Evidence-based approach to setting delta check rules.

Delta checks are a post-analytical verification tool that compare the difference in sequential laboratory results belonging to the same patient against a predefined limit. This unique quality tool highlights a potential error at the individual patient level. A difference in sequential laboratory results that exceeds the predefined limit is considered likely to contain an error that requires further investigation that can be time and resource intensive. This may cause a delay in the provision of the result to the healthcare provider or entail recollection of the patient sample. Delta checks have been used primarily to detect sample misidentification (sample mix-up, wrong blood in tube), and recent advancements in laboratory medicine, including the adoption of protocolized procedures, information technology and automation in the total testing process, have significantly reduced the prevalence of such errors. As such, delta check rules need to be selected carefully to balance the clinical risk of these errors and the need to maintain operational efficiency. Historically, delta check rules have been set by professional opinion based on reference change values (biological variation) or the published literature. Delta check rules implemented in this manner may not inform laboratory practitioners of their real-world performance. This review discusses several evidence-based approaches to the optimal setting of delta check rules that directly inform the laboratory practitioner of the error detection capabilities of the selected rules. Subsequent verification of workflow for the selected delta check rules is also discussed. This review is intended to provide practical assistance to laboratories in setting evidence-based delta check rules that best suits their local operational and clinical needs.

Development and Application of Computerized Risk Registry and Management Tool Based on FMEA and FRACAS for Total Testing Process.

Background and Objectives: Risk management is considered an integral part of laboratory medicine to assure laboratory quality and patient safety. However, the concept of risk management is philosophical, so actually performing risk management in a clinical laboratory can be challenging. Therefore, we would like to develop a sustainable, practical system for continuous total laboratory risk management. Materials and Methods: This study was composed of two phases: the development phase in 2019 and the application phase in 2020. A concept flow diagram for the computerized risk registry and management tool (RRMT) was designed using the failure mode and effects analysis (FMEA) and the failure reporting, analysis, and corrective action system (FRACAS) methods. The failure stage was divided into six according to the testing sequence. We applied laboratory errors to this system over one year in 2020. The risk priority number (RPN) score was calculated by multiplying the severity of the failure mode, frequency (or probability) of occurrence, and detection difficulty. Results: 103 cases were reported to RRMT during one year. Among them, 32 cases (31.1%) were summarized using the FMEA method, and the remaining 71 cases (68.9%) were evaluated using the FRACAS method. There was no failure in the patient registration phase. Chemistry units accounted for the highest proportion of failure with 18 cases (17.5%), while urine test units accounted for the lowest portion of failure with two cases (1.9%). Conclusion: We developed and applied a practical computerized risk-management tool based on FMEA and FRACAS methods for the entire testing process. RRMT was useful to detect, evaluate, and report failures. This system might be a great example of a risk management system optimized for clinical laboratories.

Impact of delta check time intervals on error detection capability.

Background The delta check time interval limit is the maximum time window within which two sequential results of a patient will be evaluated by the delta check rule. The impact of time interval on delta check performance is not well studied. Methods De-identified historical laboratory data were extracted from the laboratory information system and divided into children (≤18 years) and adults (>21 years). The relative and absolute differences of the original pair of results from each patient were compared against the delta check limits associated with 90% specificity. The data were then randomly reshuffled to simulate a switched (misidentified) sample scenario. The data were divided into 1-day, 3-day, 7-day, 14-day, 1-month, 3-month, 6-month and 1-year time interval bins. The true positive- and false-positive rates at different intervals were examined. Results Overall, 24 biochemical and 20 haematological tests were analysed. For nearly all the analytes, there was no statistical evidence of any difference in the true- or false-positive rates of the delta check rules at different time intervals when compared to the overall data. The only exceptions to this were mean corpuscular volume (using both relative- and absolute-difference delta check) and mean corpuscular haemoglobin (only absolute-difference delta check) in the children population, where the false-positive rates became significantly lower at 1-year interval. Conclusions This study showed that there is no optimal delta check time interval. This fills an important evidence gap for future guidance development.

Better get moving on laboratory quality assurance.

PURPOSE: With recent advances in laboratory hematology automation, emphasis is now on quality assurance processes as they are indispensable for generating reliable and accurate test results. It is therefore imperative to acquire efficient measures for recognizing laboratory malfunctions and errors to improve patient safety. The paper aims to discuss these issues. DESIGN/METHODOLOGY/APPROACH: Moving algorithm is a quality control process that monitors analyzer performance from historical records through a continuous process, which does not require additional expenditure, and can serve as an additional support to the laboratory quality control program. FINDINGS: The authors describe an important quality assurance tool, which can be easily applied in any laboratory setting, especially in cost-constrained areas where running commercial controls throughout every shift may not be a feasible option. ORIGINALITY/VALUE: The authors focus on clinical laboratory quality control measures for providing reliable test results. The moving average appears to be a reasonable and applicable choice for vigilantly monitoring each result.

