MICA: A partnership to extend the research utility of a source of real-world health data, the UK National Neonatal Research Database

MICA：旨在扩展英国国家新生儿研究数据库这一现实世界健康数据来源的研究效用的合作伙伴关系

• 批准号: MR/T016752/1
• 负责人: Neena Modi
• 金额: $ 71.24万
• 依托单位: Imperial College London
• 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%2FT016752%2F1
• 关键词: MICA; partnership; extend; research; utility

Aim: Our aim is to improve the usefulness of an established UK resource, the National Neonatal Research Database (NNRD) for parents and for researchers so that they can view data and conduct studies to improve the care of preterm and sick newborn babies more quickly, efficiently and at lower cost than presently. About 1 in 7 (100,000 each year) newborn babies is admitted to a NHS neonatal unit. Neonatal problems and the care received, affect life-long health and well-being. Background: We established the NNRD, a unique, award-winning resource, in collaboration with parents, doctors, nurses, other healthcare professionals, and researchers to improve care, treatments and outcomes for preterm and sick babies admitted to NHS neonatal units. The NNRD contains comprehensive data, updated quarterly, from the electronic medical notes of all babies admitted to NHS neonatal units in England, Scotland and Wales. Imperial College London hosts the NNRD securely on a computer server. No data that can identify any individual baby are included. The data include details of diseases, daily treatments and outcomes. To-date the NNRD has information on about one million babies; around 25,000 new babies are added each quarter. Why this work is needed: We established the NNRD because a key challenge in newborn care is the need for up-to-date, timely and accurate data for research to improve, evaluate and develop new treatments. Data are required for all of the many types of studies needed, such as improving understanding of diseases, their causes and the care provided, and to develop new medicines. Different types of studies often need similar data (e.g. age, sex, weight, disease) but traditionally, researchers collect these again and again for each new purpose. This is expensive, wastes time and increases the risk of errors. Studies can fail because data availability or quality are poor. Data also need to be up-to-date otherwise information may be misleading. For example, information on health outcomes of very preterm babies that are widely used in the UK to counsel parents and guide clinical practice was derived from research conducted over 20 years ago and no longer reflects circumstances today. The NNRD provides a single source of up-to-date data for research and other purposes. This work is needed to improve the NNRD and make it more useful. Our objectives, and what we will do: We will automate processes to check accuracy and add new data into the NNRD that we currently perform manually. At present anyone who wants to use the NNRD must request our assistance, which inevitably incurs a delay. We will identify common types of information that researchers, parents and clinicians would find useful to obtain from the NNRD. This might be to determine the number of patients with particular conditions that are admitted to neonatal units. We will also obtain views on the way in which they would like to see the results (e.g. tables or graphs). This will help us develop web-based tools to enable parents and researchers to answer common questions themselves. We will make these tools available on our website. We will also develop ways to process NNRD data so that we can apply new techniques that can help identify patterns such as where particular types of disease occur and provide clues to their causes. Additionally, we will train young scientists in handling complex health data. Why this partnership is needed: We have formed a partnership because our objectives require skills across different organisations and disciplines. Our partnership brings clinical neonatologists, academic researchers and data scientists together with the national information technology lead for the health and care system in England (NHS Digital), expertise in data tools (Strategic Intelligence Alliance for Health; SIA), the national charity for preterm and sick newborn babies (Bliss) and the national institute for health data, Health Data Research (UK HDR-UK).

