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，以识别感染感染的人 SARS-COV-2，有可能发展严重的Covid-19。路易斯安那州的人均死亡率是 截至2020年5月4日，美国。在老年人和有基础的人中有严重疾病 状况。新奥尔良人口特别容易受到严重的covid-19作为高血压， 糖尿病和肥胖猖ramp。感染后，必须将病毒引起的急性肺损伤修复到 恢复肺功能并避免急性呼吸窘迫综合征和肺纤维化。越来越多的证据 表明患有严重COVID-19的患者患有细胞因子风暴综合征，这可能加剧 多器官损伤和纤维化并发症的风险。缺乏识别和衰减严重的有效方法 由于对负责细胞因子的生物学途径的了解有限，共vid-19的进展持续存在 暴风雨综合征和老年患者的风险增加。因此，有必要确定关键 细胞因子谱，负责严重的共同-19进展以开发有效的治疗方法。此外，是 必须找到一种舞台疾病轨迹（IES）的方法，以识别具有衰减精度的治疗靶标 疾病进展和发现预防策略。为此，我们试图利用数学 SARS-COV-2诱导的肺损伤的模型，以预测急性呼吸窘迫综合征的严重程度和 通过考虑关键的细胞因子细胞相互作用，肺纤维化。我们假设该模型将准确 预测关键细胞因子和基质积累的套件的定量变化，而covid-19 在10％精度之内的进展。为此，我们在图兰（Tulane）组建了一个调查团队 具有病毒学，临床传染病研究，生物信息学和预测性的关键专业知识的大学 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 与病毒诱导的肺损伤和肺纤维化有关的标记。这将通过利用 机器学习以确定表征严重进展的生物标志物和分子途径 COVID-19以重点模型公式。在AIM 2中，我们将预测老年患者Covid-19的严重性。 将模型预测与在老年患者队列中的COVID-19疾病的血液标记中进行比较 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 可以测试和靶向的因果生物学途径和疾病过程。我们的预期结果是 确定负责肺组织损伤和指示途径的临界细胞因子相互作用 对于老年患者的不同疾病轨迹。预计这些结果将产生重要的影响 提出的预测模型将开放新的研究途径，以合理设计药物 严重的Covid-19患者的干预措施。具体而言，该研究将提供开源范例 描绘靶疗法的工具，估计其疗效并朝着特定于患者的发展发展 老年人的治疗计划。