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- Aakre, K. M., et al.
(författare)
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Analytical Considerations in Deriving 99th Percentile Upper Reference Limits for High-Sensitivity Cardiac Troponin Assays: Educational Recommendations from the IFCC Committee on Clinical Application of Cardiac Bio-Markers
- 2022
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Ingår i: Clinical chemistry. - : Oxford University Press (OUP). - 0009-9147 .- 1530-8561. ; 68:8, s. 1022-1030
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Tidskriftsartikel (refereegranskat)abstract
- The International Federation of Clinical Chemistry Committee on Clinical Application of Cardiac Bio-Markers provides evidence-based educational documents to facilitate uniform interpretation and utilization of cardiac biomarkers in clinical laboratories and practice. The committee's goals are to improve the understanding of certain key analytical and clinical aspects of cardiac biomarkers and how these may interplay in clinical practice. Measurement of high-sensitivity cardiac troponin (hs-cTn) assays is a cornerstone in the clinical evaluation of patients with symptoms and/or signs of acute cardiac ischemia. To define myocardial infarction, the Universal Definition of Myocardial Infarction requires patients who manifest with features suggestive of acute myocardial ischemia to have at least one cTn concentration above the sex-specific 99th percentile upper reference limit (URL) for hs-cTn assays and a dynamic pattern of cTn concentrations to fulfill the diagnostic criteria for MI. This special report provides an overview of how hs-cTn 99th percentile URLs should be established, including recommendations about prescreening and the number of individuals required in the reference cohort, how statistical analysis should be conducted, optimal preanalytical and analytical protocols, and analytical/biological interferences or confounds that can affect accurate determination of the 99th percentile URLs. This document also provides guidance and solutions to many of the issues posed.
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| 3. |
- Loughman, Tony, et al.
(författare)
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Analytical Validation of a Novel 6-Gene Signature for Prediction of Distant Recurrence in Estrogen Receptor-Positive, HER2-Negative, Early-Stage Breast Cancer
- 2022
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Ingår i: Clinical Chemistry. - : Oxford University Press (OUP). - 0009-9147 .- 1530-8561. ; 68:6, s. 837-847
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundOncoMasTR is a recently developed multigene prognostic test for early-stage breast cancer. The test has been developed in a kit-based format for decentralized deployment in molecular pathology laboratories. The analytical performance characteristics of the OncoMasTR test are described in this study.MethodsExpression levels of 6 genes were measured by 1-step reverse transcription-quantitative PCR on RNA samples prepared from formalin-fixed, paraffin-embedded (FFPE) breast tumor specimens. Assay precision, reproducibility, input range, and interference were determined using FFPE-derived RNA samples representative of low and high prognostic risk scores. A pooled RNA sample derived from 6 FFPE breast tumor specimens was used to establish the linear range, limit of detection, and amplification efficiency of the individual gene expression assays.ResultsThe overall precision of the OncoMasTR test was high with an SD of 0.16, which represents less than 2% of the 10-unit risk score range. Test results were reproducible across 4 testing sites, with correlation coefficients of 0.94 to 0.96 for the continuous risk score and concordance of 86% to 96% in low-/high-risk sample classification. Consistent risk scores were obtained across a > 100-fold RNA input range. Individual gene expression assays were linear up to quantification cycle values of 36.0 to 36.9, with amplification efficiencies of 80% to 102%. Test results were not influenced by agents used during RNA isolation, by low levels of copurified genomic DNA, or by moderate levels of copurified adjacent nontumor tissue.ConclusionThe OncoMasTR prognostic test displays robust analytical performance that is suitable for deployment by local pathology laboratories for decentralized use.
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- Ognissanti, Damiano, et al.
(författare)
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Estimating Analytical Errors of Glomerular Filtration Rate Measurement
- 2022
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Ingår i: Clinical Chemistry. - : Oxford University Press (OUP). - 0009-9147 .- 1530-8561. ; 68:9, s. 1211-1218
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Tidskriftsartikel (refereegranskat)abstract
- Background Few studies are available on how to optimize time points for sampling and how to estimate effects of analytical uncertainty when glomerular filtration rate (GFR) is calculated. Methods We explored the underlying regression mathematics of how analytical variation of a kidney filtration marker affects 1-compartment, slope-and-intercept GFR calculations, using 2 or 3 time points following a bolus injection, and used this to examine the results from 731 routine 3-point iohexol plasma clearance measurements. Results GFR calculations inflated analytical uncertainty if the time points were taken too late after the bolus injection and too close after each other. The uncertainty in GFR calculation was, however, the same as the analytical uncertainty if optimal time points were used. The middle of the 3 samples was of little value. The first sample should be taken as early as possible after the distribution phase. Sampling before the patient specific half-life of the kidney filtration marker resulted in an exponential error inflation whereas no error inflation was seen when sampling occurred later than 2 half-lives. Theoretical GFR uncertainty could be lowered 2.6-fold if individually optimized time points for sampling had been used in our 731 clearance measurements. Using Taylor expansions to approximate the moments of transformed random variables, the uncertainty of an individual GFR measurement could be calculated in a simple enough way to be applicable by laboratory software. Conclusions We provide a theoretical foundation to select patient-optimal time points that may both limit errors and allow calculation of GFR uncertainty.
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| 7. |
- Österlund, Tobias, 1984, et al.
(författare)
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UMIErrorCorrect and UMIAnalyzer: Software for Consensus Read Generation, Error Correction, and Visualization Using Unique Molecular Identifiers
- 2022
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Ingår i: Clinical Chemistry. - : Oxford University Press (OUP). - 0009-9147 .- 1530-8561. ; 68:11, s. 1425-1435
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Tidskriftsartikel (refereegranskat)abstract
- Background Targeted sequencing using unique molecular identifiers (UMIs) enables detection of rare variant alleles in challenging applications, such as cell-free DNA analysis from liquid biopsies. Standard bioinformatics pipelines for data processing and variant calling are not adapted for deep-sequencing data containing UMIs, are inflexible, and require multistep workflows or dedicated computing resources. Methods We developed a bioinformatics pipeline using Python and an R package for data analysis and visualization. To validate our pipeline, we analyzed cell-free DNA reference material with known mutant allele frequencies (0%, 0.125%, 0.25%, and 1%) and public data sets. Results We developed UMIErrorCorrect, a bioinformatics pipeline for analyzing sequencing data containing UMIs. UMIErrorCorrect only requires fastq files as inputs and performs alignment, UMI clustering, error correction, and variant calling. We also provide UMIAnalyzer, a graphical user interface, for data mining, visualization, variant interpretation, and report generation. UMIAnalyzer allows the user to adjust analysis parameters and study their effect on variant calling. We demonstrated the flexibility of UMIErrorCorrect by analyzing data from 4 different targeted sequencing protocols. We also show its ability to detect different mutant allele frequencies in standardized cell-free DNA reference material. UMIErrorCorrect outperformed existing pipelines for targeted UMI sequencing data in terms of variant detection sensitivity. Conclusions UMIErrorCorrect and UMIAnalyzer are comprehensive and customizable bioinformatics tools that can be applied to any type of library preparation protocol and enrichment chemistry using UMIs. Access to simple, generic, and open-source bioinformatics tools will facilitate the implementation of UMI-based sequencing approaches in basic research and clinical applications.
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