Metabolomics for prediction of hypertension in pregnancy: a systematic review and meta-analysis protocol

被引:1
作者
Mayrink, Jussara [1 ]
Leite, Debora Farias Batista [1 ,2 ]
Costa, Maria Laura [1 ]
Cecatti, Jose Guilherme [1 ]
机构
[1] Univ Estadual Campinas, Dept Gynecol & Obstet, Campinas, Brazil
[2] Univ Fed Pernambuco, Dept Maternal & Child Hlth, Recife, PE, Brazil
来源
BMJ OPEN | 2020年 / 10卷 / 12期
基金
比尔及梅琳达.盖茨基金会;
关键词
hypertension; prenatal diagnosis; epidemiology; GREAT OBSTETRICAL SYNDROMES; 1ST-TRIMESTER PREDICTION; NULLIPAROUS WOMEN; BLOOD-PRESSURE; CLINICAL RISK; PREECLAMPSIA; BIOMARKERS; MANAGEMENT; DISCOVERY; DISORDERS;
D O I
10.1136/bmjopen-2020-040652
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Introduction Hypertension is a very important cause of maternal morbidity and mortality worldwide, despite efforts on prevention. The lack of a tool to provide effective and early prediction of hypertension for a high-risk group may contribute to improving maternal and fetal outcomes. Metabolomics has figured out as a promised technology to contribute to the improvement of hypertension in pregnancy prediction. Methods and analysis Our primary outcome is hypertensive disorders of pregnancy. A detailed systematic literature search will be performed in electronic databases PubMed, EMBASE, Scopus, Web of Science, Latin America and Caribbean Health Sciences Literature, Scientific Electronic Library Online, Health Technology Assessment and Database of Abstracts of Reviews of Effects using controlled terms 'pre-eclampsia', 'hypertensive disorders', 'metabolomics' and 'prediction' (and their variations). Studies from the latest 20 years will be included, except case reports, reviews, cross-sectional studies, letter to editors, expert opinions, commentaries papers or non-human research. If possible, we will perform a meta-analysis. Two peer-reviewers will independently perform the search and in cases of discordance, a third reviewer will be consulted. Ethics and dissemination As a systematic review, ethics approval is not required. The results of this review will present the current use and performance of metabolomics for predicting gestational hypertension. Such data could potentially guide future studies and interventions to improve existing prediction models. PROSPERO registration number CRD42018097409.
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