The Computer Simulation Study And Application Of Analytical Run Length For Quantitative Tests In Clinical Laboratory

ObjectiveTo study a method of designing analytical run length for quantitative tests in clinical laboratories and develop a corresponding software. To apply the software to design analytical run length for clinical laboratories and evaluate its effectiveness.MethodsComputer simulation and statistical models combining the practical situation in clinical laboratory were used to develop the software of designing analytical run. The software was applied to12quantitative biochemistry tests of a clinical laboratory to evaluate its effectiveness. Imprecision data including coefficient of variation(CV) for analytes albumin(ALB), cholesterol(CHOL), triglyceride(TG), aspartate aminotransferase(AST), lactate dehydrogenase(LDH), alkaline phosphatase(ALP), total bilirubin(TBIL), creatinine(CREA), uric acid(URIC), phosphorus(PHOS), carbon dioxide(CO2), glucose(GLU) was acquired by analyzing internal quality control(IQC) data for6months. Bias of these tests were calculated by method comparison test and total allowable error(TEa) from EQA. Then CVs, Bias and TEa of analytes were input to a IQC design software QCCS to produce power graphs and select proper IQC rules and number of IQC measurements per run(NQ). IQC strategy was designed to content Ped>90%and Pfr<5%. Then optimal length of analytic run for these analytes was computer simulated and calculated based on the theory of average number of unacceptable patient results E(Nu). The new IQC strategy and length of analytic run for each analyte was applied to routine internal IQC paralleled with the way before redesign. IQC charts were produced by QC tests results to analyze and compare the performance of out-of-control error detection.ResultsOptimal analytical run lengths designed by the software for ALB, CHOL, TG, AST, LDH, ALP, TBIL, CREA, URIC, PHOS, CO2and GLU were39,61,900,112,279,267,363,151,230,46,158and580, respectively. After being redesigned, IQC strategies for ALB、 CHOL and PHOS could detect more out-of-control error than before.Although redesigned IQC strategies did not detect more out-of-control error for GLU、CO2、CREA and TBIL, the analytical run lengths were adjusted to save IQC cost and achieve more cost-effectiveness.ConclusionsAnalytical run length is an important factor for IQC. The software of designing analytical run length is a useful tool for quantitative tests in clinical laboratories. The IQC strategy after redesigning analytical run length can achieve more cost-effectiveness.