Klee, George G.

Background: We used simulation modeling to relate glucose meter performance criteria to insulin dosing errors for patients on a moderate glycemic control protocol (glucose target, 110–150 mg/dL) and empirically validated assumptions from simulation models using observed glucose meter and laboratory glucose values obtained nearly simultaneously. Subjects and Methods: The 25,948 glucose values from 1,513 patients on a moderate glycemic control protocol were used to represent the expected distribution of glucose values in this patient population. Simulation models were used to relate glucose meter analytical performance to insulin dosing errors assuming 10%, 15%, or 20% total allowable error (TEa). In addition, 4,017 paired glucose meter and serum laboratory glucose measurements drawn within 5 min of each other were used to generate an empiric dataset to validate simulation model assumptions relating glucose meter performance to insulin dosing errors. Results: Large (three or more category) insulin dosing errors are predicted to occur only under the 20% TEa condition. Two category insulin dosing errors were common (6–20% of all insulin dosing decisions) when 20% TEa was assumed, but frequency decreased to only 0.2% of dosing decisions when 10% TEa was modeled. When insulin dosing error rates were measured empirically by comparing paired glucose meter and laboratory glucose values, insulin dosing error rates were very similar to those predicted for the 20% TEa condition. Conclusions: Both simulation models and empiric data demonstrate that glucose meters that perform at ≥20% TEa allow large insulin dosing errors during a moderate glycemic control protocol.

Abstract Background: A statement of measurement uncertainty describes the quality of a clinical assay analysis result, and uncertainty models of clinical assays can be used to evaluate and optimize laboratory protocols designed to minimize the measurement uncertainty associated with an assay. In this study, we propose a methodology to lend systematic structure to the uncertainty modeling process. Methods: Clinical laboratory assays are typically classified based on the chemical reaction involved, and therefore, based on the assay analysis methodology. We use this fact to demonstrate that uncertainty models for assays within the same category are structurally identical in all respects except for the values of certain model parameters. This is accomplished by building uncertainty models for assays belonging to two categories – substrate assays based on optical absorbance analysis of endpoint reactions, and ion selective electrode (ISE) assays based on potentiometric measurements of electromotive force. Results: Uncertainty models for the substrate assays and the ISE assays are built, and for each category, a general mathematical framework for the uncertainty model is developed. The parameters of the general framework that vary from assay to assay for each category are identified and listed. Conclusions: Estimates of measurement uncertainty from the models were compared with estimates of uncertainty from quality control data from the clinical laboratory. We demonstrate that building a general modeling framework for each assay category and plugging in parameter values for each assay is sufficient to generate uncertainty models for an assay within a given category.

Abstract Although manufacturers are compelled by the European IVD Directive, 98/79/EC, to have traceability of the values assigned to their calibrators if suitable higher order reference materials and/or procedures are available, there is still no equivalence of results for many measurands determined in clinical laboratories. The adoption of assays with metrological traceable results will have a significant impact on laboratory medicine in that results will be equivalent across different laboratories and different analytical platforms. The IFCC WG on Allowable Errors for Traceable Results has been formed to define acceptable limits for metrological traceability chains for specific measurands in order to promote the equivalence of patient results. These limits are being developed based on biological variation for the specific measurands. Preliminary investigations have shown that for some measurands, it is possible for manufacturers to assign values to assay calibrators with a measurement uncertainty that allows the laboratory enough combined uncertainty for their routine measurements. However, for other measurands, e.g., plasma sodium, current assays are too imprecise to fulfil limits based on biological variation. Although an alternative approach based on probability theory is being investigated, the most desirable approach would be for industry to improve measurement methods so that they meet clinical requirements.

