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Background Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols. Methods In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols. Results The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors. Conclusions The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.

Background The ongoing outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a challenge for worldwide public health. A reliable laboratory assay is essential both to confirm suspected patients and to exclude patients infected with other respiratory viruses, thereby facilitating the control of global outbreak scenarios. Content In this review, we focus on the genomic, transmission, and clinical characteristics of SARS-CoV-2, and comprehensively summarize the principles and related details of assays for SARS-CoV-2. We also explore the quality assurance measures for these assays. Summary SARS-CoV-2 has some unique gene sequences and specific transmission and clinical features that can inform the conduct of molecular and serological assays in many aspects, including the design of primers, the selection of specimens, and testing strategies at different disease stages. Appropriate quality assurance measures for molecular and serological assays are needed to maintain testing proficiency. Because serological assays have the potential to identify later stages of the infection and to confirm highly suspected cases with negative molecular assay results, a combination of these two assays is needed to achieve a reliable capacity to detect SARS-CoV-2.

For many years the concept of patient-based quality control (QC) has been discussed and implemented in hematology laboratories; however, the techniques have not been widely implemented in clinical chemistry. This is mainly because of the complexity of this form of QC, as it needs to be optimized for each population and often for each analyte. However, the clear advantages of this form of QC, together with the ongoing realization of the shortcomings of “conventional” QC, have driven a need to provide guidance to laboratories to assist in deploying patient-based QC. This overview describes the components of a patient-based QC system (calculation algorithm, block size, truncation limits, control limits) and the relationship of these to the analyte being controlled. We also discuss the need for patient-based QC system optimization using patient data from the individual testing laboratory to reliably detect systematic errors while ensuring that there are few false alarms. The term patient-based real-time quality control covers many activities that use data from patient samples to detect analytical errors. These activities include the monitoring of patient population parameters such as the mean or median analyte value or using single within-patient changes such as the delta check. In this report, we will restrict the discussion to population-based parameters. This overview is intended to serve as a guide for the implementation of a patient-based QC system. The report does not cover the clinical evaluation of the population.

Background Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systematic error in skewed distributions. Our study combines delta check and MA to develop an average of deltas (AoD) strategy that monitors the mean delta of consecutive, intrapatient results. Methods Arrays of the differences (delta) between paired patient results collected within 20–28 h of each other were generated from historical data. AoD protocols were developed using a simulated annealing algorithm in MatLab (Mathworks) to select the number of patient delta values to average and truncation limits to eliminate large deltas. We simulated systematic error by adding bias to arrays for plasma albumin, alanine aminotransferase, alkaline phosphatase, amylase, aspartate aminotransferase, bicarbonate, bilirubin (total and direct), calcium, chloride, creatinine, lipase, sodium, phosphorus, potassium, total protein, and magnesium. The average number of deltas to detection (ANDED) was then calculated in response to induced systematic error. Results ANDED varied by combination of assay and AoD protocol. Errors in albumin, lipase, and total protein were detected with a mean of 6 delta pairs. The highest ANDED was calcium, with a positive 0.6-mg/dL shift detected with an ANDED of 75. However, a negative 0.6-mg/dL calcium shift was detected with an ANDED of 25. Conclusions AoD detects systematic error with relatively few paired patient samples and is a patient-based QC technique that will enhance error detection.

In this issue of Clinical Chemistry , Zhang and colleagues (1) describe the generation of quality control materials for noninvasive prenatal testing (NIPT)2. This is a very important and much-needed step forward in increasing the quality of NIPT, a step that had been largely bypassed during the very rapid development and clinical introduction of this test. Twenty-two years after the discovery of the presence of cell-free fetal DNA in maternal plasma (2) and 11 years after the first description of trisomy 21 detection using massively parallel sequencing to analyze cell-free fetal DNA (3, 4), NIPT has become the standard method of prenatal screening for trisomies 21, 13, and 18. It is not difficult to understand the reasons behind this extremely fast introduction of a clinical screening test. NIPT has 2 major advantages compared with the main existing screening tests, invasive testing and the first trimester combined screening test (FCT). Invasive testing (chorionic villus sampling and amniocentesis) is the most reliable but has the main disadvantage that it is associated with a small risk of approximately 2 in 1000 of inducing a miscarriage (5). FCT is safe but will provide pregnant women with only a risk calculation, indicating whether additional testing is needed. As a consequence, the sensitivity and specificity of FCT are relatively low, depending on the cutoff used for additional testing (6). Furthermore, FCT can be performed only during a limited time window, whereas NIPT can be performed from 10 weeks gestation onward. The market for prenatal testing is huge. According to the WHO, the number of pregnancies worldwide exceeds 200 million each year. This, in combination with the better quality and higher safety of NIPT, has led to its very rapid and often commercially driven introduction on the clinical market. Unfortunately, introduction of a clinical …

