According to French recommendations for serological screening of toxoplasmosis, some profiles must be confirmed by additional methods, extending the time taken to produce results. Thus, the Laborizon Bretagne technical platform in Nantes studied the place of the LDBIO Diagnostics® TOXOPLASMA ICT IGG-IGM (ICT) test in addition to Siemens Atellica® serology. IgG-/IgM+ and equivocal or weak positive IgG/IgM- (IgGEq/IgM-) profiles on Atellica® will be confirmed by ICT, Alinity® Abbott and Platelia® Biorad. Among the 66 IgGEq/IgM- profiles, the concordance is perfect between ICT and complementary techniques: 21 weak positives were confirmed positive, 8 equivocal were considered negative and 37 were confirmed positive. Concerning the 76 IgG-/IgM+ profiles, 68 are negative and 7 are positive by complementary techniques and ICT. One discordance was observed. The Atellica®/ICT combination allows excellent discrimination of IgG-/IgM+ and IgGEq/IgM serological profiles with consistent diagnostic orientation in 99.3% of cases. Only 1 sample was found to be discordant but required monitoring at 15 days. The observed performances are compatible with routine use. This test simplifies the analytical process, improves the time to obtain results, while guaranteeing an excellent level of quality.
Toxoplasma , Toxoplasmosis , Humans , Immunoglobulin G , Antibodies, Protozoan , Immunoglobulin M , Toxoplasmosis/diagnosis
OBJECTIVES: Monitoring quality control for a laboratory or network with multiple instruments measuring the same analyte is challenging. We present a retrospective assessment of a method to detect medically significant out-of-control error conditions across a group of instruments measuring the same analyte. The purpose of the model was to ensure that results from any of several instruments measuring the same analytes in a laboratory or a network of laboratories provide comparable results and reduce patient risk. Limited literature has described how to manage QC in these very common situations. METHODS: Single Levey-Jennings control charts were designed using peer group target mean and control limits for five common clinical chemistry analytes in a network of eight analyzers in two different geographical sites. The QC rules used were 13s/22s/R4s, with the mean being a peer group mean derived from a large population of the same instrument and the same QC batch mean and a group CV. The peer group data used to set the target means and limits were from a quality assurance program supplied by the instrument supplier. Both statistical and clinical assessments of significance were used to evaluate QC failure. Instrument bias was continually monitored. RESULTS: It was demonstrated that the biases of each instrument were not statistically or clinically different compared to the peer group's average over six months from February 2023 until July 2023. Over this period, the error rate determined by the QC model was consistent with statistical expectations for the 13s/22s/R4s rule. There were no external quality assurance failures, and no detected error exceeded the TEa (medical impact). Thus, the combined statistical/clinical assessment reduced unnecessary recalibrations and the need to amend results. CONCLUSIONS: This paper describes the successful implementation of a quality control model for monitoring a network of instruments, measuring the same analytes and using externally provided quality control targets. The model continually assesses individual instrument bias and imprecision while ensuring all instruments in the network meet clinical goals for quality. The focus of this approach is on detecting medically significant out-of-control error conditions.
Chemistry, Clinical , Laboratories , Humans , Retrospective Studies , Quality Control , Bias
