CTSA Administrative Supplement for Informatics Core: A novel AI/ML system to predict respiratory failure and ARDS in Covid-19 patients

项目总结 爱因斯坦-蒙特菲奥里临床和翻译研究所(卢旺达问题国际法庭)提出了一项行政 根据NOT-TR-20-011，CTSA计划申请补充，以应对2019年新型冠状病毒 (新冠肺炎)。具体地说，该应用程序解决了对冠状病毒大流行研究的迫切需要 一个专注于信息学和数据科学的项目，先发制人地识别患者的生命- 利用CTSA支持的核心资源，威胁SARS-CoV-2的并发症。以严重为特征的 低氧血症、呼吸急促和肺顺应性降低，急性呼吸衰竭(ARF)的诊断是 不良的预后体征，在一个亚组中，会导致急性呼吸窘迫综合征(ARDS)的发展。 布朗克斯区的新冠肺炎感染率和死亡率一直高于纽约市其他任何一个行政区。AS 作为主要的区域卫生系统，我们在新冠肺炎的经验为我们提供了指南，可能会防止未来 这场大流行的受害者。在4452名住院患者中，ARDS的黯淡前景显示，78%的我们 新冠肺炎插管患者出现急性呼吸窘迫综合征，死亡率为42%。这项提案的总体目标是 利用我们由爱因斯坦-蒙特菲奥尔CTSA(NIH/NCATS)支持的新型信息学和分析平台 1ULTR002556)，以及广泛的人工智能和深度学习资源来实施一种新颖的、 ARF和ARDS态势感知和临床决策支持系统(SA-ARDS)。我们将重新培训我们的 使用从新冠肺炎患者收集的数据的现有深度学习模型并将其实施 来自纽约大流行期间新冠肺炎应对措施的数据。SA-ARDS数据平台将提供 纵向集成的临床数据，用于研究、多机构和国家合作， 以下具体目标：目标1：整合、重新培训和验证我们新颖的、近乎实时的电子风险 评估系统(ERAS 1.0)针对ARF、ARDS和住院患者死亡率的早期识别进行了优化；目标2： 制定基于证据的、实时的、与情境相适应的情景感知临床决策 针对ARF和ARDS响应的支持系统(SA-ARDS)；以及目标3：通过我们的合作伙伴CTSA 标准化ERA1.0和SA-ARDS，并将其传播给其他卫生系统，包括 由CTSA中心和PCORI洞察网络组成的纽约市财团。我们将使用潜在的临床数据 SA-ARDS支持地方、地区和国家合作的研究。所有的方法和工具 开发的数据将通过NCATS的国家健康数据中心(CD2H)与CTSA社区共享。

PROJECT SUMMARY The Einstein-Montefiore Institute for Clinical and Translational Research (ICTR) proposes an Administrative Supplement pursuant to NOT-TR-20-011, CTSA Program Applications to Address 2019 Novel Coronavirus (Covid-19). Specifically, this application addresses the urgent need for research on the coronavirus pandemic with a project focusing on informatics and data science to preemptively identify patients with the life- threatening complications of SARS-CoV-2, using CTSA-supported core resources. Characterized by severe hypoxemia, tachypnea, and decreased lung compliance, the diagnosis of acute respiratory failure (ARF) is a bad prognostic sign, and in a subset, leads to development of acute respiratory distress syndrome (ARDS). The rates of Covid-19 infection and death in the Bronx have been higher than any other borough of NYC. As the major regional health system, our experience with Covid-19 provides guideposts that may prevent future victims of this pandemic. The bleak picture for ARDS in the 4,452 patients admitted showed that 78% of our intubated Covid-19 patients developed ARDS, with 42% mortality. The overall goal of this proposal is to leverage our novel informatics and analytics platforms enabled by the Einstein-Montefiore CTSA (NIH/NCATS 1ULTR002556), and extensive Artificial Intelligence and Deep Learning resources to implement a novel, situational awareness and clinical decision support system for ARF and ARDS (SA-ARDS). We will re-train our existing deep learning models with data collected from Covid-19 patients and contextualize its implementation with data from the Covid-19 response during the pandemic in NYC. The SA-ARDS data platform will provide longitudinally integrated clinical data for research and multi-institutional and national collaborations, with the following specific aims: Aim 1: To integrate, re-train, and validate our novel, near real-time, Electronic Risk Assessment System (ERAS 1.0) optimized for early recognition of ARF, ARDS, and inpatient mortality; Aim 2: To develop an evidence based, real-time, and context appropriate Situational Awareness clinical decision support system targeting ARF and ARDS response (SA-ARDS); and Aim 3: Through our partner CTSA organizations, to standardize and disseminate ERAS 1.0 and the SA-ARDS to other health systems, including the NYC consortium of CTSA hubs and the PCORI INSIGHT network. We will use the clinical data underlying the SA-ARDS to support research in local, regional, and national collaborations. All the methods and tools developed will be shared with the CTSA community via NCATS' National Center for Data to Health (CD2H).