Repeat patient testing quality control (RPT-QC): Background and theory.

Repeat-patient testing quality control (RPT-QC) is a version of statistical quality control (SQC) in which individual patient samples, rather than commercial control materials, are used. Whereas conventional SQC assumes control material stability and repeatedly measures the same lot of control material over time, RPT-QC uses a unique patient sample for each QC event and exploits the labile nature of patient samples under prescribed storage conditions for QC purposes. Advantages of RPT-QC include commutability, lower cost, and QC at concentrations of medical interest. Challenges include sample procurement and the establishment of control limits. The objective of this review is to compare and contrast the principles and procedures of RPT-QC and conventional SQC and to provide an overview of RPT-QC control limit establishment.

Repeat patient Testing-Quality control: An example of implementation in a network of commercial laboratories.

The theory and calculations underpinning Repeat Patient Testing-Quality Control (RPT-QC) have been presented in prior publications. This paper gives an example of the process used for implementing RPT-QC in a network of veterinary commercial reference laboratories and the stages associated with the transition to the sole use of RPT-QC. To employ RPT-QC in this commercial laboratory network, eight stages of implementation were identified: (1) education, (2) data collection, (3) calculations, (4) QC recording and documentation, (5) running RPT-QC in parallel with a commercially available quality control material (QCM), (6) development of a Standard Operating Procedure (SOP), (7) development of complementary aspects supporting RPT-QC, and (8) sole use of RPT-QC. Advantages of RPT-QC included cost savings for QCM and External Quality Assessment (EQA) participation and the ability to use commutable specimens with a veterinary matrix at a result level that is of clinical significance for the species. A disadvantage of RPT-QC using a single level of control was the inability to demonstrate stable performance over a range of results. Future avenues for investigation include ongoing refinement of control limits using a pooled standard deviation of the duplicates (SDdup), Sdup over time, investigation of blood samples from species other than the dog, and manipulation of specimens to produce "low abnormal" or "high abnormal" RPT-QC specimens.

A spreadsheet tool for designing statistical quality control programs based on patient risk parameters.

BACKGROUND: Quality control (QC) in the laboratory aims to reduce the risk of harm to a patient due to erroneous results, as highlighted by the Clinical Laboratory Standards Institute (CLSI) guidance for Statistical Quality Control (SQC) (C24-Ed4). To effectively reduce patient risk, a convenient spreadsheet tool was developed to assist laboratories in SQC design based on patient risk parameters. METHODS: In accordance with Parvin's patient risk model and the mathematical formula for calculating the expected number of unreliable final patient results [E(Nuf)], the function is edited using Excel software, and the maximum E(Nuf) [MaxE(Nuf)] value and other risk parameters based on the current QC strategy are calculated to assess the risk of the QC strategy. RESULTS: A convenient spreadsheet tool is proposed in this study. After the quality requirements, performance parameters, practical run size, QC rules and the number of QC results of test items are input, the laboratory is enabled to quickly obtain MaxE(Nuf) value, maximum run size and other data based on the strategy. The QC strategy conforming to the risk requirements can be developed by changing the QC rules or the quantity of run size. Moreover, the Power Function Graph of the QC strategy and two risk diagrams are presented simultaneously. CONCLUSIONS: Convenient spreadsheet tools can be adopted by laboratories to assess the risks of QC strategies and design appropriate risk-based SQC strategies to reduce patient risk to acceptable levels.

Statistical process control in assessing water quality in the Doce river basin after the collapse of the Fundão dam (Mariana, Brazil).

The process of extracting information from data generated in environmental monitoring programs is often carried out using statistical tools, with Statistical Process Control (SPC) showing great potential for application in environmental monitoring. In November 2015, millions of cubic metres of tailings were dumped into the basin of the River Doce with the collapse of the Fundão dam. A study of the impact of this incident requires new approaches in data monitoring and processing, so it was sought to evaluate, using SPC tools, changes in water quality in the basin of the River Doce following the collapse of the dam. Using process charts and the process capability index (PCI), water quality parameters in the Doce and Carmo rivers were evaluated between 2009 and 2020. There, turbidity has improved since 2018, and Mn since 2016. Control charts showed that by December 2020 dissolved Fe was still not within normal pre-event fluctuation patterns. The PCI value showed that the situation worsened after the event for each of the parameters, with the lowest values for Mn and E. coli. By using a reference period, SPC makes it possible to infer the permanence of the impact of extreme pollution on the waterbody, which can be used in the routine monitoring of water quality in such events.

Intelligent Quality Management 2 with IntraSpect™ technology for quality control of GEM® Premier™ 5000 blood gas analyzers- A novel application of the patient sample as its own control.