Background Patient-based real-time quality control (PBRTQC) avoids limitations of traditional quality control methods based on the measurement of stabilized control samples. However, PBRTQC needs to be adapted to the individual laboratories with parameters such as algorithm, truncation, block size, and control limit. Methods In a computer simulation, biases were added to real patient results of 10 analytes with diverse properties. Different PBRTQC methods were assessed on their ability to detect these biases early. Results The simulation based on 460 000 historical patient measurements for each analyte revealed several recommendations for PBRTQC. Control limit calculation with “percentiles of daily extremes” led to effective limits and allowed specification of the percentage of days with false alarms. However, changes in measurement distribution easily increased false alarms. Box–Cox but not logarithmic transformation improved error detection. Winsorization of outlying values often led to a better performance than simple outlier removal. For medians and Harrell–Davis 50 percentile estimators (HD50s), no truncation was necessary. Block size influenced medians substantially and HD50s to a lesser extent. Conversely, a change of truncation limits affected means and exponentially moving averages more than a change of block sizes. A large spread of patient measurements impeded error detection. PBRTQC methods were not always able to detect an allowable bias within the simulated 1000 erroneous measurements. A web application was developed to estimate PBRTQC performance. Conclusions Computer simulations can optimize PBRTQC but some parameters are generally superior and can be taken as default.

BACKGROUNDDuring the past decade, breakthroughs in sequencing technology and computational biology have provided the basis for studies of the myriad ways in which microbial communities (“microbiota”) in and on the human body influence human health and disease. In almost every medical specialty, there is now a growing interest in accurate and replicable profiling of the microbiota for use in diagnostic and therapeutic application.CONTENTThis review provides an overview of approaches, challenges, and considerations for diagnostic applications borrowing from other areas of molecular diagnostics, including clinical metagenomics. Methodological considerations and evolving approaches for microbiota profiling from mitochondrially encoded 16S rRNA-based amplicon sequencing to metagenomics and metatranscriptomics are discussed. To improve replicability, at least the most vulnerable steps in testing workflows will need to be standardized and continuous efforts needed to define QC standards. Challenges such as purity of reagents and consumables, improvement of reference databases, and availability of diagnostic-grade data analysis solutions will require joint efforts across disciplines and with manufacturers.SUMMARYThe body of literature supporting important links between the microbiota at different anatomic sites with human health and disease is expanding rapidly and therapeutic manipulation of the intestinal microbiota is becoming routine. The next decade will likely see implementation of microbiome diagnostics in diagnostic laboratories to fully capitalize on technological and scientific advances and apply them in routine medical practice.

To the Editor: The latest laboratory recommendations on high-sensitivity cardiac troponin (hs-cTn)1 endorse imprecision ≤10% at the 99th percentile with a total analytic error of

To the Editor: Droplet digital PCR (ddPCR)1 is an emerging platform that is being increasingly adopted by clinical laboratories. The partitioning of a PCR reaction into discrete droplets enables the accurate detection and quantification of molecular targets. Single molecule analysis by this manner is accurate, cost-effective, and readily performed compared with next-generation sequencing and mass spectrometry. The typical ddPCR workflow comprises 4 key steps: ( a ) preparation of the PCR reaction, ( b ) droplet generation (DG), ( c ) endpoint PCR, and ( d ), droplet analysis. The process of DG is a critical step of the workflow because this step is considered to be most prone to droplet loss (1). It is thus important to recognize that a loss of droplets during DG will affect the number of droplets recoverable for droplet analysis in an optimized assay. Consequently, lower droplet numbers can lead to a loss of analytical sensitivity and may confound the evaluation of samples for molecular targets that are present in very low concentrations. To date, informative measures to evaluate DG performance within the ddPCR workflow remain unaddressed. The Levey–Jennings chart in conjunction with the “Westgard rules” is the staple of quality management in clinical chemistry measurement systems. Here, we outline an approach that uses the Levey–Jennings chart and a set of adapted Westgard rules to monitor DG performance in …

BACKGROUNDIn clinical chemistry, quality control (QC) often relies on measurements of control samples, but limitations, such as a lack of commutability, compromise the ability of such measurements to detect out-of-control situations. Medians of patient results have also been used for QC purposes, but it may be difficult to distinguish changes observed in the patient population from analytical errors. This study aims to combine traditional control measurements and patient medians for facilitating detection of biases.METHODSThe software package “rSimLab” was developed to simulate measurements of 5 analytes. Internal QC measurements and patient medians were assessed for detecting impermissible biases. Various control rules combined these parameters. A Westgard-like algorithm was evaluated and new rules that aggregate Z-values of QC parameters were proposed.RESULTSMathematical approximations estimated the required sample size for calculating meaningful patient medians. The appropriate number was highly dependent on the ratio of the spread of sample values to their center. Instead of applying a threshold to each QC parameter separately like the Westgard algorithm, the proposed aggregation of Z-values averaged these parameters. This behavior was found beneficial, as a bias could affect QC parameters unequally, resulting in differences between their Z-transformed values. In our simulations, control rules tended to outperform the simple QC parameters they combined. The inclusion of patient medians substantially improved bias detection for some analytes.CONCLUSIONSPatient result medians can supplement traditional QC, and aggregations of Z-values are novel and beneficial tools for QC strategies to detect biases.