Approach to the interpretation of unexpected laboratory results arising in the care of patients with inborn errors of metabolism (IEM).

Endocrinologists may encounter abnormal results in routine laboratory tests while caring for patients with inborn errors of metabolism. This article provides a framework for understanding these abnormalities as: a) part of the pathophysiology of the exceptional disease, b) exceptional laboratory errors related to the exceptional disease, or c) routine laboratory errors to which any patient sample is susceptible.

Irregular analytical errors in diagnostic testing - a novel concept.

BACKGROUND: In laboratory medicine, routine periodic analyses for internal and external quality control measurements interpreted by statistical methods are mandatory for batch clearance. Data analysis of these process-oriented measurements allows for insight into random analytical variation and systematic calibration bias over time. However, in such a setting, any individual sample is not under individual quality control. The quality control measurements act only at the batch level. Quantitative or qualitative data derived for many effects and interferences associated with an individual diagnostic sample can compromise any analyte. It is obvious that a process for a quality-control-sample-based approach of quality assurance is not sensitive to such errors. CONTENT: To address the potential causes and nature of such analytical interference in individual samples more systematically, we suggest the introduction of a new term called the irregular (individual) analytical error. Practically, this term can be applied in any analytical assay that is traceable to a reference measurement system. For an individual sample an irregular analytical error is defined as an inaccuracy (which is the deviation from a reference measurement procedure result) of a test result that is so high it cannot be explained by measurement uncertainty of the utilized routine assay operating within the accepted limitations of the associated process quality control measurements. SUMMARY: The deviation can be defined as the linear combination of the process measurement uncertainty and the method bias for the reference measurement system. Such errors should be coined irregular analytical errors of the individual sample. The measurement result is compromised either by an irregular effect associated with the individual composition (matrix) of the sample or an individual single sample associated processing error in the analytical process. OUTLOOK: Currently, the availability of reference measurement procedures is still highly limited, but LC-isotope-dilution mass spectrometry methods are increasingly used for pre-market validation of routine diagnostic assays (these tests also involve substantial sets of clinical validation samples). Based on this definition/terminology, we list recognized causes of irregular analytical error as a risk catalog for clinical chemistry in this article. These issues include reproducible individual analytical errors (e.g. caused by anti-reagent antibodies) and non-reproducible, sporadic errors (e.g. errors due to incorrect pipetting volume due to air bubbles in a sample), which can both lead to inaccurate results and risks for patients.

Moving sum of number of positive patient result as a quality control tool.

BACKGROUND: Recently, the total prostate-specific antigen (PSA) assay used in a laboratory had a positive bias of 0.03 µg/L, which went undetected. Consequently, a number of post-prostatectomy patients with previously undetectable PSA concentrations (defined as <0.03 µg/L in that laboratory) were being reported as having detectable PSA, which suggested poorer prognosis according to clinical guidelines. METHODS: Through numerical simulations, we explored (1) how a small bias may evade the detection of routine quality control (QC) procedures with specific reference to the concentration of the QC material, (2) whether the use of 'average of normals' approach may detect such a small bias, and (3) describe the use of moving sum of number of patient results with detectable PSA as an adjunct QC procedure. RESULTS: The lowest QC level (0.86 µg/L) available from a commercial kit had poor probability (<10%) of a bias of 0.03 µg/L regardless of QC rule (i.e. 1:2S, 2:2S, 1:3S, 4:1S) used. The average number of patient results affected before error detection (ANPed) was high when using the average of normals approach due to the relatively wide control limits. By contrast, the ANPed was significantly lower for the moving sum of number of patient results with a detectable PSA approach. CONCLUSIONS: Laboratory practitioners should ensure their QC strategy can detect small but critical bias, and may require supplementation of ultra-low QC levels that are not covered by commercial kits with in-house preparations. The use of moving sum of number of patient results with a detectable result is a helpful adjunct QC tool.

Specimen Identification Errors in Breast Biopsies: Age Matters. Report of Two Near-Miss Events and Review of the Literature.

The consequences of patient identification errors due to specimen mislabeling can be deleterious. We describe two near-miss events involving mislabeled breast specimens from two patients who sought treatment at our institution. In both cases, microscopic review of the slides identified inconsistencies between the histologic findings and patient age, unveiling specimen identification errors. By correlating the clinical information with the microscopic findings, we identified mistakes that had occurred at the time of specimen accessioning at the original laboratories. In both cases, thanks to a timely reassignment of the specimens, the patients suffered no harm. These cases highlight the importance of routine clinical and pathologic correlation as a critical component of quality assurance and patient safety. A review of possible specimen identification errors in the anatomic pathology setting is presented.

