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Publisher's version disponible en: http://hdl.handle.net/10261/257116, COVID-19 severity and progression are determined by several host and virological factors that may influence the final outcome of SARS-CoV-2-infected patients. The objective of this work is to determine a possible association between the viral load, obtained from nasopharyngeal swabs, and the severity of the infection in a cohort of 448 SARS-CoV-2-infected patients from a hospital in Madrid during the first outbreak of the pandemic in Spain. To perform this, we have clinically classified patients as mild, moderate and severe COVID-19 according to a number of clinical parameters such as hospitalization requirement, need of oxygen therapy, admission to intensive care units and/or exitus. Here we report a statistically significant correlation between viral load and disease severity, being high viral load associated with worse clinical prognosis, independently of several previously identified risk factors such as age, sex, hypertension, cardiovascular disease, diabetes, obesity, and lung disease (asthma and chronic obstructive pulmonary disease). The data presented here reinforce the viral load as a potential biomarker for predicting disease severity in SARS-CoV-2-infected patients. It is also an important parameter in viral evolution since it relates to the numbers and types of variant genomes present in a viral population, a potential determinant of disease progression., This work was supported by Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (COVID-19 Research Call COV20/00181) co-financed by European Development Regional Fund (A way to achieve Europe). The work was also supported by grants CSIC-COV19-014 from Consejo Superior de Investigaciones Cientificas (CSIC), BFU2017-91384-EXP from Ministerio de Ciencia, Innovacion y Universidades (MICIU), PI18/00210 from Instituto de Salud Carlos III. C.P., M.C. and P.M. are supported by the Miguel Servet program of the Instituto de Salud Carlos III (CPII19/00001, CPII17/00006 and CP16/00116, respectively) cofinanced by the European Regional Development Fund (ERDF). CIBERehd (Centro de Investigacion en Red de Enfermedades Hepaticas y Digestivas) is funded by Instituto de Salud Carlos III. Institutional grants from the Fundacion Ramon Areces and Banco Santander to the CBMSO are also acknowledged. The team at CBMSO belongs to the Global Virus Network (GVN). B. M.-G. is supported by predoctoral contract PFIS FI19/00119 from Instituto de Salud Carlos III (Ministerio de Sanidad y Consumo) cofinanced by Fondo Social Europeo (FSE). R. L.-V. is supported by predoctoral contract PEJD-2019-PRE/BMD-16414 from Comunidad de Madrid. R L-R is sponsored by the IIS-Fundacion Jimenez Diaz-UAM Genomic Medicine Chair.

Background Coronavirus disease 2019 (COVID-19) is a highly variable condition. Validated tools to assist in the early detection of patients at high risk of mortality can help guide medical decisions. Objective To validate externally, as well as in patients from the second pandemic wave in Europe, our previously developed mortality prediction model for hospitalized COVID-19 patients. Methods Three validation cohorts were generated: two external with 185 and 730 patients from the first wave and one internal with 119 patients from the second wave. The probability of death was calculated for all subjects using our prediction model, which includes SpO2/FiO2 ratio, neutrophil-to-lymphocyte ratio, LDH, interleukin-6 and age. Discrimination and calibration were evaluated in the validation cohorts. The prediction model was updated by re-estimating individual risk factor effects in the overall cohort (N=1477). Results The mortality prediction model showed good performance in the external validation cohorts 1 and 2, and in the second wave validation cohort 3 (AUC 0.94, 0.86 and 0.86, respectively), with excellent calibration (calibration slope 0.86, 0.94 and 0.79; intercept 0.05, 0.03 and 0.10, respectively). The updated model accurately predicted mortality in the overall cohort (AUC 0.91), which included patients from both the first and second COVID-19 waves. The updated model was also useful to predict fatal outcome in patients without respiratory distress at the time of evaluation. Conclusions This is the first COVID-19 mortality prediction model validated in patients from the first and second pandemic waves. The COR+12 online calculator is freely available to facilitate its implementation (https://utrero-rico.shinyapps.io/COR12_Score/), We externally validated a mortality prediction model for hospitalized COVID-19 patients, which included early one-time measurements of SpO2/FiO2, N/L ratio, LDH, IL-6 and age. The model has been updated with 1477 patients from the first and second waves.