Characteristics and Outcomes Among Pregnant Women With ESRD on Hemodialysis: A Population-Based Study [05N]
Bibliographic record
Abstract
INTRODUCTION: Dialysis patients historically have had poor pregnancy outcomes, but these have improved over the years with better delivery of dialysis care. The purpose of our study is to describe baseline characteristics and pregnancy outcomes among women with end stage renal disease (ESRD) on hemodialysis (HD). METHODS: We carried out a cohort study on all births using the United States' Healthcare Cost and Utilization Project-Nationwide Inpatient Sample database between 2005 and 2015. Descriptive statistics were used to measure baseline characteristics and maternal and neonatal outcomes of women with ESRD under HD treatment. We created a composite measure of vascular mediated adverse pregnancy outcomes which included intrauterine growth restriction (IUGR), preeclampsia and intrauterine fetal death (IUFD). Then, using this composite measure, multivariate logistic regression analyses were used to identify risk factors for the development of vascular mediated adverse outcomes of pregnancy among women with ESRD who were on HD. RESULTS: We identified 307 birth records to women with a ESRD on HD among 8,765,973 births. There was an increase in the number of patients with ESRD on HR over the 10-year study period. None of the baseline characteristics were found to be significantly correlated to the occurrence of the composite outcome. CONCLUSION: The incidence of pregnant women with ESRD on HD in the US is rising, with adverse pregnancy complications for both the mother and fetus. Since no specific predictors for the adverse outcomes were found, this entire group of patients should be considered at high risk, strengthening the need for an early multidisciplinary team management.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".