Implementation of combined screening for preeclampsia in the first trimester: cost-effectiveness and clinical outcomes
Bibliographic record
Abstract
Preeclampsia (PE) is a gestational hypertensive syndrome with a worldwide incidence of 3-5%. Globally, 76,000 women and 500,000 babies die each year from complications of PE. Its timely identification and prevention through effective screening and offer of treatment, can have a vital impact on reducing maternal and fetal mortality and morbidity. In addition, there are significant healthcare economic implications. There are two primary methods for screening currently. Women can either be screened based solely on risk factors or through a combined approach that includes maternal characteristics alongside biophysical parameters such as mean arterial pressure (MAP) and uterine artery (UtA) blood flow, as well as biomarkers like pregnancy-associated plasma protein-A (Papp-A) and/or placental growth factor (PlGF). Administration of low dose aspirin (LDA) to women who screen as high-risk, using the combined screening approach has been shown to reduce the risk of PE by 62%. However, although the combined approach yields a higher detection rate for preterm PE, most international and national guidelines presently still recommend screening for PE based on maternal risk factors alone. At University College London Hospital (UCLH), we initiated the implementation of the Fetal Medicine Foundation (FMF)-combined screening for all women in their first trimester. This new protocol aims to enhance the detection rates of preterm PE and fetal growth restriction (FGR), assessing the impact on our maternity service. Our project began with a systematic review of clinical practice guidelines to understand global screening practices, revealing that despite its benefits, 74% of guidelines still recommend risk factor-based screening. We then conducted a retrospective analysis of 5,957 patient episodes, evaluating the outcomes against a hypothetical implementation of the modified combined screening algorithm. This analysis confirmed the superior performance of the combined screening approach over the existing NICE protocol in detecting both PE and FGR in our population. Notably, the application of the modified combined algorithm was cost-effective with a cost saving of £9.06 per pregnancy screened and a marginal Quality Adjusted Life Years gain. Further, we refined the clinical pathway post-first trimester screening by incorporating second trimester UtA Doppler assessments, significantly enhancing risk stratification. Our findings showed that screen-positive women with elevated UtA PI in the second trimester had an 18.8% risk of developing preterm PE, compared to just 6.5% for those with normal UtA PI. This trend was also observed in the FGR risk patterns, where the highest risk was noted in women with elevated second-trimester UtA PI. We also assessed the PE risk in high-risk women who did not develop hypertension or growth restriction up until 37 weeks, finding a significantly higher risk of PE in women delivering after 40 weeks, leading us to advocate for earlier delivery policies. Additionally, we evaluated the inter- and intra- observer variability of UtA PI measurements in the first trimester, confirming their reproducibility, which supported our decision to conduct periodic audits of sonographer performance. A future direction for this project is the prospective evaluation of the effect of implementation of first trimester FMF combined-screening for PE at UCLH between February 2023-February 2024. In evaluating this prospective data, we aim to investigate potential strategies such as earlier delivery to reduce the incidence of term PE. Finally, we plan to assess the qualitative performance of this screening approach and the associated clinical pathway by collecting patient feedback. In conclusion, our project at UCLH represents a pivotal shift in the approach to screening, providing a means of substantially improving maternal and fetal outcomes. The adoption of this comprehensive screening during the first trimester, accompanied by targeted interventions such as LDA, appropriate antenatal follow-up and timely delivery strategies, not only offers the promise of reduced prevalence of severe complications associated with PE but also a potential cost benefit within maternity services. The project's ongoing assessment and refinement of clinical pathways, including the integration of second-trimester assessments and continuous performance evaluations of sonographic techniques, highlight our commitment to advancing clinical practice through evidence-based strategies. As we continue to collect and analyze prospective data, our goal is to establish a robust model that can be adopted widely, ensuring that every woman receives the most precise and effective care during her pregnancy.
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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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".