Coronavirus Disease 2019 is a very fast-spreading infectious disease. Severe forms are marked by a high mortality rate. The objective of this study is to identify routine biomarkers that can serve as early predictors of the disease progression. This is a prospective, single-center, cohort study involving 330 SARS-CoV-2 infected patients who were admitted at the University Hospital of Blida, Algeria in the period between the 27th of March and 22nd of April 2020. The ROC curve was used to evaluate the predictive performance of biomarkers, assessed at admission, in the early warning of progression toward severity. Multivariate logistic regression was used to quantify the independent risk for each marker. After an average follow-up period of 13.9 ± 3.5 days, 143 patients (43.3%) were classified as severe cases. Six biological abnormalities were identified as potential risk markers independently related to the severity: elevated urea nitrogen (>8.0 mmol/L, OR ¼ 9.3 [2.7-31.7], p < .00001), elevated CRP (>42mg/L, OR ¼ 7.5 [2.4-23.3], p ¼ .001), decreased natremia (<133. 6 mmol/L, OR ¼ 6.0 [2.0-17.4], p ¼ .001), decreased albumin (<33.5 g/L, OR ¼ 5.2 [1.7-16.6], p ¼ .003), elevated LDH (>367 IU/L, OR ¼ 4.9 [1.7-14.2], p ¼ .003) and elevated neutrophil to lymphocyte ratio (>7.99, OR ¼ 4.2, [1.4-12.2], p ¼ .009). These easy-to-measure, time-saving and very low-cost parameters have been shown to be effective in the early prediction of the COVID-19 severity. Their use at the early admission stage can improve the risk stratification and management of medical care resources in order to reduce the mortality rate.

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Algerian medical teachers' research output was particularly low. Replacing the current promotion grid with a grid that promotes writing, developing abilities to read and write articles and developing English language proficiency are likely to improve this situation.

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