Evaluation of Imprecision, Bias and Total Error of Clinical Chemistry Analysers

Biswas, Shatarupa; Bindra, M.; Jain, Vaishali; Gokhale, Paul J.

doi:10.1007/s12291-014-0448-y

Cited by 17 publications

(13 citation statements)

References 7 publications

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“…Other study 6 but on two different analyzers showed Creatinine assay's (method not specified) TAE of 7.9 and 9.6 respectively. Another study 7 with pooled frozen serum of higher than normal creatinine levels showed error within minimal specifications.…”

Section: Resultsmentioning

confidence: 92%

Performance verification and sigma metrics of creatinine assays

2020

IJCBR

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Introduction: Creatinine enzymatic method is more accurate but its own higher cost is the main reason why the use of Jaffe assays is still in practice. The verification step is performed because assay procedure confirms to the manufacturer's claims in the end users' setup. Desirable specification based on biological variation gives Allowable Total Error (ATE) and Sigma metrics provides summative evaluation of method performance. Objectives: Verification of manufacturer's precision claims. Calculation of bias, Total Analytical Error (TAE) and sigma metrics. Materials and Methods: Modified Jaffe's (No deproteinization) and enzymatic creatinine Erba XL liquid pack on Erba EM360 fully auto analyser were used. Intra assay and inter assay imprecision done on 20 replicates each. The estimates of bias uses data from precision experiment. Results & Discussion:The performance of jaffe's and enzymatic serum creatinine is comparable to manufacturer's stated claimed value and are within desirable specification based on biological variation. The bias calculated in both assays is less than allowable bias. Total Analytical Error (TAE) is less than Allowable Total Error (ATE). Both the methods overall showed ATE ≥ bias + 4 SD. Within-run showed ATE ≥ bias + 5 SD complying Six Sigma concepts. Conclusion: Both Jaffe's and enzymatic creatinine assays gives good performance and better sigma metrics. This makes enzymatic method a better choice due to inherent advantages such as increased accuracy and less interference if cost is neglected.

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Section: Resultsmentioning

confidence: 92%

Performance verification and sigma metrics of creatinine assays

2020

IJCBR

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“…This "multi-source" data set consists of 150 remaining entries. Except slightly less populated margin areas, the histogram of the multi-source data is similar to the full-data histogram (compared in [16] [18,19] were derived from biological variations (see [20,21]). The 95% confidence intervals of λ based on the multi-source dataset are 2.23±0.5 (for κ=2.33) and 2.0±0.4 (for κ=1.96).…”

Section: The Relation Between Optimal Limits For Smc and Ramentioning

confidence: 99%

“…For example, the limits given in the German guideline (Rili-BAEK, table B1a-c column 3, in relative values [15]) can be considered as sole LSMC data [16]. In contrast to the determination of LΔ, the definition of LSMC is usually done empirically at a certain significance level and/or related to a fraction of the biological variation of the analyte [20,21]. The determination of LSMC might also base on Eq.…”

Section: Smart Utilizing the Limit Dedicated For Single Measures (Lsmc)mentioning

confidence: 99%

The Statistical Monitoring by Adaptive RMSTD Tests: an efficient, informative, and customizable method for the complete internal quality control intended for low-frequent sampling of control measures

Beier¹

2020

Preprint

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Two control mechanisms are relevant to perform an internal quality assurance: a permissible limit L_SMC applied to single measures of control samples and a retrospective statistical analysis to detect increased imprecision and baseline drifts. A common statistical metric is the root mean square (total) deviation (RMSD/RMSTD). To focus on recent changes under low-frequent sampling conditions, the monitored amount of retrospective data is usually very small. Unfortunately, the calculated RMSTD of a small data set with n<50 samples has a significant statistical uncertainty that needs to be considered in adequate limit definitions. In particular, the minimum reasonable limit L_RMSTD(n), applied to the RMSTD of a series of n samples, decreases from L_SMC (e.g., 2.33*standard_deviation+bias) for n=1 towards L_true_RMSTD for n→∞ (long-term statistics). Two mathematical approaches were derived to reliably estimate an optimal function to adjust L_RMSTD(n) to small sample sizes. This knowledge led to the development of a new quality-control method: the Statistical Monitoring by Adaptive RMSTD Tests (SMART). SMART requires just one mandatory limit (either L_SMC or L_true_RMSTD) per analyte. By definition of up to 7 possible alert levels, SMART can early recognize and evaluate both the significance of a single outlier and establishing critical trends or shifts in recent SMC data. SMART is intended to efficiently monitor and evaluate small amounts of control data.

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“…In addition, all entries by Ricos, which base on more than one reference source, are again analyzed separately, due to the assumption that these entries represent well-studied techniques to a greater extent. Biases and imprecisions by Ricos et al are derived from biological variations of each analyte (see [9,10]). Thus, they usually represent higher limits compared to the state-of-the-art limits of uncertainty.…”

Section: Common Vs Different Limits For Smc and Ramentioning

confidence: 99%

“…With regard to the entire spectrum of analytes in Ricos et al and a large decision flexibility in κ (1.65-2.33), λ would have a maximum uncertainty of 2.00 ± 0.45. This extended uncertainty, mainly originating from differences in biological variation [9,10], would be too liberal for limits based on state-of-the-art.…”

Section: Common Vs Different Limits For Smc and Ramentioning

confidence: 99%

Recommended changes of the current version of the German Rili-BAEK

Beier¹

2019

Journal of Laboratory Medicine

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Abstract A number of improvement proposals and corrections of the German Rili-BAEK (Guideline of the German Medical Association on Quality Assurance in Medical Laboratory Examinations) are discussed with special focus on the internal and external quality assurance (IQA/EQA) as well as reference intervals for quantitative results. Particular attention is paid to reconsider the retrospective analysis of control measurements. Such an analysis can be very useful to monitor establishing errors of measurement even before they become critical. The present method “Quadratischer Mittelwert der Messabweichung (QMMA)” has proved to be ineffective. Furthermore, the current idea of a common limit for single control measures and the retrospective statistics must be revised. As a more sophisticated concept, the novel Adaptive Retrospective Monitoring (ARM) has been developed. ARM is recommended as the new minimum requirement for the entire internal quality assurance. Further proposals to enhance clarity are given concerning the release decisions of medical devices and the EQA. Individualized medicine begins with a patient-specific interpretation of analytic results. This requires standardized subgroup-specific reference intervals with smooth age-related adaptations. Only large laboratories are able to ensure the desired specificity and a sufficient statistical significance of self-developed in-laboratory reference intervals. Hence, the need of a central database for harmonized reference intervals is discussed and recommended. Suitable and consistent reference intervals are also an essential prerequisite for unitless laboratory values like the zlog value.

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Evaluation of Imprecision, Bias and Total Error of Clinical Chemistry Analysers

Cited by 17 publications

References 7 publications

Performance verification and sigma metrics of creatinine assays

Performance verification and sigma metrics of creatinine assays

The Statistical Monitoring by Adaptive RMSTD Tests: an efficient, informative, and customizable method for the complete internal quality control intended for low-frequent sampling of control measures

Recommended changes of the current version of the German Rili-BAEK

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