Katanić, Stanimirov, Sekeruš, Đanić, Pavlović, Mikov, and Stankov: Drug interference with biochemical laboratory tests


Laboratory testing is an essential part of clinical decision-making, influencing 60-70% of medical decisions at all levels of healthcare (1, 2). Results of biochemical laboratory tests (BLTs) have a key role in establishment of adequate diagnosis (3-6). Biochemical laboratory tests ordering belongs to a very important pre-preanalytical phase (7, 8). Increasing frequency in BLTs ordering requires that all health professionals be aware of the recent definition of appropriateness: “prescription of the Right test, using the Right method, at the Right time, to the Right patient, with the Right costs and for producing the Right outcome”, and medical tests should be consistent with clinical guidelines (9). Despite significantly improved knowledge on biomarkers generated from BLTs, knowledge of medical students and professionals about importance of extra-analytical phase of laboratory testing is not sufficient and requires constant improvement and education (10, 11).

Laboratory practice is a cyclical process, typically divided in the preanalytical, analytical and postanalytical phase, where the preanalytical phase is considered as the most vulnerable part of the total testing process (12). Prevention of preanalytical errors and subsequent prevention of inappropriate treatment of patients due to incorrect test results, requires the permanent awareness of the primary factors linked to patient variables, sample collection and processing (13). Amongst patient characteristics, the most important are age, gender, feeding state, physiological changes and drug intake. All these patient-linked factors can modulate the results of BLTs by analytical or physiological interference with analysis. In Europe and worldwide there is a high prevalence of the use of over-the-counter (OTC) drugs, herbal preparations and dietary supplements that can significantly affect laboratory test results. The important survey in 18 European countries revealed that amongst 3600 patients, 68% were regularly taking at least one OTC drug or dietary supplement. In addition, in this large group of patients (N = 2429) taking at least one OTC drug or dietary supplement, 49% did not share this information with their physician. More detrimental, even amongst those who considered important to inform their responsible physician about consumption of OTC drugs and dietary supplements, 30% did not believe that they needed to disclose this information to laboratory staff (14). Thus, the results of this important study emphasize the significance of increasing the awareness of drug-laboratory test interactions (DLTIs) amongst patients, especially by their responsible physicians who may increase the perception of the patients that reporting the use of OTC drugs and dietary supplements improves the process of proper patient preparation for laboratory testing.

Even in an ISO15189 accredited laboratory that is certified to deliver valid and reliable examination results for their intended clinical use, laboratory results do not always correspond to the patient clinical status. Extra-laboratory factors, including fundamental procedures such as test requesting, which occur in pre-preanalytical phase, are error-prone and they account for 50-75% of all laboratory errors (7, 15). Thus, it is of vital importance to recognize that BLTs offer value only if they are analytically and clinically valid, clinically relevant and cost effective (16). These qualities of BLTs may be significantly diminished by preanalytical errors, such as unrecognized influence of drugs on clinical laboratory results. Therefore, preanalytical error may occur when the BLT is ordered inappropriately, without adequate knowledge, information or correct interpretation of DLTIs (17, 18).

Our mini-review aims to provide a comprehensive overview of the current state in this important domain of medical biochemistry. The methodology that we used in the systematic literature search for Supplementary table data is the analysis of keywords: medicine, drug, laboratory, test, interaction, interference, cholesterol, lipoprotein, triacylglycerol, triglyceride, glucose, bilirubin, urate, uric acid, creatinine, aminotransferase, CRP. The search strategy was adapted as needed and a hand-search of articles from relevant reviews was conducted to identify studies for potential inclusion. The detailed analysis of 367 references is summarized in Supplementary table 1, in which the prescription drugs are listed according to Anatomical Therapeutic Chemical (ATC) Classification, with the most common biochemical laboratory parameters that are modified by each particular drug.

Drug-laboratory test interactions

Results of numerous routinely performed and highly specialized BLTs in serum and other biological material can be influenced by one or more drugs that are prescribed to patients. Therefore, DLTIs represent an important source of diagnostic and/or therapeutic errors (19). This emphasizes the importance of knowledge and continual education regarding the possible DLTIs, for medical doctors, pharmacists and laboratory specialists (20).

