Cardiovascular Diseases Clinical Trial
Official title:
Cardiac Biomarkers:Comparison Between Analytical Methods
In the western world the prevalence of myocardial infarction is estimated at around 1-2% of the population; increases progressively with age, reaching a percentage of 10% after the 75 years. It is therefore considered the first cause of hospitalization and mortality in Western countries, as well as expenditure for the health system. It is therefore very important to have cardiac biomarkers that allow to confirm the diagnostic suspicion of coronary heart disease and/or heart failure, carry out prognostic evaluations and stratify patients as precisely as possible in relation to their actual risk. Currently as biomarkers of heart failure, natriuretic peptides (BNP and NT-proBNP) are widely used in clinical practice but, considering the complexity of heart failure and its clinical and pathophysiological heterogeneity, it is reasonable to think that a single biomarker is not sufficient . For these reasons, there is increasing interest in the scientific community in the search for new biomarkers useful for early diagnosis, for correct prognostic stratification and for evaluating the response to therapy.
The study aims to evaluate the analytical performance of different kits and molecular markers commercially available (or in pre-industrial development phase) for the assays of the cardiac biomarkers: cTnI , NT-proBNP and sST2. 500 samples (residual serum) from patients hospitalized and/or referred to the DEA Department, for which troponin and/or NT-proBNP tests have been requested routinely or urgently by clinicians, will be included in the study. The analytical performance of the various kits and the intra- and interassay imprecision will be evaluated. Comparison of methods will be calculated by Passing-Bablok regression and graph by Bland-Altman. Through the non-parametric Passing-Bablok model the obtained data will be compared with the various analytical methods, reporting them one in function of the other, together with the regression line. From the regression equation, the proportional systematic error will be evaluated as angular coefficient of the straight line and the constant systematic error as an intercept of the equation ;
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