IS RETESTING REALLY WORTHFULL IN CLINICAL LABORATORY?

IS RETESTING REALLY WORTHFULL IN CLINICAL LABORATORY?

Since the early 1970s, laboratory medicine specialists have used computer technology and automation to identify and confirm critical laboratory values.(1,2) The historic practice in clinical laboratories has been to automatically repeat laboratory values that are above or below a critical threshold or that trigger other automated “repeat rules” such as a delta check. These practices were established when laboratory instruments were far less reliable than today,(3) yet they persist in many laboratories (including ours). In fact, recent studies show that analytic issues account for only 8% to 15% of clinical laboratory– related errors, with preanalytic and postanalytic errors representing 85% to 92% of all errors.(4,5) Contemporary laboratory instruments use numerous safeguards in their hardware and software to improve the accuracy and reliability of results.

A recent summary of data from a College of American Pathologists (CAP) Q-Probes survey suggests 61% of laboratories still repeat testing for critical chemistry values.(6) The survey also suggests that laboratory test repeat practices have the potential to delay reporting by 10 to 14 minutes and waste resources without significantly preventing analytic errors.6 Recent studies have shown that the practice of repeating tests with critical laboratory values or other results that trigger automated repeating may not be necessary with today’s clinical laboratory automated analyzers.(3,7) A small study examined a total of 580 repeated tests for potassium, glucose, platelet count, or activated partial thromboplastin time and found that the repeated value was within the acceptable difference for 95.3% of the critical value tests repeated and 97.6% of all repeated tests.(3) Another study examined 5 different hematology/coagulation tests and 500 consecutive critical results and the repeated test values for each of the 5 tests. By using their internal definition of acceptable error, Toll et al7 found that 0% to 2.2% of the repeated values for these tests were outside of their acceptable criteria. They concluded that repeated testing for critical values did not offer an advantage or provide additional benefit in hematology and coagulation settings.(7) Neither of these studies examined the time delays in reporting the critical values that repeated testing invariably causes.

The CAP recently published the results of a survey of 86 laboratories, each of which reviewed 40 critical test results from 4 tests at their institutions.(8) The study found that 61% of laboratories always repeated critical results and that the median delay in reporting as a result of repeated testing was 10 to 14 minutes in most laboratories and 17 to 21 minutes in 10% of the laboratories.(6,8) Based on the findings of these studies and the Q-Probes survey, we examined the results of our repeated testing. For more than 20 years, we have repeated all tests in an automated manner when the initial values exceed the AMR of the test (high or low), the result is a critical value, the result fails a delta check, or the value exceeds a preset “review” limit. All of the instruments used are directly interfaced to our laboratory information system, and results are autoverified when all rules are satisfied. When one of the aforementioned flags occurs, results are not autoverified and are automatically repeated by the instrument. For initial values above or below the AMR of the method, the test is repeated on dilution (high values) or the sample is examined for being a short sample (below AMR) and repeated using a special “short sample” cup if necessary. For initial values within the AMR, the technologist reviews the repeated result, and if the result is in agreement, the initial value is verified manually by the technologist. If the difference between the initial and repeated results exceeds the 2 SD range of the quality control sample closest in concentration to the test sample or is greater than 10% (whichever is greater), the test is performed a third time, and the average of the 2 results that agree is reported.

Based on experience and the reports discussed, it is hypothesized that the vast majority of repeated values from clinical chemistry laboratory would agree with the initial value and that there may be only limited benefit in continuing such frequent repeat analyses. It is also hypothesized that repeated analysis for critical values is delaying notification of caregivers about these critical results. The definition of what constitutes a significant difference between repeated values is variable. Allowable error can be defined by biologic variability,(9,10) subjective opinion of what is clinically significant, clinician survey, or regulatory requirements. CAP/CLIA criteria for allowable error should be chosen because they are understood by all and are the criteria by which proficiency testing is judged in the United States. This decision can be questioned because there is not a clear rationale for some of these criteria, which may lead to some of the differences we observed in the number and frequency of errors we identified for different tests. For example, the CAP/CLIA criterion for sodium is ± 4 mmol/L (~ ± 2.8%), whereas the criterion for calcium is ± 1.0 mg/dL (~ ± 10.0%) It could be argued that the former is clinically insignificant, whereas the latter is clinically significant. It is also possible that the differences in the magnitude of what is considered allowable error led to our finding of considerably more errors for repeated sodium testing within the AMR than for calcium.