BACKGROUND Noninvasive prenatal testing (NIPT) based on cell-free DNA (cfDNA) is widely used. However, biomimetic quality control materials that have properties identical to clinical samples and that are applicable to a wide range of methodologies are still not available to support assay development, internal quality control, and proficiency testing. METHODS We developed a set of dual enzyme-digested NIPT quality control materials (DENQCMs) that comprise simulated human plasma and mixtures of mother cell line-derived cfDNA based on DNA fragmentation factor digestion (D-cfDNA) and the matched child cell line-derived cfDNA based on micrococcal nuclease digestion (M-cfDNA). Serially diluted samples positive for trisomies 21, 18, and 13 were included in the materials. To evaluate the biomimetics, DENQCMs were analyzed using random massively parallel sequencing (MPS), targeted MPS, and imaging single DNA molecule methods, and the estimated fetal fractions (FFs) were compared with expected FFs. Genome-wide analysis of cfDNA fragmentation patterns was performed to confirm their biological characteristics. RESULTS The genetic status of each DENQCM was correctly detected by 4 routine NIPT assays for the samples with FFs >5%. The chromosome Y-based and single-nucleotide polymorphism-based estimations of FFs were linearly related to those expected FFs. The MPS results exhibited a concordance of quality metrics between DENQCMs and maternal plasma, such as GC contents of cfDNA and unique read ratios. CONCLUSIONS The DENQCMs are universally applicable for different platforms. We propose DENQCMs as an approach to produce matched maternal and fetal cfDNA that will be suitable for the preparation of quality control materials for NIPT.

To the Editor: Recently, Ng et al. described a new method for optimization of moving average (MA)1 QC procedures (1). The accompanying editorial mentioned that, during the last 50 years, slow but continuous improvements have been made in the understanding and methodology of MA in the move toward continuous analytical quality assurance (2). Although these improvements are being made, general implementation of MA for continuous QC on clinical laboratories has failed and many laboratories are struggling with the implementation and application of MA QC. In this letter, we address several steps that we consider to be important to support a more general implementation of MA as a continuous QC instrument in medical laboratories. Most improvements that really affected the use of MA in clinical laboratories originate from the 1970s and 1980s. For example, the algorithms described by Bull et al. in 1974 are still the basis of the application of MA today in most, if not all, hematology analyzers (3). Interestingly, Bull et al. stated that, because their findings were based on visual inspection of MA patterns, future MA research should focus on developing objective measures of MA performance (3). Recently, 2 methods have been described that allow more objective and realistic insight into MA performance. Ng et al. (1) described an MA optimization method that used the average number of patient samples affected until error detection (ANPed), and we reported the use of bias detection curves and …

BACKGROUNDMost errors in a clinical chemistry laboratory are due to preanalytical errors. Preanalytical variability of biospecimens can have significant effects on downstream analyses, and controlling such variables is therefore fundamental for the future use of biospecimens in personalized medicine for diagnostic or prognostic purposes.CONTENTThe focus of this review is to examine the preanalytical variables that affect human biospecimen integrity in biobanking, with a special focus on blood, saliva, and urine. Cost efficiency is discussed in relation to these issues.SUMMARYThe quality of a study will depend on the integrity of the biospecimens. Preanalytical preparations should be planned with consideration of the effect on downstream analyses. Currently such preanalytical variables are not routinely documented in the biospecimen research literature. Future studies using biobanked biospecimens should describe in detail the preanalytical handling of biospecimens and analyze and interpret the results with regard to the effects of these variables.

BACKGROUND Liquid biopsy provides important information for the prognosis and treatment of cancer patients. In this study, we evaluated the effects of preanalytical conditions on gene expression and DNA methylation analyses in liquid biopsies. METHODS We tested the stability of circulating tumor cell (CTC) messenger RNA by spiking MCF-7 cells in healthy donor peripheral blood (PB) drawn into 6 collection-tube types with various storage conditions. CTCs were enriched based on epithelial cell adhesion molecule positivity, and RNA was isolated followed by cDNA synthesis. Gene expression was quantified using RT-quantitative PCR for CK19 and B2M. We evaluated the stability of DNA methylation in plasma under different storage conditions by spiking DNA isolated from MCF-7 cells in healthy donor plasma. Two commercially available sodium bisulfite (SB)-conversion kits were compared, in combination with whole genome amplification (WGA), to evaluate the stability of SB-converted DNA. SB-converted DNA samples were analyzed by real-time methylation-specific PCR (MSP) for ACTB, SOX17, and BRMS1. Quality control was assessed using Levey–Jennings graphs. RESULTS RNA-based analysis in CTCs is severely impeded by the preservatives used in many PB collection tubes (except for EDTA), as well as by time to analysis. Plasma and SB-converted DNA samples are stable and can be used safely for MSP when kept at −80 °C. Downstream WGA of SB-converted DNA compensated for the limited amount of available sample in liquid biopsies. CONCLUSIONS Standardization of preanalytical conditions and implementation of quality control steps is extremely important for reliable liquid biopsy analysis, and a prerequisite for routine applications in the clinic.

