Background: Most laboratory errors occur in the preanalytical phase. Among the most common preanalytical errors are interferences due to hemolysis, lipemia, and icterus. Our objective was to evaluate a serum interference estimation methodology by the RSD classifier, to identify other biochemical parameters affected by preanalytical interferences, and to determine the economic impact of its implementation.
Methods: The serum indices of 65,529 requests measured by the RSD system and by the analytical determination on the Cobas 711 or Cobas 8000 platforms were collected. We proceeded to the search for association patterns between the serum indices and laboratory analytical tests using data mining techniques. The influence of the preanalytical interferences was evaluated in 91 laboratory tests that include biochemistry, immunoassay, and coagulation. The savings estimation after the implementation of this methodology was made by time series models.
Results: The evaluation of the generated model showed compatibilities between the methods used (94.4% accuracy). The implementation of a protocol for serum indices estimation by the RSD would avoid the unnecessary analysis of the tests which are affected by interferences, achieving an estimated annual savings of €10,561. In addition, it allowed the estimation of bilirubin values which would add an annual savings of €4,900 in our laboratory. On the other hand, data mining techniques have allowed us to identify the following laboratory tests affected by hemolysis which are not usually considered in laboratories: iron, transferrin, fibrinogen, and alkaline phosphatase.
Conclusions: The RSD classifier is an efficient estimation method of serum indices and it allows the estimation of bilirubin values. The implementation of this methodology in our laboratory could lead to an estimated annual savings of more than €15,000 without increasing response times. Iron, alkaline phosphatase, transferrin, and fibrinogen should be included in the evaluated procedure.