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Full Length Article| Volume 224, P32-37, April 2023

Estimating the measurement uncertainties of the international sensitivity index of 12 thromboplastins through Monte Carlo simulation

Published:February 13, 2023DOI:https://doi.org/10.1016/j.thromres.2023.02.007

      Highlights

      • Measurement uncertainty (MU) estimation has become an important process in clinical laboratories.
      • Complex mathematical calculations made it difficult to estimate the MU of international sensitivity index (ISI).
      • Monte Carlo simulation (MCS) is one of the alternatives for evaluating the MU.
      • We demonstrated that MCS is adequate to estimate the MU of ISI.

      Abstract

      Background

      Measurement uncertainty (MU) estimation has become an important process in clinical laboratories; however, calculating the MUs of the international sensitivity index (ISI) of thromboplastins is difficult because of the complex mathematical calculations required in calibration. Therefore, this study quantifies the MUs of ISIs through the Monte Carlo simulation (MCS), which involves random sampling of numerical values to solve a complex mathematical calculation.

      Methods

      Eighty blood plasmas and commercially available certified plasmas (ISI Calibrate) were used to assign the ISIs of each thromboplastin. Prothrombin times were measured using reference thromboplastin and 12 commercially available thromboplastins (Coagpia PT-N, PT Rec, ReadiPlasTin, RecombiPlasTin 2G, PT-Fibrinogen, PT-Fibrinogen HS PLUS, Prothrombin Time Assay, Thromboplastin D, Thromborel S, STA-Neoplastine CI Plus, STA-Neoplastine R 15, and STA-NeoPTimal) with two automated coagulation instruments: ACL TOP 750 CTS (ACL TOP; Instrumentation Laboratory, Bedford, MA, USA) and STA Compact (Diagnostica Stago, Asnières-sur-Seine, France). Then, the MUs of each ISI were simulated through MCS.

      Results

      The MUs of ISIs ranged from 9.7 % to 12.1 % and 11.6 % to 12.0 % when blood plasma and ISI Calibrate were used, respectively. For some thromboplastins, the ISI claimed by manufacturers significantly differed from the estimated results.

      Conclusions

      MCS is adequate to estimate the MUs of ISI. These results would be clinically useful for estimating the MUs of the international normalized ratio in clinical laboratories. However, the claimed ISI significantly differed from the estimated ISI of some thromboplastins. Therefore, manufacturers should provide more accurate information about the ISI value of thromboplastins.