How accurately and consistently do laboratories measure workplace concentrations of respirable crystalline silica?

Permissible exposure limits (PELs) for respirable crystalline silica (RCS) have recently been reduced from 0.10 to 0.05 mg/m3. This raises an important question: do current laboratory practices and standards for assessing RCS concentrations permit reliable discrimination between workplaces that are in compliance and workplaces that are not? To find out, this paper examines recent laboratory performance in quantifying RCS amounts on filters sent to them to assess their proficiency. A key finding is that accredited laboratories do not reliably (e.g., with 95% confidence) estimate RCS quantities to within a factor of 2. Thus, laboratory findings indicating that RCS levels are above or below a PEL provide little confidence that this is true. The current accreditation standard only requires laboratories to achieve estimates within three standard deviations of the correct (reference) value at least two thirds of the time, rather than a more usual standard such as within 25% of the correct value at least 95% of the time. Laboratory practices may improve as the new PEL is implemented, but they are presently essentially powerless to discriminate among RCS levels over most of the range of values that have been tested, leaving employers and regulators without a reliable means to ascertain when workplace RCS levels are above or below the PEL.

A multidisciplinary approach to reducing spurious hyperkalemia in hospital outpatient clinics.

AIMS AND OBJECTIVES: To describe a multidisciplinary effort to investigate and reduce the occurence of outpatient spurious hyperkalaemia. BACKGROUND: Spurious hyperkalemia is a falsely elevated serum potassium result that does not reflect the in vivo condition of a person. A common practice of fist clenching/pumping during phlebotomy to improve vein visualisation is an under-appreciated cause of spurious hyperkalemia. DESIGN: Pre- and postinterventional study. METHOD: Objective evidence of spurious hyperkalaemia was sought by reviewing archived laboratory results. A literature review was undertaken to summarise known causes of spurious hyperkalaemia and develop a best practice in phlebotomy. Subsequently, nurses from the Urology Clinic were interviewed, observed and surveyed to understand their phlebotomy workflow and identify potential areas of improvement by comparing to the best practice in phlebotomy. Unexplained (potentially spurious) hyperkalaemia was defined as a serum potassium of >5·0 mmol/l in a patient without stage 5 chronic kidney disease or haemolysed blood sample. RESULTS AND CONCLUSION: Nurses from the Urology Clinic showed significant knowledge gap regarding causes of spurious hyperkalaemia when compared to the literature review. Direct observation revealed patients were routinely asked to clench their fists, which may cause spurious hyperkalaemia. Following these observations, several educational initiatives were administered to address the knowledge gap and stop fist clenching. The rate of unexplained hyperkalaemia at the Urology clinic reduced from a baseline of 16·0-3·8%, 58 weeks after intervention. Similar education intervention was propagated to all 18 other specialist outpatient clinic locations, which saw their rate of unexplained hyperkalaemia decrease from 5·4 to 3·7%. To ensure sustainability of the improvements, the existing phlebotomy standard operating protocol, educational and competency testing materials at variance with the best practice were revised. RELEVANCE TO CLINICAL PRACTICE: A simple intervention of avoiding fist clenching/pumping during phlebotomy produced significant reduction in the rate of spurious hyperkalemia.

Assay error detection when using common quality control targets across multiple instruments: An analysis using simulated and real-world data.

BACKGROUND: Clinical laboratories frequently implement the same tests and internal quality control (QC) rules on identical instruments. It is unclear whether individual QC targets for each analyser or ones that are common to all instruments are preferable. This study modelled how common QC targets influence assay error detection before examining their effect on real-world data. METHODS: The effect of variable bias and imprecision on error detection and false rejection rates when using common or individual QC targets on two instruments was simulated. QC data from tests run on two identical Beckman instruments (6-month period, same QC lot, n > 100 points for each instrument) determined likely real-world consequences. RESULTS: Compared to individual QC targets, common targets had an asymmetrical effect on systematic error detection, with one instrument assay losing detection power more than the other gained. If individual in-control assay standard deviations (SDs) differed, then common targets led to one assay failing QC more frequently. Applied to two analysers (95 QC levels and 45 tests), common targets reduced one instrument's error detection by ≥ 0.4 sigma on 15/45 (33%) of tests. Such targets also meant 14/45 (31%) of assays on one in-control instrument would fail over twice as frequently as the other (median ratio 1.62, IQR 1.20-2.39) using a 2SD rule. CONCLUSIONS: Compared to instrument-specific QC targets, common targets can reduce the probability of detecting changes in individual assay performance and cause one in-control assay to fail QC more frequently than another. Any impact on clinical care requires further investigation.

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.

Machine Learning for Patient-Based Real-Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory.

The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.

Preanalytical Errors in Clinical Laboratory Testing at a Glance: Source and Control Measures.