Background: Quality control (QC) in point-of-care (POC) testing has been greatly improved by automatic control processes, such as the Intelligent Quality Management (iQM®) technology found in GEM Premier blood gas analyzers (Werfen, Bedford, MA). The 2nd generation technology, iQM2, provides additional capabilities, notably the incorporation of IntraSpect software that monitors the response curves of individual tests to detect transient errors caused by micro-clots, micro-bubbles or any event that disturbs the sensor response during sample data acquisition. IntraSpect is a novel form of patient-based, real-time quality control (PBRTQC). Methods: IntraSpect pattern recognition software monitors the last 15 measurements of each patient-response curve. Control limits for slope coefficients have been established from theoretical models and empirical data. Abnormal measurement behavior is flagged to identify transient errors that invalidate test results. Results from 1,013,391 patient samples were collected on 4,985 GEM Premier 5000 cartridges and 2,765 instruments in clinical use worldwide. Results and conclusions: Total pre-analytic and transient errors detected by IntraSpect were 1.91% worldwide. iQM2 with IntraSpect technology provides a unique control function detecting transient errors that would otherwise go undetected with traditional QC. Together with the statistical QC technology in iQM2, pre-analytic, analytic, and transient analytic errors are detected much faster-seconds versus hours-than by traditional statistical QC.

Multirule procedures vs moving average algorithms for IQC: An appropriate comparison reveals how best to combine their strengths.

BACKGROUND: Moving Average Algorithms (MAA) have been widely recommended for use in Patient Based Real Time Quality Control applications (PBRTQC) to supplement or replace traditional Internal Quality Control (IQC) techniques. A recent "proof of concept" study recommends applying MAAs to IQC data to replace traditional IQC procedures because they "outperform Westgard Rules," which is a current standard of practice for IQC. METHODS: We generated power curves for multi-rule procedures with 2 and 4 control measurements per QC event, as well as a Simple Moving Average having block sizes of 5, 10, and 20 control measurements. We also assessed time to detection in terms of the Average Number of QC Events required to detect different sizes of systematic errors. RESULTS: As expected, the more control measurements included in the control technique, the better the error detection. However, when QC performance is considered on the Sigma Scale, high Sigma methods require only 1 or 2 control measurements to detect medically important systematic errors. MAAs have very low ability to detect error at the first few QC events following shift, so they suffer a lag phase in detecting medically important errors. MAAs are most useful for methods having 4.0 Sigma performance or less. Even then, large systematic shifts are more quickly detected by simple single and multirule procedures. CONCLUSIONS: Choice of control techniques (rules, means, ranges, etc.) should consider the Sigma-metric of the method. For methods having Sigmas of 4 or greater, traditional single rule and multirule procedures with Ns up to 4 are most effective; below 4 Sigma, a multirule coupled with a Simple Moving Average (SMA) rule with Ns of 4 to 8 can improve error detection.

Application of a six sigma model to evaluate the analytical performance of urinary biochemical analytes and design a risk-based statistical quality control strategy for these assays: A multicenter study.

BACKGROUND: The six sigma model has been widely used in clinical laboratory quality management. In this study, we first applied the six sigma model to (a) evaluate the analytical performance of urinary biochemical analytes across five laboratories, (b) design risk-based statistical quality control (SQC) strategies, and (c) formulate improvement measures for each of the analytes when needed. METHODS: Internal quality control (IQC) and external quality assessment (EQA) data for urinary biochemical analytes were collected from five laboratories, and the sigma value of each analyte was calculated based on coefficients of variation, bias, and total allowable error (TEa). Normalized sigma method decision charts for these urinary biochemical analytes were then generated. Risk-based SQC strategies and improvement measures were formulated for each laboratory according to the flowchart of Westgard sigma rules, including run sizes and the quality goal index (QGI). RESULTS: Sigma values of urinary biochemical analytes were significantly different at different quality control levels. Although identical detection platforms with matching reagents were used, differences in these analytes were also observed between laboratories. Risk-based SQC strategies for urinary biochemical analytes were formulated based on the flowchart of Westgard sigma rules, including run size and analytical performance. Appropriate improvement measures were implemented for urinary biochemical analytes with analytical performance lower than six sigma according to the QGI calculation. CONCLUSIONS: In multilocation laboratory systems, a six sigma model is an excellent quality management tool and can quantitatively evaluate analytical performance and guide risk-based SQC strategy development and improvement measure implementation.

A multi-test planning model for risk based statistical quality control strategies.