BACKGROUND Nonadherence to standard operating procedures (SOPs) during handling and processing of whole blood is one of the most frequent causes affecting the quality of serum and plasma. Yet, the quality of blood samples is of the utmost importance for reliable, conclusive research findings, valid diagnostics, and appropriate therapeutic decisions. METHODS UHPLC-MS-driven nontargeted metabolomics was applied to identify biomarkers that reflected time to processing of blood samples, and a targeted UHPLC-MS analysis was used to quantify and validate these biomarkers. RESULTS We found that (4E,14Z)-sphingadienine-C18-1-phosphate (S1P-d18:2) was suitable for the reliable assessment of the pronounced changes in the quality of serum and plasma caused by errors in the phase between collection and centrifugation of whole blood samples. We rigorously validated S1P-d18:2, which included the use of practicality tests on >1400 randomly selected serum and plasma samples that were originally collected during single- and multicenter trials and then stored in 11 biobanks in 3 countries. Neither life-threatening disease states nor strenuous metabolic challenges (i.e., high-intensity exercise) affected the concentration of S1P-d18:2. Cutoff values for sample assessment were defined (plasma, ≤0.085 μg/mL; serum, ≤0.154 μg/mL). CONCLUSIONS Unbiased valid monitoring to check for adherence to SOP-dictated time for processing to plasma or serum and/or time to storage of whole blood at 4 °C is now feasible. This novel quality assessment step could enable scientists to uncover common preanalytical errors, allowing for identification of serum and plasma samples that should be excluded from certain investigations. It should also allow control of samples before long-term storage in biobanks.

BACKGROUND To minimize patient risk, “bracketed” statistical quality control (SQC) is recommended in the new CLSI guidelines for SQC (C24-Ed4). Bracketed SQC requires that a QC event both precedes and follows (brackets) a group of patient samples. In optimizing a QC schedule, the frequency of QC or run size becomes an important planning consideration to maintain quality and also facilitate responsive reporting of results from continuous operation of high production analytic systems. METHODS Different plans for optimizing a bracketed SQC schedule were investigated on the basis of Parvin's model for patient risk and CLSI C24-Ed4's recommendations for establishing QC schedules. A Sigma-metric run size nomogram was used to evaluate different QC schedules for processes of different sigma performance. RESULTS For high Sigma performance, an effective SQC approach is to employ a multistage QC procedure utilizing a “startup” design at the beginning of production and a “monitor” design periodically throughout production. Example QC schedules are illustrated for applications with measurement procedures having 6-σ, 5-σ, and 4-σ performance. CONCLUSIONS Continuous production analyzers that demonstrate high σ performance can be effectively controlled with multistage SQC designs that employ a startup QC event followed by periodic monitoring or bracketing QC events. Such designs can be optimized to minimize the risk of harm to patients.

Although biomarkers have been the essence of laboratory medicine since its inception, they are relatively new tools in the development of pharmaceutical compounds. The main utility of biomarkers for pharmaceutical companies has been in making drug development a more efficient and a cost-effective process. This is primarily the case because of the drastic and perhaps alarming increase seen in the development cost of new drugs in the last few decades, which has been accompanied by a decrease in the number of drugs obtaining regulatory approval. Furthermore, the Critical Path Initiative of the US Food and Drug Administration has identified biomarkers as important tools that may help to correct this imbalance. As will become apparent in the discussion below from 4 biomarker groups in leading companies, a large number of these biomarkers have been successfully used for internal decision-making, yet they may have never left the pharmaceutical space. At present, biomarkers, at a growing number, are being codeveloped along with the therapeutic compound as companion diagnostics; this is part of the Precision Medicine paradigm of delivering the right medicine to the right patient. This development will undoubtedly move biomarkers beyond the pharmaceutical space and into the clinical laboratory, thus influencing the practice of laboratory medicine. It will also become apparent that biomarkers developed by the pharmaceutical industry must be evaluated and validated using rigorous procedures. Clinical chemists can certainly assist in this endeavor. This may, in turn, lead to a larger number of useful biomarkers, not only in drug development but also in the clinical laboratory, as companion diagnostics. Biomarkers are extensively used throughout the process of drug development. In your experience, what are the most prominent or illuminating examples of how biomarkers directly impacted the development of a drug? Sanofi team: Biomarkers have played critical roles in the development and …

The purpose of statistical quality control (SQC)2 in the clinical laboratory is to assure that reported patient results are fit for their intended use, not only when a measurement procedure is operating in its stable incontrol state, but also when out-of-control conditions occur. The value of quality-control principles and practices in the laboratory has been well recognized and appreciated for many decades. The Clinical and Laboratory Standards Institute (CLSI; then known as the NCCLS) published its first approved guideline on statistical quality-control principles and definitions for quantitative measurement procedures in 1991 (1). The fourth edition of the guideline appeared last year (2). For many years SQC design primarily involved choosing how many quality control (QC) samples to measure and what QC rules to apply to the QC results. This approach originated in an era when batch testing was common. QC samples were placed in the batch along with patient specimens. The QC sample results were used to decide if the patient results in the batch were acceptable. The goal was for the QC rule to have a low probability of rejection when the batch was in control (probability of false rejection, Pfr) and a high probability of rejection when the batch was out-of-control (probability of error detection, Ped) (3). When continuous-production analyzers became prevalent in the laboratory, a new QC planning question arose: When should QC samples be measured? In batch testing, the answer was to measure QC samples with each batch. However, with continuous-production analyzers a link between QC results and patient results within a batch no longer exists. Instead, QC results simply reflect the current state of the measurement procedure at the time they are measured. Unfortunately, the traditional QC performance measures, …

