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.

项目概要/摘要 每年大约有 30,000 名美国人患有动脉瘤性蛛网膜下腔出血 (SAH)，平均 50 多岁的年龄导致多年的生产力丧失。迟发性脑缺血（DCI）发生在每个人身上 第五位 SAH 患者在动脉瘤破裂后 3-7 天内发病，是导致动脉瘤破裂的主要原因 发病率。尽管对患者进行严密监测，但确定 DCI 的发作仍具有挑战性。 DCI 通常是在事后才被认识到的。造成这种情况的原因有以下几个：（1）大脑 缺血会导致功能丧失，并且在神经损伤患者中无法被动观察到，（2）可以 被误认为癫痫发作或谵妄等模仿行为并延误诊断，(3) 确认性检测是资源 沉重且具有潜在风险，需要超越高度怀疑的门槛。现有 DCI 预测模型无法提供必要的及时性和精确性。提高时效性和准确性 DCI 预测将使干预措施能够预防 SAH 患者中风并减少 过度激进的治疗。利用 DCI 炎症病理机制对 DCI 的影响 系统生理学，我们使用源自以下的特征创建了 DCI 的人工智能 (AI) 风险评分： 随新信息更新的普遍可用的生命体征。在数据的伪前瞻性实验中 从外部机构来看，该风险评分独特地满足了理想态势监测器的标准，该监测器不 仍然存在：连续、非侵入性、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）。世界卫生组织人工智能伦理标准 医疗保健法令规定算法应在技术将要使用的环境中进行严格测试 使用，并确保其符合安全性和有效性标准。未经测试的基于人工智能的临床的风险 决策支持是误解和过度信任，在最坏的情况下会对患者造成伤害，在最坏的情况下是不合理的 最好的。该提案包含将这一有前途的模型转化为可用于的工具的必要步骤 融入临床实践。在目标 1 中，我们将进行静默验证和模拟研究来评估 这种新颖的人工智能技术在现实临床环境中的准确性和接受度。在目标 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设计的情境设计方法，以完善 临床医生工作流程中的最佳实施。在目标 3 中，我们将生产一个基于开放标准的可互操作的 即插即用的架构，可在外部机构实施。 DCI风险的转化 将模型转变为连续监测填补了 SAH 患者管理中的一个重要空白，并且开放 标准架构能够以经济实惠且快速实现的方式传播这种新技术，同时 为标准社区提供必要的验证。