The central objective of health care is the prevention of premature death and suffering, and the restoration of functional health. Information on further action to be taken is derived from diagnostics by way of medical laboratory tests. These data, however, are only clinically useful for the patient if clinical action can be derived from them. In general, laboratory tests result in therapies being started, ended or modified; in individual cases, the pathophysiology of an illness is examined more closely through laboratory diagnostics.
In addition to the clinical and scientific aspects of the consequences of diagnostics, psychological aspects must also be taken into account when determining the indication of laboratory tests. Thus, waiting for the findings and the failure to perform a test can trigger anxiety and stress in patients . The results of a test and/or the fact that a test is done can lead to social stigmatization (e.g., in connection with HIV tests). However the findings from laboratory tests can also contribute to improved lifestyle habits, such as when it comes to smoking or physical activity. In some cases, if a hereditary disease, which would show symptoms only in the distant future (e.g., Huntington’s disease), can be ruled out by laboratory testing, this can have a liberating effect for the patient.
As with drug treatment or surgical intervention, the use of laboratory tests must also be evaluated in terms of the clinical benefit. Such an evaluation is generally complex, because most laboratory tests are used to rule out illnesses. Thus, the benefit of laboratory tests is always indirect, i.e., the treating physician must always take additional action to achieve therapeutic success. Such action may involve starting patients with positive test results on immediate treatment or sending patients with negative test results for further diagnostic tests with different methods, such as imaging. When evaluating the benefit of laboratory diagnostics, there must also be a distinction between a direct, patient-relevant benefit (i.e., a direct link to the suspected illness) and the controlling merely of surrogate parameters .
Particularly in differential diagnostics, the evaluation of the benefit depends on the incidence of the illness in the population, because this strongly affects the positive and/or negative predictive value of a test procedure . Furthermore, an evaluation of the benefit must not only look at the direct benefit to the patient, but also the (economic) benefit for the community.
The validity of the laboratory findings figures in all benefit considerations. The validity is determined by the uncertainty of the analysis itself, the day-to-day variability in terms of circadian rhythms, and also by food intake, physical activity, medication and the differences between various test assays . An example of the latter is the difference in determining cardiac peptides or creatinine between the POCT format and conventional laboratory equipment [5, 6] and/or in determining tumor markers with test systems from different manufacturers .
The evaluation of the validity of medical laboratory findings is highly complex. Many quantitative parameters – e.g., in enzymology – can be measured highly standardized and with the highest degree of precision. For other parameters (e.g., clotting factors and tumor markers), however, the result of the measurement is often very much dependent on the assay used. In the case of semi-quantitative results (e.g., in autoimmune serology, infectious serology), the evaluation of findings in the gray area may be linked to poor reproducibility. Thus, results obtained via highly standardized test methods on a specific test platform can generally be transferred also to other platforms in terms of the validity. However this transferability to other test platforms must be questioned when it comes to poorly standardized tests.
Factors influencing the frequency of repeat testing
If the half-life of the analyte in vivo as well as the performance of the test (such as the day-to-day variation coefficient, detection limit and/or lower of limit of quantification) are known, minimum periods can be defined that, if not reached, do not give rise to the expectation of changes to the measured values, which means that a repeat measurement will not yield any additional information. Typical examples for this are the HbA1c analysis , the determination of troponins with the acute coronary syndrome  or the PSA test after surgery to remove a tumor and/or an organ . This assumes that an additional condition did not affect the half-life of the analyte in this particular patient. Common causes of such changed half-life times include the development of renal insufficiency in connection with renally eliminated substances, enzyme induction through specific drugs in connection with TDM or volume changes in the case of bleeding, the administration of infusion solutions or erythrocyte concentrates. Special conditions apply to repeat frequencies of laboratory tests for special issues. Thus, the determination of HbA1c in pregnant patients should be carried out monthly, as well as for patients with poorly controlled diabetes mellitus or after changes in diabetic treatment .
