Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter April 6, 2017

Simple approach based on maternal characteristics and mean arterial pressure for the prediction of preeclampsia in the first trimester of pregnancy

  • Rebeca Silveira Rocha , Júlio Augusto Gurgel Alves , Sammya Bezerra Maia e Holanda Moura , Edward Araujo Júnior EMAIL logo , Alberto Borges Peixoto , Eduardo Félix Martins Santana , Wellington P. Martins , Camila Teixeira Moreira Vasconcelos , Fabricio Da Silva Costa and Mônica Oliveira Batista Oriá

Abstract

Aim:

To propose a simple model for predicting preeclampsia (PE) in the 1st trimester of pregnancy on the basis of maternal characteristics (MC) and mean arterial pressure (MAP).

Methods:

A prospective cohort was performed to predict PE between 11 and 13+6 weeks of gestation. The MC evaluated were maternal age, skin color, parity, previous PE, smoking, family history of PE, hypertension, diabetes mellitus and body mass index (BMI). Mean arterial blood pressure (MAP) was measured at the time of the 1st trimester ultrasound. The outcome measures were the incidences of total PE, preterm PE (delivery <37 weeks) and term PE (delivery ≥37 weeks). We performed logistic regression analysis to determine which factors made significant contributions for the prediction of the three outcomes.

Results:

We analyzed 733 pregnant women; 55 developed PE, 21 of those developed preterm PE and 34 term PE. For total PE, the best model was MC+MAP, which had an area under the receiver operating characteristic curve (AUC ROC) of 0.79 [95% confidence interval (CI)=0.76–0.82]. For preterm PE, the best model was MC+MAP, with an AUC ROC of 0.84 (95% CI=0.81–0.87). For term PE, the best model was MC, with an AUC ROC of 0.75 (0.72–0.79). The MC+MAP model demonstrated a detection rate of 67% cases of preterm PE, with a false-positive rate of 10%, positive predictive value of 17% and negative predictive value of 99%.

Conclusion:

The MC+MAP model showed good accuracy in predicting preterm PE in the 1st trimester of gestation.


Corresponding author: Prof. Edward Araujo Júnior, PhD, Department of Obstetrics, Paulista School of Medicine-Federal University of São Paulo (EPM-UNIFESP), Rua Belchior de Azevedo, 156 apto. 111-Torre Vitória, 05089-030 São Paulo-SP, Brazil, Tel./Fax: +55-11-37965944

Author’s statement

  1. Conflict of interest: Authors state no conflict of interest.

  2. Material and methods: Informed consent: Informed consent has been obtained from all individuals included in this study.

  3. Ethical approval: The research related to human subject use has complied with all the relevant national regulations, and institutional policies, and is in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee.

References

[1] Duley L. The global impact of pre-eclampsia and eclampsia. Semin Perinatol. 2009;33:130–37.10.1053/j.semperi.2009.02.010Search in Google Scholar PubMed

[2] WHO. Risking death to give life. Geneva, 2005. [Cited 30 Sept 2015]. Available from: http://www.who.int/whr/2005/chapter4/en/index1.html.Search in Google Scholar

[3] Steegers E, Dadelszen P, Duvekot JJ, Pijnenborg R. Preeclampsia. Lancet. 2010;376:631–41.10.1016/S0140-6736(10)60279-6Search in Google Scholar

[4] Bellamy L, Casas JP, Hingorani AD, Williams DJ. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. Br Med J. 2007;335:974.10.1136/bmj.39335.385301.BESearch in Google Scholar

[5] Bujold E, Roberge S, Lacasse Y, Bureau M, Audibert F, Marcoux S, et al. Prevention of preeclampsia and intrauterine growth restriction with aspirin started in early pregnancy: a meta-analysis. Obstet Gynecol. 2010;116:402–14.10.1097/AOG.0b013e3181e9322aSearch in Google Scholar PubMed

[6] Roberge S, Odibo AO, Bujold E. Aspirin for the prevention of preeclampsia and intrauterine growth restriction. Clin Lab Med. 2016;36:319–29.10.1016/j.cll.2016.01.013Search in Google Scholar PubMed

[7] Sweeting A, Park F, Hyett J. The first trimester: prediction and prevention of the great obstetrical syndromes. Best Pract Res Clin Obstet Gynaecol. 2015;29:183–93.10.1016/j.bpobgyn.2014.09.006Search in Google Scholar PubMed

[8] Alves JA, Silva BY, de Sousa PC, Maia SB, Costa Fda S. Reference range of uterine artery Doppler parameters between the 11th and 14th pregnancy weeks in a population sample from Northeast Brazil. Rev Bras Ginecol Obstet. 2013;35:357–62.10.1590/S0100-72032013000800004Search in Google Scholar

[9] Cuckle HS. Screening for pre-eclampsia: lessons from aneuploidy screening. Placenta. 2011;32:S42–S48.10.1016/j.placenta.2010.07.015Search in Google Scholar

[10] ACOG Committee on Practice Bulletins-Obstetrics. ACOG practice bulletin. Diagnosis and management of preeclampsia and eclampsia. Number 33, January 2002. Obstet Gynecol. 2002;99:159–67.10.1016/S0029-7844(01)01747-1Search in Google Scholar PubMed

[11] Miller RS, Rudra CB, Williams MA. First-trimester mean arterial pressure and risk of preeclampsia. Am J Hypertens. 2007;20:573–8.10.1016/j.amjhyper.2006.12.012Search in Google Scholar PubMed