Polypharmacy (polypragmasia) is defined as the concurrent use of five or more medications (21). The prevalence of polypharmacy in adults aged 65 years or more ranges from 26.3-39.9% across 17 European countries plus Israel, with the lowest prevalence of polypharmacy in Switzerland, Croatia and Slovenia (26.3%, 27.3% and 28.1% respectively), and highest prevalence in Portugal, Israel and Czech Republic (36.9%, 37.5% and 39.9% respectively) (22). Polypharmacy is associated with increased risk of occurrence of drug-related problems, including drug-laboratory test interactions and adverse health outcomes (23). In addition to polymedication and presence of comorbidities in elderly, age-related physiological changes of hepatic and renal function are implicated in altered drug pharmacokinetics. All these factors significantly contribute to complex and careful drug therapy in the elderly and need for improved knowledge about DLTIs (23). Medical professionals should be educated that it is essential to obtain patient data about medications as well as the timing of drug used by the patient within 10 days before collecting biological material for the correct conduction and interpretation of a laboratory test (24).

The importance of drug-drug interactions that can lead to serious unwanted effects or to a reduction in the therapeutic effects is well recognized in medical practice. Mechanisms of drug interactions with endogenous molecules in body fluids and tissues, with laboratory test components or with other drugs in case of polypharmacy are numerous, but they can all be classified into pharmacokinetic (PK) and pharmacodynamic (PD) mechanisms of interactions (25).

For PK interactions, the ADME (absorption, distribution, metabolism and excretion) principle describes the interactions at the level of proteins responsible for the disposition of drugs. After peroral intake, drug absorption in enterocytes occurs through passive or facilitated diffusion and it is controlled by the presence of drug metabolizing enzymes and drug transporters that can be either induced or inhibited by drugs (25). Numerous drugs (e.g. statins or macrolides) affect absorption of drugs by inducing or inhibiting drug transporters such as efflux ATP-binding cassette (ABC) and uptake solute carrier (SLC) families of transporters in the apical and basolateral membrane of enterocytes. In addition, duodenal enterocytes express microsomal enzymes that belong to cytochrome P450 (CYP) enzyme family (CYP3A and CYP2C subfamily), which can be induced or inhibited by drugs, thus modifying the bioavailability of drugs and their interactions (25).

Upon absorption into the systemic circulation, the distribution to tissues can occur by passive diffusion and membrane protein-mediated transport. Drugs interact with plasma proteins and binding of drugs to plasma proteins is one of many factors that determines drugs’ ADME (25). Binding of drugs occurs at the level of multiple blood constituents such as albumin, α1-acid glycoprotein, lipoproteins, red blood cells, leukocytes, platelets and α-, β- and γ-globulins. Binding between drugs and plasma proteins is usually reversible, due to weak hydrophobic and electrostatic interactions such as van der Waals and hydrogen bonding (25). Complexes between drugs and plasma proteins in the blood plasma serve as drug reservoirs for the free drug concentration, which affects drug distribution, elimination, the efficacy of the drug and/or its possible toxicity. Interactions between drugs and plasma proteins, especially the binding percentage of the drug with albumins may be affected by co-administration of other drugs or nutrients. One of the best-known examples of competitive displacement of bilirubin from albumin by sulphonamides and subsequent jaundice is described in neonates and with ibuprofen, diazepam, cyclosporine and salicylates use. Due to decreased affinity of albumin for bilirubin in neonatal period, the bilirubin may be displaced from its binding site in albumin by drugs, resulting in clinical jaundice (26).

Drug interactions with other endogenous and/or exogenous molecules due to enhanced metabolism by induction or allosteric activation of CYP enzymes may have clinical consequences. In addition, numerous drugs are CYP inhibitors (competitive, non-competitive, and mechanism-based) and these interactions have a particular importance that require specific clinical management strategy (27).

