Multi-Dimensional Outcome Prediction Algorithm for Hospitalized COVID-19 Patients

住院 COVID-19 患者的多维结果预测算法

• 批准号: 10299344
• 负责人: DAVID Owen BEENHOUWER
• 金额: $ 74.76万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-08 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10299344
• 关键词: 2019-nCoV; Algorithms; Area; Biological; Biological Markers; Blood; COVID-19; COVID-19 patient; Cardiac; Cardiovascular system; Caring; Cessation of life; Clinical; Clinical Data; Color; Complex; Country; County; Data; Decision Making; Diabetes Mellitus; Disease; Disease Outcome; Elderly; Enrollment; Family; Functional disorder; Futility; Gene Expression; Genes; Genomics; Geography; Goals; Health; Health Care Costs; Immune system; Immunologics; Inflammatory; Los Angeles; Mediating; Modeling; Molecular; Morbidity - disease rate; Multiomic Data; Natural experiment; Neurologic; New York; Obesity; Organ; Outcome; Pathway Analysis; Pathway interactions; Patients; Pattern; Personal Satisfaction; Persons; Phenotype; Population; Process; Prognosis; Proteomics; Quality of life; Reporting; Research; Resources; Risk; Role; Severity of illness; Site; Symptoms; Testing; Texas; Therapeutic; Time; Training; Triage; Ventilator; Work; acute infection; adverse outcome; algorithm development; algorithm training; base; biomarker development; candidate identification; combinatorial; coronavirus disease; cost effectiveness; demographics; design; experience; frailty; high risk; improved; male; metropolitan; military veteran; mortality; multiple omics; novel; outcome prediction; prediction algorithm; prognostic; respiratory; response; symptomatic COVID-19; tool; transcriptomics

PROJECT SUMMARY Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-mediated coronavirus disease (COVID-19) is an evolutionarily unprecedented natural experiment that causes major changes to the host immune system. Several high risk COVID-19 populations have been identified. Older adults, males, persons of color, and those with certain underlying health conditions (e.g., diabetes mellitus, obesity, etc.) are at higher risk for severe disease from COVID-19. While it is too soon to fully understand the impact of COVID-19 on overall health and well-being, there are already several reports of significant sequelae, which appear to correlate with disease severity. There is a clear and urgent need to develop prediction tests for adverse short- and long-term outcomes, especially for high-risk COVID-19 populations. We hypothesize that complementary multi-dimensional information gathered near the time of symptom onset can be used to predict new onset or worsening frailty, organ dysfunction and death within one year after COVID-19 onset. A single parameter provides limited information and is incapable of adequately characterizing the complex biological responses in symptomatic COVID-19 to predict outcome. Since they were designed for other illnesses, it is unlikely that existing clinical tools, such as respiratory, cardiovascular, and other organ function assessment scores, will precisely assess the long-term prognosis of this novel disease. Our extensive experience in biomarker development suggests that integrating molecular and clinical data increases prediction accuracy of long-term outcomes. We have chosen to test our hypothesis in a population reflecting US-demographics that is at increased risk of adverse outcomes from COVID-19. We will enroll patients, broadly reflecting US demographics, from a hospitalized civilian population in one of the country’s largest metropolitan areas and a representative National Veteran’s population. We anticipate that a prediction test that performs well in this hospitalized patient group will: help guide triaging and treatment decisions and, therefore, reduce morbidity and mortality rates, enhance patient quality of life, and improve healthcare cost-effectiveness. More accurate prognostic information will also assist clinicians in framing goals of care discussions in situations of likely futility and assist patients and families in this decision-making process. Finally, it will provide a logical means for allocating resources in short supply, such as ventilators or therapeutics with limited availability.

项目摘要 严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）介导的冠状病毒病（COVID-19）是 这是一项进化史上前所未有的自然实验，会导致宿主免疫系统发生重大变化。 已经确定了几个高风险COVID-19人群。老年人，男性，有色人种， 具有某些潜在的健康状况（例如，糖尿病、肥胖症等）患上严重的 COVID-19引起的疾病。虽然现在完全了解COVID-19对整体健康的影响还为时过早， 健康，已经有几个明显的后遗症的报告，这似乎与疾病有关 严重性。显然迫切需要开发短期和长期不利后果的预测测试， 特别是对于高风险的COVID-19人群。我们假设互补的多维 在症状发作时收集的信息可用于预测新发或恶化 在COVID-19发病后一年内出现虚弱、器官功能障碍和死亡。单个参数提供 有限的信息，无法充分表征复杂的生物反应， 症状COVID-19来预测结果。由于它们是为其他疾病设计的， 现有的临床工具，如呼吸、心血管和其他器官功能评估评分， 准确评估这种新型疾病的长期预后。我们在生物标志物领域的丰富经验 发展表明，整合分子和临床数据可以提高长期预测的准确性。 结果。我们选择在一个反映美国人口统计的人群中测试我们的假设， COVID-19导致不良后果的风险增加。我们将招募患者，广泛反映美国 人口统计数据，从该国最大的大都市地区之一的住院平民人口和 全国退伍军人代表大会。我们预计，在这种情况下表现良好的预测测试 住院患者组将：帮助指导分诊和治疗决策，从而降低发病率， 降低死亡率，提高患者的生活质量，并提高医疗保健的成本效益。更准确 预后信息还将帮助临床医生在可能无效的情况下制定护理讨论的目标 并在这个决策过程中帮助患者和家属。最后，它将提供一种逻辑手段， 分配短缺的资源，如可用性有限的麻醉剂或治疗剂。