Advances in internal quality control.

Quality control practices in the modern laboratory are the result of significant advances over the many years of the profession. Major advance in conventional internal quality control has undergone a philosophical shift from a focus solely on the statistical assessment of the probability of error identification to more recent thinking on the capability of the measurement procedure (e.g. sigma metrics), and most recently, the risk of harm to the patient (the probability of patient results being affected by an error or the number of patient results with unacceptable analytical quality). Nonetheless, conventional internal quality control strategies still face significant limitations, such as the lack of (proven) commutability of the material with patient samples, the frequency of episodic testing, and the impact of operational and financial costs, that cannot be overcome by statistical advances. In contrast, patient-based quality control has seen significant developments including algorithms that improve the detection of specific errors, parameter optimization approaches, systematic validation protocols, and advanced algorithms that require very low numbers of patient results while retaining sensitive error detection. Patient-based quality control will continue to improve with the development of new algorithms that reduce biological noise and improve analytical error detection. Patient-based quality control provides continuous and commutable information about the measurement procedure that cannot be easily replicated by conventional internal quality control. Most importantly, the use of patient-based quality control helps laboratories to improve their appreciation of the clinical impact of the laboratory results produced, bringing them closer to the patients.Laboratories are encouraged to implement patient-based quality control processes to overcome the limitations of conventional internal quality control practices. Regulatory changes to recognize the capability of patient-based quality approaches, as well as laboratory informatics advances, are required for this tool to be adopted more widely.

Implementation of patient-based real-time quality control.

The quest to use patient results as quality control for routine clinical chemistry testing has long been driven by issues of the unavailability and cost of suitable quality control material and the matrix effects of synthetic material. Hematology laboratories were early adopters of average of normals techniques, primarily because of the difficulty in acquiring appropriate, stable quality control material, while in the chemistry laboratories, the perceived advantages and availability of synthetic material outweighed the disadvantages. However, the increasing volume of testing in clinical chemistry plus the capability of computer systems to deal with large and complex calculations has now made the use of patient-based quality control algorithms feasible. The desire to use patient-based quality control is also driven by increasing awareness that common quality control rules and frequency of analysis may fail to detect clinically significant assay biases. The non-commutability of quality control material has also become a problem as laboratories seek to harmonize results across regions and indeed globally. This review describes the history of patient-based quality control in clinical chemistry, summarizes the various approaches that can be implemented by laboratory professionals, and discusses how patient-based quality control can be integrated with traditional quality control techniques.

Moving average quality control: principles, practical application and future perspectives.

Moving average quality control (MA QC) was described decades ago as an analytical quality control instrument. Although a potentially valuable tool, it is struggling to meet expectations due to its complexity and need for evidence-based guidance. For this review, relevant literature and the world wide web were examined in order to (i) explain the basic concepts and current understanding of MA QC, (ii) discuss moving average (MA) optimization methods, (iii) gain insight into practical aspects related to applying MA in daily practice and (iv) describe future prospects to enable more widespread acceptance and application of MA QC. Each of the MA QC optimization methods currently available has their own advantages and disadvantages. Recently developed simulation methods provide realistic error detecting properties for MA QC and are available for laboratories. Operational MA management issues have been identified that allow developers of MA software to upgrade their packages to support optimal MA QC application and guide laboratories on MA management issues, such as MA alarm workup. The new insights into MA QC characteristics and operational issues, together with supporting online tools, may promote more widespread acceptance and application of MA QC.

Design and implementation of quality control plans that integrate moving average and internal quality control: incorporating the best of both worlds.

Background New moving average quality control (MA QC) optimization methods have been developed and are available for laboratories. Having these methods will require a strategy to integrate MA QC and routine internal QC. Methods MA QC was considered only when the performance of the internal QC was limited. A flowchart was applied to determine, per test, whether MA QC should be considered. Next, MA QC was examined using the MA Generator (www.huvaros.com), and optimized MA QC procedures and corresponding MA validation charts were obtained. When a relevant systematic error was detectable within an average daily run, the MA QC was added to the QC plan. For further implementation of MA QC for continuous QC, MA QC management software was configured based on earlier proposed requirements. Also, protocols for the MA QC alarm work-up were designed to allow the detection of temporary assay failure based on previously described experiences. Results Based on the flowchart, 10 chemistry, two immunochemistry and six hematological tests were considered for MA QC. After obtaining optimal MA QC settings and the corresponding MA validation charts, the MA QC of albumin, bicarbonate, calcium, chloride, creatinine, glucose, magnesium, potassium, sodium, total protein, hematocrit, hemoglobin, MCH, MCHC, MCV and platelets were added to the QC plans. Conclusions The presented method allows the design and implementation of QC plans integrating MA QC for continuous QC when internal QC has limited performance.