The biochemical interactions of drugs with body molecules are described as PD response. Interactions with other drugs may induce additive, synergistic or antagonistic PD response. Interactions with BLTs are classified as a pharmacological type of DLTIs (28, 29).

Timely and adequate recognition of significant DLTIs is critical to prevent common clinical consequences such as incorrectly interpreted test results, delayed or non-treated condition due to erroneous diagnosis, unnecessary extra tests, or inadequate therapy. The distribution of interactions by clinical importance according to comprehensive recent review, positions antibacterial agents, specifically cephalosporins, as the most frequently reported drugs that affect the BLTs (glucose and creatinine in blood) (29). Amongst other FDA-approved drugs that interfere with laboratory results, the second most frequent DLTIs are those that appear in patients taking psychotropic drugs, such as antidepressants, antidyskinesia agents and antipsychotic drugs. In these patients, the most frequent DLTIs are false positivity for ketone bodies and false negativity for glucose in urine, as well as false positivity/elevation for phenylketonuria test and pregnancy test results in blood. Other clinically important DLTIs comprise those induced by contrast media (proteinemia, bilirubin, creatinine, iron, calcium, coagulation factors); by proton-pump inhibitors (higher serum concentrations of chromogranin A) and acetaminophen interference with continuous glucose monitor (CGM) sensing, which results in erroneously high readings (29). Several CGM devices are designed to eliminate interference from acetaminophen, but there is still important interference from ascorbic acid and antineoplastic drug, hydroxyurea (30).

An extensive on-line database of the effects of drugs, disease, preanalytical variables, and herbals on laboratory tests, created by principal editor DS Young, contains information on more than 135,000 effects on more than 5,000 tests, with > 50,000 DLTIs (19, 31). Table 1 presents the selection of the most useful DLTIs databases with internet addresses. The prevalence of DLTIs is variable and depending on the hospital ward, literature reports high prevalence of up to 43% of patients who had laboratory results influenced by drugs (32, 33).

Table 1

Selection of DLTIs databases with internet addresses

Database Web address Accessed
AACC Effects on Clinical Laboratory Tests (John Wiley and Sons, Inc., on behalf of the American Association for Clinical Chemistry) https://clinfx.wiley.com/aaccweb/aacc/login Feb 3rd 2023
First DataBank MedKnowledge Database. Hearst Health Network https://www.fdbhealth.com/solutions/medknowledge-drug-database Feb 3rd 2023
Dailymed database (The National Library of Medicine (NLM), a National Institutes of Health (NIH) institute) https://dailymed.nlm.nih.gov/dailymed/ Feb 3rd 2023
Exeter Clinical Laboratory. Blood Sciences department at the Royal Devon & Exeter NHS Foundation Trust, UK. https://www.exeterlaboratory.com/blood-sciences/ Feb 3rd 2023
Drug effects in clinical chemistry (the Swedish Society for Clinical Chemistry in collaboration with the National Corporation of Pharmacies) https://www.tryding.se/ Feb 3rd 2023
Multirec (Multirec Ltd, Turku, Finland) https://www.multirec.fi/products/mr-dle/ Feb 3rd 2023

Consequences of unrecognized DLTIs as preanalytical variables may significantly disturb the analytical process and postanalytical phase. Results of BLTs may be misinterpreted and lead to incorrect or delayed diagnosis, to extra costs for unnecessary additional tests or inadequate therapy, all of which have an important negative clinical impact (34). Linking the laboratory and pharmacology, BLTs and drugs prescriptions, presents an important approach to improve the utilization and quality of both laboratory testing and pharmacotherapy, as well as to provide opportunities for improved outcomes and learning (35, 36).

The main concern of health providers regarding the patients’ safety is to reduce diagnostic errors (defined as incorrect, missed, or delayed diagnoses) that may be the consequence of miscommunication, misinterpretation and missing results (37). In clinical laboratories settings, the basis for accurate laboratory tests and improved quality of all phases of the testing process is the implementation of the laboratory information system (LIS).