目的：我们的目标是提高一个既定的英国资源，国家新生儿研究数据库（NNRD）的父母和研究人员的有用性，使他们能够查看数据和进行研究，以改善早产和患病新生儿的护理更快，更有效，成本比目前更低。大约每7个新生儿中就有1个（每年10万个）被送往NHS新生儿病房。新生儿问题和得到的护理影响到终生的健康和福祉。背景资料：我们建立了NNRD，这是一个独特的屡获殊荣的资源，与父母，医生，护士，其他医疗保健专业人员和研究人员合作，以改善NHS新生儿病房收治的早产儿和患病婴儿的护理，治疗和结果。NNRD包含全面的数据，每季度更新一次，来自英格兰，苏格兰和威尔士NHS新生儿病房所有婴儿的电子医疗记录。伦敦帝国理工学院将NNRD安全地托管在计算机服务器上。没有任何数据可以识别任何单个婴儿。这些数据包括疾病、日常治疗和结果的详细信息。到目前为止，国家残疾人权利委员会掌握了大约100万名婴儿的信息;每个季度大约增加25 000名新婴儿。为什么需要这项工作：我们建立了NNRD，因为新生儿护理的一个关键挑战是需要最新，及时和准确的研究数据，以改进，评估和开发新的治疗方法。所有许多类型的研究都需要数据，例如提高对疾病、其原因和所提供的护理的认识，以及开发新药。不同类型的研究通常需要相似的数据（例如年龄，性别，体重，疾病），但传统上，研究人员会为每个新的目的一次又一次地收集这些数据。这是昂贵的，浪费时间，并增加了错误的风险。研究可能会因为数据可用性或质量差而失败。数据也需要是最新的，否则信息可能会误导。例如，在英国广泛用于咨询父母和指导临床实践的关于极早产儿健康结果的信息来自20多年前进行的研究，不再反映今天的情况。NNRD为研究和其他目的提供最新数据的单一来源。需要开展这项工作，以改进《国家残疾人权利公约》，使其更加有用。我们的目标和我们将做什么：我们将自动化流程，以检查准确性，并将新数据添加到我们目前手动执行的NNRD中。目前，任何人想使用NNRD都必须请求我们的帮助，这不可避免地会造成延误。我们将确定研究人员，家长和临床医生会发现有用的信息从NNRD获得的常见类型。这可能是为了确定新生儿病房收治的患有特殊疾病的患者数量。我们亦会就他们希望以何种方式（例如表格或图表）看到结果，征询他们的意见。这将帮助我们开发基于网络的工具，使家长和研究人员能够自己回答常见问题。我们将在我们的网站上提供这些工具。我们还将开发处理NNRD数据的方法，以便我们可以应用新技术来帮助识别特定类型疾病发生的模式，并提供其原因的线索。此外，我们将培训年轻科学家处理复杂的健康数据。为什么需要这种伙伴关系：我们已经形成了伙伴关系，因为我们的目标需要跨不同组织和学科的技能。我们的合作伙伴关系将临床病理学家，学术研究人员和数据科学家与英格兰卫生和护理系统的国家信息技术领导者（NHS Digital），数据工具的专业知识（Strategic Intelligence Alliance for Health; SIA），早产和患病新生儿的国家慈善机构（布利斯）以及国家健康数据研究所，健康数据研究（UK HDR-UK）。

项目成果

Facilitating quality improvement through routinely recorded clinical information.

通过定期记录临床信息促进质量改进。

• DOI: 10.1016/j.siny.2021.101195
• 发表时间: 2021
• 期刊: Seminars in fetal & neonatal medicine
• 影响因子: 3
• 作者: Modi N

Incorporating parent, former patient and clinician perspectives in the design of a national UK double-cluster, randomised controlled trial addressing uncertainties in preterm nutrition.

• DOI: 10.1136/bmjpo-2021-001112
• 发表时间: 2021
• 期刊: BMJ paediatrics open
• 影响因子: 2.6
• 作者: Lammons W;Moss B;Battersby C;Cornelius V;Babalis D;Modi N
• 通讯作者: Modi N

Pilot feasibility study of a digital technology approach to the systematic electronic capture of parent-reported data on cognitive and language development in children aged 2 years.

• DOI: 10.1136/bmjhci-2023-100781
• 发表时间: 2023-06
• 期刊: BMJ HEALTH & CARE INFORMATICS
• 影响因子: 4.1
• 作者: Modi, Neena;Ribas, Ricardo;Johnson, Samantha;Lek, Elizabeth;Godambe, Sunit;Fukari-Irvine, Edit;Ogundipe, Enitan;Tusor, Nora;Das, Nayan;Udayakumaran, Abinithya;Moss, Becky;Banda, Victor;Ougham, Kayleigh;Cornelius, Victoria;Arasu, Anusha;Wardle, Steve;Battersby, Cheryl;Bravery, Amanda
• 通讯作者: Bravery, Amanda

Improving the Efficiency and Impact of Clinical Research: A Game Changer for 21st Century Neonatology.

提高临床研究的效率和影响：21 世纪新生儿学的游戏规则改变者。

• DOI: 10.1159/000506865
• 发表时间: 2020
• 期刊: Neonatology
• 影响因子: 2.5
• 作者: Modi N

Post-natal growth of very preterm neonates - Authors' reply.

极早产新生儿的产后生长 - 作者的答复。

• DOI: 10.1016/s2352-4642(22)00025-6
• 发表时间: 2022
• 期刊: The Lancet. Child & adolescent health
• 作者: Greenbury SF