[12] Wright D, Gallo DM, Gil Pugliese S, Casanova C, Nicolaides KH. Contingent screening for preterm pre-eclampsia. Ultrasound Obstet Gynecol. 2016;47:554–9.10.1002/uog.15807Search in Google Scholar PubMed

[13] Gurgel Alves JA, Praciano de Sousa PC, Bezerra Maia E Holanda Moura S, Kane SC, da Silva Costa F. First-trimester maternal ophthalmic artery Doppler analysis for prediction of pre-eclampsia. Ultrasound Obstet Gynecol. 2014;44:411–8.10.1002/uog.13338Search in Google Scholar PubMed

[14] Bezerra Maia E Holanda Moura S, Praciano PC, Gurgel Alves JA, Martins WP, Araujo Júnior E, Kane SC, et al. Renal interlobar vein impedance index as a first-trimester marker does not predict hypertensive disorders of pregnancy. J Ultrasound Med. 2016;35:2641–8.10.7863/ultra.15.11002Search in Google Scholar PubMed

[15] Poon LC, Kametas NA, Pandeva I, Valencia C, Nicolaides KH. Mean arterial pressure at 11(+0) to 13(+6) weeks in the prediction of preeclampsia. Hypertension. 2008;51:1027–33.10.1161/HYPERTENSIONAHA.107.104646Search in Google Scholar PubMed

[16] Giguère Y, Massé J, Thériault S, Bujold E, Lafond J, Rousseau F, et al. Screening for pre-eclampsia early in pregnancy: performance of a multivariable model combining clinical characteristics and biochemical markers. BJOG. 2015;122:402–10.10.1111/1471-0528.13050Search in Google Scholar PubMed

[17] O’Gorman N, Wright D, Syngelaki A, Akolekar R, Wright A, Poon LC, et al. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11–13 weeks gestation. Am J Obstet Gynecol. 2016;214:103.e1–103.e12.10.1016/j.ajog.2015.08.034Search in Google Scholar PubMed

[18] Skråstad RB, Hov GG, Blaas HG, Romundstad PR, Salvesen KÅ. Risk assessment for preeclampsia in nulliparous women at 11–13 weeks gestational age: prospective evaluation of two algorithms. Br J Obstet Gyneacol. 2015;122:1781–8.10.1111/1471-0528.13194Search in Google Scholar PubMed

[19] Al-Rubaie Z, Askie LM, Ray JG, Hudson HM, Lord SJ. The performance of risk prediction models for pre-eclampsia using routinely collected maternal characteristics and comparison with models that include specialised tests and with clinical guideline decision rules: a systematic review. Br J Obstet Gyneacol. 2016;123:1441–52.10.1111/1471-0528.14029Search in Google Scholar PubMed

[20] Leung C, Saaid R, Pedersen L, Park F, Poon L, Hyett J. Demographic factors that can be used to predict early-onset pre-eclampsia. J Matern Fetal Neonatal Med. 2015;28:535–9.10.3109/14767058.2014.923837Search in Google Scholar PubMed

[21] Poon LC, Kametas NA, Chelemen T, Leal A, Nicolaides KH. Maternal risk factors for hypertensive disorders in pregnancy: a multivariate approach. J Hum Hypertens. 2010;24:104–10.10.1038/jhh.2009.45Search in Google Scholar PubMed

[22] Akolekar R, Syngelaki A, Poon L, Wright D, Nicolaides KH. Competing risks model in early screening for preeclampsia by biophysical and biochemical markers. Fetal Diagn Ther. 2013;33:8–15.10.1159/000341264Search in Google Scholar PubMed

[23] Lisonkova S, Joseph KS. Incidence of preeclampsia: risk factors and outcomes associated with early- versus late-onset disease. Am J Obstet Gynecol. 2013;209:544.e1–544.e12.10.1016/j.ajog.2013.08.019Search in Google Scholar PubMed

[24] Gabbay-Benziv R, Oliveira N, Baschat AA. Optimal first trimester preeclampsia prediction: a comparison of multimarker algorithm, risk profiles and their sequential application. Prenat Diagn. 2016;36:34–39.10.1002/pd.4707Search in Google Scholar PubMed

[25] Oliveira N, Magder LS, Blitzer MG, Baschat AA. First-trimester prediction of pre-eclampsia: external validity of algorithms in a prospectively enrolled cohort. Ultrasound Obstet Gynecol. 2014;44:279–85.10.1002/uog.13435Search in Google Scholar PubMed

[26] Wright D, Syngelaki A, Akolekar R, Poon LC, Nicolaides KH. Competing risks model in screening for preeclampsia by maternal characteristics and medical history. Am J Obstet Gynecol. 2015;213:62.e1–10.10.1016/j.ajog.2015.02.018Search in Google Scholar PubMed

[27] Markandu ND, Whitcher F, Arnold A, Carney C. The mercury sphygmomanometer should be abandoned before it is proscribed. J Hum Hypertens. 2000;14:31–6.10.1038/sj.jhh.1000932Search in Google Scholar PubMed

[28] Poon LC, Maiz N, Valencia C, Plasencia W, Nicolaides KH. First-trimester maternal serum pregnancy-associated plasma protein-A and pre-eclampsia. Ultrasound Obstet Gynecol. 2009;33:23–33.10.1002/uog.6280Search in Google Scholar PubMed

Received: 2016-12-24
Accepted: 2017-3-8
Published Online: 2017-4-6
Published in Print: 2017-10-26

©2017 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 29.3.2024 from https://www.degruyter.com/document/doi/10.1515/jpm-2016-0418/html
Scroll to top button