Critical Care Informatics

重症监护信息学

基本信息

批准号：
10020401
负责人：
Leo Anthony G Celi
金额：
$ 39.63万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10020401
关键词：
Academia Accident and Emergency department Address Admission activity Adoption Adult Archives Boston Caring Clinical Clinical Data Clinical Research Code Collaborations Communication Communities Computer software Country Credentialing Critical Care Critical Illness Data Data Collection Data Set Data Sources Databases Development Diagnostic radiologic examination Disease Educational workshop Electronic Health Record Funding Guidelines Healthcare Home environment Hospitals Image Industry Informatics Infrastructure Institution Intensive Care Intensive Care Units International Intervention Investigation Israel Joints Knowledge Laboratories Licensing Link London Medical Medical Technology Medical center Modification Operating Rooms Paris, France Patient Care Patients Performance Pharmaceutical Technology Physiological Physiology Population Process Reproducibility Research Research Project Grants Resolution Resources Retrospective Studies Standardization Technology Thoracic Radiography United States Universities Update Validation access restrictions base clinical care clinical database clinical decision support data archive data harmonization data modeling data resource data sharing data standards education resources evidence base health data improved international center online course open source prediction algorithm repository research study structured data success support tools symposium tool treatment effect unstructured data

项目摘要

Abstract Critical care units are home to some of the most sophisticated patient technology within hospitals. The result- ing data have the potential to improve our understanding of disease and to improve clinical care. Critically ill patients are an ideal population for clinical database investigations because the value of many treatments and interventions they receive remains largely unproven, and high-quality studies supporting or discouraging speciﬁc practices are relatively sparse [4]. Standardized critical care guidelines currently in use are dependent on an evidence base that is surprisingly weak considering the amount of data generated in the ICU [13]. The MIT Laboratory for Computational Physiology (LCP) developed and maintains the publicly available Medical Information Mart for Intensive Care (MIMIC), containing highly detailed data associated with 53,423 distinct adult ICU admissions at the Beth Israel Deaconess Medical Center in Boston [21]. MIMIC is now a widely used resource worldwide for clinical research studies, exploratory and validation analyses performed by pharmaceutical and medical technology companies, as well as for university, conference and online courses, tutorials and workshops. LCP recently released the open eICU Collaborative Research Database [24] in collaboration with Philips Healthcare, comprising de-identiﬁed clinical data associated with approximately 200,000 critical care admissions to over two hundred hospitals throughout the United States. We now intend to expand the success of our open-access, open-source approach to critical care research by releasing large new intra-operative, emergency department and imaging datasets. Importantly, we have made exciting progress with the global consortium our group is spearheading around the development of high resolution critical care databases. With our assistance, colleagues at Oxford, London, Paris, Sao Paulo, Madrid, and Beijing have made signiﬁcant progress in building their own versions of MIMIC and transforming them into the OMOP common data model. Multi-center research is challenging, because different institutions collect and store data in (sometimes dras- tically) different formats. The adoption and harmonization of data standards is a critical requirement in order for the data to be properly archived, integrated across institutions, and shared for reuse. This proposal seeks funding to: (a) support and expand our publicly available critical care data resources into new domains including pre-ICU care in the ED and OR, and serial chest X-ray imaging; b) develop the technical infrastructure needed to integrate data from international critical care units; and c) conduct research aimed at understanding and addressing the complexities of using multicenter and federated datasets in the development of predictive and clinical decision support tools, as well as in observational retrospective studies.

抽象的重症监护病房是医院内一些最复杂的患者技术的所在地。结果 - ING数据有可能提高我们对疾病的理解并改善临床护理。批判性患者是临床数据库调查的理想人群，因为许多治疗的价值和他们接受的干预措施在很大程度上尚未得到证实，并且高质量的研究支持或灰心实践相对稀疏[4]。当前正在使用的标准化重症监护指南取决于考虑到ICU中产生的数据量，证据基础令人惊讶地较弱[13]。 MIT计算生理学实验室（LCP）开发并维护公开可用重症监护（MIMIC）的医学信息MART，其中包含与53,423相关的高度详细数据在波士顿的贝丝以色列执事医疗中心，独特的成人ICU入学[21]。模仿现在是全球广泛使用的资源用于临床研究，探索性和验证分析。制药和医疗技术公司以及大学，会议和在线课程，教程和讲习班。 LCP最近与飞利浦合作发布了Open EICU合作研究数据库[24] 医疗保健，完成与大约200,000个重症监护招生相关的临床数据全美的200多家医院。我们现在打算扩大我们的成功通过释放大型新的术中紧急情况，开放式，开源方法进行重症监护研究部门和成像数据集。重要的是，我们在全球财团中取得了令人兴奋的进步小组围绕着高分辨率重症监护数据库的发展。在我们的协助下牛津，伦敦，巴黎，圣保罗，马德里和北京的同事在建造方面取得了重大进展他们自己的模仿版本并将其转换为OMOP共同数据模型。多中心研究受到挑战，因为不同的机构收集和存储数据（有时Dras- 确实）不同的格式。采用和协调数据标准是至关重要的要求数据要正确存档，整合到机构中并共享以进行重复使用。该提案寻求资金：（a）支持和扩展我们公开可用的重症监护数据资源新领域，包括ED和OR中的ICU护理，以及串行的胸部X射线成像； b）开发技术基础设施需要整合来自国际重症监护单位的数据； c）进行针对的研究了解和解决在开发中使用多中心和联合数据集的复杂性预测性和临床决策支持工具以及观察性回顾性研究。