Comorbidities and Risk Score in COVID-19 Patients

Background A virus causing clusters of severe pneumonia was first detected in the city of Wuhan, China, in December 2019. This pathogen was designated as SARS-CoV-2. Although of probable zoonotic origin, human-to-human transmission has rapidly fuelled the spread of SARS-CoV-2 infection globally. On February 20th, the first case of locally acquired SARS-CoV-2 infection was diagnosed in Northern Italy in a critically ill, hospitalized young man with no travel history to known areas of viral circulation or link to a probable or confirmed coronavirus infectious disease 2019 (COVID-19) case. Prior to this date, only three cases of COVID-19 had been reported in Central Italy, all with a travel history to Wuhan. Following this unexpected finding, case counts, and death tolls has increased rapidly in the country with a total of 192,994 confirmed cases and 25,969 deaths as of 24 April 2020. Study rationale Multiple variables have been described as possible risk factors for SARS-CoV-2 susceptibility, severity and prognosis, among which age, sex and comorbidities play an important role. Centers for Disease Control and Prevention (CDC) listed the underlying medical conditions that have shown to increase the risk of severe illness from SARS-CoV-2. While some comorbidities, such as serious heart conditions and chronic kidney disease have a consistent and strong evidence as bad prognostic factors in SARS-CoV-2 infection, others as HIV have a limited evidence and heterogeneous results. Further, despite it is well-known that the burden of co-existing diseases may be additive or even multiplicative, the effect of specific disease cluster on COVID-19 adverse outcomes has never been evaluated. Finally, the proposed models and risk scores currently available to predict disease severity and mortality are poorly reported and at high risk of bias, raising concern that their predictions could be unreliable when applied in daily practice. A reliable risk/prognostic score developed by a multidimensional and cross-validated approach will pave the way for future research on frail sectors of the population and on the use of health system resources. At the clinical level, a prognostic score will allow to predict severity and mortality risk in patients requiring hospitalization and to stratify patients according to clinical severity helping clinicians in their therapeutic decision-making. Objectives The primary objective of the study is to evaluate the role of patient's comorbidities on clinical outcome in patients hospitalized for SARS-CoV-2. The investigators will confirm risk predictors already known and provide evidence for the uncertain ones. The investigators will also develop a prognostic score able to predict negative clinical outcomes (primarily short-term mortality), that will be useful to stratify patients at hospital admission according to their different risk profiles, and therefore to "tailor" the individuals' level of care. A secondary objective could be that to extend this approach at the susceptible population level, especially the elders, to stratify according to the higher risk of being infected by SARS-CoV-2, hospitalized and to have a dismal outcome (not developed here but related to a possible amendment). Sample size The investigators expect the total number of patients with complete data to be approximately 2500, based on the expected recruitment of each center. Analysis Plan Data will be summarized by counts and percentage and quartiles for categorical and continuous variables, respectively. Multi-state models will be used to describe patient's hospital mortality and discharge. In-hospital mortality will be estimate accounting for discharge as competing event. Kaplan-Meier estimator will be used to estimate mortality up to 3-months from admission. The role of patient's comorbidities on clinical outcome will be evaluated by the Cox model adjusting for relevant confounders. A clinically-based prognostic score will be developed including comorbidities and other risk factors. The score will be constructed by a multidimensional approach and Lasso approach will be used to select relevant risk factors. The Area Under the Receiving Operating Characteristics curve (AUC) and Brier score will be used to evaluate model performance and the final score will be cross-validated. A sensitivity analysis will be performed using a training and test validation approach. The use of regression trees for a practical definition of risk subgroups and latent variable models will also be considered. Multiple imputation will be performed if missing would exceed 10%. Data collection Consecutive patients hospitalized for SARS-CoV-2 up to October 2020 will be included. Given the difficulty in systematically obtaining written informed consent and given the great public interest of the project, the research will be conducted in the context of the authorizations guaranteed by Article 89 of the General Data Protection Regulation (GDPR) EU Regulation 2016/679, which guarantees processing for purposes of public interest, for scientific or historical research or for statistical purposes of health data.