The influence of undetected hemolysis on POCT potassium results in the emergency department.

OBJECTIVES: This study aimed to evaluate discrepancies in potassium measurements between point-of-care testing (POCT) and central laboratory (CL) methods, focusing on the impact of hemolysis on these measurements and its impact in the clinical practice in the emergency department (ED). METHODS: A retrospective analysis was conducted using data from three European university hospitals: Technische Universitat Munchen (Germany), Hospital Universitario La Paz (Spain), and Erasmus University Medical Center (The Netherlands). The study compared POCT potassium measurements in EDs with CL measurements. Data normalization was performed in categories for potassium levels (kalemia) and hemolysis. The severity of discrepancies between POCT and CL potassium measurements was assessed using the reference change value (RCV). RESULTS: The study identified significant discrepancies in potassium between POCT and CL methods. In comparing POCT normo- and mild hypokalemia against CL results, differences of -4.20 % and +4.88 % were noted respectively. The largest variance in the CL was a +4.14 % difference in the mild hyperkalemia category. Additionally, the RCV was calculated to quantify the severity of discrepancies between paired potassium measurements from POCT and CL methods. The overall hemolysis characteristics, as defined by the hemolysis gradient, showed considerable variation between the testing sites, significantly affecting the reliability of potassium measurements in POCT. CONCLUSIONS: The study highlighted the challenges in achieving consistent potassium measurement results between POCT and CL methods, particularly in the presence of hemolysis. It emphasised the need for integrated hemolysis detection systems in future blood gas analysis devices to minimise discrepancies and ensure accurate POCT results.

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.

Failure Mode and Effects Analysis (FMEA) at the preanalytical phase for POCT blood gas analysis: proposal for a shared proactive risk analysis model.

OBJECTIVES: Proposal of a risk analysis model to diminish negative impact on patient care by preanalytical errors in blood gas analysis (BGA). METHODS: Here we designed a Failure Mode and Effects Analysis (FMEA) risk assessment template for BGA, based on literature references and expertise of an international team of laboratory and clinical health care professionals. RESULTS: The FMEA identifies pre-analytical process steps, errors that may occur whilst performing BGA (potential failure mode), possible consequences (potential failure effect) and preventive/corrective actions (current controls). Probability of failure occurrence (OCC), severity of failure (SEV) and probability of failure detection (DET) are scored per potential failure mode. OCC and DET depend on test setting and patient population e.g., they differ in primary community health centres as compared to secondary community hospitals and third line university or specialized hospitals. OCC and DET also differ between stand-alone and networked instruments, manual and automated patient identification, and whether results are automatically transmitted to the patient's electronic health record. The risk priority number (RPN = SEV × OCC × DET) can be applied to determine the sequence in which risks are addressed. RPN can be recalculated after implementing changes to decrease OCC and/or increase DET. Key performance indicators are also proposed to evaluate changes. CONCLUSIONS: This FMEA model will help health care professionals manage and minimize the risk of preanalytical errors in BGA.

Serum index rules prevent risk of analysing uncentrifuged tubes on automated biochemistry analysers.

Around 1.5% of the total clinical biochemistry tests performed in laboratories are affected by preanalytical errors. Large, automated chemistry analysers prevent errors and interference by using control systems such as spectrophotometric measurements to evaluate serum indices, i.e. haemolysis (H), icterus (I), and lipemia/turbidity (L). However, still preanalytical errors can remain undetected. Our laboratory experienced an incident caused by laboratory-induced preanalytical errors, where approximately 100 sedimented lithium heparin samples bypassed centrifugation and entered our automated analyser. Based on index results, we investigated the possibility of using turbidimetry measurement, as a mean to prevent analysis on uncentrifuged sedimented whole blood. 14078 L-indices from 8 days in August 2019 were extracted from the middleware and used to develop and evaluate stop rules. Similarly, a one-day validation dataset was identified in December 2020 and used for an independent validation. Three different types of stop rules were evaluated: (1) A single L-index result above a cut-off; (2) A sequence of an L-index results above a cut-off; (3) A simple moving average of n results above a cut-off. A stop rule using 3 consecutive L-indices of 40-60 was found to be superior. However, practical implementation in the instrument middleware on a Roche Cobas 8000 only allowed a simple moving average of 110 (n = 5). This rule was found to be able to identify and stop sedimented whole blood analysis. Additionally, the rule has minimal impact on daily routine production in the laboratory.

Erroneous diagnosis of primary fibrosarcoma of the breast: The value of the multidisciplinary breast team approach.

