ContinuOuS Monitoring Tool for Delayed Cerebral IsChemia (COSMIC)

迟发性脑缺血持续监测工具 (COSMIC)

• 批准号: 10736589
• 负责人: Soojin Park
• 金额: $ 63.66万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10736589
• 关键词: Address; Age; Age of Onset; Algorithms; American; Aneurysmal Subarachnoid Hemorrhages; Architecture; Artificial Intelligence; Authorization documentation; Big Data to Knowledge; Biological Availability; Brain; Brain Injuries; Cerebral Ischemia; Classification; Clinical; Clinical Trials; Code; Collaborations; Communities; Custom; Data; Decision Making; Delirium; Devices; Diagnosis; Diagnostic tests; Ensure; Ethics; Evaluation; Fast Healthcare Interoperability Resources; Future; Generations; Healthcare; Heart; Hemorrhage; Hospitalization; Hospitals; Hour; Inflammatory; Informatics; Institution; Intensive Care Units; Intervention; Laboratories; Life; Methodology; Modeling; Monitor; Morbidity - disease rate; Nature; Nervous System Trauma; Observational Study; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Performance; Persons; Physiological; Physiology; Play; Probability; Procedures; Production; Productivity; Research Design; Resources; Risk; Risk Assessment; Ruptured Aneurysm; Safety; Seizures; Signal Transduction; Signs and Symptoms; Site; Specialist; Stroke; Stroke prevention; Subarachnoid Hemorrhage; Symptoms; Syndrome; Technology; Testing; Time; Translating; Translations; Trust; United States National Institutes of Health; Update; Validation; Virginia; World Health Organization; aggressive therapy; authority; clinical decision support; clinical diagnosis; clinical practice; clinical risk; computer human interaction; design; diagnostic strategy; disability; effectiveness study; effectiveness testing; experimental study; health information technology; improved; individual patient; innovation; interoperability; iterative design; loss of function; machine learning model; multidisciplinary; new technology; novel; open source; predictive modeling; prospective; prototype; real time model; simulation; skills; stroke symptom; support tools; tool; user centered design

Project Summary/Abstract Approximately 30,000 Americans suffer an aneurysmal subarachnoid hemorrhage (SAH) each year, at a mean age in the mid-50s leading to many years of lost productivity. Delayed cerebral ischemia (DCI) occurs in every fifth patient with SAH with onset between 3-7 days after aneurysm rupture, and is the leading cause of morbidity. Identifying the onset of DCI is challenging even though patients are closely monitored in intensive care units, and too often DCI is only recognized in retrospect. There are several reasons for this: (1) cerebral ischemia results in loss of function and is not passively observable in a neurologically injured patient, (2) can be mistaken for mimics such as seizure or delirium and delay diagnosis, (3) confirmatory testing is resource heavy and carries potential risk which necessitates surpassing a high threshold of suspicion. Existing DCI prediction models do not offer the necessary timeliness nor precision. Improving timeliness and precision of DCI prediction would enable interventions to prevent strokes in patients with SAH as well as reduce overly aggressive treatment. Leveraging the impact that the inflammatory pathomechanism of DCI has on systemic physiology, we created an artificial intelligence (AI) risk score for DCI using features derived from universally available vital signs that updates with new information. In a pseudo-prospective experiment on data from external institutions, this risk score uniquely met the criteria for an ideal situational monitor that does not yet exist: continuous, non-invasive, independent of pretest probability, operator-independent, quantitative, and timely (12 hours before clinical diagnosis). The World Health Organization standard of ethics for AI in healthcare decrees that algorithms should be tested rigorously in the setting in which the technology will be used, and ensure that it meets standards of safety and efficacy. The risks of an untested AI based clinical decision support are misinterpretation and over-trusting with harm to patients at worst, and inconsequence at best. This proposal encompasses the necessary steps to translate this promising model into a tool that can be integrated into clinical practice. In Aim 1, we will perform a Silent Validation and Simulation Study to evaluate the accuracy and acceptance of this novel AI technology in a realistic clinical setting. In Aim 2, we will use Contextual Design methodology for user-centered participatory design and rapid agile prototyping to refine the optimal implementation in clinician workflow. In Aim 3, we will produce an open standards-based interoperable architecture that will be plug and play for implementation at external institutions. The translation of a DCI risk model into a continuous monitor fills an important gap in the management of patients with SAH, and an open standards architecture enables affordable and rapidly achievable dissemination of this novel technology, while providing an essential validation for the standards community.

项目摘要/摘要 每年约有30,000名美国人患动脉瘤性蛛网膜下腔出血（SAH） 50年代中期的年龄导致生产力失去多年。延迟的大脑缺血（DCI）发生在每个 第五名SAH患者在动脉瘤破裂后3-7天之间发病，是主要原因 发病率。即使患者受到密集监测 护理单位，而DCI经常仅在回顾中得到认可。这样的原因有几个：（1）大脑 缺血导致功能丧失，并且在神经损伤的患者中无法被动观察，（2）可以 被误认为模仿，例如癫痫发作或ir妄和延迟诊断，（3）确认性测试是资源 重型和潜在的风险需要超过高度怀疑的阈值。现有DCI 预测模型没有提供必要的及时性或精确度。提高及时性和精度 DCI预测将使干预措施可以防止SAH患者的中风，并减少 过于积极的治疗。利用DCI的炎症性致病机理对 系统生理学，我们使用从 普遍可用的生命体征，以新信息更新。在伪培训实验中 从外部机构中，此风险分数独特地符合理想情况监视器的标准 但存在：连续的，无创的，独立于预测试概率，独立于操作员，定量和 及时（临床诊断前12小时）。世界卫生组织的AI道德标准 医疗保健法令，应在技术将在该技术的情况下进行严格测试算法 使用并确保它符合安全和功效的标准。未经测试的AI临床的风险 决策支持是误解和过度怀念对最坏的患者的伤害，而无关紧要 最好的。该建议涵盖了将这种有前途的模型转化为一个可以是的工具的必要步骤 整合到临床实践中。在AIM 1中，我们将进行无声验证和模拟研究以评估 在现实的临床环境中，这种新型AI技术的准确性和接受。在AIM 2中，我们将使用 以用户为中心的参与式设计和快速敏捷原型制作的上下文设计方法，以完善 在临床医生工作流程中的最佳实施。在AIM 3中，我们将产生基于公开标准的互操作性 将在外部机构实施的插件。 DCI风险的翻译 连续监视器中的模型填补了SAH患者管理的重要空白，开放 标准体系结构可以负担得起且可以快速实现这一新技术的传播，而 为标准社区提供基本验证。