Pseudonormokalemia case report - What does it mean to have normal blood potassium?

This case report describes a case of pseudonormokalemia, true hypokalemia. Often, only laboratory values outside the normal range gain attention and false normal results are at risk of not being noticed. However, a disease state may be masked by another pathological process. Here, a 50-year old male was admitted to the Department of Internal Medicine due to sepsis from a dental infection. Initially, serum potassium measurement revealed a normal value of 4 mmol/L (reference interval 3.8-5.1 mmol/L). Thrombocyte number was above 500x109/L. Due to our policy to recommend a repeated measurement of potassium in whole blood or heparin plasma if a patient has thrombocytosis, pseudonormokalemia was identified because the heparin plasma potassium value was only 2.9 mmol/L (reference interval 3.5-4.8 mmol/L). The physiological difference between serum and plasma concentration is no more than 0.3 mmol/L. In this case, potassium concentration were falsely elevated in the serum sample, probably caused by the high number of platelets releasing potassium during clotting. Interpretative comments in patients with thrombocytosis over 500x109/L recommending plasma potassium measurement are helpful. The best way to eliminate pseudohyperkalemia and pseudonormokalemia phenomena caused by thrombocytosis is to completely change towards heparin plasma as the standard material.

Influence of haemolysis on blood biochemistry profiles in cattle

Although haemolysis is the most common source of preanalytical error in clinical laboratories, its influence on cattle biochemistry remains poorly understood. The effect of haemolysis and its clinical relevance were investigated in 70 samples in which haemolysis was artificially induced (by spiking with increasing amounts of haemolysate, yielding 0.0%, 0.2%, 0.5%, 1.0%, 2.5%, 5.0% and 10% haemolysis degree (HD)), focusing on key parameters for bovine metabolic health assessment, including albumin, alkaline phosphatase (ALP), aspartate aminotransferase (AST), blood urea nitrogen (BUN), calcium (Ca), cholesterol, creatinine, creatine kinase (CK), gamma-glutamyl transferase (GGT), globulins, magnesium (Mg), phosphorus (P), total bilirubin (TBIL) and total proteins (TP). Preanalytical haemolysis significantly affected most (8 of 14) of the biochemical parameters analysed, leading to significant increases in concentrations of albumin (starting at 5% HD), cholesterol (at 5% HD) and P (at 10% HD) and to significant decreases in Ca (at 2.5% HD), creatinine (at 5% HD), globulins (at 10% HD), TBIL (at 2.5% HD) and TP (at 10% HD). Comparison of the present and previous data indicated that, for each parameter, the HD required to produce significant bias and the clinical relevance of over- and underestimation are variable and appear to depend on the analytical technique used. Therefore, different laboratories should evaluate the influence of haemolysis in their analytical results and provide advice to clinicians accordingly. Affected parameters should be interpreted together with clinical signs and other analytical data to minimize misinterpretations (false or masked variations). Finally, due to the significant impact on numerous parameters and the limited potential for correction, we recommend rejection of samples with >10% HD.

Artificial intelligence in the pre-analytical phase: State-of-the art and future perspectives

The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine. Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis. Therefore, this opinion paper aims to discuss the state-of-the-art and some future possibilities of AI in the preanalytical phase.

Patient Stratification for Antibiotic Prescriptions Based on the Bound-Free Phase Detection Immunoassay of C-Reactive Protein in Serum Samples