Diagnostic errors occur in the preanalytic, analytic, and postanalytic phases of specimen processing. Correlating clinical and imaging information with gross and microscopic findings is crucial to limit errors and unnecessary treatment. Herein, we report the case of a 54-year-old woman who presented with left breast bloody nipple discharge and subsequently underwent central duct excision. Pathology revealed a high-grade sarcoma. The patient presented to our institution for further management. Upon secondary pathology review and DNA fingerprinting analysis, the correct interpretation was rendered. Our case demonstrates the importance of clinical correlation and review of pathology slides prior to definitive therapy.

The Hemolyzed Sample: To Analyse Or Not To Analyse.

Preanalytical errors constitute about 40-65% of laboratory errors, of which 60% are due to hemolysis. This leads to imprecise reporting and misinterpretation of the actual concentration of analytes. Hence the aim of this study was to estimate the extent of different degrees of interference by visible hemolysis. 25 hemolysed samples along with their fresh unhemolysed sample were studied. Hemolyzed serum was mixed with unhemolyzed serum in predefined serial ratios from 100%, 70%, 50%, 30% and 10% to achieve different grades of hemolysis. Each dilution was analysed for BUN, creatinine, uric acid, phosphorus, Na, K, total protein, amylase, lipase, LDH, tacrolimus and methotrexate. Percentage difference of each dilution of the hemolyzed sample as compared to the unhemolyzed sample was calculated and considered acceptable only if less than TEa. It was observed that Percentage difference of BUN, creatinine, amylase and lipase in all dilutions of hemolyzed samples were within TEa while phosphorus, Na, K, total protein and LDH were beyond the acceptance criteria. Hence It was concluded that it may be safe to analyse a hemolysed sample for BUN, creatinine, amylase, lipase, tacrolimus and methotrexate while uric acid may be estimated in a moderately hemolysed sample. Phosphorus, sodium, potassium, total protein and LDH must never be analyzed in any hemolysed sample.

Comparison of the effect of gel used in two different serum separator tubes for thyroid function tests.

BACKGROUND: Selection and verification of blood collection tubes is an important preanalytical issue in clinical laboratories. Today, gel tubes are commonly used with many advantages, although they are known to cause interference in immunoassay methods. In this study, we aimed to compare SSTs of two different suppliers (Ayset clot activator & Gel and Becton Dickinson (BD) Vacutainer SST II advance) with reference tubes and evaluate the effect of storage time in terms of commonly used endocrine tests such as thyroid-stimulating hormone (TSH), free thyroxine (fT4), and free triiodothyronine (fT3). METHODS: Fifty-five volunteers were included in the study. Samples were taken into three different tubes and analyzed for serum TSH, fT4, and fT3 on Architect ci8200 Immunoassay System. Clinical decision levels were estimated using total allowable error (TEa). RESULTS: No difference was found between tubes in terms of TSH, fT3, and fT4 levels. From a statistical standpoint, TSH and fT4 levels were no longer stable during 24, 48, and 72 hours storage time periods. However, their variations were not clinically significant. CONCLUSION: Ayset clot activator & Gel tubes and BD Vacutainer SST II advance tubes have comparable results with glass tube in terms of TSH, fT3, and fT4 levels on Architect ci8200 Immunoassay Systems. From a clinical standpoint, serum TSH, fT4, and fT3 concentrations may be considered as stable when storing these tubes over 72 hours.

Evaluation of Preanalytical Quality Indicators by Six Sigma and Pareto`s Principle.

Preanalytical steps are the major sources of error in clinical laboratory. The analytical errors can be corrected by quality control procedures but there is a need for stringent quality checks in preanalytical area as these processes are done outside the laboratory. Sigma value depicts the performance of laboratory and its quality measures. Hence in the present study six sigma and Pareto principle was applied to preanalytical quality indicators to evaluate the clinical biochemistry laboratory performance. This observational study was carried out for a period of 1 year from November 2015-2016. A total of 1,44,208 samples and 54,265 test requisition forms were screened for preanalytical errors like missing patient information, sample collection details in forms and hemolysed, lipemic, inappropriate, insufficient samples and total number of errors were calculated and converted into defects per million and sigma scale. Pareto`s chart was drawn using total number of errors and cumulative percentage. In 75% test requisition forms diagnosis was not mentioned and sigma value of 0.9 was obtained and for other errors like sample receiving time, stat and type of sample sigma values were 2.9, 2.6, and 2.8 respectively. For insufficient sample and improper ratio of blood to anticoagulant sigma value was 4.3. Pareto`s chart depicts out of 80% of errors in requisition forms, 20% is contributed by missing information like diagnosis. The development of quality indicators, application of six sigma and Pareto`s principle are quality measures by which not only preanalytical, the total testing process can be improved.

