Accepted Manuscript 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 PII: DOI: Reference:
S2210-7789(17)30050-8 http://dx.doi.org/10.1016/j.preghy.2017.07.146 PREGHY 324
To appear in:
Pregnancy Hypertension: An International Journal of Women's Cardiovascular Health
Received Date: Revised Date: Accepted Date:
8 March 2017 29 May 2017 24 July 2017
Please cite this article as: Rocha, R.S., Gurgel Alves, J.A., Bezerra Maia e Holanda Moura, S., Araujo Júnior, E., Martins, W.P., Vasconcelos, C.T.M., Da Silva Costa, F., Oriá, M.O.B., Comparison of three algorithms for prediction preeclampsia in the first trimester of pregnancy, Pregnancy Hypertension: An International Journal of Women's Cardiovascular Health (2017), doi: http://dx.doi.org/10.1016/j.preghy.2017.07.146
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Title: Comparison of three algorithms for prediction preeclampsia in the first trimester of pregnancy
Short title: Algorithms for prediction preeclampsia
Type of article: Full length article Authors: Rebeca Silveira Rocha,1 Júlio Augusto Gurgel Alves,2 Sammya Bezerra Maia e Holanda Moura,3 Edward Araujo Júnior,4 Wellington P. Martins,5 Camila Teixeira Moreira Vasconcelos,1 Fabricio Da Silva Costa,6 Mônica Oliveira Batista Oriá 1
Institutions: 1
Department of Nursing, Federal University of Ceará (UFC), Fortaleza, State of Ceará,
Brazil 2
Department of Maternal and Child, Federal University of Ceará (UFC), Fortaleza, State
of Ceará, Brazil 3
Department of Obstetrics and Gynecology, University of Fortaleza (UNIFOR),
Fortaleza, State of Ceará, Brazil 4
Department of Obstetrics, Paulista School of Medicine–Federal University of São
Paulo (EPM-UNIFESP), São Paulo, State of São Paulo, Brazil 5
Department of Obstetrics and Gynecology, Ribeirão Preto Medical School, University
of São Paulo (FMRP-USP), Ribeirão Preto, State of São Paulo, Brazil 6
Department of Obstetrics and Gynaecology, Monash University Faculty of Medicine
Nursing and Health Sciences, Clayton, Victoria, Australia.
Address for correspondence: Prof. Edward Araujo Júnior, PhD (Corresponding author) Rua Belchior de Azevedo, 156 apto. 111 - Torre Vitória CEP 05089-030 São Paulo–SP, Brazil Phone/Fax: +55-11-37965944; E-mail:
[email protected]
2
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 ( 30 years First pregnancy First pregnancy BMI ≥ 30 kg/m² BMI ≥ 35 kg/m² BMI ≥ 30 kg/m² Family history of PE Family history of PE Inter-pregnancy interval > 10 years ACOG: American College of Obstetricians and Gynecologists; NICE: National Institute for Clinical Excellence, PE: pre-eclampsia, BMI: body mass index,
16
Table 2. Clinical and obstetrical data of participants according to outcome group. Variable Clinical Variable Age (years) BMI (kg/m²) Prior PE Family history of PE Nulliparity Smoking Diabetes mellitus Chronic hypertension Skin color White Mixed Black Obstetrical Variables GA at delivery (weeks) Birth weight (g)
No PE (N = 678)
PE (N = 55)
p value*
26.0 ± 6.7 25.2 ± 4.7 37 (5%) 80 (12%) 330 (49%) 41 (6%) 17 (3%) 27 (4%)
27.1 ± 6.4 28.3 ± 4.7 8 (14%) 18 (33%) 30 (54%) 1 (3%) 1 (5%) 6 (11%)
0.23 < 0.001 0.01
