Predictive Modeling of COVID-19 Progression in Older Patients

The objective of this proposal is to develop a predictive model to identify individuals who are infected withSARS-CoV-2 and at risk of developing severe COVID-19. Louisiana has the 5th highest death rate per capita inthe United States as of May 4th, 2020. Severe disease is seen in older individuals and those with underlyingconditions. 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 toregain lung function and avoid acute respiratory distress syndrome and pulmonary fibrosis. Mounting evidencesuggests that patients with severe COVID-19 have cytokine storm syndrome, which may exacerbatemultiorgan injury and risk of fibrotic complications. Lack of effective ways to identify and attenuate severeCOVID-19 progression persist due to limited understanding of the biological pathways responsible for cytokinestorm syndrome and increased risk in older patients. Therefore, there is a need to determine the criticalcytokine profiles responsible for severe COVID-19 progression to develop effective treatments. Further, it isessential to find a way to stage disease trajectory(ies) to identify therapeutic targets with precision to attenuatedisease progression and uncover preventive strategies. Towards this end, we seek to leverage a mathematicalmodel of SARS-CoV-2-induced lung damage to predict severity of acute respiratory distress syndrome andpulmonary fibrosis by considering key cytokine-cell interactions. We hypothesize that the model will accuratelypredict quantitative changes in suites of key cytokines and matrix accumulation with varying COVID-19progression within 10% accuracy. To accomplish this, we have assembled an investigative team at TulaneUniversity with key expertise in virology, clinical infectious disease research, bioinformatics, and predictivemathematical models of tissue remodeling. In Aim 1 of the proposal, we will identify the critical cytokinemarkers linked to viral-induced lung damage and pulmonary fibrosis. This will be accomplished by leveragingmachine learning to determine the biomarkers and molecular pathways characterizing progression of severeCOVID-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 atdifferent stages of disease progression. The model will be refined and informed by cytokine data to discerncausal biological pathways and disease processes that can be tested and targeted. Our expected outcome isto have determined the critical cytokine interactions responsible for lung tissue damage and dictating pathwaysfor varying disease trajectories in older patients. These results are expected to have an important impact asthe proposed predictive model will open new avenues of research to rationally design pharmaceuticalinterventions for severe COVID-19 patients. Specifically, the study will provide a paradigm-shifting open-sourcetool to delineate target therapeutics, estimate their efficacy, and move towards development of patient-specifictreatment plans for older individuals.