The accuracy of diagnostic results in clinical laboratory testing is paramount for informed healthcare decisions and effective patient care. While the focus has traditionally been on the analytical phase, attention has shifted towards optimizing the preanalytical phase due to its significant contribution to total laboratory errors. This review highlights preanalytical errors, their sources, and control measures to improve the quality of laboratory testing. Blood sample quality is a critical concern, with factors such as hemolysis, lipemia, and icterus leading to erroneous results. Sources of preanalytical errors encompass inappropriate test requests, patient preparation lapses, and errors during sample collection, handling, and transportation. Mitigating these errors includes harmonization efforts, education and training programs, automated methods for sample quality assessment, and quality monitoring. Collaboration between laboratory personnel and healthcare professionals is crucial for implementing and sustaining these measures to enhance the accuracy and reliability of diagnostic results, ultimately improving patient care.

Root Cause Analysis: Unraveling Common Laboratory Challenges.

Diverse errors occur in a pathology laboratory and manual mistakes are the most common. There are various advancements to replace manual procedures with digitized automation techniques. Guidelines and protocols are available to run a standard pathology laboratory. But, even with such attempts to reinforce and strengthen the protocols, the complete elimination of errors is yet not possible. Root cause analysis (RCA) is the best way forward to develop an error-free laboratory, In this review, the importance of RCA, common errors taking place in laboratories, methods to carry out RCA, and its effectiveness are discussed in detail. The review also highlights the potential of RCA to provide long-term quality improvement and efficient laboratory management.

Quality impact on diagnostic blood specimen collection using a new device to relieve venipuncture pain.

A new device called Buzzy(®) has been recently presented that combines a cooling ice pack and a vibrating motor in order to relieve the venipuncture pain. The aim of this study was to evaluate the impact of Buzzy(®) use during diagnostic blood specimen collection by venipuncture for routine immunochemistry tests. Blood was collected from 100 volunteers by a single, expert phlebotomist. A vein was located on the left forearm without applying tourniquet, in order to prevent any interference from venous stasis, and blood samples were collected using a 20-G straight needle directly into 5 mL vacuum tubes with clot activator and gel separator. In sequence, external cold and vibration by Buzzy(®) was applied on the right forearm-5 cm above the chosen puncture site-for 1 min before venipuncture and continued until the end of the same procedure already done in the left forearm. The panel of tests included the following: glucose, total cholesterol, HDL-cholesterol, triglycerides, total protein, albumin, c-reactive protein, urea, creatinine, uric acid, alkaline phosphatase, amylase, AST, ALT, g-glutamyltransferase, lactate dehydrogenase, creatine kinase, total bilirubin, phosphorus, calcium, magnesium, iron, sodium, potassium, chloride, lipase, cortisol, insulin, thyroid-stimulating hormone, total triiodothyronine, free triiodothyronine, total thyroxine, free thyroxine and haemolysis index. Clinically significant differences between samples were found only for: total protein, albumin and transferrin. The Buzzy(®) can be used during diagnostic blood specimens collection by venipuncture for the majority of the routine immunochemistry tests. We only suggest avoiding this device during blood collection when protein, albumin and transferrin determinations should be performed.

Quality Assurance for Veterinary In-Clinic Laboratories.

Quality assurance and the implementation of a quality management system are as important for veterinary in-clinic laboratories as for reference laboratories. Elements of a quality management system include the formulation of a quality plan, establishment of quality goals, a health and safety policy, trained personnel, appropriate and well-maintained facilities and equipment, standard operating procedures, and participation in external quality assurance programs. Quality assurance principles should be applied to preanaltyic, analytic, and postanalytic phases of the in-clinic laboratory cycle to ensure that results are accurate and reliable and are released in a timely manner.

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.

An Objective Approach to Deriving the Clinical Performance of Autoverification Limits.

This study describes an objective approach to deriving the clinical performance of autoverification rules to inform laboratory practice when implementing them. Anonymized historical laboratory data for 12 biochemistry measurands were collected and Box-Cox-transformed to approximate a Gaussian distribution. The historical laboratory data were assumed to be error-free. Using the probability theory, the clinical specificity of a set of autoverification limits can be derived by calculating the percentile values of the overall distribution of a measurand. The 5th and 95th percentile values of the laboratory data were calculated to achieve a 90% clinical specificity. Next, a predefined tolerable total error adopted from the Royal College of Pathologists of Australasia Quality Assurance Program was applied to the extracted data before subjecting to Box-Cox transformation. Using a standard normal distribution, the clinical sensitivity can be derived from the probability of the Z-value to the right of the autoverification limit for a one-tailed probability and multiplied by two for a two-tailed probability. The clinical sensitivity showed an inverse relationship with between-subject biological variation. The laboratory can set and assess the clinical performance of its autoverification rules that conforms to its desired risk profile.