Development and internal validation of a multivariable model to predict perinatal death in pregnancy hypertension
Bibliographic record
Abstract
OBJECTIVE: To develop and internally validate a prognostic model for perinatal death that could guide community-based antenatal care of women with a hypertensive disorder of pregnancy (HDP) in low-resourced settings as part of a mobile health application. STUDY DESIGN: Using data from 1688 women (110 (6.5%) perinatal deaths) admitted to hospital after 32weeks gestation with a HDP from five low-resourced countries in the miniPIERS prospective cohort, a logistic regression model to predict perinatal death was developed and internally validated. Model discrimination, calibration, and classification accuracy were assessed and compared with use of gestational age alone to determine prognosis. MAIN OUTCOME MEASURES: Stillbirth or neonatal death before hospital discharge. RESULTS: The final model included maternal age; a count of symptoms (0, 1 or ⩾2); and dipstick proteinuria. The area under the receiver operating characteristic curve was 0.75 [95% CI 0.71-0.80]. The model correctly identified 42/110 (38.2%) additional cases as high-risk (probability >15%) of perinatal death compared with use of only gestational age <34weeks at assessment with increased sensitivity (48.6% vs. 23.8%) and similar specificity (86.6% vs. 90.0%). CONCLUSION: Using simple, routinely collected measures during antenatal care, we can identify women with a HDP who are at increased risk of perinatal death and who would benefit from transfer to facility-based care. This model requires external validation and assessment in an implementation study to confirm performance.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".