Comparison of three algorithms for prediction preeclampsia in the first trimester of pregnancy

Rebeca Silveira Rocha, Júlio Augusto Gurgel Alves, Sammya Bezerra Maia E Holanda Moura, Edward Araujo Júnior, Wellington P. Martins, Camila Teixeira Moreira Vasconcelos, Fabricio Da Silva Costa, Mônica Oliveira Batista Oriá

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14 Citations (Scopus)

Abstract

Objective: To compare a new simple algorithm for preeclampsia (PE) prediction among Brazilian women with two international guidelines - National Institute for Clinical Excellence (NICE) and American College of Obstetricians and Gynecologists (ACOG). Methods: We performed a secondary analysis of two prospective cohort studies to predict PE between 11 and 13 + 6. weeks of gestation, developed between August 2009 and January 2014. Outcomes measured were total PE, early PE (<34. weeks), preterm PE (<37. weeks), and term PE (≥37. weeks). The predictive accuracy of the models was assessed using the area under the receiver operator characteristic curve (AUC-ROC) and via calculation of sensitivity and specificity for each outcome. Results: Of a total of 733 patients, 55 patients developed PE, 12 at early, 21 at preterm and 34 at term. The AUC-ROC values were low, which compromised the accuracy of NICE (AUC-ROC: 0.657) and ACOG (AUC-ROC: 0.562) algorithms for preterm PE prediction in the Brazilian population. The best predictive model for preterm PE included maternal factors (MF) and mean arterial pressure (MAP) (AUC-ROC: 0.842), with a statistically significant difference compared with ACOG (p. <. 0.0001) and NICE (p = 0.0002) guidelines. Conclusion: The predictive accuracies of NICE and ACOG guidelines to predict preterm PE were low and a simple algorithm involving maternal factors and MAP performed better for the Brazilian population.

Original languageEnglish
Pages (from-to)113-117
Number of pages5
JournalPregnancy Hypertension
Volume10
DOIs
Publication statusPublished - Oct 2017

Keywords

  • First trimester pregnancy
  • Maternal characteristics
  • Mean arterial pressure
  • Prediction
  • Preeclampsia

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