Explaining laboratory test results to patients: what the clinician needs to know
BMJ 2015; 351 doi: https://doi.org/10.1136/bmj.h5552 (Published 03 December 2015) Cite this as: BMJ 2015;351:h5552
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Re: Explaining laboratory test results to patients: what the clinician needs to know. Maurice John O’Kane and Berenice Lopez. 351:doi 10.1136/bmj.h5552
With great interest we read the article by O’Kane and Lopez: “Explaining laboratory test results to patients: what the clinician needs to know”. The authors provide a comprehensive overview and discuss the possibilities and limitations of laboratory tests for diagnosis, monitoring, risk stratification and screening. Of particular interest, they discuss the variability of test results over time due to (pre)analytical and biological variation. Awareness of day to day variability within an individual, is particularly important when monitoring disease progression or response to treatment. Importantly, reference values have limited value in the setting of monitoring as they are based on variation between individuals, whereas the interpretation of serial measurements requires information on the day to day variation within an individual. O’Kane and Lopez recommend laboratory test results to be considered as 'ranges' instead of single numbers and to determine whether the change between subsequent laboratory test results is real by looking at the degree of overlap of the ranges. [1] This is what experienced clinicians intuitively do when assessing whether serial laboratory results reflect a true change, or rather reflect expected variability. There is however a more formal way to objectively distinguish between ‘true changes’ and random variation between serial test results. For a change between consecutive measurements to become significant, the difference must be larger than the change that can reasonably be expected due to normal biological and analytical variation. This threshold value is termed the Reference Change Value (RCV). The RCV can be calculated for each laboratory test and depends on the biological within-person variability (CVI) and the analytical variability (CVA). [2]
RCV= √2 * Zscore* √(CVa^2 + CVi^2 )
In this formula, the Z-score represents the number of standard deviations and correspond to the desired probability. Commonly used Z-scores are 1.96 and 2.56.These Z-scores calculate the percentage increase or decrease that is required to become statistically significant, with a false positive rate of 5%, (p <0.05) and 1% (p <0.01) respectively.
For easy calculation of the RCV we developed Labtracker+ [3], a free, CE certified, medical smartphone app for iOS. The RCV principle is used to calculate the probability of a true change between serial laboratory results. Over 100 laboratory parameters are currently available in Labtracker+. Decision support by Labtracker, using the RCV principle, may be a useful addition to clinical intuition.
References:
[1] O’Kane and Lopez, Explaining laboratory test results to patients: what the clinician needs to know. BMJ 2015; 351: h5552
[2] Fraser, C.G. and Harris, C.G., Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci, 1989. 27(5): p. 409-37
[3] https://appsto.re/nl/T3lr8.i
Competing interests: No competing interests
The article by O’Kane and Lopez precisely illustrates the technical issues in a clear manner. We, however wish to make further comments regarding the tests done in the microbiology laboratory because of the distinctive nature of this specialty:
• Appropriate and relevant clinical details are essential to unsure the correct test id performed in the first instance and the results can be interpreted correctly.
• When requesting a microbiology test, it is imperative to state the site and nature of the specimen and the relevant clinical condition of the patient in order to ensure correct interpretation.
• The collection of microbiology samples is essentially a procedure in its own right eg. aseptic collection of blood cultures.
• The implication of microbiology tests has potential wider population implications namely because of the concept of infection prevention and control.
• Unlike other pathology specialties there is generally no need in microbiology to repeat tests in order to establish whether they have become negative or not following treatment, as further testing is entirely dependent on the clinical condition of the patient.
• In the end the patient must be treated and not the result, hence the rapport between the clinicians and clinical microbiologists is crucial in order to optimise management of patients.
Molecular biology is revolutionising microbiology and pathology services as a whole. This will add a further layer of complexity in the interpretation of results.
For the time being we are still performing and relying on traditional technologies in microbiology, but the way we do it is with culture and sensitivity!
Clinical Microbiology and Infection Prevention and Control Department.
The Rotherham NHS Foundation Trust
Competing interests: No competing interests
Not highlighting abnormal results by the lab. A system safety failure
I am concerned about recent changes in pathology result reporting. In several, if not all GP systems, results that are abnormal are highlighted so when results are checked they stand out. Both locally to us and in discussion with colleagues in other areas this is happening less. Examples include loss of highlighting high psa, high lipids and high creatinine, as well as positive chlamydia results.
It is hard enough for clinicians to sort this out, and it is in my view an example of a system safety failure. It will be even harder for patients viewing their own results. Who makes these decisions and how can they be influenced?
Competing interests: No competing interests