CHIMERA: Collaborative Healthcare Innovation through Mathematics, EngineeRing and AI

CHIMERA：通过数学、工程和人工智能进行协作医疗创新

• 批准号: EP/T017791/1
• 负责人: Rebecca Shipley
• 金额: $ 134.99万
• 依托单位: University 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: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT017791%2F1
• 关键词: CHIMERA; Collaborative; Healthcare; Innovation; through

Hospitals collect a wealth of physiological data that provide information on patient health. Full use of this data is significantly limited by its complexity and by a limited mechanistic understanding of the relationship between internal physiology and external measurement. Addressing this challenge requires multidisciplinary collaboration between mathematicians developing new biomechanical models, clinicians who measure and interpret the data to treat patients, and statistical and computational scientists to bridge the two-way translation between model output and real-life data. CHIMERA is designed to foster such collaboration to generate new understanding of physiology, new methods for relating physiology to real time data, and, finally, to translate these into practice, improving outcomes for patients by supporting clinical decision making.CHIMERA will start by focusing on the most critically ill patients within hospital intensive care units: such patients have by far the most monitoring data and are most likely to benefit from improved understanding of what that data can tell us about their underlying physical state. Each year about 20,000 children and 300,000 adults in the UK need intensive care. These critically ill patients are continuously monitored at the bedside, including measurements of heart rate, breathing rate, blood pressure and other vital sign data. However, the wealth of these physiological data are not currently used to inform clinical decision making and clinicians can only really use real-time snapshots of the physiology to guide their decisions.CHIMERA will address this unmet opportunity to use individual patient physiological data to support clinical decision making, with the potential to impact on patient management across the UK and beyond. This will be achieved through a multidisciplinary Hub which brings together experts in mathematics, statistics, data science and machine learning, with unique, high volume and rich data sets from both adult and paediatric Intensive Care Units provided through embedded Project Partnerships with Great Ormond Street Hospital (GOSH) and University College London Hospital (UCLH). CHIMERA will deliver new mathematical frameworks to learn the biophysical relationships that govern the interdependencies between physiological variables, based on data sets for thousands of patients through these project partners. Clinical impact will be achieved through an extensive series of clinically-led, multidisciplinary workshops themed around specific opportunities to improve care, for example identifying deteriorating patients in advance of an adverse event such as heart attack or stroke, or advance warning systems to diagnose sepsis. These workshops will include partnering with the Alan Turing Institute (the national centre for AI and Data Science), will be open to national participation, and will provide a mechanism to fund new projects by making available seed corn funding, PhD studentships and researcher resource for new interdisciplinary teams and partnerships. CHIMERA will build new links with clinical centres, companies and academic units across the UK and internationally, expand to work with a variety of patient monitoring data, and provide dedicated support to nurture new projects, funding bids and collaborations. In this way, we will build CHIMERA to a self-sustaining, multidisciplinary and vibrant Centre for the application of mathematical and data sciences tools in patient care.

医院收集了大量的生理数据，提供了关于病人健康的信息。由于数据的复杂性和对内部生理学和外部测量之间关系的有限的机制理解，这些数据的充分利用受到很大的限制。解决这一挑战需要数学家之间的多学科合作，数学家开发新的生物力学模型，临床医生测量和解释数据来治疗患者，统计和计算科学家在模型输出和现实数据之间架起双向转换的桥梁。CHIMERA旨在促进这种合作，以产生对生理学的新理解，将生理学与实时数据联系起来的新方法，并最终将其转化为实践，通过支持临床决策来改善患者的预后。CHIMERA将首先关注医院重症监护病房的危重患者：这些患者迄今为止拥有最多的监测数据，并且最有可能从更好地了解这些数据所能告诉我们的有关其潜在身体状态的信息中受益。在英国，每年大约有2万名儿童和30万名成年人需要重症监护。这些危重病人在床边被持续监测，包括心率、呼吸频率、血压和其他生命体征数据的测量。然而，这些丰富的生理数据目前并未用于临床决策，临床医生只能真正使用生理的实时快照来指导他们的决策。CHIMERA将利用这一未满足的机会，利用个体患者生理数据来支持临床决策，并有可能影响英国及其他地区的患者管理。这将通过一个多学科中心来实现，该中心汇集了数学、统计学、数据科学和机器学习方面的专家，并通过与大奥蒙德街医院（GOSH）和伦敦大学学院医院（UCLH）的嵌入式项目合作伙伴关系提供来自成人和儿科重症监护病房的独特、大量和丰富的数据集。CHIMERA将通过这些项目合作伙伴提供新的数学框架，以学习控制生理变量之间相互依赖的生物物理关系，这些关系基于数千名患者的数据集。临床影响将通过一系列广泛的以临床为主导的多学科研讨会来实现，这些研讨会的主题是改善护理的具体机会，例如在心脏病发作或中风等不良事件发生之前识别病情恶化的患者，或诊断败血症的预警系统。这些研讨会将包括与阿兰·图灵研究所（国家人工智能和数据科学中心）合作，将向国家参与开放，并将通过为新的跨学科团队和伙伴关系提供种子玉米资金，博士生和研究人员资源来提供资助新项目的机制。CHIMERA将与英国和国际上的临床中心、公司和学术单位建立新的联系，扩大与各种患者监测数据的合作，并为培育新项目、资助投标和合作提供专门的支持。通过这种方式，我们将把CHIMERA建设成为一个自我维持的、多学科的、充满活力的中心，将数学和数据科学工具应用于患者护理。

项目成果

Evaluation of blood pressure trajectories and outcome in critically ill children with initial hypertension on admission to Paediatric Intensive Care.

评估入院儿科重症监护室初始高血压的危重儿童的血压轨迹和结果。

• DOI: 10.1016/j.accpm.2022.101149
• 发表时间: 2022
• 期刊: Anaesthesia, critical care & pain medicine
• 作者: Peros T

Pragmatic trials for critical illness in neonates and children.

新生儿和儿童危重疾病的实用试验。

• DOI: 10.1016/s2352-4642(22)00345-5
• 发表时间: 2023
• 期刊: The Lancet. Child & adolescent health
• 作者: Schlapbach LJ