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个预测模型来识别感染者 SARS-CoV-2 并有发展为严重 COVID-19 的风险。路易斯安那州的人均死亡率排名第五 截至 2020 年 5 月 4 日的美国。严重疾病常见于老年人和患有潜在疾病的人 状况。新奥尔良人口特别容易感染严重的 COVID-19，如高血压、 糖尿病和肥胖症十分猖獗。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 表明重症 COVID-19 患者患有细胞因子风暴综合征，这可能会加剧 多器官损伤和纤维化并发症的风险。缺乏有效的方法来识别和减轻严重的 由于对细胞因子的生物学途径了解有限，COVID-19 的进展持续存在 风暴综合症和老年患者的风险增加。因此，需要确定临界值 导致 COVID-19 严重进展的细胞因子谱，以开发有效的治疗方法。进一步地，它是 找到一种方法来分期疾病轨迹以精确识别治疗靶点以减轻影响至关重要 疾病进展并揭示预防策略。为此，我们寻求利用数学方法 SARS-CoV-2 诱导的肺损伤模型可预测急性呼吸窘迫综合征的严重程度 通过考虑关键的细胞因子-细胞相互作用来研究肺纤维化。我们假设该模型将准确地 预测关键细胞因子套件和基质积累随不同 COVID-19 的数量变化 进展精度在 10% 以内。为了实现这一目标，我们在杜兰大学组建了一个调查小组 大学在病毒学、临床传染病研究、生物信息学和预测方面拥有关键专业知识 组织重塑的数学模型。在提案的目标 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相关的标记物。这将通过利用 机器学习来确定表征严重疾病进展的生物标志物和分子途径 COVID-19 聚焦模型制定。在目标 2 中，我们将预测老年患者中 COVID-19 的严重程度。 模型预测将与老年患者队列中的 COVID-19 疾病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完善和告知，以辨别 可以测试和瞄准的因果生物学途径和疾病过程。我们的预期结果是 确定导致肺组织损伤和决定途径的关键细胞因子相互作用 针对老年患者不同的疾病轨迹。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开辟新的研究途径 针对重症 COVID-19 患者的干预措施。具体来说，该研究将提供一种范式转变的开源 描述目标治疗方法、评估其疗效并朝着针对患者具体情况进行开发的工具 老年人的治疗计划。