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Alternative for reducing calibration standard use in mass spectrometry.

To the Editor:

Recent publications have discussed alternative calibration strategies for quantitative clinical mass spectrometry and proposed terminology for these approaches (1-3). Like Grant (3), we believe that mass spectrometry laboratories generally use more calibration standards than necessary. We also note that conventional calibration methods fail to use all information available from recent calibrations. Preparation of calibration curves is often more an exercise in curve preparation than a useful evaluation of patient sample accuracy and imprecision, which is best evaluated by QC samples prepared identically to patient samples.

Here we report a calibration strategy using a single calibrator generating a provisional response factor (PRF), [1] which then is compared to a historical response factor (HRF). If, based on predetermined rules, agreement between the two is acceptable, then a current response factor (CRF) is calculated:

CRF = W X HRF + PRF - W

X PRF, (1)

where Wrepresents a weighting factor. Unacceptable agreement between PRF and HRF triggers a mitigation strategy. In most such cases, bringing the previous CRF forward gives acceptable accuracy. This corresponds to temporarily setting W= 1 in Eq. (1). Once CRF is determined it is used for quantification of the current run, and the HRF is updated:

HRF = CRF. (2)

Patient sample acceptance is based on QC results of control samples included in each batch.

This method uses both historical and current information to generate a best estimate for the calibration parameter, CRF. This scheme effectively performs signal averaging, controlled by a weighting factor, W, which governs how much historical vs current information is used for calibration. Choosing a large number for Wputs an emphasis on the historical information, which stabilizes the CRF against random statistical fluctuations in the process.

An advantage of this method is that the HRF and CRF values are allowed to adjust over time because they are updated using PRF in Eqs. (1) and (2). This compensates for slow process changes, such as degradation of internal standard (IS) stock solution concentration. Regarding more rapid changes, Pauwels et al. (1) point out that bad IS preparation contributed substantially to the variance obtained in their approach. With our strategy, the introduction of a bad lot of IS may initially produce erroneous, possibly unacceptable QC results if the change of IS concentration is large, but over a period of several runs the method will self-correct. If Wis large (e.g., 0.9), it takes more time to adjust to new values of HRF and CRF. These errors can be minimized by replacing the IS solution or by other methods alluded to by both Olson et al. (2) and Pauwels et al. (1).

After trying several weighting factors (e.g., 0.5 and 0.9), we empirically found by subjective judgment that W = 0.75 provided a good compromise between stabilizing the process against random variations and the time to self-adjust after sudden unexpected systematic process changes. In both theory and practice, the dampening effect provided an improvement in the imprecision obtained (Table 1).

This algorithm presumes knowledge of a HRF and is therefore not self-starting. An initial HRF can be determined using a conventional multipoint calibration.

Analyzing many runs, we have found that patient sample batches may be unnecessarily rejected due purely to a bad calibration curve. Our strategy allows such batches to be processed and judged by acceptability of QC samples. We find use of the weighted mean RF can provide not only better imprecision but more accurate sample determinations as well. Traditional multipoint calibration can be performed every 6 months, to verify method linearity, as is standard for analytical measurement range evaluation. In some situations, such as adjustment of collision energy, it may be necessary to reestablish and reset the HRF. One can also adapt the method in Rule et al. (4) to certain nonlinear calibration schemes that can be characterized by a response factor.

CLSI document C43-A2 (5) suggests that historical calibration curves may be used if they are shown to be linear over time. At least 2 standards are suggested for each batch of samples with 1 standard at the threshold concentration. In our approach, we extract and analyze a sample at the limit of quantification before each run to verify acceptable method and instrument performance. Interestingly, in high-volume core laboratories it is common to use just 2 calibration standards to verify or make adjustments to a multipoint calibration. For example, 2 calibration standards may be evaluated just once every 7-28 days (Siemens, ADVIA Centaur package inserts).

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafingor revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: A.L. Rockwood, ARUP Laboratories.

Expert Testimony: None declared.

Patents: G.S. Rule, patent no. 14/207,346.

References

(1.) Pauwels S, Peersman N, Gerits M, Desmet K, Vermeersch P. Response factor-based quantification for mycophenolic acid. Clin Chem 2014;60:692-4.

(2.) Olson MT, Breaud A, Harlan R, Emezienna N, Schools S, Yergey AL, Clarke W. Alternative calibration strategies for the clinical laboratory: application to nortriptyline therapeutic drug monitoring. Clin Chem 2013;59:920-7.

(3.) Grant RP. The march of the masses. Clin Chem 2013; 59:871-3.

(4.) Rule GS, Clark ZD, Yue B, Rockwood AL. Correction for isotopic interferences between analyte and internal standard in quantitative massspectrometry by a nonlinear calibration function. Anal Chem 2013;85: 3879-85.

(5.) Gas chromatography/mass spectrometry confirmation of drugs; approved guideline, 2nd ed. Wayne (PA): CLSI; 2010. Document No. C43-A2.

Geoffrey S. Rule [2]

Alan L. Rockwood [2,3] *

[1] Nonstandard abbreviations: PRF, provisional response factor; HRF, historical response factor; CRF, current response factor; IS, internal standard.

[2] Institute for Clinical and Experimental Pathology ARUP Laboratories Salt Lake City, UT

[3] Department of Pathology University of Utah School of Medicine Salt Lake City, UT

* Address correspondence to this author at: ARUP Laboratories 500 Chipeta Way Salt Lake City, UT 84108 Fax 801-584-5207 E-mail alan.rockwood@aruplab.com

Previously published online at DOI: 10.1373/clinchem.2014.229880
Table 1. Comparison of QC means, CVs, and bias, along
with patient sample comparisons for 3 analytes by both
conventional and alternative calibration approaches. (a)

QC (n = 64)             Androstenedione, ng/mL
                                (CV, %)

Low
  Conventional               0.147 (6.88)
  Alternative                 0.143(6.57)
  Bias, %                        -2.80
High
  Conventional                2.44 (4.74)
  Alternative                 2.37 (4.40)
  Bias, %                        -2.95
Deming regression
  n                               355
  Slope (95% CI)          0.992(0.989-0.995)
  Intercept (95% CI)   -0.010 (-0.014 to -0.005)
[S.sub.y|x]                      0.033

QC (n = 64)             DHEA, ng/mL (CV, %) (b)

Low
  Conventional               0.141 (8.75)
  Alternative                 0.139(8.49)
  Bias, %                        -1.44
High
  Conventional                2.36 (7.77)
  Alternative                 2.33(6.91)
  Bias, %                        -1.29
Deming regression
  n                               758
  Slope (95% CI)          1.006(1.003-1.008)
  Intercept (95% CI)   -0.061 (-0.075 to -0.046)
[S.sub.y|x]                      0.175

QC (n = 64)              Testosterone, ng/dL
                               (CV, %)

Low
  Conventional                12.0(7.73)
  Alternative                 11.6(6.30)
  Bias, %                       -3.45
High
  Conventional                241 (6.43)
  Alternative                 234(5.11)
  Bias, %                       -2.99
Deming regression
  n                              872
  Slope (95% CI)         0.976 (0.973-0.978)
  Intercept (95% CI)   -0.826 (-1.361 to-0.291)
[S.sub.y|x]                      7.35

(a) Data are from a total of 32 runs acquired over a
period of 2 weeks.

(b) DHEA, didehydroepiandrosterone.
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Title Annotation:Letters to the Editor
Author:Rule, Geoffrey S.; Rockwood, Alan L.
Publication:Clinical Chemistry
Date:Feb 1, 2015
Words:1297
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