One of the first LIS that includes patients’ medication data was described more than 25 years ago (38). Laboratory information system is a key component of a successfully implemented electronic health record (EHR), which combines the clinical documentation module that captures the patient’s clinical data such as diagnosis, procedure, complication and medication (39). In secondary and tertiary health care, the implementation of a LIS that is integrated with an EHR, significantly improved within-laboratory turnaround time, decreased test requests and preanalytical errors, while increasing efficiency and improving provider satisfaction. In addition to improved quality of patient care and reduced errors, LIS links communication between clinical and laboratory medical services. The laboratory information system is an integral part of laboratory data and process management and includes automatic monitoring and evaluation of potential effects of drugs on laboratory tests (40). In modern laboratories, the analytical phase represents the component of the testing process that is the least error-prone (only 15% of all mistakes), due to highly automated and standardized support provided by a LIS (41).

Classification of DLTIs

Drug-laboratory test interactions fall into two broad categories: physiological (pharmacological, biological, in vivo) and analytical (methodological, in vitro) interference (18).

The first type of DLTIs is the most frequent category that refers to the influences of drugs and their metabolites on BLTs, which are independent of the BLT method used in laboratory. This physiological type of DLTIs can be identified when the change of laboratory parameter under the influence of drug is expected (the intended effects of drugs) (19). An illustrative example of this is the decrease of thyroid stimulating hormone upon thyroid hormone replacement therapy (19, 42). On the other hand, the identification of DLTI is difficult in case of unwanted or toxic drug effects such as idiosyncratic drug reactions (IDR) (43). Such DLTIs may lead to wrong, missed or delayed diagnosis, which is the definition of diagnostic error (44).

Recently published data indicate that additional unnecessary diagnostic procedures are carried out due to DLTI, which is well known in medical practice (19). In patients with neuroendocrine tumors (NETs), standard of care requires testing of the most important biochemical tumour markers: chromogranin A (CgA, diagnostic sensitivity and specificity within the range of 60-90%) and neuron-specific enolase (NSE) (45). In addition to the higher circulating CgA concentrations that have been demonstrated in serum or plasma of patients with different NETs, the stimulated CgA release is also possible from secretory granules of gastric enterochromaffin-like cells (ECLs) in non-NET patients who used proton pump inhibitors (PPIs). Even the short-term application of PPIs for 7 days stimulates hyperplasia of gastric ECLs, in which PPIs-induced gastrin elevation enhances the transcription of gene coding for CgA protein. Thus, in clinical practice, the use of PPIs is the most common cause of false (non-NET) CgA increase, and PPIs need to be discontinued for at least 14 days before a CgA test (46). Clinical and laboratory specialists need better awareness of this physiological type of DLTI. The results of an important retrospective study revealed the additional costs and discomfort for patients due to unnecessary diagnostic work-up, which was performed as a consequence of CgA and PPI interaction. Repeated CgA measurement (until CgA concentrations were normalized upon PPIs discontinuation) and somatostatin receptor PET imaging could have been avoided. Additional importance of this DLTI is underlined by high prevalence of NET (57%) in patients with both elevated CgA and prescribed PPIs (34).

Analytical (or in vitro) interactions between drugs and results of BLTs increase the chance of errors in the laboratory analytical process and important clinical consequences. Thus, adequate knowledge of such DLTIs may prevent errors in test interpretation, while the avoidance can be achieved by selection of an appropriate laboratory test method that is not influenced by drugs. Important examples comprise analytical DLTIs with commonly used drugs and BLTs that are used to guide the clinical decisions. There is an increase of up to additional 200 μmol/L of creatinine due to positive interference of cephalosporins (excluding cefotaxime and ceftazidime) with the Jaffe analytical method for creatinine (cefoxitin at concentrations ≥ 100 µg/mL for up to 2 hours post-infusion) (47). Falsely higher blood glucose values are determined by capillary blood glucose meters, in patients receiving intravenous vitamin C therapy (48). High-dose intravenous vitamin C (6 g/day for ≥ 5 days) is associated with lower mortality in patients with severe sepsis and septic shock (48). The cornerstones of therapy for patients in septic shock are adequate hemodynamic resuscitation, vasopressor therapy, and ventilation support, and in these patients high-dose vitamin C therapy decreases the fluid and vasopressors requirements (49). However, the well-known interference between vitamin C and glycaemia measurement method may result in erroneously recognized pseudohyperglycaemia and improperly indicated insulin therapy, leading to dangerous, possibly even fatal consequences. Since the hexokinase spectrophotometric method does not interfere with vitamin C, its use is recommended for point-of-care glucose monitoring in patients receiving intravenous high-dose ascorbic acid therapy (49). Considering the ascorbate PK, glycaemia measurements should be performed for at least 8-10h after intravenous vitamin C therapy (50).

