Multimorbidity and Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCT)
Multimorbidity and Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCT)
批准号:
MR/W014432/1
负责人:
Krishnarajah Nirantharakumar
金额:
$375.72万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
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英文摘要
What is the problem?One in five pregnant women have two or more active long-term health conditions. These can be both physical conditions (like diabetes or raised blood pressure), and mental health conditions (such as depression or anxiety). Often women also have to take several medications to manage their different health needs. Having two or more health conditions is also becoming increasingly common in pregnant women as women are increasingly older when they start having a family and as obesity and mental health conditions are on the rise in general.We don't really understand what the consequences are of multiple health conditions or medications for mothers and babies. This can make pregnancy, healthcare and managing medications more complicated. Without deeper understanding of the problem, women with several long-term health conditions may not have the best and safest experience of care before, during and after pregnancy because services have not been designed with their health needs in mind. What will we do?Our research is divided into five work packages. The first work package will examine how health conditions accumulate over time and identify what makes a woman more at risk of developing two or more long-term health conditions before pregnancy. The second work package will explore women's experiences of care during pregnancy, birth and after birth. We will work together with families and health professionals to establish how care could be improved. The third work package will look further at how having two or more long-term health conditions may affect pregnant women and their children. We will do this in three ways: we will identify outcomes that women, health professionals and researchers feel should be reported in research; we will examine how often women experience pregnancy complications; and we will explore how frequently women and their children develop additional long-term ill health.In the fourth work package we will describe how medications are prescribed. We will investigate how taking combinations of medication may affect pregnant women and their babies. In our fifth work package, we will build a prediction model to help identify how likely a previously healthy pregnant woman will develop multiple long-term conditions after pregnancy. We can do this by using health information collected during or just after pregnancy. This is because we know that some complications in pregnancy may be a warning sign of future illnesses.What will our research achieve?We will help women and their healthcare professionals make informed decisions about their care and medication use by providing accessible information on risk. For example, the risks associated with pregnancy; the risks associated with combinations of medications during pregnancy; and the future risk of developing long-term health conditions after a pregnancy complication. Our work will also identify important time points to intervene and ways to prevent pregnancy complications or developing future long-term health conditions. This will reduce the health burden for women, partners, carers and reduce avoidable healthcare and economic cost in the long run for society. Working together with women and healthcare professionals, we will produce recommendations on how to plan and design services that meet the needs of women and their families before, during and after pregnancy.
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DOI:
10.1136/bmjopen-2022-067585
发表时间:
2023-03-06
期刊:
BMJ open
影响因子:
2.9
作者:
[]
通讯作者:
DOI:
10.1186/s12916-023-03058-4
发表时间:
2023-09-12
期刊:
BMC MEDICINE
影响因子:
9.3
作者:
[Azcoaga-Lorenzo, Amaya, Fagbamigbe, Adeniyi Francis, Agrawal, Utkarsh, Black, Mairead, Usman, Muhammad, Lee, Siang Ing, Eastwood, Kelly-Ann, Moss, Ngawai, Plachcinski, Rachel, Nelson-Piercy, Catherine, Brophy, Sinead, O'Reilly, Dermot, Nirantharakumar, Krishnarajah, Mccowan, Colin]
通讯作者:
Mccowan, Colin
DOI:
10.1136/bmjopen-2021-050058
发表时间:
2021-07-12
期刊:
BMJ open
影响因子:
2.9
作者:
[Haider S, Thayakaran R, Subramanian A, Toulis KA, Moore D, Price MJ, Nirantharakumar K]
通讯作者:
Nirantharakumar K
mmVAE: multimorbidity clustering using Relaxed Bernoulli ß-Variational Autoencoders
mmVAE:使用松弛伯努利变分自动编码器进行多病态聚类
DOI:
--
发表时间:
2022
期刊:
Machine Learning for Health
影响因子:
--
作者:
[Charles Gadd]
通讯作者:
Charles Gadd
England's preconception health report: convenient and valuable data.
英国的孕前健康报告:方便且有价值的数据。
DOI:
10.1111/1471-0528.17483
发表时间:
2023
期刊:
an international journal of obstetrics and gynaecology
影响因子:
--
作者:
[Black M]
通讯作者:
Black M
共 8 条
Multimorbid Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCCT)
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批准号:MR/V005243/1
-
项目类别:Research Grant
-
资助金额:$12.79万
-
财政年份:2020
-
负责人:Krishnarajah Nirantharakumar
-
依托单位:
Automated Clinical Epidemiology Studies (ACES) platform for complex epidemiology study designs and diverse databases
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批准号:MR/S003878/1
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项目类别:Fellowship
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资助金额:$38.16万
-
财政年份:2018
-
负责人:Krishnarajah Nirantharakumar
-
依托单位:
海外基金