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Patient safety and clinical effectiveness as imperatives for achieving harmonization inside and outside the clinical laboratory.

For clinical laboratorians, the case for standardization and harmonization has been evident for more than 4 decades, since Radin first proposed using traceable reference standards for calibration as a means to harmonize laboratory results produced with different in vitro diagnostic methods (1). Over the intervening period, the subject has been reviewed (2) and debated extensively--both inside and outside the laboratory community--yet a striking majority of our physician and surgeon colleagues still fail to grasp or understand the limitations of current laboratory measurements, the lack of interchangeability of results obtained by different analytical methods, and the resulting effects on interpretation, clinical decision-making, and patient management.

Some incremental progress has been made in addressing these issues through professional organizations, multidisciplinary practice guidelines developed by national and international committees, peer-review publications, and the global in vitro diagnostic manufacturing industry. Notable successes include the National Glycohemoglobin Standardization Program (including the recommendations of the Diabetes Control and Complications Trial), the National Cholesterol Education Program, and the National Kidney Disease Education Program, which have helped drive improvements in laboratory methods for hemoglobin A1c, total cholesterol, and creatinine, respectively, and helped establish clinical practice guidelines based on laboratory measurements that meet defined performance criteria. As recently as October 2010, the AACC hosted an international conference for a diverse group of stakeholders to improve harmonization of laboratory results and to make recommendations for the future.

Regrettably, personnel in the clinical laboratory must continue to cope on a day-to-day basis with the blissful ignorance or blatant denial of these multifactorial methodologic differences by many practicing clinicians and, sadly, a few fellow laboratory colleagues. Harmonization of results remains the "holy grail." How often have we been asked, "Isn't one test result the same as another from a different laboratory?" Even after careful explanation and intensive efforts to educate, many practitioners choose to interpret and act on test results with little consideration of the potential implications and outcome to the patient. We are all familiar with the laboratory tests whose differences and limitations frequently present a challenge to explain to clinicians--troponin I, natriuretic peptides, thyroid-stimulating hormone, fertility hormones, tumor markers, and, more recently, vitamin D metabolites and many molecular-diagnostic assays (e.g., bcr-abl, BK virus). The lack of understanding of these limitations and differences by clinicians becomes a greater threat to patient safety and quality of care in the information technology age (3, 4), in which the focus tends to be on looking at cumulative merged results, reacting to flagged abnormal findings, and plotting graphical trends in electronic health records, while at the same time disregarding actual quantitative values and critical details in appended comments (e.g., failure to recognize different sources of testing and/or the specific methods used).

In parallel with ongoing efforts to educate clinicians and laboratorians alike about the risks of misinterpretation, misdiagnosis, and improper monitoring of patient results obtained with different laboratory methods (either within or between facilities), the desire to find a solution to minimize or mitigate the lack of harmonization is increasing, both inside and outside the clinical laboratory. Historically, one of the earliest and greatest educational efforts in this area, which concerned the use of tumor markers in the diagnosis and monitoring of neoplastic diseases, is exemplified by the 2008 publication of the National Academy of Clinical Biochemistry (NACB) [2] guidelines for tumor markers (5, 6). These guidelines highlighted the challenges with the lack of standardization and harmonization of current tumor marker assays, the different molecular variants and isoforms detected by these assays, the lack of interchange ability of results obtained with the different methods used to monitor patients with known disease, and the need to reestablish new baselines when making conversions. These and other identified limitations confound test interpretation and impede the ability to create specific, defined, and uniformly accepted cutoff values for clinical decision-making.

In this issue of Clinical Chemistry, Stephan et al. present findings (7) from a study of a large cohort of patients that compared the effects of different prostate-specific antigen (PSA) methods (for total PSA and the percentage of free PSA), as well as calibration changes, on the results of logistic regression-based nomograms for predicting the risk of prostate cancer (PCa). Multivariate models, including artificial neural networks and logistic regression-based nomograms, have been proposed since the late 1990s (8, 9) as an adjunct for PCa risk prediction, for monitoring disease progression, and for guiding therapeutic intervention, to help address the low specificity of measurements of total and the percentage of free PSA alone for diagnostic and prognostic assessments. Recently, such combined approaches, which can account for other factors such as age, digital rectal examination findings, and prostate volume, have been advocated widely (10) and embraced by the medical community; however, an important assumption of these approaches has been that variation in the PSA measurement methodology used does not affect assessment outcomes. The NACB guidelines, in discussing the promise of nomograms for predicting PCa risk, suggested that these tools may be "the most accurate means of individualizing therapy and predicting outcome, and reflect the most recent advances in patient management" (6). The expert panel cautioned, however, that it might be difficult to select the best nomogram when several competing versions apply to the same clinical decision.

A critical and comprehensive comparison of the effects of assay-dependent variation in measurements of total PSA and the percentage of free PSA on commonly used PCa risk-prediction nomograms has not previously been well documented. To clinical chemists and pathologists familiar with the issue of the lack of PSA assay harmonization, it may seem intuitively obvious that some differences in the performance and outcomes of these nomograms could be observed with different assays and calibration methods. Despite the advances in the availability of WHO reference materials and assay improvements from all manufacturers, we are still far from achieving the desired harmonization and interchangeability of PSA results across all available methods (11, 12). Stephan and his colleagues, who were early proponents of the value of PCa nomograms, have documented the obvious (and not so obvious) limitations of these nomograms, the use of which may alter clinical decisions, depending on the PSA methods used. In this large retrospective study of nearly 800 patients (which included some assays that are no longer commercially available), Stephan et al. have documented very important PSA test method-dependent differences between 5 commonly used assays when results are applied to 5 of the most widely used and readily available ("plug and play") regression-based predictive nomogram models. Not only did results of the 5 assays lead to different PCa probabilities with the same nomogram, but the various nomograms (which differ in their inclusion of other factors, such as the percentage of free PSA, sampling density, and prostate volume) also produced different PCa probabilities when the same PSA assay was used. Although the authors' ROC curve analyses yielded comparable areas under the curves, there were significant differences between the 5 assays in the diagnostic sensitivities and specificities at various PCa probability cutoff values for the majority of the evaluated nomograms.

