Development and internal validation of a multivariable model to predict perinatal death in pregnancy hypertension

- Symptoms, dipstick proteinuria and maternal age identify fetuses at risk of perinatal death.
- The area under the receiver operating characteristic curve of the model was 0.75 [95% CI 0.71-0.80].
- This perinatal risk model improves detection of perinatal risk beyond gestational age alone.

ObjectiveTo 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 designUsing data from 1688 women (110 (6.5%) perinatal deaths) admitted to hospital after 32 weeks 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 measuresStillbirth or neonatal death before hospital discharge.ResultsThe 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 <34 weeks at assessment with increased sensitivity (48.6% vs. 23.8%) and similar specificity (86.6% vs. 90.0%).ConclusionUsing 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.