C-reactive protein is a well-studied host response biomarker, whose diagnostic performance depends on its accurate classification into concentration zones defined by clinical scenario-specific cutoff values. We validated a newly developed, bead-based, bound-free phase detection immunoassay (BFPD-IA) versus a commercial CE-IVD enzyme-linked immunosorbent assay (ELISA) kit and a commercial CE-IVD immunoturbidimetric assay (ITA) kit. The latter was performed on a fully automated DPC Konelab 60i clinical analyzer used in routine diagnosis. We classified 53 samples into concentration zones derived from four different sets of cutoff values that are related to antibiotic prescription scenarios in the case of respiratory tract infections. The agreements between the methods were ELISA/ITA at 87.7%, ELISA/BFPD-IA at 87.3%, and ITA/-BFPD-IA at 93.9%, reaching 98-99% in all cases when considering the calculated relative combined uncertainty of the single measurement of each sample. In a subgroup of 37 samples, which were analyzed for absolute concentration quantification, the scatter plot slopes' correlations were as follows: ELISA/ITA 1.15, R2 = 0.97; BFPD-IA/ELISA 1.12, R2 = 0.95; BFPD-IA/ITA 0.95, R2 = 0.93. These very good performances and the agreement between BFPD-IA and ITA (routine diagnostic), combined with BFPD-IA's functional advantages over ITA (and ELISA)-such as quick time to result (~20 min), reduced consumed reagents (only one assay buffer and no washing), few and easy steps, and compatibility with nucleic-acid-amplification instruments-render it a potential approach for a reliable, cost-efficient, evidence-based point-of-care diagnostic test for guiding antibiotic prescriptions.

Erroneous potassium results: preanalytical causes, detection, and corrective actions

Potassium is one of the most requested laboratory tests. Its level is carefully monitored and maintained in a narrow physiological range. Even slightly altered potassium values may severely impact the patient's health, which is why an accurate and reliable result is of such importance. Even if high-quality analytics are available, there are still numerous ways in which potassium measurements may be biased, all of which occur in the preanalytical phase of the total laboratory testing process. As these results do not reflect the patient's in-vivo status, such results are referred to as pseudo-hyper/hypokalemia or indeed pseudo-normokalemia, depending on the true potassium result. Our goal in this review is to present an in-depth analysis of preanalytical errors that may result in inaccurate potassium results. After reviewing existing evidence on this topic, we classified preanalytical errors impacting potassium results into 4 categories: 1) patient factors like high platelet, leukocytes, or erythrocyte counts; 2) the sample type 3) the blood collection procedure, including inappropriate equipment, patient preparation, sample contamination and others and 4) the tube processing. The latter two include sample transport and storage conditions of whole blood, plasma, or serum as well as sample separation and subsequent preanalytical processes. In particular, we discuss the contribution of hemolysis, as one of the most frequent preanalytical errors, to pseudo-hyperkalemia. We provide a practical flow chart and a tabular overview of all the discussed preanalytical errors including possible underlying mechanisms, indicators for detection, suggestions for corrective actions, and references to the according evidence. We thereby hope that this manuscript will serve as a resource in the prevention and investigation of potentially biased potassium results.

Measuring haemolysis in cattle serum by direct UV-VIS and RGB digital image-based methods

A simple, rapid procedure is required for the routine detection and quantification of haemolysis, one of the main sources of unreliable results in serum analysis. In this study, we compared two different approaches for the rapid determination of haemolysis in cattle serum. The first consisted of estimating haemolysis via a simple direct ultraviolet-visible (UV-VIS) spectrophotometric measurement of serum samples. The second involved analysis of red, green, blue (RGB) colour data extracted from digital images of serum samples and relating the haemoglobin (Hb) content by means of both univariate (R, G, B and intensity separately) and multivariate calibrations (R, G, B and intensity jointly) using partial least squares regression and artificial neural networks. The direct UV-VIS analysis and RGB-multivariate analysis using neural network methods were both appropriate for evaluating haemolysis in serum cattle samples. The procedures displayed good accuracy (mean recoveries of 100.7 and 102.1%, respectively), adequate precision (with coefficients of variation from 0.21 to 2.68%), limit of detection (0.14 and 0.21 g L-1, respectively), and linearity of up to 10 g L-1.

A Deep Learning-Based System for Assessment of Serum Quality Using Sample Images

BACKGROUND

Serum quality is an important factor in the pre-analytical phase of laboratory analysis. Visual inspection of serum quality (including recognition of hemolysis, icterus, and lipemia) is widely used in clinical laboratories but is time-consuming, subjective, and prone to errors.

METHODS

Deep learning models were trained using a dataset of 16427 centrifuged blood images with known serum indices values (including hemolytic index, icteric index, and lipemic index) and their performance was evaluated by five-fold cross-validation. Models were developed for recognizing qualified, unqualified and image-interfered samples, predicting serum indices values, and finally composed into a deep learning-based system for the automatic assessment of serum quality.