In addition to the known analytical parameters of laboratory tests – i.e., deviation in the measurement from the ‘true value’ – one must also take into account pre-analysis and, if necessary, post-analysis. Some factors of pre-analysis, such as circadian rhythms  or food intake , are known and are therefore controllable, in theory at least. In contrast to the situation of analysis, with its highly developed quality management system in the laboratory, serious errors in pre-analysis and post-analysis are not rare , nor can they be fully controlled: the biggest source of major errors is by far the misidentification of patients. This happens almost always at the time a sample is taken at the doctor’s office, a clinic or hospital ward or when the findings are assigned to a patient record (typically by slipping the findings in the wrong patient record). According to a large number of studies, serious pre-analytical errors occur in approx. 0.1%–0.5% of all samples submtitted for testing [15–18], i.e., an almost daily occurrence at every major medical facility.
Another important source of pre-analytical errors is the submission of unsuitable test material – particularly due to artificial hemolysis, wrong collection containers, insufficient sample volumes and/or incorrect mixing ratios regarding the anticoagulant. The submission of unsuitable material is a much more common source of errors than the misidentification of patients. However the evaluation of the consequences of such pre-analytical errors for the analytical findings is problematic: for example, in the case of low hemolysis, the use of LDH may still be possible to rule out hemolytic anemia, but the sample would be inappropriate for follow-ups with a known diagnosis. The marking of the findings in the event of a problem in the measured value caused by icterus, lipemia and hemolysis (if the measured value deviates from the ‘true value’ by more than 10%/15%/20%) is arbitrary and not always helpful in assessments: thus, when looking at the total error, a deviation of 10% may be irrelevant (e.g., in the triglyceride analysis), but it may also be crucial to the diagnostics (e.g., in the analysis of creatinine) [19, 20]. The evaluation of ‘unsuitable test material’, therefore, is highly uncertain even in combination with automated analyses of serum indices . Problems due to in vitro hemolysis, the most common cause of unsuitable test material, should therefore always be eliminated through timely repeat analyses using materials obtained and transported correctly . This can be done easily if it is not an emergency situation.
Avoiding repeat testing
The number of laboratory tests should be optimized to ensure the responsible use of existing resources in medical treatment. Avoiding multiple analyses of an analyte, among other issues, is one way to achieve this goal. Many public health efforts – e.g., the obligation to hand over the findings or the networking of inpatient and outpatient care – are designed to reduce the number of repeat tests. However, the patients’ active desire to have certain repeat tests done has a significant influence on the frequency of requests . On the other hand, an excessive reduction in the frequency of tests also carries the risk of overlooking changes in the health of the patients or making wrong medical decisions due to a laboratory result that does not apply (e.g., in the case of the misidentification of patients).
However the focus on avoiding laboratory tests does reveal that the decision not to repeat laboratory tests (and the resulting potential damage to the patients and/or society) is much more common than overly frequent requests. A meta-analysis of studies conducted between 1997 and 2012 (at least in those carried out outside of Germany) found that the rate of forgone, necessary laboratory tests was twice that of requests made with excessive frequency .
The numbers available on the frequency of avoidable repeat tests vary greatly. An Israeli study identified 19% of tests as avoidable , while a Dutch study found only 0.56% to be avoidable, in accordance with a cost reduction of only 0.33% . The share of redundant requests is very small particularly for high-volume and cost-efficient tests. Requests for repeat testing are much more common for rare tests. However, the data available on what is a meaningful time span between repeat tests for such rare tests are inadequate .
The optimal use of medical laboratory tests is influenced by a multitude of factors. Apart from specifications on optimal frequency (i.e., frequency and time interval of repeat tests), key factors include the use of stepwise diagnostics and/or diagnostic paths [27, 28], avoidance of obsolete tests , the replacement of historically grown profiles with current profiles , and the timely use of innovations.
Principles for the repetition of medical laboratory tests
The repetition of medical laboratory tests is necessary in certain circumstances and, given specific constellations, may even be mandatory under the law. Examples of this are the determination of the blood group from several blood samples and the blood count before and after a transfusion to document the therapeutic success , the confirmation of a positive HIV finding from a second blood sample , the detection of bacteria in the urine of pregnant patients  or, if necessary, the repetition of a genetic test after 10 years, because findings have to be deleted after 10 years under the German Genetic Diagnostics Act . Similar to blood grouping and HIV tests, a repeat of other laboratory tests may also be indicated where the laboratory test may have serious consequences for the patient. This may be the case, e.g., if the medical laboratory test cannot be confirmed by other diagnostic methods (such as other laboratory or clinical methods, through imaging), if the result is unexpected in light of the clinical picture (e.g., also with pre-operative screening), if there are unexplained fluctuations in the TDM, and also if there is a lack of clarity in the allocation of patients to sample material (“misidentification of patients”). Even if the findings of the laboratory test necessitate further testing with increased invasiveness and a significant risk to the patient (which is also true of some invasive diagnostics) or treatment with serious side effects (e.g., pathogen detection in connection with osteomyelitis or endocarditis), confirmation of the laboratory findings from a second sample may be useful.