Failure of internal quality control in detecting significant reagent lot shift in serum creatinine measurement.

BACKGROUND: Internal quality control (IQC) in clinical laboratories is carried out to monitor analytical stability. Usually, the satisfactory results of the IQC ensure the acceptability of the examination results. Here, we reported that patients' creatinine results are unreliable, although the internal quality control is satisfactory. METHODS: Creatinine levels were analyzed from two quality control materials and twenty patients' specimens using two different lots of reagents. Lot-to-lot comparison was performed. The daily median values of serum creatinine levels of patients were calculated from the test results recorded in our laboratory information system. RESULTS: Although IQC was consistent, serum creatinine concentrations were higher using lot B (median: 153 µmol/L; interquartile range: 122-522 µmol/L) than using lot A (median: 133 µmol/L; interquartile range: 76-508 µmol/L) for 20 patients (P = .001). The Deming linear regression showed a best fit of y = 0.9394 × x + 45.66. R2  = .8919, and mean percentage difference between two lots was 34%. The new lot was considered unacceptable. Likewise, the median serum creatinine level from the 360 patients using lot B was 102 µmol/L, which was significantly higher than the daily medians of patients using lot A (median: 66 µmol/L; range: 61-70 µmol/L) in the previous month. CONCLUSION: The variations in creatinine concentrations proved to be due to different lots of reagents. However, IQC materials tested using both lots of reagent exhibited minimal variation. Therefore, IQC alone is insufficient for assessing laboratory analytical results. This finding prompts us to be vigilant in potential pitfall of interpreting test results based on satisfactory IQC alone.

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.

Impact of combining data from multiple instruments on performance of patient-based real-time quality control.

INTRODUCTION: It is unclear what is the best strategy for applying patient-based real-time quality control (PBRTQC) algorithm in the presence of multiple instruments. This simulation study compared the error detection capability of applying PBRTQC algorithms for instruments individually and in combination using serum sodium as an example. MATERIALS AND METHODS: Four sets of random serum sodium measurements were generated with differing means and standard deviations to represent four simulated instruments. Moving median with winsorization was selected as the PBRTQC algorithm. The PBRTQC parameters (block size and control limits) were optimized and applied to the four simulated laboratory data sets individually and in combination. RESULTS: When the PBRTQC algorithm were individually optimized and applied to the data of the individual simulated instruments, it was able to detect bias several folds faster than when they were combined. Similarly, the individually applied algorithms had perfect error detection rates across different magnitudes of bias, whereas the error detection rates of the algorithm applied on the combined data missed smaller biases. The performance of the individually applied PBRTQC algorithm performed more consistently among the simulated instruments compared to when the data were combined. DISCUSSION: While combining data from different instruments can increase the data stream and hence, increase the speed of error detection, it may widen the control limits and compromising the probability of error detection. The presence of multiple instruments in the data stream may dilute the effect of the error when it only affects a selected instrument.

Moving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision.

INTRODUCTION: An increase in analytical imprecision (expressed as CVa) can introduce additional variability (i.e. noise) to the patient results, which poses a challenge to the optimal management of patients. Relatively little work has been done to address the need for continuous monitoring of analytical imprecision. METHODS: Through numerical simulations, we describe the use of moving standard deviation (movSD) and a recently described moving sum of outlier (movSO) patient results as means for detecting increased analytical imprecision, and compare their performances against internal quality control (QC) and the average of normal (AoN) approaches. RESULTS: The power of detecting an increase in CVa is suboptimal under routine internal QC procedures. The AoN technique almost always had the highest average number of patient results affected before error detection (ANPed), indicating that it had generally the worst capability for detecting an increased CVa. On the other hand, the movSD and movSO approaches were able to detect an increased CVa at significantly lower ANPed, particularly for measurands that displayed a relatively small ratio of biological variation to CVa. CONCLUSION: The movSD and movSO approaches are effective in detecting an increase in CVa for high-risk measurands with small biological variation. Their performance is relatively poor when the biological variation is large. However, the clinical risks of an increase in analytical imprecision is attenuated for these measurands as an increased analytical imprecision will only add marginally to the total variation and less likely to impact on the clinical care.