RESULTS

The area under the receiver operating characteristic curve (AUC) of the developed model for recognizing qualified, unqualified and image-interfered samples was 0.987, 0.983, and 0.999 respectively. As for subclassification of hemolysis, icterus, and lipemia, the AUCs were 0.989, 0.996, and 0.993. For serum indices and total bilirubin predictions, the Pearson's correlation coefficients (PCCs) of the developed model were 0.840, 0.963, 0.854, and 0.953 respectively. Moreover, 30.8% of serum indices tests were deemed unnecessary due to the preliminary application of the deep learning-based system.

CONCLUSIONS

The deep learning-based system is suitable for the assessment of serum quality and holds the potential to be used as an accurate, efficient, and rarely interfered solution in clinical laboratories.

Influence of Haemolysis on the Mineral Profile of Cattle Serum

Simple Summary

The results of blood tests routinely used in clinical chemistry can be altered by haemolysis, the disruption of red blood cells. Haemolysis of serum samples is recognized to be the leading cause of preanalytical errors in clinical laboratories. The influence of haemolysis must be specifically studied for each analyte and species of clinical interest, as it is often not known how serum samples are affected. Little is known about the potential alterations in the concentrations of mineral elements in haemolyzed serum in general and the phenomenon has not been specifically studied in bovine serum samples. We investigate how haemolysis affects the mineral content of bovine samples.

Abstract

Haemolysis of serum samples is the leading cause of preanalytical errors in clinical laboratories. Little is known about the potential alterations in the concentrations of mineral elements in haemolyzed serum and the phenomenon has not been specifically studied in bovine serum samples. We investigate how haemolysis affects the mineral content of bovine samples. We used ICP-MS to measure the concentrations of 12 mineral elements (Ca, Co, Cr, Cu, Fe, Mg, Mn, Mo, Ni, P, Se and Zn) in bovine whole blood, serum and gradually haemolyzed samples and observed significant differences between the different types of samples, particularly in the Fe and Zn concentrations. However, in practice, the high interindividual variability makes it difficult to establish whether a given value corresponds to normal or haemolyzed samples. In response to this problem, we propose to consider that a result is significantly biased when the haemolysis threshold (the degree of haemolysis above which the concentration of an element in serum is significantly altered) of a given element is surpassed. The haemolysis threshold values for the different elements considered were found as follows: 0.015 g Hb L⁻¹ for Fe, 2 g for Zn, 4 g for Cr and 8 g for Ca, Se and Mo.

Laboratory-related issues in the measurement of cardiac troponins with highly sensitive assays

A number of assay-related issues can affect the performance of cardiac troponin (cTn) measurement in everyday practice. In this respect, it is vital that all information on cTn assays is known and that the performance characteristics of assays are objectively assessed and adequately described. The advent of the latest generation of more sensitive cTn assays has heralded a new wave of information about low concentrations of cTn in blood. These recent generation assays have improved analytical sensitivity and corresponding performance at low cTn concentrations when compared to their predecessors, providing a convincing goal for laboratory medicine in helping clinicians in the diagnosis of acute myocardial infarction. Crucial to the clinical utility of highly sensitive cTn assays is the laboratorians' role in closely scrutinizing proposed assays and defining their value in relation to available evidence. Analytical, as well as pre-analytical and post-analytical, aspects must be documented. In this review, we describe what laboratory professionals should know about their cTn assay performance characteristics and the pre-analytical prerequisites for robustness to ensure optimal post-analytical reporting.

Measurement of Serum Neuron-Specific Enolase in Neuroblastoma: Is There a Clinical Role?

BACKGROUND

The measurement of neuron-specific enolase (NSE) in serum is frequently requested for diagnosis, risk stratification, and treatment monitoring of neuroblastoma (NB) in the pediatric population. However, authoritative clinical practice guidelines advise about the poor diagnostic performance of NSE.