To the Editor: Dried blood spot (DBS)1 collection is an established sampling method for new born screening and is increasingly used in other domains, including therapeutic drug monitoring, toxicology, microbiology, and genetics. Advantages of DBS sampling are the low blood volume requirements, minimally invasive collection, favorable stability of many analytes, and the potential of patient self-sampling at home. Importantly, the introduction of more sensitive techniques in the clinical laboratory has paved the way for analysis of small volume DBS samples in clinical chemistry (1). A well-recognized concern for accurate analyte quantification using DBS is the hematocrit (Ht) effect (2). First, Ht influences blood viscosity and thereby fluidity of blood on the paper. This can lead to different volumes of blood in equally sized punches and to varying analyte concentrations within the spot, resulting in a potential bias. Second, Ht may influence analyte extraction from the paper. Third, many analytes in clinical chemistry are currently measured in plasma and/or serum, whereas DBS samples are whole blood lysates. Therefore, for many compounds, Ht is necessary to accurately convert DBS measurements to plasma/serum values. Previously proposed techniques to …

BACKGROUND QC planning based on risk management concepts can reduce the probability of harming patients due to an undetected out-of-control error condition. It does this by selecting appropriate QC procedures to decrease the number of erroneous results reported. The selection can be easily made by using published nomograms for simple QC rules when the out-of-control condition results in increased systematic error. However, increases in random error also occur frequently and are difficult to detect, which can result in erroneously reported patient results. METHODS A statistical model was used to construct charts for the 1ks and X/χ2 rules. The charts relate the increase in the number of unacceptable patient results reported due to an increase in random error with the capability of the measurement procedure. They thus allow for QC planning based on the risk of patient harm due to the reporting of erroneous results. RESULTS 1ks Rules are simple, all-around rules. Their ability to deal with increases in within-run imprecision is minimally affected by the possible presence of significant, stable, between-run imprecision. X/χ2 rules perform better when the number of controls analyzed during each QC event is increased to improve QC performance. CONCLUSIONS Using nomograms simplifies the selection of statistical QC procedures to limit the number of erroneous patient results reported due to an increase in analytical random error. The selection largely depends on the presence or absence of stable between-run imprecision.

“Quality is never an accident. It is always the result of intelligent effort.” So observed John Ruskin, Victorian writer and critic of art, architecture, and society (1), unknowingly foreshadowing the emergence of quality science in the 20th century. Statistical QC came from Bell Telephone Laboratories, where Shewhart developed the statistical control chart in 1924, and Dodge and Romig pioneered statistical methods for acceptance sampling rather than 100% inspection of products. These beneficial techniques were not widespread until the manufacturing demands of World War II necessitated better control of product quality. With Deming's involvement, quality science was further developed in postwar Japanese manufacturing (2). Levey and Jennings introduced clinical laboratory statistical QC in 1950 (3). The Levey–Jennings chart remains a staple of laboratory QC. Development of stabilized control materials enhanced QC practices (4), and the testing of controls at fixed intervals became and remains the QC mainstay. Control-based QC has many strengths, including sensitivity to small changes in bias and ready availability of controls. Test intervals may be adjusted to account for assay stability, regulations or a laboratory's experience. Decision rules may be selected to maximize both sensitivity and specificity of QC procedures (5, 6). Weaknesses of control-based QC include the cost of materials and labor, scope limited to the analytical component, and matrix-effect masking of clinically relevant drift. Furthermore, a risk inherent in the intermittency of control-based QC is that analytical instability goes undetected until the next QC episode, while the laboratory releases clinically significant erroneous results. Ideally, QC would be continuous. In a limited sense, continuous QC exists for sample QC through delta checks, anion gap calculations, indices of hemolysis, icterus, lipemia, etc., and for analyzer performance through function checks, temperature sensors, automated reaction pattern analysis, etc. In this issue of Clinical Chemistry , Ng et …

The analysis of circulating cell-free DNA for the detection of cancer-associated genetic and genomic changes, frequently called liquid biopsy, has become an important tool for the management of cancer patients. For example, the analysis of epidermal growth factor receptor ( EGFR ) mutations in the plasma of cancer patients suffering from non-small cell lung cancers has been rapidly adopted as a routine clinical service for guiding the use of EGFR tyrosine kinase inhibitors in many countries (1). The noninvasive nature of liquid biopsy, as well as its rapid turn-around time, makes it a favorable choice for patients over tissue biopsy. In some patients, the EGFR mutation status can be determined by circulating tumoral DNA analysis before imaging-guided tumor biopsies are performed. Recently, whole-exome and targeted sequencing of cancer-associated genes from plasma DNA have also been performed in patients with advanced cancer to search for cancer-associated alterations that are potential therapeutic targets and to detect disease recurrence (2, 3). With the increasing utilization of liquid biopsies, there is a growing demand for quality control materials for circulating tumoral DNA analysis for platform comparison, assay development, internal quality control, and proficiency testing. However, the production of good-quality control materials that biologically resemble authentic circulating DNA in cancer patients is challenging and was the subject of a study by Zhang and colleagues reported in this issue of Clinical Chemistry (4). Circulating cell-free DNA exhibits a characteristic nucleosomal size profile predominantly comprising DNA fragments …