BACKGROUND: Efforts to improve QC for multi-test analytic systems should focus on risk-based bracketed SQC strategies, as recommended in the CLSI C24-Ed4 guidance for QC practices. The objective is to limit patient risk by controlling the expected number of erroneous patient test results that would be reported over the period an error condition goes undetected. METHODS: A planning model is described to provide a structured process for considering critical variables for the development of SQC strategies for continuous production multi-test analytic systems. The model aligns with the principles of the CLSI C24-Ed4 "roadmap" and calculation of QC frequency, or run size, based on Parvin's patient risk model. Calculations are performed using an electronic spreadsheet to facilitate application of the planning model. RESULTS: Three examples of published validation data are examined to demonstrate the application of the planning model for multi-test chemistry and enzyme analyzers. The ability to assess "what if" conditions is key to identifying the changes and improvements that are necessary to simplify the overall system to a manageable number of SQC procedures. CONCLUSIONS: The planning of risk based SQC strategies should align operational requirements for workload and reporting intervals with QC frequency in terms of the run size or the number of patient samples between QC events. Computer tools that support the calculation of run sizes greatly facilitate the planning process and make it practical for medical laboratories to quickly assess the effects of critical variables.

Planning SQC strategies and adapting QC frequency for patient risk.

BACKGROUND: Risk-based Statistical QC strategies are recommended by the CLSI guidance for Statistical Quality Control (C24-Ed4). Using Parvin's patient risk model, QC frequency can be determined in terms of run size, i.e., the number of patient samples between QC events. Run size provides a practical goal for planning SQC strategies to achieve desired test reporting intervals. METHODS: A QC Frequency calculator is utilized to evaluate critical factors (quality required for test, precision and bias observed for method, rejection characteristics of SQC procedure) and also to consider patient risk as a variable for adjusting run size. RESULTS: We illustrate the planning of SQC strategies for a HbA1c test where two levels of controls show different sigma performance, for three different HbA1c analyzers used to achieve a common quality goal in a network of laboratories, and for an 18 test chemistry analyzer where a common run size is achieved by changes in control rules and adjustments for the patient risk of different tests. CONCLUSIONS: Run size provides a practical characteristic for adapting QC frequency to systematize the SQC strategies for multiple levels of controls or multiple tests in a chemistry analyzer. Patient risk can be an important variable for adapting run size to fit the laboratory's desired reporting intervals for high volume continuous production analyzers.

Statistical Quality and Process Control in Biopharmaceutical Manufacturing-Practical Issues and Remedies.

Statistical quality and process controls (SQC and SPC) are used for monitoring, trending, and ultimately improving biopharmaceutical manufacturing processes and operations. The purpose of this paper is to highlight characteristic features of bioprocess data and their impact on typical SQC and SPC applications, specifically control charts for individual observations (I-chart) and estimation of process performance index (Ppk). Simulated data were used in an attempt to mimic bioprocess data by inducing inhomogeneity, nonstationarity, autocorrelation, and outliers. The first specific part highlights the roles of within and overall standard deviation (SD) estimates for 3σ limits and their impacts on frequently applied sensitizing rules for control charts, i.e. Nelson's rules 1-4. The second part deals with the often-asked question of how many observations are required for estimation of robust 3σ limits. In the third part, five popular approaches for treating censored data (results below or equal to limit of quantification, ≤LOQ) were compared and their impact on 3σ limits and Ppk estimates were assessed. The final section summarizes the typical issues faced by the practitioner in the application of SQC and SPC and provides remedies for setting up robust and efficient control charts for biopharmaceutical process monitoring. Overall, this study shows that process monitoring and subsequent assessment without taking into consideration this atypical nature of biopharmaceutical process can lead to increased false alarm rates, thus impacting the batch release or even possibility of rejecting good batches.

Real-time quality control of nuchal translucency measurements using the exponentially weighted moving average chart.

OBJECTIVE: The establishment of ongoing audits for first-trimester nuchal translucency (NT) measurements is of paramount importance. The exponentially weighted moving average (EWMA) chart has been published as an efficient tool for NT quality control with the advantages of being suitable for real-time long-term monitoring. This study aimed to assess the efficacy of real-time NT quality control using EWMA charts. MATERIALS AND METHODS: This was an ongoing prospective study conducted from January 2011 to December 2017 at the Centre for Fetal Medicine Gennet in Prague. The quality of NT measurements was assessed using the NT retrospective distribution parameters and EWMA charts, and the results were presented to the sonographers during collective meetings. RESULTS: Overall, 28,928 NT measurements obtained from six sonographers were eligible for the study. Looking at individual EWMA charts, we observed four main outcomes. First, there was a clear improvement in the performance of sonographers with initially poor performances. Second, the performance of sonographers with an initially satisfactory quality was maintained. Third, there was an observed deterioration of the performance without the audits. Last, the sonographers appreciated an unequivocal and straightforward graphical presentation of EWMA curves. CONCLUSION: EWMA proved to be an efficient and suitable tool for real-time monitoring of NT quality and led to an overall improvement of the sonographers' performance.