Content

We critically appraised the available literature evaluating the diagnostic and prognostic value of NSE in the management of NB, paying special attention to the definition of appropriate threshold levels. In addition, we discuss the interfering conditions causing artifactual increases of NSE concentrations in serum and potentially influencing the clinical evaluation of patients with suspected NB.

Summary

No definitive evidence supports the use of serum NSE for diagnosis and monitoring of NB. The risk of obtaining false-positive NSE results associated with confounders (e.g., sample hemolysis) and other pathophysiologic conditions (e.g., inflammation) is remarkable and hampers the diagnostic value of this test. NSE may be helpful to define the risk of death of patients with NB, mainly in the advanced stages of disease. However, further studies validating currently marketed immunoassays and defining threshold values useful for this scope are warranted.

Hemolytic specimens in complete blood cell count: Red cell parameters could be revised by plasma free hemoglobin

Introduction

Hemolysis is the main cause of unqualified clinical samples. In this study, we established a method for detecting and evaluating hemolysis in whole blood test. We used a mathematical formula for correcting the influence of hemolysis on complete blood cell count (CBC) so as to avoid re‐venipuncture and obtain more accurate parameters of red blood cell detection, reduce the burden of patients, and improve the efficiency of diagnosis and treatment.

Methods

Hemolytic samples were selected and then corrected using the new formula. Plasma free hemoglobin (fHB) was used as the criterion to determine the degree of hemolysis; the uncertainty of measurement is acceptable as the limit value of deviation between the measured value and the revised value. Hemolysis simulation analysis in vitro and continuous monitoring of clinical patients were used to verify the correction effect.

Results

A total of 83 clinical samples with hemolysis were collected and analyzed; fHB 1.4 g/L was selected as the unacceptable value for clinical hemolysis detection. In hemolytic samples, the red blood cell parameters corrected by formula are significantly different from those uncorrected and had a good consistency with those before hemolysis.

Conclusion

The results show that the hemolysis phenomenon of CBC has a significant impact on routine blood testing. By using the new formula, the influence of hemolysis on erythrocyte and related parameters can be quickly and easily corrected, thus avoiding venipuncture again for re‐examination, reducing diagnostic errors, and saving medical resources.

Managing hemolyzed samples in clinical laboratories

Hemolysis is conventionally defined as membrane disruption of red blood cells and other blood cells that is accompanied by subsequent release of intracellular components into the serum or plasma. It accounts for over 60% of blood sample rejections in the laboratory and is the most common preanalytical error in laboratory medicine. Hemolysis can occur both in vivo and in vitro. Intravascular hemolysis (in vivo) is always associated with an underlying pathological condition or disease, and thus careful steps should always be taken by the laboratory to exclude in vivo hemolysis with confidence. In vitro hemolysis, on the other hand, is highly preventable. It may occur at all stages of the preanalytical phase (i.e. sample collection, transport, handling and storage), and may lead to clinically relevant, yet spurious, changes in patient results by interfering with laboratory measurements. Hemolysis interference is exerted through several mechanisms: (1) spectrophotometric interference, (2) release of intracellular components, (3) sample dilution and (4) chemical interference. The degree of interference observed depends on the level of hemolysis and also on the assay methodology. Recent evidence shows that preanalytical practices related to detection and management of hemolyzed samples are highly heterogeneous and need to be standardized. The Working Group for Preanalytical Phase (WG-PRE) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has published many recommendations for facilitating standardization and improvement of this important preanalytical issue. Some key EFLM WG-PRE publications related to hemolysis involve: (i) a call for more transparency and some practical recommendations for improving the harmonization of the automatic assessment of serum indices and their clinical usefulness, specifically the hemolysis index (H-index), (ii) recommendations on how to manage local quality assurance of serum or plasma hemolysis/icterus/lipemia-indices (HIL-indices) and (iii) recommendations on how to detect and manage hemolyzed samples in clinical chemistry testing. In this review we provide a comprehensive overview of hemolysis, including its causes and effects on clinical laboratory assays. Furthermore, we list and discuss the most recent recommendations aimed at managing hemolyzed samples in everyday practice. Given the high prevalence of hemolyzed blood samples, the associated costs, the great heterogeneity in how hemolysis is handled across healthcare settings, countries and continents, and increasing patient cross-border mobility, standardization and quality improvement processes aimed at combatting this important preanalytical problem are clearly warranted.