As for drugs that require therapeutic drug monitoring (TDM), control measurements should generally be taken at the start of the drug treatment, after changes to the dose of the drug or any co-medication, as well as in cases of suspected changes to the pharmacokinetics, such as due to changes in the liver, kidney or heart functions . As a rule, the determination of drug concentrations makes sense only in the steady state, which is reached after 4–5 elimination half-lives. This means that repeat measurements are indicated already after 1 day for drugs with a short half-life (t 1/2), such as cyclosporine ( t 1/2 approx. 6 h), while it would be only after 6–7 days for drugs with a long half-life, such as digoxin ( t 1/2 approx. 36 h). Immediately after the start of treatment, frequent concentration controls (and/or activity determination) may be necessary in consideration of the t 1/2 until the therapeutic range has been reached, especially for drugs with varying inter-individual kinetics and narrow therapeutic index, for example, in the case of vitamin K antagonists . Repeat tests, however, are generally needed only rarely for well-controlled and stable patients who undergo maintenance treatment. Thus, the recommendation calls for controls every 3–6 months for psychotropic drugs , or every 1–3 months for immunosuppressants with an uncomplicated clinical course .
Examples of repeat frequencies for laboratory tests
Table 1 shows examples of recommendations regarding the frequency and/or minimum time intervals of repeat tests. These are usually based on international or only local consensus opinions. Quite often, minimum time intervals can be defined only for certain diagnoses and/or patient groups. Most of the recommendations mentioned for time intervals also apply to outpatients seen by a family physician: in contrast to inpatients, the influence of further factors is of little significance in their case. In the event of missing data, test frequencies for other parameters may be derived from known parameters, taking into account formation kinetics and specific analytical performance data. Thus, for example, the recommendation for determining cystatin C corresponds to the recommendation for the determination of creatinine . As an example of recommended test frequencies in connection with a complex illness, Figure 1 shows the time intervals for control tests on patients with a monoclonal gammopathy of undetermined significance (MGUS).
Further development of the recommendations
The data situation for determining the optimal frequencies for the repetition of laboratory tests is currently highly unsatisfactory. Generally applicable and widely accepted test intervals have been mentioned only for few tests in the literature. These test intervals, as a rule, apply only to selected groups of patients, as shown in Figure 1. It is therefore necessary to work out further diagnostics and treatment guidelines with the input of laboratory physicians. While the guidelines available so far do recommend regular laboratory tests, the test intervals mentioned therein are usually not sufficiently grounded in science, i.e., scientific, medical laboratory concepts for defining such intervals have not been taken into account adequately thus far.
In the practical implementation of time intervals for controlling requests, IT systems are used on a regular basis. Frequently, the time intervals are set in such a way that premature requests are suppressed. It must therefore be ensured that the test can be requested more often as well in justified individual cases in order to avoid disadvantaging patients (what is known as “e-iatrogenesis”) . Rather than suppressing a request automatically, it would make more sense if the IT system, when processing a request, were to give an indication that the desired test had been requested prior to the expiry of the proposed test interval. This would then allow the requesting physician to decide actively whether the request should be carried out or be canceled.
The general use of time intervals for request control can be recommended only for few analytes at this time . By contrast, when using diagnostic pathways, the concept of given test intervals is extremely useful and also technically feasible . The major advantage of diagnostic pathways is that the control of requests can be realized not only on the basis of a known diagnosis, but also on the basis of new findings obtained over the course of the pathway, which makes it possible to adjust it gradually.
Conflict of interest statement
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
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Original German online version at: http://www.degruyter.com/view/j/labm.2014.38.issue-5/labmed-2014-0016/labmed-2014-0016.xml?format=INT. The German article was translated by Compuscript Ltd. and authorized by the authors.