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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2273-2280 |
| Number of pages | 8 |
| Journal | Clinical Chemistry and Laboratory Medicine |
| Volume | 51 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2013 |
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Keywords
- Clinical assay classification
- Clinical measurement uncertainty
- Uncertainty modeling
ASJC Scopus subject areas
- Clinical Biochemistry
- Biochemistry, medical
Cite this
Category-specific uncertainty modeling in clinical laboratory measurement processes. / Ramamohan, Varun; Yih, Yuehwern; Abbott, James T.; Klee, George G.
In: Clinical Chemistry and Laboratory Medicine, Vol. 51, No. 12, 01.12.2013, p. 2273-2280.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Category-specific uncertainty modeling in clinical laboratory measurement processes
AU - Ramamohan, Varun
AU - Yih, Yuehwern
AU - Abbott, James T.
AU - Klee, George G.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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.
AB - 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.
KW - Clinical assay classification
KW - Clinical measurement uncertainty
KW - Uncertainty modeling
UR - http://www.scopus.com/inward/record.url?scp=84889560936&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889560936&partnerID=8YFLogxK
U2 - 10.1515/cclm-2013-0357
DO - 10.1515/cclm-2013-0357
M3 - Article
C2 - 23969469
AN - SCOPUS:84889560936
VL - 51
SP - 2273
EP - 2280
JO - Clinical Chemistry and Laboratory Medicine
JF - Clinical Chemistry and Laboratory Medicine
SN - 1434-6621
IS - 12
ER -