Although this study may not have examined the effects on all currently used nomograms, such as those described by Finne et al. (13), and did not take into account other PSA assays, its findings raise some well--founded concerns that merit the attention of both clinicians and the laboratory community. Yes, the conclusions of Stephan et al. may state the obvious--namely, the accuracy of predicted PCa probabilities produced with different nomograms is affected and can be compromised by the lack of harmonization of PSA assays. Moreover, the variations in PCa risk prediction and discrimination can be substantial and unacceptable, depending on the model used and differences in calibration, even with the same assay. Nonetheless, this study reinforces the point that until harmonization of results is truly achieved in the laboratory, caution is still warranted. Few tools outside the laboratory can substitute for harmonization for accurately informing and appropriately guiding clinical decision-making and ensuring safe and effective patient care.

In the US, PCa remains the most common cancer in men and the second-leading cause of cancer deaths. Although the incidence of PCa and PCa deaths has declined steadily over the last 2 decades because of early detection and treatment (6), we must remain vigilant in raising awareness that current laboratory results are neither standardized nor harmonized and may lead to erroneous decisions with serious consequences (i.e., just plugging the numbers into a risk-calculation nomogram can give misleading results). The decade-old Institute of Medicine report (14) on building a safer health system states that "to err is human," but no where in the report does it say that "to forgive is divine," especially now that we have knowledge of factors that can lead to the wrong call and potential harm.

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) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: No authors declared any potential conflicts of interest.

References

(1.) Radin N. What is a standard? Clin Chem 1967;13:55-76.

(2.) Vesper HW, Miller WG, Myers GL. Reference materials and commutability. Clin Biochem Rev 2007;28:139-47.

(3.) Linder JA, Ma J, Bates DW, Middleton BW, Stafford RS. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med 2007;167:1400-5.

(4.) Henricks WH. "Meaningful use" of electronic health records and its relevance to laboratories and pathologists. J Pathol Inform 2011;2:7.

(5.) Sturgeon CM, Hoffman BR, Chan DW, Ch'ng SL, Hammond E, Hayes DF, et al. National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines for use of tumor markers in clinical practice: quality requirements. Clin Chem 2008;54:e1-10.

(6.) Sturgeon CM, Duffy MJ, Stenman UH, Lilja H, Branner N, Chan DW, et al. National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines for use of tumor markers in testicular, prostate, colorectal, breast, and ovarian cancers. Clin Chem 2008;54:e11-79.

(7.) Stephan C, Siemssen K, Cammann H, Friedersdorff F, Deger S, Schrader M, et al. Between-method differences in prostate-specific antigen assays affect prostate cancer risk prediction by nomograms. Clin Chem 2011;57:995 1004.

(8.) Kattan MW, Eastham JA, Stapleton AMF, Wheeler TM, Scardino PT. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 1998;90:766-71.

(9.) Virtanen A, Gomari M, Kranse R, Stenman UH. Estimation of prostate cancer probability by logistic regression: free and total prostate-specific antigen, digital rectal examination, and heredity are significant variables. Clin Chem 1999;45:987-94.

(10.) Chun FK, Karakiewicz PI, Briganti A, Walz J, Kattan MW, Huland H, Graefen M. A critical appraisal of logistic regression-based nomograms, artificial neural networks, classification and regression-tree models, look-up tables and risk-group stratification models for prostate cancer. BJU Int 2007;99: 794-800.

(11.) Chan DW, Sokoll LJ. WHO first international standards for prostate-specific antigen: the beginning of the end of assay discrepancies? Clin Chem 2000;46:1291-2.

(12.) Ishibashi M. Standardization of prostate-specific antigen (PSA) assays: Can interchangeability of PSA measurements be improved? Clin Chem 2006;52: 1-2.

(13.) Finne P, Finne R, Bangma C, Hugosson J, Hakama M, Auvinen A, Stenman UH. Algorithms based on prostate-specific antigen (PSA), free PSA, digital rectal examination and prostate volume reduce false-positive PSA results in prostate cancer screening. Int J Cancer 2004;111:310-35.

(14.) Kohn LT, Corrigan JM, Donaldson MS, eds. To err is human: building a safer health system. Washington (DC): National Academy Press; 2000.

Ronald W. McLawhon [1] *

[1] University of California, San Diego, School of Medicine, La Jolla, CA.

* Address correspondence to the author at: University of California, San Diego, School of Medicine, 200 West Arbor Dr. #8320, MPF/402 Dickinson Suite 4-420, San Diego, CA 92103-8320. Fax 619-543-3730; e-mail rmclawhon@ucsd.edu.

Received May 12, 2011; accepted May 13, 2011

Previously published online at DOI: 10.1373/clinchem.2011.166041

[2] Nonstandard abbreviations: NACB, National Academy of Clinical Biochemistry; PSA, prostate-specific antigen; PCa, prostate cancer.
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Title Annotation:Editorials
Author:McLawhon, Ronald W.
Publication:Clinical Chemistry
Date:Jul 1, 2011
Words:2026
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