Key factors influencing the incidence of hemolysis: A critical appraisal of current evidence.

Hemolysis is a leading cause of pre-analytical laboratory errors. The identification of contributing factors is an important step towards the development of effective practices to reduce and prevent hemolysis. We performed a review of PUBMED, Embase, Medline and CINAHL to identify articles published between January 2000 and August 2016 that identified factors influencing in vitro hemolysis rates. The 40 studies included in this review provide excellent evidence that hemolysis rates are higher in Emergency Departments (EDs), for non-antecubital draws, for specimens drawn using an intravenous catheter compared to venipuncture and for samples transported by pneumatic tube compared to by hand. There is also good evidence that hemolysis rates are higher when specimens are not collected by professional phlebotomists, larger volume specimen tubes are used, specimen tubes are filled less than halfway and tourniquet time is greater than one minute. The results of this review suggest that hospitals and clinical laboratories should consider deploying phlebotomists in EDs, drawing all blood through a venipuncture, using the antecubital region as the optimum blood collection site and transporting specimens by laboratory assistant/other personnel, or if this in not practical, ensuring that pneumatic transport systems are validated, maintained and monitored. Studies also recommend making hemolysis a hospital-wide issue and ensuring high-quality staff training and adherence to standard operating procedures to reduce hemolysis rates. Awareness of the factors that influence hemolysis rates, and adoption of strategies to mitigate these risk factors, is an important step towards creating quality practices to reduce hemolysis rates and improve the quality of patient care.

Potential misdiagnosis of von Willebrand disease and haemophilia caused by ineffective mixing of thawed plasma.

INTRODUCTION: von Willebrand disease (VWD) reflects a loss or dysfunction in von Willebrand factor (VWF), while haemophilia represents a loss or dysfunction of clotting factors such as factor VIII (FVIII) or FIX. Their diagnosis requires laboratory testing, with this potentially compromised by preanalytical events, including poor sample quality. This study assessed the effect of inadequate mixing as a potential cause of VWD and haemophilia misdiagnosis. METHODS: After completion of requested testing, 48 consecutive patient samples comprising separate aliquots from single collections were individually pooled, appropriately mixed, then frozen in separate aliquots, either at -20°C or -80°C for 2-7 days. Each sample set was then thawed and the separate aliquots subjected to separate mixing protocols (several inversions, blood roller, vortex) vs a non-mix sample, and all aliquots then tested for various VWF and factor assays. RESULTS: Non-mixing led to substantial reduction in VWF and factors in about 25% of samples, that in some cases could lead to misdiagnosis of VWD or haemophilia. Interestingly, there were also some differences observed with respect to different mixing protocols. CONCLUSIONS: Our study identified ineffective or variable mixing of thawed plasma samples as potential causes of misdiagnosis of VWD or haemophilia. Further education regarding the importance of appropriate mixing appears warranted.

Preanalytical Errors in a Hematology Laboratory: An Experience from a Tertiary Care Center.

BACKGROUND: Laboratory errors arise at any stage of testing. Detecting these inaccuracies before results are revealed might delay diagnosis and treatment, causing patient distress. Here, we studied the preanalytical errors in a hematology laboratory. METHODS: This one-year retrospective analysis was conducted at the laboratory of a tertiary care hospital and included information on blood samples that were taken for hematology tests from both outpatients and inpatients. Laboratory records included sample collection and rejection information. The type and frequency of preanalytical errors were expressed as a proportion of total errors and sample number. Microsoft Excel was utilized to enter data. The results were presented in the form of frequency tables. RESULTS: This research included 67,892 hematology samples. For preanalytical errors, 886 samples (1.3%) were discarded. The most common preanalytical error was insufficient sample (54.17%), and the least common was an empty/damaged tube (0.4%). Erroneous samples in the emergency department were mostly insufficient and clotted, whereas pediatric sample errors were caused by insufficient and diluted samples. CONCLUSION: Inadequate samples and clotted samples account for the vast majority of preanalytical factors. Insufficiency and dilutional errors were most frequent from pediatric patients. Adherence to best laboratory practices can drastically cut down on preanalytical errors.

Evaluation of errors related to surgical pathology specimens of different hospital departments with a patient safety approach: a case study in Iran.

