Predictive Modeling of COVID-19 Progression in Older Patients

老年患者 COVID-19 进展的预测模型

• 批准号: 10162283
• 负责人: S MICHAL JAZWINSKI
• 金额: $ 37.99万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10162283
• 关键词: 2019-nCoV; Acute Lung Injury; Adult Respiratory Distress Syndrome; Attenuated; Autopsy; Bioinformatics; Biological; Biological Markers; Blood; COVID-19; Cell Communication; Cessation of life; China; Cities; Clinical; County; Data; Data Set; Death Rate; Development; Diabetes Mellitus; Disease; Disease Progression; Elderly; Epithelial Cells; Fibrosis; Formulation; Future; Hypertension; Individual; Infection; Infectious Diseases Research; Intervention; Learning; Link; Louisiana; Lung; Machine Learning; Maps; Modeling; Molecular; Molecular Genetics; Monitor; Obesity; Outcome; Pathway Analysis; Pathway interactions; Patients; Pharmacologic Substance; Plasma; Population; Population Study; Prevention strategy; Process; Pulmonary Fibrosis; RNA; Research; Respiratory physiology; Risk; Risk Factors; Role; Severities; Specimen; Structure of parenchyma of lung; Syndrome; Testing; Tissue Model; Treatment Efficacy; United States; Universities; Validation; Viral; Virus; base; biomarker identification; cell injury; cohort; coronavirus disease; cytokine; cytokine release syndrome; design; effective therapy; evidence base; extracellular; genetic predictors; individualized medicine; lung injury; mathematical model; molecular pathology; multiorgan injury; nonhuman primate; older patient; open source; pandemic disease; predictive modeling; predictive test; repaired; research study; targeted treatment; therapeutic target; therapy design; tool; treatment planning; virology; wound healing

The objective of this proposal is to develop a predictive model to identify individuals who are infected with SARS-CoV-2 and at risk of developing severe COVID-19. Louisiana has the 5th highest death rate per capita in the United States as of May 4th, 2020. Severe disease is seen in older individuals and those with underlying conditions. The New Orleans population is particularly susceptible to severe COVID-19 as hypertension, diabetes and obesity are rampant. After infection, acute lung injury caused by the virus must be repaired to regain lung function and avoid acute respiratory distress syndrome and pulmonary fibrosis. Mounting evidence suggests that patients with severe COVID-19 have cytokine storm syndrome, which may exacerbate multiorgan injury and risk of fibrotic complications. Lack of effective ways to identify and attenuate severe COVID-19 progression persist due to limited understanding of the biological pathways responsible for cytokine storm syndrome and increased risk in older patients. Therefore, there is a need to determine the critical cytokine profiles responsible for severe COVID-19 progression to develop effective treatments. Further, it is essential to find a way to stage disease trajectory(ies) to identify therapeutic targets with precision to attenuate disease progression and uncover preventive strategies. Towards this end, we seek to leverage a mathematical model of SARS-CoV-2-induced lung damage to predict severity of acute respiratory distress syndrome and pulmonary fibrosis by considering key cytokine-cell interactions. We hypothesize that the model will accurately predict quantitative changes in suites of key cytokines and matrix accumulation with varying COVID-19 progression within 10% accuracy. To accomplish this, we have assembled an investigative team at Tulane University with key expertise in virology, clinical infectious disease research, bioinformatics, and predictive mathematical models of tissue remodeling. In Aim 1 of the proposal, we will identify the critical cytokine markers linked to viral-induced lung damage and pulmonary fibrosis. This will be accomplished by leveraging machine learning to determine the biomarkers and molecular pathways characterizing progression of severe COVID-19 to focus model formulation. In Aim 2, we will predict the severity of COVID-19 in older patients. Model predictions will be compared to blood markers of COVID-19 disease in cohorts of older patients at different stages of disease progression. The model will be refined and informed by cytokine data to discern causal biological pathways and disease processes that can be tested and targeted. Our expected outcome is to have determined the critical cytokine interactions responsible for lung tissue damage and dictating pathways for varying disease trajectories in older patients. These results are expected to have an important impact as the proposed predictive model will open new avenues of research to rationally design pharmaceutical interventions for severe COVID-19 patients. Specifically, the study will provide a paradigm-shifting open-source tool to delineate target therapeutics, estimate their efficacy, and move towards development of patient-specific treatment plans for older individuals.

该提案的目的是开发一种预测模型，以识别感染了 SARS-CoV-2和发展严重COVID-19的风险。路易斯安那州的人均死亡率排名第五， 美国截至2020年5月4日。严重的疾病见于老年人和那些有潜在 条件新奥尔良的人口特别容易受到严重的COVID-19的影响，如高血压， 糖尿病和肥胖症猖獗。感染后，病毒引起的急性肺损伤必须修复， 恢复肺功能，避免急性呼吸窘迫综合征和肺纤维化。越来越多的证据 表明严重COVID-19患者患有细胞因子风暴综合征， 多器官损伤和纤维化并发症的风险。缺乏识别和减轻严重 COVID-19进展持续存在，原因是对细胞因子生物学途径的了解有限 风暴综合征和老年患者的风险增加。因此，需要确定关键的 研究导致COVID-19严重进展的细胞因子谱，以开发有效的治疗方法。进一步应 必须找到一种方法来分期疾病轨迹，以精确地确定治疗靶点， 疾病进展和发现预防策略。为此，我们寻求利用数学 SARS-CoV-2诱导的肺损伤模型预测急性呼吸窘迫综合征的严重程度和 肺纤维化，通过考虑关键的细胞相互作用。我们假设这个模型 预测不同COVID-19的关键细胞因子和基质积累的定量变化 准确率在10%以内。为此，我们在杜兰大学组建了一个调查小组 在病毒学，临床传染病研究，生物信息学和预测方面具有关键专长的大学 组织重塑的数学模型在该提案的目标1中，我们将确定关键的细胞因子 与病毒引起的肺损伤和肺纤维化有关的标志物。这将通过利用 机器学习，以确定生物标志物和分子途径表征严重 COVID-19聚焦模型制定。在目标2中，我们将预测COVID-19在老年患者中的严重程度。 模型预测将与年龄较大患者队列中COVID-19疾病的血液标志物进行比较， 疾病进展的不同阶段。该模型将通过细胞因子数据进行优化和通知，以识别 因果生物途径和疾病过程，可以测试和针对。我们的预期结果是 确定了导致肺组织损伤的关键细胞因子相互作用， 老年患者的不同疾病轨迹。这些结果预计将产生重要影响， 所提出的预测模型将为合理设计药物制剂开辟新的研究途径。 为重症COVID-19患者提供干预措施。具体来说，这项研究将提供一个范式转变的开源 用于描绘目标疗法、评估其疗效并转向开发患者特定疗法的工具 老年人的治疗计划。