Cost‐effectiveness of first‐trimester screening with early preventative use of aspirin in women at high risk of early‐onset pre‐eclampsia
Bibliographic record
Abstract
OBJECTIVE: Pre-eclampsia (PE) remains a leading cause of maternal and fetal morbidity and mortality. A first-trimester screening algorithm predicting the risk of early-onset PE has been developed and validated. Early prediction coupled with initiation of aspirin at 11-13 weeks in women identified as high risk is effective at reducing the prevalence of early-onset PE. The aim of this study was to evaluate the cost-effectiveness of this first-trimester screening program coupled with early use of low-dose aspirin in women at high risk of developing early-onset PE, in comparison to current practice in Canada. METHODS: A decision analysis was performed based on a theoretical population of 387 516 live births in Canada in 1 year. The clinical and financial impact of early preventative screening using the Fetal Medicine Foundation algorithm for prediction of early-onset PE coupled with early (< 16 weeks) use of low-dose aspirin in those at high risk was simulated and compared with current practice using decision-tree analysis. The probabilities at each decision point and associated costs of utilized resources were calculated based on published literature and public databases. RESULTS: Of the theoretical 387 516 births per year, the estimated prevalence of early PE based on first-trimester screening and aspirin use was 705 vs 1801 cases based on the current practice. This was associated with an estimated total cost of C$9.52 million with the first-trimester screening program compared with C$23.91 million with current practice for the diagnosis and management of women with early-onset PE. This equals an annual cost saving to the Canadian healthcare system of approximately C$14.39 million. CONCLUSIONS: The implementation of a first-trimester screening program for PE and early intervention with aspirin in women identified as high risk for early PE has the potential to prevent a significant number of early-onset PE cases with a substantial associated cost saving to the healthcare system in Canada. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".