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Validation of the fullPIERS model for predicting severe maternal outcomes in preeclampsia in five Brazilian centers
Pedro do Valle Teichmann1, José Paulo Siqueira Guida2,3, Maria Laura Costa2,3
1Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
Objective:
The objective of this study is to validate the fullPIERS (Pre-eclampsia Integrated Estimate of Risk) prognostic model in a large, multicenter cohort of Brazilian women with preeclampsia and to determine the optimal cutoff for clinical application in the Brazilian context.
Methods:
This was a secondary analysis of five prospective Brazilian studies evaluating the fullPIERS model, conducted in tertiary obstetric referral centers across three geographic regions (Porto Alegre, Campinas, Recife, Belo Horizonte, and Joinville). A total of 1581 pregnant women with confirmed preeclampsia were included. The primary outcome was the occurrence of one or more severe maternal complications within 48 h of assessment. FullPIERS risk estimates were calculated using routinely collected clinical and laboratory variables. Model performance was assessed by receiver operating characteristic (ROC) curve analysis.
Results:
Severe maternal complications occurred in 235 women (14.86%), with HELLP (hemolysis, elevated liver enzymes and low platelets) syndrome being the most frequent (9.5%). The fullPIERS model demonstrated moderate discriminative ability, with an area under the ROC curve of 0.753 (95% confidence interval: 0.714-0.791). The optimal cutoff value of 2.35% yielded a sensitivity of 58.3%, specificity of 84.6%, and a negative predictive value (NPV) of 94.8%.
Conclusion:
In this large, geographically diverse Brazilian cohort, the fullPIERS model showed substantial clinical utility for ruling out severe maternal complications in women with preeclampsia. Its high NPV supports its use in referral decision-making and prioritization of resources. Given its reliance on routinely available clinical data, fullPIERS might serve as a valuable tool to optimize maternal care pathways, particularly in resource-limited settings.
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