BACKGROUND: Nucleic acid amplification technologies significantly improved the limit of detection (LOD) for diagnostic assays. The ability of these assays to amplify fewer than 10 target copies of DNA or RNA imposes new requirements on the preparation of clinical samples. We report a statistical method to determine how large of an aliquot is necessary to reproducibly provide a detectable number of cells. METHODS: We determined the success probability (p) based on aliquot size and sample volume. The binomial distribution, based on p and the concentration of cells in sample, was used to calculate the probability of getting no target objects in an aliquot and to determine the minimum number of objects per aliquot necessary to generate a reproducible clinical assay. RESULTS: The described method was applied to find a minimum aliquot volume required for a set LOD, false-negative rate (FNR), and %CV. For example, to keep FNR <0.01% for 0.5%, 1% and 2% aliquots (minimum 2000, 1000, and 500 cells per sample) are required. Comparison between experimental and predicted FNR demonstrated good correlation for the small volume aliquots and/or low concentration of target. When 4 muL of 200 copies/mL of plasmid is amplified, predicted and experimental FNRs are 47.2% and 44.9%. CONCLUSION: This probability model is a useful tool to predict the impact of aliquot volume on the LOD and reproducibility of clinical assays. Even for samples for which pathogens are homogeneously distributed, it is theoretically impossible to collect a single pathogen consistently if the concentration of pathogen is below a certain limit.
BACKGROUND: Nucleic acid amplification technologies significantly improved the limit of detection (LOD) for diagnostic assays. The ability of these assays to amplify fewer than 10 target copies of DNA or RNA imposes new requirements on the preparation of clinical samples. We report a statistical method to determine how large of an aliquot is necessary to reproducibly provide a detectable number of cells. METHODS: We determined the success probability (p) based on aliquot size and sample volume. The binomial distribution, based on p and the concentration of cells in sample, was used to calculate the probability of getting no target objects in an aliquot and to determine the minimum number of objects per aliquot necessary to generate a reproducible clinical assay. RESULTS: The described method was applied to find a minimum aliquot volume required for a set LOD, false-negative rate (FNR), and %CV. For example, to keep FNR <0.01% for 0.5%, 1% and 2% aliquots (minimum 2000, 1000, and 500 cells per sample) are required. Comparison between experimental and predicted FNR demonstrated good correlation for the small volume aliquots and/or low concentration of target. When 4 muL of 200 copies/mL of plasmid is amplified, predicted and experimental FNRs are 47.2% and 44.9%. CONCLUSION: This probability model is a useful tool to predict the impact of aliquot volume on the LOD and reproducibility of clinical assays. Even for samples for which pathogens are homogeneously distributed, it is theoretically impossible to collect a single pathogen consistently if the concentration of pathogen is below a certain limit.
Authors: Patricia M Agreda; Gerard H Beitman; Erin C Gutierrez; James M Harris; Kristopher R Koch; William D LaViers; Sharon V Leitch; Courtney E Maus; Ray A McMillian; William A Nussbaumer; Marcus L R Palmer; Michael J Porter; Gregory A Richart; Ryan J Schwab; Laurence M Vaughan Journal: J Clin Microbiol Date: 2013-05-15 Impact factor: 5.948