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Validation of salivary uric acid remote self-monitoring for early prediction of hypertensive disorders of pregnancy: study protocol for a prospective, observational, multicentre cohort study

Chmielewska, B; Reading, I; Bhide, A; Ganapathy, R; Thilaganathan, B (2025) Validation of salivary uric acid remote self-monitoring for early prediction of hypertensive disorders of pregnancy: study protocol for a prospective, observational, multicentre cohort study. BMJ Open, 15 (6). e094421. ISSN 2044-6055 https://doi.org/10.1136/bmjopen-2024-094421
SGUL Authors: Thilaganathan, Baskaran

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Abstract

Introduction Hypertensive disorders of pregnancy (HDP), including gestational hypertension and pre-eclampsia, affect approximately 10% of pregnancies worldwide and contribute significantly to fetal and maternal morbidity and mortality. Early identification of HDP would facilitate targeted surveillance and personalised care in order to mitigate the severity of complications and improve pregnancy outcomes. Uric acid is a marker of oxidative stress, inflammation and endothelial dysfunction, and has been proposed as a predictor of hypertensive disease. Salurate is a salivary uric acid test that has the potential to identify pregnant women at risk of developing HDP several weeks before clinical manifestation. Methods and analysis This is a prospective, multicentre, observational, cohort study with health economics evaluation. Women aged 16 and above, with a viable singleton pregnancy at <16 weeks gestation and a combined first trimester pre-eclampsia risk of >1:300 will be eligible for recruitment. Participants will perform weekly remote salivary uric acid testing from enrolment until the conclusion of pregnancy and upload results of colourimetric paper tests via a smartphone application. We will validate a predictive algorithm that analyses colour data from several consecutive samples to detect patterns that predict whether HDP is likely to occur. The primary outcome is test performance for the prediction of HDP. Secondary outcomes include adherence to sampling and test performance for predicting gestational diabetes, stillbirth and fetal growth restriction. Data on pregnancy outcomes will be collected from the medical notes, compared with the predictions made by the algorithm and subjected to statistical analysis. Ethics and dissemination Approval has been obtained from Cambridge East Research Ethics Committee (REC reference 24/EE/0123), Medicines and Healthcare products Regulatory Agency (CI/2024/0038/GB) and Health Research Authority (IRAS ID 337290). Results of the study will be published in peer-reviewed journals and presented at national and international conferences. Trial registration number ISRCTN17992452. Protocol version 4, 4 July 2024.

Item Type: Article
Additional Information: © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
SGUL Research Institute / Research Centre: Academic Structure > Cardiovascular & Genomics Research Institute
Academic Structure > Cardiovascular & Genomics Research Institute > Vascular Biology
Journal or Publication Title: BMJ Open
ISSN: 2044-6055
Language: en
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
10053908Innovate UKhttps://doi.org/10.13039/501100006041
URI: https://openaccess.sgul.ac.uk/id/eprint/117676
Publisher's version: https://doi.org/10.1136/bmjopen-2024-094421

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