      Keywords

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      References

        • Lim Y.K.
        • Kweon O.J.
        • Lee M.K.
        • Kim B.
        • Kim H.R.
        Top-down and bottom-up approaches for the estimation of measurement uncertainty in coagulation assays.
        Clin. Chem. Lab. Med. 2020; 58: 1525-1533
        • Eurachem/CITAC
        Eurachem/CITAC Guide: Quantifying Uncertainty in Analytical Measurement.
        Third edition. Eurachem/CITAC, Gembloux, Belgium2012
        • Lim Y.K.
        • Kweon O.J.
        • Lee M.-K.
        • Kim H.R.
        Estimation of measurement uncertainty of factor assays using the Monte Carlo simulation.
        Am. J. Clin. Pathol. 2021; 156: 717-721
        • JCGM
        Evaluation of Measurement Data – Guide to the Expression of Uncertainty in Measurement.
        JCGM, Paris, France2008
        • Farrance I.
        • Frenkel R.
        Uncertainty in measurement: a review of Monte Carlo simulation using Microsoft Excel for the calculation of uncertainties through functional relationships, including uncertainties in empirically derived constants.
        Clin. Biochem. Rev. 2014; 35: 37
        • Boyd J.C.
        • Bruns D.E.
        Monte Carlo simulation in establishing analytical quality requirements for clinical laboratory tests: meeting clinical needs.
        Methods Enzymol. 2009; 467: 411-433
        • Ramamohan V.
        • Chandrasekar V.
        • Abbott J.
        • Klee G.G.
        • Yih Y.
        A Monte Carlo approach to the estimation & analysis of uncertainty in clinical laboratory measurement processes.
        IISE Trans. Healthc. Syst. 2012; 2: 1-13
        • Papadopoulos C.E.
        • Yeung H.
        Uncertainty estimation and Monte Carlo simulation method.
        Flow Meas. Instrum. 2001; 12: 291-298
        • Qin Y.
        • Zhou R.
        • Wang W.
        • Yin H.
        • Yang Y.
        • Yue Y.
        • et al.
        Uncertainty evaluation in clinical chemistry, immunoassay, hematology and coagulation analytes using only external quality assessment data.
        Clin. Chem. Lab. Med. 2018; 56: 1447-1457
        • Padoan A.
        • Antonelli G.
        • Aita A.
        • Sciacovelli L.
        • Plebani M.
        An approach for estimating measurement uncertainty in medical laboratories using data from long-term quality control and external quality assessment schemes.
        Clin. Chem. Lab. Med. 2017; 55: 1696-1701
        • Sobas F.
        • Benattar N.
        • Bellisario A.
        • Marin S.
        • Nougier C.
        • Lienhart A.
        • et al.
        Impact of quality control matrix effect: application to the calculation of uncertainty of measurement in one-stage clotting factor VIII assay.
        Blood Coagul. Fibrinolysis. 2010; 21: 498-501
        • Riley R.S.
        • Rowe D.
        • Fisher L.M.
        Clinical utilization of the international normalized ratio (INR).
        J. Clin. Lab. Anal. 2000; 14: 101-114
        • WHO
        WHO Expert Committee on Biological Standardization, Sixty-Second Report. (WHO Technical Report Series ; No. 979). WHO, Geneva, Switzerland2013: 271-316
        • Dorgalaleh A.
        • Favaloro E.J.
        • Bahraini M.
        • Rad F.
        Standardization of prothrombin time/international normalized ratio (PT/INR).
        Int. J. Lab. Hematol. 2021; 43: 21-28
        • JCGM
        International Vocabulary of Metrology – Basic and General Concepts and Associated Terms (VIM).
        3rd edition. JCGM, Paris, France2012
        • CLSI
        Expression of Measurement Uncertainty in Laboratory Medicine; Approved Guideline. CLSI Document EP29-A.
        Clinical and Laboratory Standards Institute, Wayne, PA2012
        • CLSI
        Procedures for Validation of INR and Local Calibration of PT/INR Systems; Approved Guideline. CLSI Document H54-A.
        Clinical and Laboratory Standards Institute, Wayne, PA2005
        • CLSI
        Evaluation of Precision of Quantitative Measurement Procedures; Approved Guideline—Third Edition. CLSI Document EP05-A3.
        Clinical and Laboratory Standards Institute, Wayne, PA2014
        • Sadler W.A.
        Imprecision profiling.
        Clin. Biochem. Rev. 2008; 29: S33-S36
      1. Package ‘VFP’.
      2. Package ‘ggplot2’.
        (Accessed July 2022)
        • van den Besselaar A.M.
        • Witteveen E.
        • van der Meer F.J.
        Uncertainty of international sensitivity index and international normalized ratio.
        J. Thromb. Haemost. 2013; 11: 1615-1617
        • van den Besselaar A.
        • Chantarangkul V.
        • Angeloni F.
        • Binder N.B.
        • Byrne M.
        • Dauer R.
        • et al.
        International collaborative study for the calibration of proposed international standards for thromboplastin, rabbit, plain, and for thromboplastin, recombinant, human, plain.
        J. Thromb. Haemost. 2018; 16: 142-149
        • Ng V.L.
        Prothrombin time and partial thromboplastin time assay considerations.
        Clin. Lab. Med. 2009; 29: 253-263
        • Aral H.
        • Usta M.
        • Cilingirturk A.M.
        • Inal B.B.
        • Bilgi P.T.
        • Guvenen G.
        Verifying reference intervals for coagulation tests by using stored data.
        Scand. J. Clin. Lab. Invest. 2011; 71: 647-652