Preterm preeclampsia screening and prevention: a comprehensive approach to implementation in a real-world setting
Bibliographic record
Abstract
BACKGROUND: Preeclampsia significantly impacts maternal and perinatal health. Early screening using advanced models and primary prevention with low-dose acetylsalicylic acid for high-risk populations is crucial to reduce the disease's incidence. This study assesses the feasibility of implementing preterm preeclampsia screening and prevention by leveraging information from our current aneuploidy screening program in a real-world setting with geographic separation clinical site and laboratory analysis site. METHODS: A prospective cohort study involved pregnant individuals undergoing nuchal translucency scans between 11 and 14 weeks. Risk for preterm preeclampsia was assessed using the Fetal Medicine Foundation algorithm, which includes maternal risk factors, uterine artery Doppler, mean arterial pressure and serum markers (Placental growth factor, PlGF and Pregnancy-associated plasma protein-A, PAPP-A). High-risk patients were offered low-dose acetylsalicylic acid prophylaxis. Feasibility outcomes, such as recruitment rates, protocol adherence, operational impact, integration with existing workflows, screening performance and pregnancy outcomes, were evaluated. RESULTS: Out of 974 participants, 15.6% were deemed high-risk for preterm preeclampsia. The study achieved high recruitment (82.1%) and adherence rates, with 95.4% of high-risk patients prescribed low-dose acetylsalicylic acid. Screening performance, adjusted for low-dose acetylsalicylic acid use, showed a detection rate of 88.9-90% (FPR 13.0% and 12.7%) for preterm preeclampsia. High-risk group for preeclampsia had higher incidences of adverse outcomes, including preterm preeclampsia (7.5 vs 0.4%; p < 0.001), preterm delivery (21.2 vs 6.2%; p < 0.001), low birth weight (23.3 vs 5.6%; p < 0.001) and birthweight < 10th percentile (11% vs 5.6%; p = 0.015) compared to low-risk group. The integration of preeclampsia screening had a minimal effect on the time required for aneuploidy screening, with results obtained within a rapid turnaround time. CONCLUSIONS: The study confirms the feasibility of integrating comprehensive preeclampsia screening into clinical practice, notwithstanding geographic separation between laboratory and clinical settings. It underscores the need for broader adoption and enhanced infrastructure to optimize patient care and outcomes across diverse healthcare settings. TRIAL REGISTRATION: Clinical trial: NCT04412681 (2020-06-02).
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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 | 0.063 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".