Diagnostic tests, such as laboratory analysis of body fluids, represent an important part of today’s healthcare. The use of diagnostics is expanding and tests are becoming increasingly complex. Therefore, diagnostic test interpretation is becoming more complicated, especially for non-laboratory professionals .
A common source of diagnostic error is the lack of knowledge of the presence of drug-laboratory test interactions (DLTIs). Misinterpretation of test results may lead to an erroneous diagnosis, unnecessary extra diagnostic tests, therapy or follow-up.
There are two main categories of DLTIs: physiological and analytical interactions. Physiological interactions are in vivo processes, in which drugs affect patients’ laboratory test results. Test results may reveal an intended or unintended effect of a drug. Intended effects of drugs will generally not result in diagnostic misinterpretation, for example, an elevation in free thyroxin levels due to levothyroxine treatment. However, unintended effect of drugs often can lead to diagnostic confusion. A clear example of an unintended effect of drugs is an elevated level of chromogranin A by the frequently prescribed proton pump inhibitors (PPIs). An elevated level of chromogranin A can be indicative of the activity of a neuroendocrine tumor. Case reports describe expensive imaging with no abnormalities and a normalized chromogranin A level after the discontinuation of the PPI . This example illustrates that unnecessary discomfort and expenditure could have been avoided if this unintended physiological interaction had been recognized promptly.
Analytical interactions are in vitro processes. In these cases, the interactions between drugs and laboratory tests disturb the analytical process, which may have an important negative clinical impact, as affected laboratory test results may not reflect the clinical situation of the patient. These analytical interactions should be avoided by using an alternative assay, or erroneous test interpretations should be eliminated by warning systems. An extreme example of the danger of an analytical drug-test interaction is an erroneously high glucose level that can occur in continuous ambulatory peritoneal dialysis (CAPD) patients, because some glucose test strips cannot distinguish glucose from other sugars (e.g. icodextrin or maltose) that can be present in CAPD fluid . The improper administration of insulin has resulted in fatal consequences in a number of these cases .
Impact of DLTI in clinical practice
The number of DLTIs described in the literature is substantial, approximately 50,000 .
Therefore, the application of a knowledge-based electronic expert system with DLTI information seems necessary. An expert system may send automatic messages about interactions based on algorithms, which use data from pharmacy and laboratory data systems.
To build DLTI algorithms, relevant information about interactions is conditional. Information about DLTI can be found in the literature, but is often fragmentarily described and sometimes even contradictory effects are reported, i.e. the effect of a drug on a laboratory test may result in either an increase or decrease of measured values . Therefore, several DLTI databases have been introduced to provide an overview of interactions and the corresponding available literature , , , .
In a literature review , only four studies were found about automated DLTI decision support in clinical practice , , , . The added value of the system was evaluated with extensive surveys among physicians receiving DLTI messages in two studies ,  and a retrospective evaluation of patient reports by an expert panel in one study .
Apart from the prevalence of DLTIs, another important issue that was examined in the studies was the clinical usefulness of the interaction messages. In one study, the medical staff reported 30% of the messages to be useful and in 4% of cases their medical policy changed because of the DLTI message . In another study, all the physicians considered the DLTI messages to be useful and 74% of the physicians reported to sometimes refrain from additional further examinations as a consequence of DLTI ‘reminders’ .
The existing literature shows a high prevalence of DLTIs in a variable range of laboratory tests and drugs. However, it is likely that the prevalence of DLTIs is even higher, as the interactions are not systematically examined or reported.
Studies have shown the added value of automated decision support applications to alert healthcare professionals on possible DLTIs. The effectiveness of such a system increases when a refined set of clinical rules is determined in cooperation with healthcare professionals who use the system , . These refined clinical rules are needed to prevent excessive numbers of DLTI messages and consequently the so-called ‘alert fatigue’ of physicians .
Although the benefit of DLTI decision support was already shown in the past, it is not widely implemented today. To implement a DLTI decision support tool, interoperability of a laboratory information system, an electronic patient record and a decision support application are crucial. The interoperability of information technology (IT)-systems is not yet realized in many laboratories and hospitals. To implement such an IT-system, an intensive cooperation between medical IT-specialists, laboratory specialists, pharmacists and physicians is needed.
DLTIs could potentially disturb the diagnostic process in a large number of patients, as many patients receive drugs and thousands of laboratory test results are produced in each hospital every day. Further research is needed to better estimate the prevalence and impact of DLTIs in daily practice. Decision support applications probably improve DLTI recognition by healthcare professionals. These DLTI decision support tools could prevent diagnostic errors and consequently improve diagnosis and treatment of patients.
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About the article
Published Online: 2019-02-12
Published in Print: 2019-03-26
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: Quality Foundation of the Dutch Medical Specialists (SKMS), Grant Number: 42678870.
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.