Pilot study on harmonization of cardiac troponin I immunoassays using patients and quality control plasma samples. On behalf of the Italian Section of the European Ligand Assay Society (ELAS) and of the Study Group on Cardiovascular Biomarkers of the Società Italiana di Biochimica Clinica (SIBioC)

Abstract

BackgroundThe aim of this study is to evaluate whether it is possible to reduce the between-methods variability of troponin I (cTnI) immunoassays using mathematical algorithms calculated from the results of both patients' samples and quality control materials distributed in an external quality assessment (EQA) scheme. MethodsWe collected 122 heparinized plasma samples of patients admitted to the emergency department with thoracic pain or supraventricular tachyarrhythmia. Moreover, we also analyzed 20 control samples distributed in an EQA and 26 plasma pools prepared from healthy subjects and patients with myocardial infarction. We evaluated 4 different methods for cTnI assay: STAT Architect High Sensitive TnI (Abbott Diagnostics), ADVIA Centaur Troponin I Ultra (Siemens Healthcare Diagnostics), ST AIA-Pack cTnI Third Generation (Tosoh Bioscience), and Access AccuTnI+3 (Beckman Coulter Diagnostics). ResultsSystematic differences between cTnI methods were observed. However, correlation coefficients (R from 0.976 to 0.990) between the log-transformed cTnI values measured in all 168 samples were significantly better (p=0.0037) than those obtained considering only the 122 patients' samples. cTnI values measured in EQA and pool samples were included within the 95% prediction intervals of linear regressions calculated with those of patients' samples. After the recalibration of cTnI values based on the robust principal component analysis approach the between-methods variability decreased significantly (about 40% around the cut off values). ConclusionsOur pilot study suggests that EQA schemes for cTnI immunoassay methods, based on both quality control samples with tested commutability and robust statistical analyses, are able to evaluate between-methods variability as well as allow a reliable recalibration and harmonization of results.