Risk of Preeclampsia and Pregnancy Complications in Women With a History of Acute Kidney Injury
Bibliographic record
Abstract
An episode of clinically recovered acute kidney injury (r-AKI) has been identified as a risk factor for future hypertension and cardiovascular disease. Our objective was to assess whether r-AKI was associated with future preeclampsia and other adverse pregnancy outcomes and to identify whether severity of AKI or time interval between AKI and pregnancy was associated with pregnancy complications. We conducted a retrospective cohort study of women who delivered infants between 1998 and 2016 at Massachusetts General Hospital. AKI was defined using the 2012 Kidney Disease Improving Global Outcomes laboratory criteria with subsequent clinical recovery (estimate glomerular filtration rate, >90 mL/min per 1.73 m 2 before conception). AKI was further classified by severity (Kidney Disease Improving Global Outcomes stages 1–3) and time interval between AKI episode and the start of pregnancy. Women with r-AKI had an increased rate of preeclampsia compared with women without previous r-AKI (22% versus 9%; P <0.001). Infants of women with r-AKI were born earlier (gestational age, 38.2±3.0 versus 39.0±2.2 weeks; P <0.001) and were more likely to be small for gestational age (9% versus 5%; P =0.002). Increasing severity of r-AKI was associated with increased risk of preeclampsia for stages 2 and 3 AKI (adjusted odds ratio, 3.5; 95% confidence interval, 2.1–5.7 and adjusted odds ratio, 6.5; 95% confidence interval, 3.5–12.0, respectively), but not for stage 1 (adjusted odds ratio, 1.7; 95% confidence interval, 0.9–3.2). A history of AKI before pregnancy, despite apparent full recovery, was associated with increased risk of pregnancy complications. Severity and timing of the AKI episode modified the risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".