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The verification of analytical methods is a requirement of the standard NF EN ISO 15 189. It consists of evaluating the performance of an analytical method according to a well-defined protocol and then comparing it with pre-established analytical objectives. The mastery of this approach must be the concern of any biologist. Through this work we present the results of the protocol of verification of the method of determination of microalbumin by comparing two automats: Alinity ci ® and Architect ci-8200® Abbott. Microalbumin monitoring in urine is an important element in the treatment of diabetes mellitus types I and II. It can also be used to predict diabetic nephropathy, which is the leading cause of death in people with insulin-dependent diabetes.

IntroductionWith the spread of the Covid-19 pandemic and its overwhelming impact on health systems in several countries, the importance of identifying predictors of severity is of paramount importance. The objective of this study is to determine the relationship between death and the biological parameters of patients with Covid-19. Materials and methodsThis is an analytical retrospective cohort study conducted on 326 patients admitted to the Mohammed VI University Hospital in Oujda, Morocco. The statistical analysis concerned the biological parameters carried out on the admission of the patients, in addition to age and sex. The comparison between the two surviving and non-surviving groups was made by a simple analysis than a multivariate analysis by logistic regression. Next, a survival analysis was performed by the Kaplan-Meier method and then by Cox regression. ResultsA total of 326 patients were included in the study, including 108 fatal cases. The mean age was 64.66 ± 15.51 and the sex ratio was 1.08:1 (M:F). Age, procalcitonin, liver enzymes, and coagulation factors were significantly higher in patients who died of Covid-19 and are therefore considered to be the main prognostic factors identified in this study. ConclusionKnowledge and monitoring of predictive biomarkers of poor prognosis in patients with Covid-19 could be of great help in the identification of patients at risk and in the implementation of an effective diagnostic and therapeutic strategy to predict severe disease forms.