BACKGROUND: Most surgical specimen errors occur in the pre-analysis stage, which can be prevented. This study aims to identify errors related to surgical pathology specimens in one of the most comprehensive healthcare centers in Northeast Iran. METHODS: The present study is descriptive and analytical research conducted cross-sectionally in 2021 at Ghaem healthcare center in the Mashhad University of Medical Sciences on the basis of a census sampling. We used a standard checklist to collect information. Professors and pathologists evaluated the validity and reliability of the checklist using Cronbach's alpha calculation method of 0.89. We analyzed the results using statistical indices, SPSS 21 software, and the chi-square test. RESULTS: Out of 5617 pathology specimens studied, we detected 646 errors. The highest number of errors is the mismatch of the specimen with the label (219 cases; 3.9%) and the non-compliance of the patient's profile in the specimen sent with the label (129 cases; 2.3%), and the lowest errors are the inappropriate volume of the fixator(24 cases; 0.4%), and they accounted for insufficient sample size (25 cases; 0.4%). Based on Fisher's exact test results, there was a significant difference between the proportion of errors in different departments and months. CONCLUSION: Considering the frequency of labeling errors in the stage before the analysis in the pathology department, the use of barcode imprinted in specimen containers, the removal of the paper request for pathology, the use of radio frequency chip technology, the use of the rechecking system and improving communication in different departments can be effective in reducing these errors.

Determination of lipemia acceptance thresholds for 31 immunoassay analytes.

BACKGROUND: Lipemia is one of common endogenous interferences that can compromises sample quality and potentially influence results of various laboratory methods. Determination of the lipemic index or triglyceride concentrations are used to define the degree of lipemia. This study was aimed to establish lipemic index (LI) and triglyceride thresholds above where significant interference exists for 31 immunoassay analytes measured on Roche Cobas 6000. MATERIALS AND METHODS: The study was carried out following CLSI C56-A and EP07-ED3:2018 guidelines using sample pools spiked with increasing concentrations of lipid emulsion solution, reaching 70 mmol/L. To define the LI and triglyceride thresholds, the bias from concentration in the native sample was calculated at different lipemia degree and compared with allowable error limits based on biological variation or state-of-the-art technology. RESULTS: No lipemia interference was observed for 27 out of 31 analytes even at the highest concentrations of lipid emulsion (LI ranging from 1737 to 2086 mg/dL, triglyceride concentration 60.34-73.99 mmol/L). However, progesterone, 25-OH vitamin D, testosterone, and estradiol were negatively affected by lipemia at 217 mg/dL (9.58 mmol/L), 222 mg/dL (10.66 mmol/L), 478 mg/dL (18.81 mmol/L), and 941 mg/dL (35.82 mmol/L) of the LI (triglyceride concentration), respectively. CONCLUSION: Most immunoassays evaluated in this study were found to be robust to lipemia interference. By using these thresholds, laboratories can report the immunoassay results from analyzing a lipemic patient sample in many cases.

Reducing Specimen Rejection Rates Using Concentration-Dependent Hemolysis Rejection Thresholds.

BACKGROUND: Using middleware solutions, it is possible to implement concentration-dependent analyte-specific hemolysis rejection limits. This makes day-to-day reporting of clinical specimens more efficient and potentially lowers sample rejection rates compared to a "one-size-fits-all" approach (i.e., solely based on a single cutoff provided in the package insert). METHODS: Hemolysis interference studies were performed at multiple analyte concentrations for three frequently ordered tests. For each assay, concentration-dependent hemolysis rejection limits were designed based on the total allowable error (TAE) for the analyte as well as the clinical significance of such incurred inaccuracy at the respective concentrations. In general, the following rationale was used: if the interference exceeds 10% (or package insert cutoffs), a comment is placed on the result. If the interference exceeds the TAE, the result will not be reported. Reduction in specimen rejection rates were estimated by comparing the incurred specimen rejection rates when package inserts' vs concentration-dependent hemolysis interference limits were applied to a data set in our institute during a three-month period. RESULTS: Concentration-dependent analyte-specific hemolysis rejection thresholds were designed for three commonly ordered assays that are especially susceptible to hemolysis interference. It is estimated that these novel thresholds for aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and direct bilirubin (DBIL) reduced specimen rejection rates from 9.3% to 1.3%, 31.4% to 4.8%, and 19.9% to 7.1%, respectively. CONCLUSIONS: Concentration-dependent analyte-specific hemolysis rejection thresholds for three commonly ordered assays can reduce rejection rates without significantly compromising the quality of test results.

Undefilled blood tube containing EDTA: Is it an inappropriate sample for HbA1c assay?