European survey on preanalytical sample handling - Part 2: Practices of European laboratories on monitoring and processing haemolytic, icteric and lipemic samples. On behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase (WG-PRE)

Introduction

No guideline currently exists on how to detect or document haemolysis, icterus or lipemia (HIL) in blood samples, nor on subsequent use of this information. The EFLM WG-PRE has performed a survey for assessing current practices of European laboratories in HIL monitoring. This second part of two coherent articles is focused on HIL.

Materials and methods

An online survey, containing 39 questions on preanalytical issues, was disseminated among EFLM member countries. Seventeen questions exclusively focused on assessment, management and follow-up actions of HIL in routine blood samples.

Results

Overall, 1405 valid responses from 37 countries were received. A total of 1160 (86%) of all responders stating to analyse blood samples - monitored HIL. HIL was mostly checked in clinical chemistry samples and less frequently in those received for coagulation, therapeutic drug monitoring and serology/infectious disease testing. HIL detection by automatic HIL indices or visual inspection, along with haemolysis cut-offs definition, varied widely among responders. A quarter of responders performing automated HIL checks used internal quality controls. In haemolytic/icteric/lipemic samples, most responders (70%) only rejected HIL-sensitive parameters, whilst about 20% released all test results with general comments. Other responders did not analysed but rejected the entire sample, while some released all tests, without comments. Overall, 26% responders who monitored HIL were using this information for monitoring phlebotomy or sample transport quality.

Conclusion

Strategies for monitoring and treating haemolytic, icteric or lipemic samples are quite heterogeneous in Europe. The WG-PRE will use these insights for developing and providing recommendations aimed at harmonizing strategies across Europe.

European survey on preanalytical sample handling - Part 1: How do European laboratories monitor the preanalytical phase? On behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase (WG-PRE)

Introduction

Compared to other activities of the testing process, the preanalytical phase is plagued by a lower degree of standardization, which makes it more vulnerable to errors. With the aim of providing guidelines and recommendations, the EFLM WG-PRE issued a survey across European medical laboratories, to gather information on local preanalytical practices. This is part one of two coherent articles, which covers all practices on monitoring preanalytical quality except haemolysis, icterus and lipemia (HIL).

Materials and methods

An online survey, containing 39 questions dealing with a broad spectrum of preanalytical issues, was disseminated to EFLM member countries. The survey included questions on willingness of laboratories to engage in preanalytical issues.

Results

Overall, 1405 valid responses were received from 37 countries. 1265 (94%) responders declared to monitor preanalytical errors. Assessment, documentation and further use of this information varied widely among respondents and partially among countries. Many responders were interested in a preanalytical online platform, holding information on various aspects of the preanalytical phase (N = 1177; 87%), in a guideline for measurement and evaluation of preanalytical variables (N = 1235; 92%), and in preanalytical e-learning programs or webinars (N = 1125; 84%). Fewer responders were interested in, or already participating in, preanalytical EQA programs (N = 951; 71%).

Conclusion

Although substantial heterogeneity was found across European laboratories on preanalytical phase monitoring, the interest in preanalytical issues was high. A large majority of participants indicated an interest in new guidelines regarding preanalytical variables and learning activities. This important data will be used by the WG-PRE for providing recommendations on the most critical issues.

Pre-analytical quality control in hemostasis laboratories: visual evaluation of hemolysis index alone may cause unnecessary sample rejection

Background

Visual inspection is the most widespread method for evaluating sample hemolysis in hemostasis laboratories. The hemolysis index (HI) was determined visually (visual index, VI) and measured on an ACL TOP 750 (IL Werfen) system with a hemolysis-icterus-lipemia index (HIL) module. These values were compared with those measured on clinical chemistry systems Unicel DXC600 and AU680 and with quantitation of free-hemoglobin (Hb) performed by a spectrophotometric measurement method (SMM).

