Algorithms for appropriate patient stratification in the face of a new SARS-CoV-2 pandemic

Although the SARS-Cov-2 pandemic has passed, the virus continues to cause infection and death. Immunological response is crucial to overcome the disease. Elucidating the prognostic value of measurable immunological variables at the time of admission and throughout the disease progression could provide stratification tools, improving the management of patients. A total of 1,096 samples belonging to 979 hospitalised patients molecularly diagnosed with SARS-CoV-2 infection, together with their medical records were retrospectively analysed. Demographic data, including age, sex, date of symptom onset and admission and T, B and NK cells and serum cytokine levels, were collected as predictor variables. Stratification of patients was performed according to their severity (grade 1: uncomplicated disease; grade 2: mild pneumonia; grade 3: severe pneumonia), their admission to Intensive Care Unit and their final outcome (death vs. alive). On admission, more severe patients were older, had lower lymphocyte subpopulation counts and higher levels of IL-6, IL-8, IL-10 and IP-10. The trend of lymphocyte counts throughout the disease progression since the symptom onset was to increase both in less and severe patients (even though in these last the levels remained significantly lower), except for NK cells, which decreased. Regarding cytokine levels, IL-6 and IL-8 tended to increase, whereas IL-10 and IP-10 tended to decrease in more severe patients, although particularly IL-10 remained significantly higher in them as compared with less severe patients, and, interestingly, its tendency was to increase in those who died. When considering the clinical evolution, an increase of IL-8 serum levels and a decrease of NK cells were significantly associated with a worsening, while an increase of CD19+ B and CD8+ T cells and a decrease of IL-6, IL-10, MCP-1 and IP-10 cytokine circulating levels were significantly related to an improvement. By integrating all the results, 13 optimal classification trees were constructed using evolutionary algorithms to predict COVID-19 outcome. The age and the study of lymphocyte populations in a county hospital, together with cytokine serum level quantitation in a third-level hospital, are enough variables to predict the outcome of patients hospitalised due to SARS-CoV-2 infection.