Introduction: Blood samples having inappropriate volume are a substantial part of preanalytical errors. Inadequate sample volume for glycated haemoglobin (HbA1c) test may be a common problem of patients with diabetes mellitus having vascular changes. In this study, we compared HbA1c concentrations of underfilled and appropriately filled blood collection tubes. Materials and methods: To compare HbA1c concentrations, blood samples were collected into 2 mL tubes containing K3-EDTA from 109 subjects. Two blood samples (underfilled and appropriately filled) were drawn from a patient by the same personnel and materials. HbA1c measurements were assayed on a Cobas 6000 analyser module c 501 (Roche Diagnostics, Mannheim, Germany). The HbA1c% results were compared by t-test and Wilcoxon's signed-rank statistical methods (SPSS Inc., Chicago, USA). Bias analysis was performed using Microsoft Excel 4.0. Results: Underfilled samples were classified three groups (group 1, N = 44; group 2, N = 36; and group 3, N = 29) according to the filling ratio of the samples; 0.5 mL and below (< 25%), 0.5-1.0 mL (25-50%), and 1.0-2.0 mL (> 50%), respectively. When we compared underfilled tubes with pairing filled tubes, there was a statistically significant difference only with tubes filled less than 25% (P = 0.030). Furthermore, we have done bias analysis between paired tubes according to the diagnostic cut-off value of 6.5%. The bias was more prominent in up to 50% underfilled blood tubes (1.1%), when HbA1c concentrations were below the diagnostic cut-off of 6.5%. Conclusions: We suggest that the blood tubes with EDTA for HbA1c measurement should be filled with at least 50% to avoid clinical variations.

Delta checks.

Delta check is an electronic error detection tool. It compares the difference in sequential results within a patient against a predefined limit, and when exceeded, the delta check rule is considered triggered. The patient results should be withheld for review and troubleshooting before releasing to the clinical team for patient management. Delta check was initially developed as a tool to detect wrong-blood-in-tube (sample misidentification) errors. It is now applied to detect errors more broadly within the total testing process. Recent advancements in the theoretical understanding of delta check has allowed for more precise application of this tool to achieve the desired clinical performance and operational set up. In this Chapter, we review the different pre-implementation considerations, the foundation concepts of delta check, the process of setting up key delta check parameters, performance verification and troubleshooting of a delta check flag.

Deviating glucose results in an international dual-center study. A root cause investigation.

During a dual-center study on obese and normal weight children and adolescents, focusing on glucose metabolism, we observed a marked difference in glucose results (N = 16,840) between the two sites, Salzburg, Austria and Uppsala, Sweden (P < 0.001). After excluding differences in patient characteristics between the two populations as cause of this finding, we investigated other preanalytic influences. Finally, only the tubes used for blood collection at the two sites were left to evaluate. While the Vacuette FC-Mix tube (Greiner Bio-One, Kremsmünster, Austria) was used in Uppsala, in Salzburg blood collections were performed with a lithium heparin tube (LH-Monovette, Sarstedt, Germany). To prove our hypothesis, we collected two blood samples in either of these tubes from 51 children (Salzburg N = 27, Uppsala N = 24) and compared the measured glucose results. Indeed, we found the suspected bias and calculated a correction formula, which significantly diminished the differences of glucose results between the two sites (P = 0.023). Our finding is in line with those of other studies and although this issue should be widely known, we feel that it is widely neglected, especially when comparing glucose concentrations across Europe, using large databases without any information on preanalytic sample handling.

Effect of haemolysis on an enzymatic measurement of ethanol.

INTRODUCTION: We investigated the interference of haemolysis on ethanol testing carried out with the Synchron assay kit using an AU680 autoanalyser (Beckman Coulter, Brea, USA). MATERIALS AND METHODS: Two tubes of plasma samples were collected from 20 volunteers. Mechanical haemolysis was performed in one tube, and no other intervention was performed in the other tube. After centrifugation, haemolysed and non-haemolysed samples were diluted to obtain samples with the desired free haemoglobin (Hb) values (0, 1, 2, 5, 10 g/L). A portion of these samples was then separated, and ethanol was added to the separated sample to obtain a concentration of 86.8 mmol/L ethanol. After that, these samples were diluted with ethanol-free samples with the same Hb concentration to obtain samples containing 43.4, 21.7, and 10.9 mmol/L. Each group was divided into 20 equal parts, and an ethanol test was carried out. The coefficient of variation (CV), bias, and total error (TE) values were calculated. RESULTS: The TE values of haemolysis-free samples were approximately 2-5%, and the TE values of haemolysed samples were approximately 10-18%. The bias values of haemolysed samples ranged from nearly - 6.2 to - 15.7%. CONCLUSIONS: Haemolysis led to negative interference in all samples. However, based on the 25% allowable total error value specified for ethanol in the Clinical Laboratory Improvement Amendments (CLIA 88) criteria, the TE values did not exceed 25%. Consequently, ethanol concentration can be measured in samples containing free Hb up to 10 g/L.