Acute Kidney Injury in Pregnancy: The Changing Landscape for the 21st Century
Bibliographic record
Abstract
Pregnancy-related acute kidney injury (Pr-AKI) remains a large public health problem, with decreasing incidences in developing countries but seemingly increasing incidences in the United States and Canada. These epidemiologic changes are reflective of the advances in medical and obstetric care, as well as changes in underlying maternal risk factors. The risk factors associated with advanced maternal age, such as hypertension, diabetes, chronic kidney disease, and those associated with reproductive technologies such as multiple gestations, are increasing. Traditional causes of Pr-AKI, such as septic abortions and puerperal sepsis, have been replaced by hypertensive diseases, such as preeclampsia and thrombotic microangiopathies comprising thrombotic thrombocytopenic purpura (TTP) and atypical hemolytic uremic syndrome (aHUS). In this review, we discuss the global impact of Pr-AKI on maternal and fetal outcomes, the predominant etiologies, and key clinical features to distinguish diagnoses, such as preeclampsia/hemolysis elevated liver function test and low platelet (HELLP) syndrome, acute fatty liver disease of pregnancy (AFLP), and other thrombotic microangiopathies. New insights into the pathogenesis of preeclampsia, TTP/aHUS, and AFLP that have unearthed possible therapeutic targets are summarized. We also delve into special consideration needed to give to pyelonephritis and postobstructive causes of Pr-AKI. With each diagnosis, we offer the latest treatment recommendations, such as the positive reports from the use of eculizumab to treat aHUS. In the end, we hope to arm the clinician with the best tools to understand and address this morbid problem that does not seem to be disappearing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".