BACKGROUND Maintaining consistency of results over time is a challenge in laboratory medicine. Lot-to-lot reagent changes are a major threat to consistency of results. METHODS For the period October 2007 through July 2012, we reviewed lot validation data for each new lot of insulin-like growth factor 1 (IGF-1) reagents (Siemens Healthcare Diagnostics) at Mayo Clinic, Rochester, MN, and the University of Virginia, Charlottesville, VA. Analyses of discarded patient samples were used for comparison of lots. For the same period, we determined the distributions of reported patient results for each lot of reagents at the 2 institutions. RESULTS Lot-to-lot validation studies identified no reagent lot as significantly different from the preceding lot. By contrast, significant lot-to-lot changes were seen in the means and medians of 105 668 reported patient IGF-I results during the period. The frequency of increased results increased nearly 2-fold to a high of 17%, without detectable changes in the underlying patient demographics. Retrospective statistical analysis indicated that lot-to-lot comparison protocols were underpowered and that validation studies for this assay required testing >100 samples to achieve 90% power to detect reagent lots that would significantly alter the distributions of patient results. CONCLUSIONS The number of test samples required for adequate lot-to-lot validation protocols is high and may be prohibitively large, especially for low-volume or complex assays. Monitoring of the distributions of patient results has the potential to detect lot-to-lot inconsistencies relatively quickly. We recommend that manufacturers implement remote monitoring of patient results from analyzers in multiple institutions to allow rapid identification of between-lot result inconsistency.

BACKGROUND Metabolomics is a powerful tool that is increasingly used in clinical research. Although excellent sample quality is essential, it can easily be compromised by undetected preanalytical errors. We set out to identify critical preanalytical steps and biomarkers that reflect preanalytical inaccuracies. METHODS We systematically investigated the effects of preanalytical variables (blood collection tubes, hemolysis, temperature and time before further processing, and number of freeze–thaw cycles) on metabolomics studies of clinical blood and plasma samples using a nontargeted LC-MS approach. RESULTS Serum and heparinate blood collection tubes led to chemical noise in the mass spectra. Distinct, significant changes of 64 features in the EDTA-plasma metabolome were detected when blood was exposed to room temperature for 2, 4, 8, and 24 h. The resulting pattern was characterized by increases in hypoxanthine and sphingosine 1-phosphate (800% and 380%, respectively, at 2 h). In contrast, the plasma metabolome was stable for up to 4 h when EDTA blood samples were immediately placed in iced water. Hemolysis also caused numerous changes in the metabolic profile. Unexpectedly, up to 4 freeze–thaw cycles only slightly changed the EDTA-plasma metabolome, but increased the individual variability. CONCLUSIONS Nontargeted metabolomics investigations led to the following recommendations for the preanalytical phase: test the blood collection tubes, avoid hemolysis, place whole blood immediately in ice water, use EDTA plasma, and preferably use nonrefrozen biobank samples. To exclude outliers due to preanalytical errors, inspect the biomarker signal intensities reflecting systematic as well as accidental and preanalytical inaccuracies before processing the bioinformatics data.

One of the most frequent questions asked by the new clinical chemist is, “How do I set up the quality control for a particular analyzer?” Two new documents described below will readily help both the neophyte and practicing chemist. The practice of quality control in the clinical laboratory has been evolving in fits and starts. Unfortunately, today's laboratory quality control practices are obscured by undeserved complexity which robs the quality control practitioner (primarily the bench medical technologist) of tangible quality control intuition and in Deming's words, also robs her of pride of workmanship (1). In US clinical laboratories, quality control practices are hugely divergent in addition to being costly (2). In a recent review, Kazmierczak stated, “the costs associated with performing quality control testing and the costs associated with evaluating, reviewing and maintaining quality control records are not trivial” (3). He cited a developing nation hospital study that found the costs of quality to be 22% of total direct laboratory expenses, with 89% of the costs for maintaining quality associated with calibration and analysis of quality control material necessary to confirm the accuracy and reliability of test results (4). Simplicity is required in today's quality control practices and in this month's issue of Clinical Chemistry , Yago and Alcover, Spanish clinical chemists, provide highly instructive nomograms that permit the selection of simple quality control rules to yield appropriately low frequencies of defective results in analytic runs of 100 (5). These surprisingly orderly, ready-to-use nomograms provide a welcome contrast to the complex rule sets employed by many practicing clinical chemists and elaborated with or without the aid of quality control selection software. The nomograms of Yago and Alcover yield the number of defective analyses per analytical run of 100 samples. These nomograms visually demonstrate the importance of selecting analyzers with …

