PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP)

心脏骤停的精准护理 - ICECAP (PRECICECAP)

• 批准号: 10842647
• 负责人: Jonathan Elmer
• 金额: $ 33.16万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-25 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10842647
• 关键词: Acute Brain Injuries; Artificial Intelligence; Attention; Automation; Biomedical Research; Brain; Brain imaging; Cardiopulmonary; Caring; Cessation of life; Clinical; Collaborations; Complex; Computer software; Critical Care; Critical Illness; Custom; Data; Data Scientist; Data Set; Electroencephalography; Emergency Care; Engineering; Ensure; FAIR principles; Frequencies; Funding; Generations; Goals; Grant; Healthcare; Heart; Heart Arrest; Industry; Intensive Care; Knowledge; Label; Libraries; Lung; Machine Learning; Manuals; Measures; Methods; Mission; Modeling; Modernization; Monitor; Morbidity - disease rate; Multicenter Studies; National Institute of Neurological Disorders and Stroke; Neurologic; Organ; Output; Oxygen; Parents; Patients; Phase; Physiological; Prediction of Response to Therapy; Preparation; Process; Public Health; Readiness; Recovery of Function; Reproducibility; Reproducibility of Results; Research; Research Personnel; Resolution; Running; Sampling; Scientist; Signal Transduction; Software Tools; Standardization; Supervision; Testing; Traumatic Brain Injury; United States National Institutes of Health; Validation; Vision; Visualization; Work; analytical method; biomarker signature; clinically relevant; cloud based; complex data; cost; data curation; data ecosystem; data integration; data pipeline; data quality; deep learning; disability; discrete data; flexibility; hands-on learning; heuristics; improved; industry partner; innovation; insight; interest; long term recovery; mortality; novel; novel marker; patient subsets; personalized care; precision medicine; prospective; quality assurance; repository; usability

Project Summary: The PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) parent R01 study aims to discover novel biomarker signatures after cardiac arrest that predict treatment responsiveness and long-term recovery. Cardiac arrest is a major public health problem and top cause of morbidity and mortality nationally. Improving survival and functional recovery is of critical importance. We hypothesize that through innovative, multi-parametric data driven approaches we will be able to identify distinct clinically relevant subgroups of patients. Our primary analytical plan allows us to achieve our aims but is not readily generalizable or usable by others. An ever-growing number of NIH-funded projects amass and analyze similar datasets and develop their own custom solutions. This ad hoc approach is costly, inefficient, and threatens both rigor and reproducibility. The objective of this supplement to PRECICECAP is to complete a publicly available software platform that allows end-to-end curation of complex neurocritical care data to create artificial intelligence/machine learning (AI/ML) ready analytical datasets from raw waveform data. Building on our previous work, we will address the remaining gaps needed to achieve this goal by: 1) developing standard processes to ensure data quality, segmentation, and sampling; 2) feature engineering; and, 3) external validation of our entire pipeline. This work aligns with and supports NIH goals for modernizing the biomedical research data ecosystem by developing a software product that can handle AI/ML on this type of complex data. It will also allow the sharing of a finalized analytical data set for use by others. By continuing our proven collaboration between clinician investigators, data scientists and industry, we will take NIH-supported data from the PRECICECAP study and make it broadly available and easily usable. The project delivers an important software tool that can be used by others conducting similar research, advancing the NIH’s mission to make complex data FAIR (Findable, Accessible, Interoperable, and Reusable). The result will facilitate an open, wide collaboration between scientists, using similar data.

项目概要： 心脏骤停的精密护理- ICECAP（PRECICECAP）母研究R 01旨在发现 心脏骤停后预测治疗反应性和长期 复苏心脏骤停是一个主要的公共卫生问题，也是发病率和死亡率的首要原因 全国范围内。提高生存率和功能恢复至关重要。我们假设 通过创新的多参数数据驱动方法，我们将能够识别不同的 临床相关患者亚组。我们的主要分析计划使我们能够实现我们的目标， 不容易被其他人推广或使用。越来越多的NIH资助项目 收集和分析类似的数据集，并开发自己的定制解决方案。这种特别的方法是 成本高，效率低，并且威胁到严格性和可重复性。本补充的目的是 PRECICECAP将完成一个公开可用的软件平台，允许端到端管理 复杂的神经重症监护数据，以创建人工智能/机器学习（AI/ML） 原始波形数据的分析数据集。在我们以前工作的基础上，我们将讨论 通过以下方式实现这一目标所需的剩余差距：1）制定标准流程，以确保数据 质量，分割和采样; 2）特征工程;以及，3）我们整个产品的外部验证 渠道.这项工作符合并支持NIH实现生物医学研究数据现代化的目标 通过开发一个软件产品，可以处理这种类型的复杂数据的AI/ML。它 还将允许分享最后确定的分析数据集，供其他方面使用。通过继续我们经过验证的 临床研究人员，数据科学家和行业之间的合作，我们将采取NIH支持的 （c）利用PRECICECAP研究的数据，使其广泛提供和易于使用。项目 提供了一个重要的软件工具，可供其他人进行类似的研究，推进 NIH的使命是使复杂数据公平（可查找、可解释、可互操作和可重用）。 这一结果将促进科学家之间利用类似数据进行公开、广泛的合作。