The performance of risk prediction models for pre‐eclampsia using routinely collected maternal characteristics and comparison with models that include specialised tests and with clinical guideline decision rules: a systematic review
Bibliographic record
Abstract
BACKGROUND: Risk prediction models may be valuable to identify women at risk of pre-eclampsia to guide aspirin prophylaxis in early pregnancy. OBJECTIVE: To assess the performance of 'simple' risk models for pre-eclampsia that use routinely collected maternal characteristics; compare with 'specialised' models that include specialised tests; and to guideline recommended decision rules. SEARCH STRATEGY: MEDLINE, Embase and PubMed were searched to June 2014. SELECTION CRITERIA: We included studies that developed or validated pre-eclampsia risk models using maternal characteristics with or without specialised tests and reported model performance. DATA COLLECTION AND ANALYSIS: We extracted data on study characteristics; model predictors, validation and performance including area under the curve (AUC), sensitivity and specificity. MAIN RESULTS: We identified 29 studies that developed 70 models including 22 simple models. Studies included 151-9149 women with a pre-eclampsia prevalence of 1.2-9.5%. No single predictor was included in all models. Four simple models were externally validated, with a model using parity, pre-eclampsia history, race, chronic hypertension and conception method to predict early-onset pre-eclampsia achieving the highest AUC (0.76, 95% CI 0.74-0.77). Nine studies comparing simple versus specialized models in the same population reported AUC favouring specialised models. A simple model achieved fewer false positives than a guideline recommended risk factor list, but sensitivity to classify risk for aspirin prophylaxis was not assessed. CONCLUSION: Validated simple pre-eclampsia risk models demonstrate good risk discrimination that can be improved with specialised tests. Further research is needed to determine their clinical value to guide aspirin prophylaxis compared with decision rules. TWEETABLE ABSTRACT: Pre-eclampsia risk models using maternal factors show good risk discrimination to guide aspirin prophylaxis.
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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 | 0.027 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".