ABSTRACT
BACKGROUND: Limit of detection (LOD) is an important performance characteristic of clinical laboratory tests. Verification, as recommended by the CLSI EP17-A2 guideline, is done by testing a sample with a claimed LOD concentration. Claimed LOD is verified if the 95% CI for the population proportion, calculated from observed proportion of positive results, contains the expected detection rate of 95% (CLSI EP17-A2; Clin Chem 2004;50:732-40). Claimed LOD, verification sample concentration, and observed rate of positive results are subjects to systematic and random errors that can cause false failure or false acceptance of the LOD verification. The aim of this study was to assess the probability to pass or fail verification of claimed LOD with various numbers of tests as function of the ratio of test sample concentration and actual LOD for PCR-based molecular diagnostics tests and provide recommendations for study design. METHODS: A method of calculating the probability of passing the claimed LOD verification following CLSI EP17-A2 guideline recommendations, based on the Poisson-binomial probability model, have been developed for PCR-based assays. RESULTS: Calculations and graphs have shown that the probability of passing LOD verification depends on the number of tests and has local minima and maxima between 0.975 and 0.995 for the number of tests from 20 to 1000 on samples having actual LOD concentration. The probability of detecting the difference between claimed LOD and actual LOD increases with the number of tests performed. Graphs and tables with examples are included. CONCLUSIONS: Method, tables, and graphs helping in planning LOD verification study in molecular diagnostics are provided along with the recommendations on what to do in case of failure to verify the LOD claim.
ABSTRACT
Significant advancements in molecular diagnostics have been made since the inception and application of PCR-based technologies in clinical diagnostic laboratories and the management of HIV-1 infected patients. More recently, real-time PCR has improved the overall performance of assays used for detecting and quantifying HIV-1 RNA viral load in patients undergoing antiretroviral treatment. The effects of these changes and the interpretations of the HIV-1 viral load results are discussed in this review in the context of the different assays used, the viral dynamics of the HIV-1 virus, and the recent changes to HIV-1 treatment guidelines.
Subject(s)
HIV Infections/diagnosis , RNA, Viral/analysis , Viral Load/immunology , Guidelines as Topic/standards , HIV Infections/immunology , HIV Infections/virology , HIV-1/genetics , HIV-1/immunology , HIV-1/pathogenicity , Humans , Real-Time Polymerase Chain Reaction/standards , Sensitivity and Specificity , Viral Load/methodsABSTRACT
BACKGROUND: Errors of calibrator-assigned values lead to errors in the testing of patient samples. The ability to estimate the uncertainties of calibrator-assigned values and other variables minimizes errors in testing processes. International Organization of Standardization guidelines provide simple equations for the estimation of calibrator uncertainty with simple value-assignment processes, but other methods are needed to estimate uncertainty in complex processes. METHODS: We estimated the assigned-value uncertainty with a Monte Carlo computer simulation of a complex value-assignment process, based on a formalized description of the process, with measurement parameters estimated experimentally. This method was applied to study uncertainty of a multilevel calibrator value assignment for a prealbumin immunoassay. RESULTS: The simulation results showed that the component of the uncertainty added by the process of value transfer from the reference material CRM470 to the calibrator is smaller than that of the reference material itself (<0.8% vs 3.7%). Varying the process parameters in the simulation model allowed for optimizing the process, while keeping the added uncertainty small. The patient result uncertainty caused by the calibrator uncertainty was also found to be small. CONCLUSIONS: This method of estimating uncertainty is a powerful tool that allows for estimation of calibrator uncertainty for optimization of various value assignment processes, with a reduced number of measurements and reagent costs, while satisfying the requirements to uncertainty. The new method expands and augments existing methods to allow estimation of uncertainty in complex processes.