Abstract DNA methylation profiles could signify the risk of prostate cancer and predict the progression of cancer before the appearance of clinical symptoms. Specific CpG sites methylation of genes identified in our study could be used to improve the sensitivity and specificity for early diagnosis and to predict the risk of prostate cancer recurrence. To identify methylation changes associated with prostate cancer, we measured gene methylation using the Infinium Human Methylation27 bead chips. Tissue DNA isolation and bisulfite conversion was done using ZR Genomic DNA kit and EZ methylation kit. The methylation27 microarrays was used to analyze methylation of 27,578 CpG sites of 14,495 genes in 198 tumor tissues and 40 matched normal tissues. Methylation data was processed using the Genome Studio software, differentially methylated genes identified using one-way analysis of variance, and correction for multiple testing done using the false discovery rate q-value. To identify putative diagnostic and prognostic biomarkers we performed four subgroup comparisons: tumor vs. matched normal, recurrent tumors vs. non-recurrent tumors, clinical recurrence vs. biochemical recurrence, and systemic recurrence vs. local recurrence. Clinicopathological and molecular features including patient preoperative PSA levels, Gelason score, TNM stage, GPSM, recurrence type, were also evaluated for subgroup discrimination. Discriminative subgroup candidate methlyation markers were selected using a p value <0.05 and a mean difference between two contrast groups >0.05. Between the tumor and matched normal tissue samples, 164 CpG sites representing 147 genes were identified as significantly differentially methylated (p<9.99E-25, fold change>2.0). Between recurrent tumor and non-recurrent tumor tissues there were 78 CpG sites representing 75 genes that were significantly differentially methylated (p <0.002, fold change >1.5). Between clinically recurrent and biochemically recurrent tumors, 16 CpG sites representing 16 genes were significantly differentially methylated (p <0.05, fold change >1.5). Between systemic recurrent and local recurrent tumors there were 73 CpG sites representing 68 genes that were significantly differentially methylated (p <0.01, fold change >1.5) Our analysis revealed differential methylation of the genes, implicating their role in prostate cancer development and progression. We demonstrated that the hypermethylation of genes could be used as a sensitive molecular tool in detection of prostate tumorigenesis and prediction of tumor progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 109. doi:10.1158/1538-7445.AM2011-109

Abstract Background: Laboratory protocols used to calibrate commercial clinical chemistry systems affect test result quality. Mathematical models of system uncertainty can be developed using performance parameters provided by the manufacturer for various subsystems. These models can be used to evaluate protocols for specific laboratory operations. Methods: A mathematical model was developed to estimate the uncertainty inherent in the Roche Diagnostics P-Modular system, and included uncertainties associated with the sample and reagent pipettes, spectrometer and the calibration process. The model was then used to evaluate various alternate calibration protocols: calibration based on mean of replicate measurements (n=1–6) and calibration based on conditional acceptance when the following quality control specimen was within one standard deviation of target. The effect of calibrator concentrations on assay measurement uncertainty was also studied, and calibrator concentrations that minimize uncertainty at a specific concentration were identified. Results: The simulation model produced uncertainty estimates of 3.5% for the serum cholesterol assay and identified sample pipette (40%) and spectrometer (21%) as the largest contributors to measurement uncertainty. Each additional replicate calibrator measurements result in diminishing reductions in measurement uncertainty, with maximum reductions (19%) achieved with five replicate measurements. The conditional acceptance of calibration only when the control was within 1s resulted in an 18% reduction. Conclusions: The model can be used to evaluate the utility of laboratory protocols and establish realistic assay performance targets. The model also can help instrument manufacturers and laboratorians identify major contributors to assay measurement uncertainty, which helps improve performance in future assay systems.

Abstract Purpose: The aim of this study was to investigate the promoter hypermethylation as diagnostic markers to detect malignant prostate cells and as prognostic markers to predict the clinical recurrence of prostate cancer. Experimental Design: DNA was isolated from prostate cancer and normal adjacent tissues. After bisulfite conversion, methylation of 14,495 genes was evaluated using the Methylation27 microarrays in 238 prostate tissues. We analyzed methylation profiles in four different groups: (i) tumor (n = 198) versus matched normal tissues (n = 40), (ii) recurrence (n = 123) versus nonrecurrence (n = 75), (iii) clinical recurrence (n = 80) versus biochemical recurrence (n = 43), and (iv) systemic recurrence (n = 36) versus local recurrence (n = 44). Group 1, 2, 3, and 4 genes signifying biomarkers for diagnosis, prediction of recurrence, clinical recurrence, and systemic progression were determined. Univariate and multivariate analyses were conducted to predict risk of recurrence. We validated the methylation of genes in 20 independent tissues representing each group by pyrosequencing. Results: Microarray analysis revealed significant methylation of genes in four different groups of prostate cancer tissues. The sensitivity and specificity of methylation for 25 genes from 1, 2, and 4 groups and 7 from group 3 were shown. Validation of genes by pyrosequencing from group 1 (GSTP1, HIF3A, HAAO, and RARβ), group 2 (CRIP1, FLNC, RASGRF2, RUNX3, and HS3ST2), group 3 (PHLDA3, RASGRF2, and TNFRSF10D), and group 4 (BCL11B, POU3F3, and RASGRF2) confirmed the microarray results. Conclusions: Our study provides a global assessment of DNA methylation in prostate cancer and identifies the significance of genes as diagnostic and progression biomarkers of prostate cancer. Clin Cancer Res; 18(10); 2882–95. ©2012 AACR.