Numerous important examples of DLTIs are described in our literature survey presented in Supplementary table 1, in which we provided a comprehensive and relevant list of interactions between drugs and BLTs, which may improve the knowledge of healthcare providers, including physicians, pharmacists and laboratory specialists. In Table 2, we selected several examples of the most commonly prescribed drugs and DLTIs. Within the limitations of our comprehensive review, we directly consulted and analysed only the primary sources i.e., the scientific articles published in PubMed, however for construction of Supplementary table 1 we did not consult several DLTIs databases that also provide the overview of interactions and the corresponding available literature.

Table 2

Selection of most commonly prescribed drugs and DLTIs

Drug Laboratory parameter Change Mechanism
Ascorbic acid Total cholesterol Decrease Negative interaction with Trinder’s reaction
Triglycerides Decrease
Uric acid Decrease
Creatinine Increase Positive interaction with Jaffe reaction
Total bilirubin Decrease /
Acetaminophen Glucose Increase Falsely elevated continuous glucose monitor (CGM) sensing
Cefalotin Creatinine Increase Positive interaction with Jaffe reaction
Ceftriaxone Total bilirubin Increase Competitive binding to albumin
Ciprofloxacin Glucose Increase/decrease Impaired glucose homeostasis
Fluoxetine Triglycerides Increase /
Isoflurane Total bilirubin Increase /
Ritonavir Triglycerides Increase Increased production of very low-density lipoprotein
Simvastatin Glucose Increase Impaired insulin secretion, impaired glucose uptake by the cells
Warfarin Uric acid Increase Enhanced uric acid production

Concluding remarks

In conclusion, we believe that synergy of advanced analytical methods and interdisciplinary researchers as well as improved digitalization and automation of laboratory medicine with implementation of artificial intelligence into analysis of complicated medical data, is the pathway for improved precision and continuous optimization of the laboratory processes. This is an opportunity to advance healthcare with very important improvements for patients, but also for health professionals, data scientists, engineers and analytical chemists, with the main aim to reduce the prevalence of incorrect diagnosis, inadequate treatment and unnecessary follow-up due to DLTIs.


Supported by the grant from Ministry of Education, Science and Technological Development, Republic of Serbia III41012 (grant number 451-03-68/2022-14/200114), and Project of special interest for sustainable development in the Autonomous Province of Vojvodina (grant number 142-451-2532/2021-01). The authors are grateful to Prof Dr Sinisa Dovat, MD, PhD and Dr Joseph Schramm, MD, Pennsylvania State University College of Medicine, Hershey, PA, USA, for their constructive scientific suggestions and linguistic editing of this paper.


[1] Conflicts of interest Potential conflict of interest

None declared.



Hicks AJ, Carwardine ZL, Hallworth MJ, Kilpatrick ES. Using clinical guidelines to assess the potential value of laboratory medicine in clinical decision-making. Biochem Med (Zagreb). 2021;31:010703. https://doi.org/10.11613/BM.2021.010703


Schünemann HJ, Reinap M, Piggott T, Laidmäe E, Köhler K, Pōld M, et al. The ecosystem of health decision making: from fragmentation to synergy. Lancet Public Health. 2022;7:e378–90. https://doi.org/10.1016/S2468-2667(22)00057-3


Ferraro S, Braga F, Panteghini M. Laboratory medicine in the new healthcare environment. Clin Chem Lab Med. 2016;54:523–33. https://doi.org/10.1515/cclm-2015-0803


Sikaris KA. Enhancing the Clinical Value of Medical Laboratory Testing. Clin Biochem Rev. 2017;38:107–14.


