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: University of Birmingham
• 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
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV005243%2F1
• 关键词: Multimorbid; Pregnancy; Determinants; Clusters; Consequences

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.

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，有时他们得不到最好的护理。患有多发性硬化症的患者可能需要与不同的专家协调预约，他们的药物需要仔细管理。在怀孕期间，这些挑战可能会增加妇女与多发性硬化症。我们知道怀孕期间的多胎妊娠越来越普遍，但我们不明白为什么会这样，以及对母亲和婴儿的后果是什么。如果对这个问题没有更深入的了解，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好的护理体验，因为服务不是针对她们的具体需求。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、疾病和公共卫生领域的专家。我们将与有多次妊娠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，以了解有多少女性在怀孕期间患有多发性硬化症以及她们患有哪些疾病。我们将尝试确定年龄，体重，文化或社会背景，教育水平和以前怀孕的次数等因素是否会影响妇女在怀孕期间是否患有多发性硬化症。我们还将了解哪些疾病在怀孕期间聚集在一起（集群），哪些集群最常见，以及某些集群是否对某些女性的影响比其他女性更大。在研究的第二部分，我们将比较在怀孕期间有和没有多Mortocarcinoma的母亲发生了什么。我们将发现患有多重抑郁症的女性是否更容易在怀孕期间（例如妊娠糖尿病），怀孕后（例如产后抑郁症）以及长期（例如心脏病）发生疾病。我们还将关注妊娠期多发性硬化症妇女的子女的健康和福祉。我们研究的第三部分将集中在妊娠期药物治疗上。我们将了解患有多发性硬化症的妇女在怀孕期间服用什么药物，以及这些药物如何影响怀孕期间母亲和婴儿的健康。这些知识将有助于医生在怀孕期间安全地开药。我们知道，妊娠并发症是妇女未来患病的警告信号。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妇女的长期健康。我们可以利用这些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，讨论为妊娠期多发性硬化症妇女提供的服务。我们将了解这些服务是否适当和方便，以及如何改善服务。展望未来，这将有助于我们与妇女及其伴侣共同设计保健服务。让公众参与我们将与有多次怀孕经验的妇女合作。他们的见解将有助于确保我们的项目立足于妇女的经验，并确保项目的所有阶段都致力于改善对妇女的照顾。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，皇家妇产科医师学院）、NHS网络（例如，当地产科服务）以及慈善机构和妇女网络（例如，产妇之声伙伴关系）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。通过加强了解，我们将能够规划和设计各种服务，满足妇女及其家庭在婴儿出生之前、期间和之后的需要。

项目成果

Prevalence of polypharmacy in pregnancy: a systematic review.

• DOI: 10.1136/bmjopen-2022-067585
• 发表时间: 2023-03-06
• 期刊: BMJ open
• 影响因子: 2.9

Maternal multimorbidity and preterm birth in Scotland: an observational record-linkage study.

• 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

• 发表时间: 2022
• 期刊: Machine Learning for Health
• 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
• 作者: Black M

Maternal and child outcomes for pregnant women with pre-existing multiple long-term conditions: protocol for an observational study in the UK.

• DOI: 10.1136/bmjopen-2022-068718
• 发表时间: 2023-02-24
• 期刊: BMJ open
• 影响因子: 2.9