ContinuOuS Monitoring Tool for Delayed Cerebral IsChemia (COSMIC)

项目概要/摘要 每年大约有30，000名美国人患有蛛网膜下腔出血（SAH），平均 50年代中期的年龄导致多年的生产力损失。迟发性脑缺血（DCI）发生在每个 第5例SAH患者在动脉瘤破裂后3-7天内发作，是动脉瘤破裂的主要原因。 发病率。确定DCI的发作是具有挑战性的，即使患者在密集的监测中被密切监测， 护理单位，而且DCI往往只在回顾时才被认识到。这有几个原因：（1）大脑 局部缺血导致功能丧失，并且在神经损伤的患者中不能被动地观察到，（2）可以 误认为癫痫或谵妄等拟态，延误诊断;（3）确证性试验是资源 重，并带有潜在的风险，这需要超过怀疑的高门槛。现有DCI 预测模型不能提供必要的及时性和精确性。提高信息的及时性和准确性 DCI预测将使干预措施能够预防SAH患者的卒中， 过于激进的治疗。利用DCI的炎症病理机制对 系统生理学，我们创建了一个人工智能（AI）的风险评分DCI使用的功能来自 普遍可用的生命体征，更新新的信息。在一个关于数据的伪前瞻性实验中 从外部机构来看，这个风险分数唯一符合理想情况监测器的标准， 然而，仍然存在：连续的，非侵入性的，独立于预测试概率的，独立于操作者的，定量的， 及时（临床诊断前12小时）。世界卫生组织人工智能道德标准 医疗保健法令规定，算法应该在技术将被应用的环境中进行严格的测试。 使用，并确保其符合安全性和有效性标准。未经测试的人工智能临床应用的风险 决策支持是误解和过度信任，在最坏的情况下会伤害患者， 最好这项建议包括采取必要步骤，将这一有希望的模式转化为一种工具， 融入临床实践。在目标1中，我们将进行无声验证和模拟研究，以评估 这种新型AI技术在现实临床环境中的准确性和可接受性。在目标2中，我们将使用 情境设计方法论，用于以用户为中心的参与式设计和快速敏捷原型设计， 临床医生工作流程中的最佳实施。在目标3中，我们将创建一个基于开放标准的可互操作的 该架构将是即插即用的，以便在外部机构实施。DCI风险的转换 模型转换为连续监测填补了SAH患者管理的重要空白， 标准体系结构使这一新技术能够以负担得起的价格快速传播， 为标准社区提供必要的验证。

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.