Landeck L, Kneip C, Reischl J, Asadullah K. Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology. Exp Dermatol. 2016;25:333–9. https://doi.org/10.1111/exd.12948


Revuelta-López E, Barallat J, Cserkóová A, Gálvez-Montón C, Jaffe AS, Januzzi JL, et al. Pre-analytical considerations in biomarker research: focus on cardiovascular disease. Clin Chem Lab Med. 2021;59:1747–60. https://doi.org/10.1515/cclm-2021-0377


Lippi G, Simundic AM. The EFLM strategy for harmonization of the preanalytical phase. Clin Chem Lab Med. 2018;56:1660–6. https://doi.org/10.1515/cclm-2017-0277


Cadamuro J, Baird G, Baumann G, Bolenius K, Cornes M, Ibarz M, et al. Preanalytical quality improvement - an interdisciplinary journey, on behalf of the European Federation for Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for Preanalytical Phase (WG-PRE). Clin Chem Lab Med. 2022;60:662–8. https://doi.org/10.1515/cclm-2022-0117


Lippi G, Bovo C, Ciaccio M. Inappropriateness in laboratory medicine: an elephant in the room? Ann Transl Med. 2017;5:82. https://doi.org/10.21037/atm.2017.02.04


Dukic L, Jokic A, Kules J, Pasalic D. The knowledge and understanding of preanalytical phase among biomedicine students at the University of Zagreb. Biochem Med (Zagreb). 2016;26:90–7. https://doi.org/10.11613/BM.2016.009


Kulkarni KK, Bhandari AP, Unni AK. Questionnaire-based study to assess knowledge of preanalytical phase of laboratory testing among trainee doctors in a tertiary care hospital medical college. J Lab Physicians. 2020;12:178–83. https://doi.org/10.1055/s-0040-1720945


Simundic AM, Lippi G. Preanalytical phase – a continuous challenge for laboratory professionals. Biochem Med (Zagreb). 2012;22:145–9. https://doi.org/10.11613/BM.2012.017


Grzych G, Sivadas A. The rising importance of pre-analytical phase in medical and research laboratory, a new challenge in the omics era. Biomed J Sci Tech Res. 2020;27:20889–91. https://doi.org/10.26717/BJSTR.2020.27.004523


Simundic AM, Filipi P, Vrtaric A, Miler M, Gabaj NN, Kocsis A, et al. Patient’s knowledge and awareness about the effect of the over-the-counter (OTC) drugs and dietary supplements on laboratory test results: a survey in 18 European countries. Clin Chem Lab Med. 2018;57:183–94. https://doi.org/10.1515/cclm-2018-0579


Plebani M, Sciacovelli L, Aita A, Chiozza ML. Harmonization of pre-analytical quality indicators. Biochem Med (Zagreb). 2014;24:105–13. https://doi.org/10.11613/BM.2014.012


Horvath AR. From evidence to best practice in laboratory medicine. Clin Biochem Rev. 2013;34:47–60.


Siest G, Dawkins SJ, Galteau MM. Drug effects on clinical laboratory tests. J Pharm Biomed Anal. 1983;1:247–57. https://doi.org/10.1016/0731-7085(83)80038-7


van Balveren JA, Verboeket-van de Venne WPHG, Doggen CJM, Cornelissen AS, Erdem-Eraslan L, de Graaf AJ, et al. Clinical usefulness of drug-laboratory test interaction alerts: a multicentre survey. Clin Chem Lab Med. 2021;59:1239–45. https://doi.org/10.1515/cclm-2020-1770


van Balveren JA, Verboeket-van de Venne WPHG, Erdem-Eraslan L, de Graaf AJ, Loot AE, Musson REA, et al. Diagnostic error as a result of drug-laboratory test interactions. Diagnosis (Berl). 2019;6:69–71. https://doi.org/10.1515/dx-2018-0098


van Balveren JA, Verboeket-van de Venne WPHG, Erdem-Eraslan L, de Graaf AJ, Loot AE, Musson REA, et al. Impact of interactions between drugs and laboratory test results on diagnostic test interpretation - a systematic review. Clin Chem Lab Med. 2018;56:2004–9. https://doi.org/10.1515/cclm-2018-0900


