AI-multi-omics-based Prognostic Stratification of COVID-19 Patients in Acute and Chronic State

COVID-19 is a pandemic disease with tremendous consequences spreading at fast pace. Key measures to control and manage this currently untreatable disease, and to provide adequate patient care are rapid and reliable diagnosis as well as severity assessment. While most patients develop only mild symptoms or are asymptomatic altogether, others exhibit severe courses that are associated with a high mortality rate. Of the initially moderate to severe cases, some recover completely while others deteriorate with the need of being put on invasive ventilation support or even extracorporeal membrane oxygenation. The long term effects on the lung in severe cases are largely unknown to date. First publications suggest that pulmonary fibrosis could develop. In addition, over the last weeks we have learned that the prognosis depends not only on pathological changes in lung tissue but also on changes in a variety of other pathological sites that the virus attacks such as the vessel wall. The findings on chest imaging for COVID-19 are often typical, but ultimately not specific and overlap with other infections, including influenza, H1N1. Despite early positive reports, the role of radiology in the management of COVID-19 remains to be defined. In this project we aim to develop and test an AI-based multi-omics system that combines and uses information from chest CT, laboratory parameters, and clinical data to a) assess the current state of a patient in the acute phase and to forecast seven-day progression and b) to predict chronicity (chronic lung damage). More specifically, our research is aimed at investigating whether an ensemble of multi-omic, patient-specific information can be used to predict patient outcome and ultimately optimize patient care. In addition, we intend to interpret and understand the importance of each item in the ensemble in terms of learning and disease outcome prediction. Special emphasis will be placed on the vascular situation and certain lung changes (bronchiectasis). Furthermore, through the predictive capabilities of the proposed AI-based multi-omics approach, we aim to better understand how COVID-19 progresses over time, since it is largely unknown why patients differ in disease progression. Finally, once the AI system is established, we intend to monitor treatment response in patients receiving different treatments (e.g. Remdesivir, Actrema). To date, we have access to more than 2'000 chest CTs of patients with laboratory-proven COVID-19 infection as well as their lab parameters, age, gender, and patient history. The majority of COVID-19 positive cases will be provided from centers in Northern Italy. As controls, we will include 1'200 cases with similar symptoms who have had pathological CT findings (pneumonias of various causes) before 12/2019 to rule out that these symptoms could have been caused by COVID-19 and 1'000 negative controls with normal chest CT. We will measure the performance of the AI-based computational engine, as well as the contribution of each individual variable to the classification and prediction performance of the proposed system. Quantitative metrics used to analyze the results include sensitivity, specificity, accuracy, positive predictive value and area under the curve (AUC) of the Receiver Operating Characteristic (ROC).Determining faster and more accurate prognostic stratification for COVID-19 patients based on the proposed AI-system is expected to contribute to better and more appropriate patient care and improve the efficiency of physicians. Better understanding of the different parameters and their interaction for the outcome of COVID-19 patients can shed new insights regarding the disease progression patterns of COVID-19, which could be of crucial importance for the treatment of critical patients.