First-Trimester Screening Program for the Risk of Pre-eclampsia Using a Multiple-Marker Algorithm: A Health Technology Assessment.
Bibliographic record
Abstract
Background: Pre-eclampsia is when high blood pressure develops after 20 weeks of pregnancy and either proteinuria, maternal end-organ dysfunction, or uteroplacental dysfunction causing fetal growth restriction also develops. The Fetal Medicine Foundation has created an algorithm ("the FMF algorithm") that uses maternal factors in combination with biophysical and biochemical markers to identify people at high risk for pre-eclampsia so that they can been offered acetylsalicylic acid (Aspirin) as a preventive measure. We conducted a health technology assessment to evaluate the safety, effectiveness, and cost-effectiveness of a first-trimester population-wide screening program for pre-eclampsia risk that uses the FMF algorithm ("the FMF-based screening program"). We also evaluated the accuracy of the FMF algorithm, the budget impact of publicly funding the population-wide FMF-based screening program, and patient preferences and values. Methods: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each study using the Risk of Bias in Non-randomized Studies-of Interventions tool and the Quality Assessment of Diagnostic Accuracy Studies-Comparative tool, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature search and conducted a cost-effectiveness analysis comparing the FMF-based screening program to standard care (screening for risk of pre-eclampsia using maternal factors alone) from a public payer perspective. We also analyzed the budget impact of publicly funding a population-wide FMF-based screening program in Ontario. We spoke with people who have experience with pregnancy and preeclampsia and their family members through direct interviews to gather preferences and values surrounding pre-eclampsia and the potential screening program. Results: weeks' gestation; risk ratios ranged from 0.64 (95% confidence interval [CI] 0.46-0.93) to 0.70 (95% CI 0.58-0.84) (GRADE: Moderate). It may reduce the risks of low birth weight (risk ratio 0.89 [95% CI 0.85-0.94]) and low Apgar score (risk ratio 0.73 [95% CI 0.63-0.85]) (GRADE: Low). Evidence on the effectiveness of the FMF-based screening program in reducing the risk of stillbirth and neonatal death was highly uncertain (GRADE: Very low). In addition, the FMF algorithm can improve the detection rate of pre-eclampsia with delivery at less than 37 weeks' gestation or at less than 34 weeks' gestation compared with conventional algorithms, although there are concerns about bias and applicability across studies. The population-wide FMF-based screening program is more effective and more costly than standard care. The incremental cost-effectiveness ratio of the population-wide FMF-based screening program compared with standard care is $3,446 per prevented case of pre-eclampsia with delivery at less than 37 weeks. The annual budget impact of publicly funding the population-wide FMF-based screening program in Ontario ranges from an additional $1.23 million in year 1 to $3.56 million in year 5, for a total of $8.50 million over the next 5 years. The population-wide FMF-based screening program was seen as valuable by those who have experienced pregnancy and their family members. Strong emphasis was placed on providing education and equitable access as part of any screening program, and participants valued the potential clinical benefits that the population-wide FMF-based screening program could provide. Conclusions: The FMF-based screening program is likely more effective than standard care in reducing the risk of pre-eclampsia with delivery at less than 37 weeks' gestation. Also, the FMF algorithm can improve the detection rate of pre-eclampsia with delivery at less than 37 weeks' gestation or at less than 34 weeks' gestation when compared with conventional algorithms. The population-wide FMF-based screening program is more effective and more costly than standard care. We estimate that publicly funding the population-wide FMF-based screening program in Ontario would result in additional costs of $8.50 million over the next 5 years. Pregnant people and their family members valued the potential equitable access, information, and clinical benefits that the population-wide FMF-based screening program could provide.
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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 | 0.038 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".