Literature DB >> 31733203

The competing risk approach for prediction of preeclampsia.

David Wright1, Alan Wright1, Kypros H Nicolaides2.   

Abstract

The established method of the assessment of the risk for development of preeclampsia is to identify risk factors from maternal demographic characteristics and medical history; in the presence of such factors, the patient is classified as high risk and in their absence as low risk. Although this approach is simple to perform, it has poor performance of the prediction of preeclampsia and does not provide patient-specific risks. This review describes a new approach that allows the estimation of patient-specific risks of delivery with preeclampsia before any specified gestational age by maternal demographic characteristics and medical history with biomarkers obtained either individually or in combination at any stage in pregnancy. In the competing risks approach, every woman has a personalized distribution of gestational age at delivery with preeclampsia; whether she experiences preeclampsia or not before a specified gestational age depends on competition between delivery before or after the development of preeclampsia. The personalized distribution comes from the application of Bayes theorem to combine a previous distribution, which is determined from maternal factors, with likelihoods from biomarkers. As new data become available, what were posterior probabilities take the role as the previous probability, and data collected at different stages are combined by repeating the application of Bayes theorem to form a new posterior at each stage, which allows for dynamic prediction of preeclampsia. The competing risk model can be used for precision medicine and risk stratification at different stages of pregnancy. In the first trimester, the model has been applied to identify a high-risk group that would benefit from preventative therapeutic interventions. In the second trimester, the model has been used to stratify the population into high-, intermediate-, and low-risk groups in need of different intensities of subsequent monitoring, thereby minimizing unexpected adverse perinatal events. The competing risks model can also be used in surveillance of women presenting to specialist clinics with signs or symptoms of hypertensive disorders; combination of maternal factors and biomarkers provide patient-specific risks for preeclampsia that lead to personalized stratification of the intensity of monitoring, with risks updated on each visit on the basis of biomarker measurements.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bayes theorem; biomarker; mean arterial pressure; personalized distribution; placental growth factor; preeclampsia; soluble fms-like tyrosine kinase-1

Year:  2019        PMID: 31733203     DOI: 10.1016/j.ajog.2019.11.1247

Source DB:  PubMed          Journal:  Am J Obstet Gynecol        ISSN: 0002-9378            Impact factor:   8.661


  16 in total

1.  Prediction of preeclampsia throughout gestation with maternal characteristics and biophysical and biochemical markers: a longitudinal study.

Authors:  Adi L Tarca; Andreea Taran; Roberto Romero; Eunjung Jung; Carmen Paredes; Gaurav Bhatti; Corina Ghita; Tinnakorn Chaiworapongsa; Nandor Gabor Than; Chaur-Dong Hsu
Journal:  Am J Obstet Gynecol       Date:  2021-04-16       Impact factor: 8.661

2.  Correlation of serum vitamin A and vitamin E levels with the occurrence and severity of preeclampsia.

Authors:  Sijing Duan; Yong Jiang; Kai Mou; Yi Wang; Shanshan Zhou; Bingxin Sun
Journal:  Am J Transl Res       Date:  2021-12-15       Impact factor: 4.060

3.  Serum Neurofilament Light: a Potential Diagnostic and Prognostic Biomarker in Obstetric Posterior Reversible Encephalopathy Syndrome.

Authors:  Xiaobo Fang; Yanling Liang; Weixi Zhang; Qiong Wang; Jingsi Chen; Jia Chen; Yongqiang Lin; Yanli Chen; Li Yu; Haibin Wang; Dunjin Chen
Journal:  Mol Neurobiol       Date:  2021-09-22       Impact factor: 5.590

4.  Multivariate logistic regression analysis of preeclampsia in patients with pregnancy induced hypertension and the risk predictive value of monitoring platelet, coagulation function and thyroid hormone in pregnant women.

Authors:  Li Zeng; Chunfang Liao
Journal:  Am J Transl Res       Date:  2022-09-15       Impact factor: 3.940

Review 5.  Prevention, Diagnosis, and Management of Hypertensive Disorders of Pregnancy: a Comparison of International Guidelines.

Authors:  Rachel G Sinkey; Ashley N Battarbee; Natalie A Bello; Christopher W Ives; Suzanne Oparil; Alan T N Tita
Journal:  Curr Hypertens Rep       Date:  2020-08-27       Impact factor: 5.369

Review 6.  Maternal Morbidity and Mortality: Are We Getting to the "Heart" of the Matter?

Authors:  Jasmina Varagic; Patrice Desvigne-Nickens; Joyonna Gamble-George; Lisa Hollier; Christine Maric-Bilkan; Megan Mitchell; Victoria L Pemberton; Nicole Redmond
Journal:  J Womens Health (Larchmt)       Date:  2020-12-01       Impact factor: 2.681

7.  Ultrasound estimated subcutaneous and visceral adipose tissue thicknesses and risk of pre-eclampsia.

Authors:  Heidrun Pétursdóttir Maack; Inger Sundström Poromaa; Linda Lindström; Ajlana Mulic-Lutvica; Katja Junus; Anna-Karin Wikström
Journal:  Sci Rep       Date:  2021-11-23       Impact factor: 4.379

Review 8.  Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders.

Authors:  Eleanor P Thong; Drishti P Ghelani; Pamada Manoleehakul; Anika Yesmin; Kaylee Slater; Rachael Taylor; Clare Collins; Melinda Hutchesson; Siew S Lim; Helena J Teede; Cheryce L Harrison; Lisa Moran; Joanne Enticott
Journal:  J Cardiovasc Dev Dis       Date:  2022-02-10

9.  The Importance of Doppler Analysis of Uterine Circulation in Pregnancy for a Better Understanding of Preeclampsia.

Authors:  Edin Medjedovic; Asim Kurjak
Journal:  Med Arch       Date:  2021-12

Review 10.  First Trimester Prediction of Adverse Pregnancy Outcomes-Identifying Pregnancies at Risk from as Early as 11-13 Weeks.

Authors:  Alexandra Bouariu; Anca Maria Panaitescu; Kypros H Nicolaides
Journal:  Medicina (Kaunas)       Date:  2022-02-22       Impact factor: 2.430

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