Masnoon N, Shakib S, Kalisch-Ellett L, Caugey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17:230. https://doi.org/10.1186/s12877-017-0621-2


Pazan F, Wehling M. Polypharmacy in older adults: a narrative review of definitions, epidemiology and consequences. Eur Geriatr Med. 2021;12:443–52. https://doi.org/10.1007/s41999-021-00479-3


Khezrian M, McNeil CJ, Murray AD, Myint PK. An overview of prevalence, determinants and health outcomes of polypharmacy. Ther Adv Drug Saf. 2020;11:2042098620933741. https://doi.org/10.1177/2042098620933741


Silva RS, Domingueti CP, Tinoco MS, Veloso JC, Pereira ML, Baldoni AO, et al. Interference of medicines in laboratory exams. J Bras Patol Med Lab. 2021;57:1–15. https://doi.org/10.5935/1676-2444.20210014


Roberts AG, Gibbs ME. Mechanisms and the clinical relevance of complex drug-drug interactions. Clin Pharmacol. 2018;10:123–34. https://doi.org/10.2147/CPAA.S146115


Sethi PK, White CA, Cummings BS, Hines RN, Muralidhara S, Bruckner JV. Ontogeny of plasma proteins, albumin and binding of diazepam, cyclosporine, and deltamethrin. Pediatr Res. 2016;79:409–15. https://doi.org/10.1038/pr.2015.237


Hakkola J, Hukkanen J, Turpeinen M, Pelkonen O. Inhibition and induction of CYP enzymes in humans: an update. Arch Toxicol. 2020;94:3671–722. https://doi.org/10.1007/s00204-020-02936-7


Niu J, Straubinger RM, Mager DE. Pharmacodynamic drug-drug interactions. Clin Pharmacol Ther. 2019;105:1395–406. https://doi.org/10.1002/cpt.1434


Yao H, Rayburn ER, Shi Q, Gao L, Hu W, Li H. FDA-approved drugs that interfere with laboratory tests: A systematic search of US drug labels. Crit Rev Clin Lab Sci. 2017;54:1–17. https://doi.org/10.1080/10408363.2016.1191425


Heinemann L. Interferences with CGM systems: practical relevance? J Diabetes Sci Technol. 2022;16:271–4. https://doi.org/10.1177/19322968211065065


Young DS. Effects of drugs on clinical laboratory tests, 5th ed. Washington: American Association of Clinical Chemistry, 2000.


Kailajärvi M, Takala T, Grönroos P, Tryding N, Viikari J, Irjala K, et al. Reminders of drug effects on laboratory test results. Clin Chem. 2000;46:1395–400. https://doi.org/10.1093/clinchem/46.9.1395


van Balveren JA, Verboeket-van de Venne WPHG, Doggen CJM, Erdem-Eraslan L, de Graaf AJ, Krabbe JG, et al. Added value of drug-laboratory test interaction alerts in test result authorisation. Clin Chem Lab Med. 2022;60:e108–11. https://doi.org/10.1515/cclm-2022-0077


van Balveren JA, Erdem-Eraslan L, Verboeket-van de Venne WPHG, Doggen CJM, Hofland J, Oosterhuis WP, et al. Awareness of drug laboratory test interactions is important for prevention of unnecessary additional diagnostics: An example. Clin Chim Acta. 2022;530:99–103. https://doi.org/10.1016/j.cca.2022.03.013


Schiff GD, Klass D, Peterson J, Shah G, Bates DW. Linking laboratory and pharmacy: opportunities for reducing errors and improving care. Arch Intern Med. 2003;163:893–900. https://doi.org/10.1001/archinte.163.8.893


van Balveren JA, Verboeket-van de Venne WPHG, Doggen CJM Erdem-Eraslan L, de Graaf AJ, Krabbe JG, et al. Real-time monitoring of drug laboratory test interactions: a proof of concept. Clin Chem Lab Med. 2021;60:235–42. https://doi.org/10.1515/cclm-2021-0790


