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

基本信息

批准号：
10656282
负责人：
DAVID Owen BEENHOUWER
金额：
$ 66.11万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
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/10656282
关键词：
2019-nCoV Algorithms Area Biological Biological Markers Blood COVID-19 COVID-19 impact COVID-19 patient COVID-19 risk Cardiac Cardiovascular system Caring Cessation of life Clinical Clinical Data Complex Country County Data Data Correlations Decision Making Diabetes Mellitus Dimensions Disease Disease Outcome Elderly Family Functional disorder Futility Gene Expression Genes Genomics Geography Goals Health Health Care Costs Hospitalization 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 Phenotype Population Process Prognosis Proteomics Quality of life Reporting Research Resource Allocation Risk Role Severity of illness Site Symptoms Testing Texas Therapeutic Time Training Triage Ventilator Work acute infection adverse outcome algorithm development algorithm training biomarker development candidate identification combinatorial coronavirus disease cost effectiveness demographics design experience frailty high risk improved information gathering male metropolitan military veteran mortality multiple omics novel outcome prediction participant enrollment people of color prediction algorithm predictive test prognostic respiratory response symptomatic COVID-19 tool transcriptomics

2019-nCoV Algorithms Area Biological Biological Markers Blood COVID-19 COVID-19 impact COVID-19 patient COVID-19 risk Cardiac Cardiovascular system Caring Cessation of life Clinical Clinical Data Complex Country County Data Data Correlations Decision Making Diabetes Mellitus Dimensions Disease Disease Outcome Elderly Family Functional disorder Futility Gene Expression Genes Genomics Geography Goals Health Health Care Costs Hospitalization 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 Phenotype Population Process Prognosis Proteomics Quality of life Reporting Research Resource Allocation Risk Role Severity of illness Site Symptoms Testing Texas Therapeutic Time Training Triage Ventilator Work acute infection adverse outcome algorithm development algorithm training biomarker development candidate identification combinatorial coronavirus disease cost effectiveness demographics design experience frailty high risk improved information gathering male metropolitan military veteran mortality multiple omics novel outcome prediction participant enrollment people of color prediction algorithm predictive test prognostic respiratory response symptomatic COVID-19 tool transcriptomics

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项目摘要

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.

项目摘要严重的急性呼吸道综合征冠状病毒2（SARS-COV-2）介导的冠状病毒病（COVID-19）为进化前所未有的自然实验，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人群。老年人，男性，有色人种以及在某些潜在的健康状况（例如糖尿病，肥胖等）的情况下，严重的风险更高 Covid-19的疾病。虽然现在已经完全了解Covid-19对整体健康的影响还为时过早幸福感，已经有几份有关后遗症的报道，似乎与疾病相关严重程度。明确而迫切需要为广告进行短期和长期结果的预测测试，特别是对于高风险的Covid-19人群。我们假设互补的多维症状发作时收集的信息可用于预测新的发作或担心脆弱的，器官功能障碍和死亡在Covid-19发作后的一年内。单个参数提供有限的信息，并且无法充分表征复杂的生物学反应有症状的Covid-19预测结果。由于它们是为其他疾病而设计的，因此不太可能现有的临床工具，例如呼吸道，心血管和其他器官功能评估评估，将精确地评估了这种新型疾病的长期预后。我们在生物标志物方面的丰富经验发展表明，整合分子和临床数据会提高长期的预测准确性结果。我们选择在反映美国数字学的人群中检验我们的假设 COVID-19的不良后果风险增加。我们将注册患者，广泛反映我们人口统计，来自该国最大的大都市之一的住院平民和代表国家退伍军人的人口。我们预计在此过程中表现良好的预测测试住院的患者组将：帮助指导分列和治疗决策，因此减少了发病率和死亡率，提高患者的生活质量并提高医疗保健成本效益。更准确预后信息还将帮助临床医生在可能徒劳的情况下实现护理讨论的目标并协助患者和家人进行这一决策过程。最后，它将为短暂供应资源，例如呼吸机或可用性有限的治疗。