disorders of pregnancy, including gestational hypertension, pre-eclampsia, and eclampsia, and other maternal and fetal/infant outcomes in living kidney donors compared with a matched group of nondonors of similar baseline health. Design and Setting: Protocol for a population-based, matched cohort study using Canadian administrative health care databases. The protocol will be run separately in 3 provinces, Ontario, Alberta, and British Columbia, and results will be combined statistically using meta-analysis. Participants: The cohort will include women aged 18 to 48 years who donated a kidney between July 1992 and March 2022 and had at least one postdonation singleton pregnancy of ≥20 weeks gestation between January 1993 and February 2023. We expect to include at least 150 living kidney donors with over 200 postdonation pregnancies from Ontario and a similar number of donors and pregnancies across Alberta and British Columbia combined. Nondonors will include women from the general population with at least one pregnancy of ≥20 weeks gestation between January 1993 and February 2023. Nondonors will be randomly assigned cohort entry dates based on the distribution of nephrectomy dates in donors. The sample of nondonors will be restricted to those aged 18 to 48 years on their cohort entry dates with delivery dates at least 6 months after their assigned entry dates. A concern with donor and nondonor comparisons is that donors are healthier than the general population. To reduce this concern, we will also apply 30+ exclusion criteria to further restrict the nondonor group so that they have similar health measures at cohort entry as the donors. Donor and nondonor pregnancies will then be matched (1:4) on 5 potential confounders: delivery date, maternal age at delivery date, time between cohort entry and delivery date, neighborhood income quintile, and parity at delivery date. Measurements: The primary outcome will be a composite of maternal gestational hypertension, preeclampsia, or eclampsia. Secondary maternal outcomes will include components of the primary outcome, early pre-eclampsia, severe maternal morbidity, cesarean section, postpartum hemorrhage, and gestational diabetes. Fetal/infant/neonatal outcomes will include premature birth/low birth weight, small for gestational age, neonatal intensive care unit admission, stillbirth, and neonatal death. Methods: The primary unit of analysis will be the pregnancy. We will compute the risk ratio of the primary composite outcome in donors versus nondonors using a log-binomial mixed regression model with random effects to account for the correlation within women with multiple pregnancies and within matched sets of donors and nondonors. We will perform the statistical analyses within each province and then combine aggregated results using meta-analytic techniques to produce overall estimates of the study outcomes. Limitations: Due to regulations that prevent individual-level records from being sent to other provinces, we cannot pool individual-level data from all 3 provinces. Conclusion: Compared to prior studies, this study will better estimate the donation-attributable risk of adverse maternal, fetal/infant, and neonatal outcomes. Transplant centers can use the results to counsel female living donor candidates of childbearing age and to inform recommended practices for the follow-up and care of living kidney donors who become pregnant.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".