It is interesting that when the data for these 2 tests is examined closely, this is not the case. There is only 1 additional calcium repeated result of the 3,079 results within the AMR in which the difference is between 0.5 and 1.0 mg/ dL. In contrast, for the 31 errors within the AMR for sodium, 19 exhibited a difference between the initial and repeated tests that exceeded 10 mmol/L. Results below the AMR (linear low) may be due to “short sampling” or other preanalytic or analytic error. Results above the AMR are repeated on dilution to obtain a final result, and the absolute or percentage differences from the repeated results are most frequently greater than the CAP/ CLIA-derived allowable error. Clearly, when initial results are outside the AMR, repeated testing will continue to be necessary.

The results of several studies for repeated testing when automated chemistry testing; the repeated testing is unnecessary and delays the reporting of results, which is a particularly important problem for critical values. Findings of these studies also suggest that for some tests such as sodium and pO2, repeat testing may be necessary to detect some large errors in the initial result. The reasons for these errors are unclear at this time and may require prospective evaluation. Finally, while it may not be necessary to repeat the analysis for samples that trigger a delta check flag, it will still be necessary for technologists to check the identity and feasibility of the result for the previous sample. Delta checks provide a means for identifying mislabeled samples, sample integrity problems as a result of preanalytic problems, and random analytic errors. While all these studies strongly suggests that random analytic errors are rare, it does not address the first 2 causes of a delta check flag, and these will need to be investigated by the laboratory. The delays observed in reporting critical values that result in repeated testing were similar to those described by survey participants in the Q-Probes study.(6,8) It is not surprising that the delays for blood gases were shorter than the delays for other chemistry tests because the analytic time is much shorter. However, the median delays observed for tests such as potassium and glucose are far greater than the actual analytic time. This is most likely due to a technologist taking time to review results, determine if a third test is necessary, and making a decision about manual verification of the final result. Weaknesses of these studies are that the data are from a single laboratory and only several types of automated analyzers. The error rates may vary depending on instrumentation, quality assurance practices, or other variables of individual laboratories. For example, because using multiple instruments performing the same test (eg, 7 Roche Modular P units), calibrations is not accepted if the quality control samples are outside of 1 SD, which may minimize error rates.

Conclusion

Finally, the number of repeated tests observed for the immunoassay and therapeutic drug monitoring categories may be too small to make firm conclusions. Nevertheless, these results suggest that repeat testing for many automated chemistry tests, including critical values, can be stopped and should also serve as a catalyst for other laboratories to examine the value of their repeat testing practices. Doing so can improve patient care by delivering critical values more rapidly and could potentially save 2% to 3% of reagent costs for many tests.

References

1. Lundberg GD. When to panic over abnormal values. MLO Med Lab Obs. 1972;4:47-54.

2. Lundberg GD. Managing the Patient-Focused Laboratory. Oradell, NJ: Medical Economics Books; 1975.

3. Chima HS, Ramarajan V, Bhansali D. Is it necessary to repeat critical values in the laboratory? Lab Med. 2009;40:453-457.

4. Goswami B, Singh B, Chawla R, et al. Evaluation of errors in a clinical laboratory: a one-year experience. Clin Chem Lab Med. 2010;48:63-66.

5. Carraro P, Plebani M. Errors in a stat laboratory: types and frequencies 10 years later. Clin Chem. 2007;53:1338-1342.

6. Paxton A. Critical value repeats: redundancy, necessity? CAP Today. December 2010;24:1.

7. Toll A, Liu JM, Gulati G, et al. Does routine repeat testing of critical values offer any advantage over single testing? Arch Pathol Lab Med. 2011;135:440-444.

8. Lehman CM, Howanitz PJ, Karcher DS. QP102—Utility of Repeat Testing of Critical Values Data Analysis and Critique. Q-PROBES. Northfield, IL: College of American Pathologists; 2010:1-12.

9. Ricos C, Alvarez V, Cava F, et al. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest. 1999;59:491-500.

10. Fraser CG. General strategies to set quality specifications for reliability performance characteristics. Scand J Clin Lab Invest. 1999;59:487-490.