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

CTSA 信息学核心行政补充：一种预测 Covid-19 患者呼吸衰竭和 ARDS 的新型 AI/ML 系统

• 批准号: 10158737
• 负责人: MARLA J KELLER
• 金额: $ 100.34万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-09 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10158737
• 关键词: 2019-nCoV; Acute; Acute respiratory failure; Address; Administrative Supplement; Adult Respiratory Distress Syndrome; Arrhythmia; Artificial Intelligence; Award; Awareness; COVID-19; COVID-19 pandemic; Cessation of life; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Research; Collaborations; Communities; Competence; Complex; Coronavirus; Data; Data Science; Data Scientist; Detection; Deterioration; Development; Diagnosis; Engineering; Expert Systems; Fatal Outcome; Functional disorder; Funding; Future; Generations; Goals; Health; Health system; Healthcare; Heart Injuries; Hospitals; Hypoxemia; Infection; Informatics; Injury to Kidney; Inpatients; Institutes; Intervention; Lead; Life; Link; Literature; Lung Compliance; Mechanical ventilation; Medical center; Methods; Modeling; Morbidity - disease rate; New York City; Organ; Parents; Patients; Pneumonia; Population; Preventive; Process; Public Health; Publishing; Research; Research Personnel; Research Support; Resources; Respiratory Failure; Risk Assessment; Seasons; Severities; Software Engineering; Source; Standardization; System; Thromboembolism; Time; Training; Translating; Translational Research; United States National Institutes of Health; Upper Respiratory Infections; base; care delivery; care providers; clinical decision-making; cohort; complex data; coronavirus disease; data modeling; deep learning; design; evidence base; experience; flu; health data; infection rate; innovation; machine learning algorithm; mortality; multidisciplinary; novel; pandemic disease; preemptive intervention; prevent; prognostic; programs; response; software systems; tool

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).

项目总结