Determination of hemolysis cut-offs for biochemical and immunochemical analytes according to their value.

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Dupuy, Anne Marie | Bargnoux, Anne-Sophie | Kuster, Nils | Cristol, Jean-Paul | Badiou, Stéphanie

Edité par CCSD ; De Gruyter -

International audience. Background All general biochemistry instruments allow the measure of hemolysis index (HI), and suppliers provide an acceptable HI for each assay without consideration of the analyte value or its clinical application. Our first objective was to measure the impact of hemolysis degree on plasma biochemical and immunochemical analytes to determine the maximum allowable HI for each of them using four calculation methods as significant bias in comparison to manufacturer's data. The second objective was to assess whether the maximum allowable HI varied according to the analyte values. Methods Twenty analytes were measured in hemolyzate-treated plasma to determine the HI leading to a significant change compared to baseline value. Analytes were assessed at one (3 analytes), two (5 analytes) and three (12 analytes) values according to their sensitivity to hemolysis and their clinical impact. We used four calculation methods as significant limit from baseline value: the total change limit (TCL), the 10% change (10%Δ), the analytical change limit and the reference change value. Results Allowable HI was significantly different according to the threshold chosen for most analytes and was also dependent on the analyte value for alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, creatine kinase, iron, haptoglobin and high sensitivity troponin T. No hemolysis interference was observed for albumin, creatinine, C-reactive protein, and procalcitonin even at an HI value of 11 g/L. Conclusions This study highlights that TCL is the most appropriate calculation method to determine allowable HI in practice for biochemical and immunochemical parameters using Cobas 8000© from Roche Diagnostics. In addition, different allowable HI were found according to analyte value leading to optimization of resampling to save time in patient care.

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