Potential biological therapies for severe preeclampsia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Preeclampsia remains a significant danger to both mother and child and current prevention and treatment management strategies are limited. The objective of this systematic review was to investigate the current literature on evidence for the use of the regenerative capacity of mesenchymal stem cell (MSC) therapy, the anticoagulant activity of antithrombin (AT), or the free radical scavenging activity of alpha-1-microglobulin (A1M) as potential novel treatments for severe preeclampsia and Hemolysis, Elevated Liver enzymes, Low Platelet count (HELLP). METHOD: We conducted a systematic review of potential biological therapies for preeclampsia. We screened MEDLINE and Embase from inception through May 2017 for studies using AT, A1M or MSCs as potential treatments for preeclampsia and/or HELLP. A meta-analysis was performed to pool data from randomized control trials (RCTs) with homogenous outcomes using the inverse variance method. The Newcastle-Ottawa Scale, the Cochrane risk of bias tool for RCTs, and SYRCLE's risk of bias tool for animal studies were used to investigate potential bias of studies. RESULTS: The literature search retrieved a total of 1015 articles, however, only 17 studies met the selection criteria: AT (n = 9, 8 human and 1 animal); A1M (n = 4, 3 animal and 1 ex-vivo); and, MSCs (n = 4, 3 animal and 1 ex-vivo). A meta-analysis of AT therapy versus placebo and a meta-analysis for AT therapy with heparin versus heparin alone did not show significant differences between study groups. Animal and ex-vivo studies demonstrated significant benefits in relevant outcomes for A1M and MSCs versus control treatments. Most RCT studies were rated as having a low risk of bias across categories with some studies showing an unclear risk of bias in some categories. The two cohort studies both received a total of four out of nine stars (a rating of "poor" quality). Most animal studies had an unclear risk of bias across most categories, with some studies having a low risk of bias in some categories. CONCLUSIONS: The findings of this review are strengthened by rigorous systematic search and review of the literature. Results of our meta-analyses do not currently warrant further exploration of AT as a treatment of preeclampsia in human trials. Results of animal and ex-vivo studies of A1M and MSCs were encouraging and supportive of initiating human investigations.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".