The accuracy of the fullPIERS model in predicting adverse maternal and perinatal outcomes: evidence from a tertiary care maternity unit

Abstract

Objective: Low-and middle-income countries face significant challenges in managing women diagnosed with pre-eclampsia, from making the clinical decision about whether to deliver to transferring these women to healthy facilities where they can receive appropriate care. The aim of this study was to evaluate the performance and accuracy of the fullPIERS model in a referral Brazilian maternity hospital-to assess maternal and fetal morbidity and impatient mortality at birth admission. Methods: A cross-sectional study analyzed pregnant women with preeclampsia diagnosis, between 2014 and 2023. The full PIERS model was applied to a database retrospectively collected and its accuracy to predict maternal and perinatal outcomes during the hospital stay was determined through a receiver operating curve. Results: Analyzing 207 pregnant women with fullPIERS had an Area Under the Curve (AUC) for adverse maternal outcome discrimination of 0.672 (0.576-0.767 95% CI, p<0.001) and AUC 0.582, (0.504-0.6661 95% CI, p = 0.041) for maternal and perinatal outcomes. Nevertheless, the model had no discrimination utility to assess perinatal outcomes (AUC 0.561, 0.480-0.642 95% CI, p = 0.642). Conclusion: The fullPIERS model had limited performance in identifying women at increased risk of adverse outcomes birth admission and absent utility to assess perinatal outcomes. Future studies, combining different tools and validated in low-and middle-income countries should be carried out to improve maternal health.