Griffin JA, Carr K, Bersani K, Piniella N, Motta-Calderon D, Malik M, et al. Analyzing diagnostic errors in the acute setting: a process-driven approach. Diagnosis (Berl). 2021;9:77–88. https://doi.org/10.1515/dx-2021-0033


Forsström JJ, Grönroos P, Irjala K, Heiskanen J, Torniainen K. Linking patient medication data with laboratory information system. Int J Biomed Comput. 1996;42:111–6. https://doi.org/10.1016/0020-7101(96)01188-9


Petrides AK, Bixho I, Goonan EM, Bates DW, Shaykevich S, Lipsitz SR, et al. The benefits and challenges of an interfaced electronic health record and laboratory information system: effects on laboratory processes. Arch Pathol Lab Med. 2017;141:410–7. https://doi.org/10.5858/arpa.2016-0146-OA


Kailajärvi M, Takala T, Gronroos P, Tryding N, Viikari J, Irjala K, et al. Reminders of drug effects on laboratory test results. Clin Chem. 2000;46:1395–400. https://doi.org/10.1093/clinchem/46.9.1395


Kammergruber R, Durner J. Laboratory information system and necessary improvements in function and programming. J Lab Med. 2018;42:277–87. https://doi.org/10.1515/labmed-2018-0038


Jonklaas J. Optimal thyroid hormone replacement. Endocr Rev. 2022;43:366–404. https://doi.org/10.1210/endrev/bnab031


Benesic A, Rotter I, Dragoi D, Weber S, Leiti A, Buchholtz ML, et al. Development and validation of a test to identify drugs that cause idiosyncratic drug-induced liver injury. Clin Gastroenterol Hepatol. 2018;16:1488–1494.e5. https://doi.org/10.1016/j.cgh.2018.04.049


Abimanyi-Ochom J, Bohingamu Mudiyanselage S, Catchpool M, Firipis M, Arachchige Dona SW, Watts JJ. Strategies to reduce diagnostic errors: a systematic review. BMC Med Inform Decis Mak. 2019;19:174. https://doi.org/10.1186/s12911-019-0901-1


Oberg K, Couvelard A, Delle Fave G, Gross D, Grossman A, Jensen RT, et al. ENETS consensus guidelines for standard of care in neuroendocrine tumours: biochemical markers. Neuroendocrinology. 2017;105:201–11. https://doi.org/10.1159/000472254


Mosli HH, Denis A, Kocha W, Asher LJ, Van Uum SHM. Effect of short-term proton pump inhibitor treatment and its discontinuation on chromogranin A in healthy subjects. J Clin Endocrinol Metab. 2012;97:E1731–5. https://doi.org/10.1210/jc.2012-1548


Wang D, Emanuel AJ, Neyens RR, Bakharev E, Babic N. Cefoxitin-serum creatinine interference in a patient with nontuberculous mycobacteria ventriculomeningitis. J Pharm Pract. 2021;34:658–61. https://doi.org/10.1177/0897190020921613


Jung SY, Lee MT, Baek MS, Kim WY. Vitamin C for ≥ 5 days is associated with decreased hospital mortality in sepsis subgroups: a nationwide cohort study. Crit Care. 2022;26:3. https://doi.org/10.1186/s13054-021-03872-3


Rosengrave P, Spencer E, Williman J, Mehrtens J, Morgan S, Doyle T, et al. Intravenous vitamin C administration to patients with septic shock: a pilot randomised controlled trial. Crit Care. 2022;26:26. https://doi.org/10.1186/s13054-022-03900-w


Sharma E, Resta C, Park P. A Case of Factitious Hyperglycemia in a patient on intravenous ascorbic acid. Case Rep Endocrinol. 2018;2018:7063137. https://doi.org/10.1155/2018/7063137