Methods

The HI was measured in 356 sodium citrate plasma samples, 306 of which were visibly hemolyzed to varying degrees and 50 were not hemolyzed. The analytical performance of each method was evaluated.

Results

Linear regression analysis, calculated between SMM and the other systems in the study, returned coefficients of determination r2 = 0.853 (AU680), r2 = 0.893 (DXC600) and r2 = 0.917 (ACL TOP 750). An r2 = 0.648 was obtained for linear regression analysis between VI and ACL TOP 750. In addition, ACL TOP 750 showed an excellent correlation in multivariate analysis (r2 = 0.958), showing good sensitivity (0.939) and specificity (0.934) and a diagnostic accuracy of 94%. By comparison, DXC600 and AU680 showed coefficients of determination of 0.945 and 0.923, respectively. A cut-off was set at 0.15 g/L free-Hb, as determined by the automated method, such that any hemostasis samples measuring above this threshold should not be analyzed. Based on this criterion, samples were classified as accepted or rejected, and the number of samples discarded during VI or ACL TOP 750 measurements was compared.

Conclusions

This study confirmed that hemostasis laboratories should consider introducing an objective, automated and standardized method to check samples for hemolysis. By relying solely on visual inspection, up to 50% of samples could be unnecessarily rejected. The ACL TOP 750 system demonstrated a satisfactory analytical performance, giving results comparable to those of the reference method.

Practical recommendations for managing hemolyzed samples in clinical chemistry testing

We suggest here a pragmatic approach for managing results of clinical chemistry testing in hemolyzed samples collected from adults/older children, attempting to balance the need to produce quality laboratory data with clinical urgency of releasing test results. Automatic measurement of the hemolysis index (H-index) in serum or plasma is highly advisable, whilst low-quality assessment of this test remains less good than a visual inspection. Regarding its practical use, when the H-index value does not generate an analytically significant bias, results can be released, whilst when the value is associated with analyte variation in a range between analytically and clinically significant bias (i.e. variation does not exceed the reference change value [RCV]), results of hemolysis-sensitive tests can be released in association with a comment describing the direction in which data are potentially altered, suggesting the need to collect another sample. When the H-index is associated with analyte variation exceeding clinically significant bias (i.e. variation exceeds the RCV), results of hemolysis-sensitive tests should be suppressed and replaced with a comment that biased results cannot be released because the sample is preanalytically compromised and advising the recollection of another sample. If H-index values reach an even higher critical cut-off (i.e. H-index corresponding to a cell-free hemoglobin concentration ≥10 g/L), all laboratory data may be unreliable and should hence be suppressed and replaced with a comment that all data cannot be released because the sample is grossly hemolyzed, also suggesting the recollection of another sample. Due to inaccuracy and imprecision, the use of corrective formulas for adjusting data of hemolysis-sensitive tests is discouraged.

Internal quality assurance of HIL indices on Roche Cobas c702

Automatic assessment of hemoglobin (H), lipaemia (L) and icterus (I) in serum or plasma (HIL indices) is the mainstay for evaluating sample quality. We planned this study to verify whether in-house prepared internal quality control (IQC) materials may be suitable for quality assurance of HIL indices. Pools containing different values of each of the three HIL indices were prepared from routine plasma samples, divided in aliquots and frozen at -20°C. Stability of frozen materials was assessed by thawing one aliquot of each pool after different days of freezing (1, 4, 8, 15, 22 and 29), and by measuring HIL indices on baseline fresh samples and frozen-thawed aliquots with Roche Cobas c702. Five fresh liquid IQCs materials were also measured at the same time points. Intra-assay and inter-assay imprecision of HIL indices calculated with commercial IQC materials ranged between 1.1-2.0% and 1.6-3.3%, respectively. When target values of HIL indices were calculated using frozen-thawed aliquots, the inter-assay imprecision of in-house prepared materials was optimal, even better than that of commercial liquid IQCs (H-index, 0.8% versus 1.6%; L-index, 2.2% versus 2.5%; I-index, 0.8% versus 3.3%). In conclusion, in-house prepared IQC materials are cost-effective alternatives to commercial liquid IQCs for HIL quality assurance.