BACKGROUNDWe undertook this study to evaluate method differences for 5 components analyzed by immunoassays, to explore whether the use of method-dependent reference intervals may compensate for method differences, and to investigate commutability of external quality assessment (EQA) materials.METHODSTwenty fresh native single serum samples, a fresh native serum pool, Nordic Federation of Clinical Chemistry Reference Serum X (serum X) (serum pool), and 2 EQA materials were sent to 38 laboratories for measurement of cobalamin, folate, ferritin, free T4, and thyroid-stimulating hormone (TSH) by 5 different measurement procedures [Roche Cobas (n = 15), Roche Modular (n = 4), Abbott Architect (n = 8), Beckman Coulter Unicel (n = 2), and Siemens ADVIA Centaur (n = 9)]. The target value for each component was calculated based on the mean of method means or measured by a reference measurement procedure (free T4). Quality specifications were based on biological variation. Local reference intervals were reported from all laboratories.RESULTSMethod differences that exceeded acceptable bias were found for all components except folate. Free T4 differences from the uncommonly used reference measurement procedure were large. Reference intervals differed between measurement procedures but also within 1 measurement procedure. The serum X material was commutable for all components and measurement procedures, whereas the EQA materials were noncommutable in 13 of 50 occasions (5 components, 5 methods, 2 EQA materials).CONCLUSIONSThe bias between the measurement procedures was unacceptably large in 4/5 tested components. Traceability to reference materials as claimed by the manufacturers did not lead to acceptable harmonization. Adjustment of reference intervals in accordance with method differences and use of commutable EQA samples are not implemented commonly.

BACKGROUND According to the traditional approach to statistical QC planning, the performance of QC procedures is assessed in terms of its probability of rejecting an analytical run that contains critical size errors (PEDC). Recently, the maximum expected increase in the number of unacceptable patient results reported during the presence of an undetected out-of-control error condition [Max E(NUF)], has been proposed as an alternative QC performance measure because it is more related to the current introduction of risk management concepts for QC planning in the clinical laboratory. METHODS We used a statistical model to investigate the relationship between PEDC and Max E(NUF) for simple QC procedures widely used in clinical laboratories and to construct charts relating Max E(NUF) with the capability of the analytical process that allow for QC planning based on the risk of harm to a patient due to the report of erroneous results. RESULTS A QC procedure shows nearly the same Max E(NUF) value when used for controlling analytical processes with the same capability, and there is a close relationship between PEDC and Max E(NUF) for simple QC procedures; therefore, the value of PEDC can be estimated from the value of Max E(NUF) and vice versa. QC procedures selected by their high PEDC value are also characterized by a low value for Max E(NUF). CONCLUSIONS The PEDC value can be used for estimating the probability of patient harm, allowing for the selection of appropriate QC procedures in QC planning based on risk management.

BACKGROUND Data to standardize and harmonize the differences between cardiac troponin assays are needed to support their universal status in diagnosis of myocardial infarction. We characterized the variation between methods, the comparability of the 99th-percentile cutoff thresholds, and the occurrence of outliers in 4 cardiac troponin assays. METHODS Cardiac troponin was measured in duplicate in 2358 patient samples on 4 platforms: Abbott Architect i2000SR, Beckman Coulter Access2, Roche Cobas e601, and Siemens ADVIA Centaur XP. RESULTS The observed total variances between the 3 cardiac troponin I (cTnI) methods and between the cTnI and cardiac troponin T (cTnT) methods were larger than expected from the analytical imprecision (3.0%–3.7%). The between-method variations of 26% between cTnI assays and 127% between cTnI and cTnT assays were the dominant contributors to total variances. The misclassification of results according to the 99th percentile was 3%–4% between cTnI assays and 15%–17% between cTnI and cTnT. The Roche cTnT assay identified 49% more samples as positive than the Abbott cTnI. Outliers between methods were detected in 1 patient (0.06%) with Abbott, 8 (0.45%) with Beckman Coulter, 10 (0.56%) with Roche, and 3 (0.17%) with Siemens. CONCLUSIONS The universal definition of myocardial infarction should not depend on the choice of analyte or analyzer, and the between- and within-method differences described here need to be considered in the application of cardiac troponin in this respect. The variation between methods that cannot be explained by analytical imprecision and the discordant classification of results according to the respective 99th percentiles should be addressed.

To the Editor: Commutability of External Quality Assessment (EQA)1 materials is a key requirement for their use in accuracy-based EQA surveys (1–3). In a recent paper, Korzun et al. (4) evaluated commutability of 4 frozen pools for measurements of direct HDL cholesterol (HDLC) and LDL cholesterol (LDLC). These pools were used in the CDC's Lipid Standardization Program to assess accuracy of direct HDLC measurements only (4). Among the results presented using the medical requirement acceptance criteria for bias (4% for LDLC and 5% for HDLC), the authors found that 1 of the 4 frozen pools was commutable for most of the HDLC methods, whereas none were commutable for LDLC methods. The authors concluded that frozen pools prepared according to the CLSI C37 protocol may not always be commutable and especially for direct LDLC assays. In 2013, Laboratoire national de metrologie et d'essais (LNE) organized a similar study to assess commutability of 5 freshly prepared frozen serum pools prepared according to the CLSI C37-A protocol for HDLC and LDLC. The pools were shipped frozen and analyzed along with 20–25 fresh clinical specimens by 31 medical laboratories operating HDLC and LDLC routine methods on the most popular clinical chemistry analyzers: Roche Cobas, Siemens Vista, Abbott Architect, Ortho CD …

