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Multimorbid Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCCT)

Multimorbid Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCCT)
多病态妊娠:决定因素、聚类、后果和轨迹 (MuM-PreDiCCT)
批准号:
MR/V005243/1
负责人:
Krishnarajah Nirantharakumar
金额:
$12.79万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --

项目摘要

项目成果

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中文摘要
翻译
问题多发病是指人们患有一种以上的长期疾病。患有几种长期疾病的人很难控制自己的病情,有时他们得不到最高质量的护理。患有多发病的患者可能不得不协调与不同专家的预约,他们的药物需要谨慎管理。在怀孕期间,这些挑战可能会增加患有多发性疾病的妇女。我们知道,怀孕期间的多发病正变得越来越普遍,但我们不明白为什么会出现这种情况,以及对母亲和婴儿会产生什么后果。如果没有对这个问题的深入了解,患有几种长期疾病的妇女就不会在怀孕前、怀孕期间和怀孕后获得最佳的护理体验,因为服务并不是根据她们的特定需求量身定做的。我们的目标和方法我们的合作将汇集来自英国7个学术机构的数据分析、疾病和公共卫生方面的专家。我们将与有妊娠期多发病经验的妇女密切合作。首先,我们的数据专家将查看电子健康记录,以找出有多少女性在怀孕期间患有多发病,以及她们患有什么疾病。我们将尝试确定年龄、体重、文化或社会背景、教育水平和既往怀孕次数等因素是否会影响女性在怀孕期间是否患有多发病。我们还将找出哪些疾病在怀孕期间集中在一起(集群),哪些集群最常见,以及一些集群是否比其他集群对某些女性的影响更大。在这项研究的第二部分,我们将比较患有和不患有多发病的母亲在怀孕期间发生的情况。我们将找出患有多发病的妇女是否更有可能在怀孕期间(例如妊娠期糖尿病)、怀孕后(例如产后抑郁症)以及长期内(例如心脏病)患上疾病。我们还将研究怀孕期间患有多发性疾病的妇女的子女的健康和福祉。我们研究的第三部分将集中在怀孕期间的药物治疗上。我们将了解患有多发病的妇女在怀孕期间服用哪些药物,以及这些药物在怀孕期间如何影响母亲和婴儿的健康。这些知识将帮助医生在怀孕期间安全地开出处方。我们知道,怀孕期间的并发症是女性未来疾病的警告信号。作为该项目的一部分,我们将能够更多地了解不同的妊娠并发症如何影响女性的长期健康。我们可以利用这一知识在可能的情况下采取预防措施。最后,我们将与妇女和医疗保健专业人员会面,讨论为怀孕期间患有多发病的妇女提供的服务。我们将了解这些服务的适当性和可及性,以及如何改进服务。展望未来,这将有助于我们与妇女及其伴侣共同设计卫生服务。让公众参与我们将与有妊娠多病经验的妇女合作。他们的见解将有助于确保我们的项目以妇女的经验为基础,并确保该项目的所有阶段都推动改善对妇女的护理。分享我们的发现我们的团队拥有庞大的网络,我们将通过专业组织(例如皇家妇产科学院)、NHS网络(例如当地产科服务)以及慈善机构和妇女网络(例如孕妇之声伙伴关系)与医疗保健专业人员分享我们的发现。我们的影响这项研究将使医疗保健专业人员和妇女更好地了解怀孕期间的多发病。通过这种加深的了解,我们将能够规划和设计满足妇女及其家庭在婴儿出生前、期间和之后的需求的服务。
英文摘要
The ProblemMultimorbidity is when people suffer from more than one long-term illness. It can be difficult for people with several long-term illnesses to manage their conditions and sometimes they don't receive the best quality care. Patients with multimorbidity may have to coordinate appointments with different specialists and their medications need to be managed carefully. During pregnancy, these challenges may increase for women with multimorbidity. We know that multimorbidity in pregnancy is becoming more common, but we don't understand why this is and what the consequences are for mothers and babies. Without this deeper understanding of the problem, women with several long-term illnesses won't have the best experience of care before, during and after pregnancy because services are not tailored to their specific needs. Our aims and approachOur collaboration will bring together experts in data analysis, diseases and public health from 7 academic institutions in the UK. We will work in close partnership with women with experience of multimorbidity in pregnancy. Firstly, our data specialists will look at electronic health records to find out how many women have multimorbidity in pregnancy and what illnesses they have. We will try and identify if factors such as age, weight, cultural or social background, level of education and number of previous pregnancies influences whether a woman has multimorbidity in pregnancy. We will also find out which illnesses group together(cluster) during pregnancy, which clusters are most common and whether some clusters affect some women more than others. In the second part of the study, we will compare what happens to mothers with and without multimorbidity during pregnancy. We will find out whether women with multimorbidity are more likely to develop illnesses during the pregnancy (e.g. gestational diabetes), after the pregnancy (e.g postnatal depression) and also in the longer-term (e.g. heart-disease). We will also look at the health and wellbeing of children of women with multimorbidity in pregnancy.The third part of our research will focus on medications in pregnancy. We will find out what medicines women with multimorbidity take during pregnancy and how the medications affect the health of the mother and the baby during pregnancy. This knowledge will help doctors prescribe safely during pregnancy. We know that complications in pregnancy are a warning sign of future illnesses in women. As part of this project, we will be able to find out more about how different pregnancy complications affect the longer-term health of women. We can use this knowledge to put preventative measures in place where possible. Finally, we will meet with women and healthcare professionals to discuss the services available for women with multimorbidity in pregnancy. We will find out how appropriate and accessible these services are and how services can be improved. Going forward, this will help us to jointly design health services with women and their partners. Involving the publicWe will work in partnership with women with experience of multimorbidity in pregnancy. Their insights will help ensure that our project is grounded in the experiences of women and that all stages of the project drive towards improving care for women. Sharing our findingsOur team has large networks and we will share our findings with healthcare professionals, through professional organisations (e.g. Royal Colleges of Obstetricians and Gynaecologists); through NHS networks (e.g. Local Maternity Services) and through charities and women's networks (e.g. Maternity Voice Partnerships). Our ImpactThis study will give healthcare professionals and women a much better understanding of multimorbidity in pregnancy. Through this enhanced understanding, we will be able to plan and design services that meet the needs of women and their families before, during and after the birth of their babies.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
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
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 7 条
    Multimorbidity and Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCT)
    • 批准号:
      MR/W014432/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $375.72万
    • 财政年份:
      2021
    • 负责人:
      Krishnarajah Nirantharakumar
    • 依托单位:
    Automated Clinical Epidemiology Studies (ACES) platform for complex epidemiology study designs and diverse databases
    • 批准号:
      MR/S003878/1
    • 项目类别:
      Fellowship
    • 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