Development and internal validation of a multivariable model to predict perinatal death in pregnancy hypertension
Payne, Beth A.
Ukah, U. Vivian
Ansermino, J. Mark
Hall, David R.
Hutcheon, Jennifer A.
Magee, Laura A.
von Dadelszen, Peter
- Publisher: Elsevier BV
Pregnancy Hypertension: An International Journal of Women's Cardiovascular Health,
Obstetrics and Gynaecology | Internal Medicine | Stillbirth | Pre-eclampsia | Perinatal death | Prognosis | Low-resourced setting
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.\ud Study Design\ud \ud Using 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.\ud \ud Main outcome measures: Stillbirth or neonatal death before hospital discharge.\ud Results\ud \ud 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 <34 weeks at assessment with increased sensitivity (48.6% vs. 23.8%) and similar specificity (86.6% vs. 90.0%).\ud Conclusion\ud \ud 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.