To the Editor: In contrast with chemistry or immunoassay analyzers, quantification for liquid chromatography-tandem mass spectrometry (LC-MS/MS)1 assays is mostly done by time-consuming multipoint calibration in each run (CR) (1). Few reports on alternative quantification methods have been published, with most of them being impractical for clinical purposes (1–3) and none of them covering a large period of time (1–4). We evaluated the performance of quantification based on an experimentally derived response factor (RF) used for 1 year without change (4) for mycophenolic acid (MPA) compared to CR. We also examined the variables influencing RF quantification performance. MPA was extracted by protein precipitation using a reagent containing deuterated internal standard (IS) and analyzed on an Alliance HPLC 2795 separations module coupled to a Quattro Micro tandem MS (both Waters Corp.). From June 2012 to May 2013, the first 10 patient samples, QCs of each run, and external QCs were quantified by CR and RF. CR was performed using a weighted (1/ x ) linear regression of 7 calibrators. RF quantification was performed by multiplying the observed response ratio (RR) (area analyte/area IS) by …

BACKGROUNDProficiency testing (PT), or external quality assessment (EQA), is intended to verify on a recurring basis that laboratory results conform to expectations for the quality required for patient care.CONTENTKey factors for interpreting PT/EQA results are knowledge of the commutability of the samples used and the process used for target value assignment. A commutable PT/EQA sample demonstrates the same numeric relationship between different measurement procedures as that expected for patients' samples. Noncommutable PT/EQA samples frequently have a matrix-related bias of unknown magnitude that limits interpretation of results. PT/EQA results for commutable samples can be used to assess accuracy against a reference measurement procedure or a designated comparison method. In addition, the agreement of the results between different measurement procedures for commutable samples reflects that which would be seen for patients' samples. PT/EQA results for noncommutable samples must be compared to a peer group mean/median of results from participants who use measurement procedures that are expected to have the same or very similar matrix-related bias. Peer group evaluation is used to asses whether a laboratory is using a measurement procedure in conformance to the manufacturer's specifications and/or in conformance to other laboratories using the same technology. A noncommutable PT/EQA sample does not give meaningful information about the relationship of results for patients' samples between different measurement procedures.SUMMARYPT/EQA provides substantial value to the practice of laboratory medicine by assessing the performance of individual laboratories and, when commutable samples are used, the status of standardization or harmonization among different measurement procedures.

BACKGROUND The Diabetes Control and Complications Trial (DCCT) and United Kingdom Prospective Diabetes Study (UKPDS) established the importance of hemoglobin A1c (Hb A1c) as a predictor of outcome in patients with diabetes mellitus. In 1994, the American Diabetes Association began recommending specific Hb A1c targets, but lack of comparability among assays limited the ability of clinicians to use these targets. The National Glycohemoglobin Standardization Program (NGSP) was implemented in 1996 to standardize Hb A1c results to those of the DCCT/UKPDS. CONTENT The NGSP certifies manufacturers of Hb A1c methods as traceable to the DCCT. The certification criteria have been tightened over time and the NGSP has worked with the College of American Pathologists in tightening proficiency-testing requirements. As a result, variability of Hb A1c results among clinical laboratories has been considerably reduced. The IFCC has developed a reference system for Hb A1c that facilitates metrological traceability to a higher order. The NGSP maintains traceability to the IFCC network via ongoing sample comparisons. There has been controversy over whether to report Hb A1c results in IFCC or NGSP units, or as estimated average glucose. Individual countries are making this decision. SUMMARY Variability among Hb A1c results has been greatly reduced. Not all countries will report Hb A1c in the same units, but there are established equations that enable conversion between different units. Hb A1c is now recommended for diagnosing diabetes, further accentuating the need for optimal assay performance. The NGSP will continue efforts to improve Hb A1c testing to ensure that clinical needs are met.

BACKGROUNDGood laboratory practice includes verifying that each new lot of reagents is suitable for use before it is put into service. Noncommutability of quality control (QC) samples with clinical patient samples may preclude their use to verify consistency of results for patient samples between different reagent lots.METHODSPatient sample results and QC data were obtained from reagent lot change verification records for 18 QC materials, 661 reagent lot changes, 1483 reagent lot change–QC events, 82 analytes, and 7 instrument platforms. The significance of between-lot differences in the results for QC samples compared with those for patient samples was assessed by a modified 2-sample t test adjusted for heterogeneity of QC and patient sample measurement variances.RESULTSOverall, 40.9% of reagent lot change–QC events had a significant difference (P < 0.05) between results for QC samples compared with results for patient samples between 2 reagent lots. For QC results with differences CONCLUSIONSOccurrence of noncommutable results for QC materials was frequent enough that the QC results could not be used to verify consistency of results for patient samples when changing lots of reagents.