Quicker detection risk-adjusted cumulative sum charting procedures.

When a patient is operated on, the surgical outcome depends on two major factors: (i) the patient's health condition and (ii) the surgical process comprising the surgeon, the supporting staff, operating environment, and equipment. An outcome is usually represented by one if a patient dies within 30 days of an operation and zero otherwise. Another method of measuring the outcome is to use survival time with truncation on the 30th day for monitoring purposes. In order to monitor a surgical process effectively, the health condition of a patient must be taken into consideration. This is usually done using a log-likelihood ratio statistic based on an outcome, that is, risk adjusted according to the health condition of the patient. The 30-day wait results in delay in signaling when a deterioration occurs. The consequence of having to wait even though a death has occurred is the potential loss of lives because of delay in signaling. Regular updating of patients' information can improve the sensitivity of a charting procedure. The main objective of this article is to develop and study the class of risk-adjusted cumulative sum procedures that are updated on a regular basis based on patients' current conditions, without having to wait 30 days. Our study shows that these charts do in fact signal earlier and there are differences among the various updating techniques and monitoring statistics.

The clinical significance of modifying X-ray tube current-time product based on prior image deviation index for digital radiography.

PURPOSE: The absorbed dose at the image receptor in digital X-ray systems increases with an incorrect adjustment of the X-ray tube current-time product (mAs). Accordingly, the exposure index, target exposure index, and deviation index (DI) are proposed as absorbed dose optimization tools. We aimed at reducing the variation of DI in a short period by employing the mAs value determined by previously used mAs and DI. METHODS: We developed software that automatically calculates mAs for subsequent X-ray examinations based on mAs and DI values from prior examinations. Portable chest X-ray examinations in an intensive care unit (ICU) were performed for 16 weeks. The software was not used for the first 10 weeks in 406 cases and was used for the remaining 6 weeks in 216 cases. The changes in the non-conformance rate of DI for 16 weeks were evaluated using the p-chart used for quality control. The effect of the software on image noise was also evaluated. RESULTS: In total, 42% of cases had a DI range of -1 to 1 without using the software; this increased to 81% when using the software. Averages and variances of DI in cases with and without the software demonstrated statistically significant differences. From the p-chart, the non-conformance rate of DI was shown to decrease when using software. The software also worked for reducing the variation in image noise. CONCLUSIONS: Our method reduced the variation in DI in a short period of time.

Monitoring data quality for telehealth systems in the presence of missing data.

BACKGROUND: All-in-one station-based health monitoring devices are implemented in elder homes in Hong Kong to support the monitoring of vital signs of the elderly. During a pilot study, it was discovered that the systolic blood pressure was incorrectly measured during multiple weeks. A real-time solution was needed to identify future data quality issues as soon as possible. METHODS: Control charts are an effective tool for real-time monitoring and signaling issues (changes) in data. In this study, as in other healthcare applications, many observations are missing. Few methods are available for monitoring data with missing observations. A data quality monitoring method is developed to signal issues with the accuracy of the collected data quickly. This method has the ability to deal with missing observations. A Hotelling's T-squared control chart is selected as the basis for our proposed method. FINDINGS: The proposed method is retrospectively validated on a case study with a known measurement error in the systolic blood pressure measurements. The method is able to adequately detect this data quality problem. The proposed method was integrated into a personalized telehealth monitoring system and prospectively implemented in a second case study. It was found that the proposed scheme supports the control of data quality. CONCLUSIONS: Data quality is an important issue and control charts are useful for real-time monitoring of data quality. However, these charts must be adjusted to account for missing data that often occur in healthcare context.

Establishing Evidence-Based Statistical Quality Control Practices.

OBJECTIVES: To establish an objective, scientific, evidence-based process for planning statistical quality control (SQC) procedures based on quality required for a test, precision and bias observed for a measurement procedure, probabilities of error detection and false rejection for different control rules and numbers of control measurements, and frequency of QC events (or run size) to minimize patient risk. METHODS: A Sigma-Metric Run Size Nomogram and Power Function Graphs have been used to guide the selection of control rules, numbers of control measurements, and frequency of QC events (or patient run size). RESULTS: A tabular summary is provided by a Sigma-Metric Run Size Matrix, with a graphical summary of Westgard Sigma Rules with Run Sizes. CONCLUSION: Medical laboratories can plan evidence-based SQC practices using simple tools that relate the Sigma-Metric of a testing process to the control rules, number of control measurements, and run size (or frequency of QC events).

Quality control limits: Are we setting them too wide?

BACKGROUND: Quality control charts (Levey Jennings Charts) are based on estimates of variation. There are two general approaches for estimating variation: those based on short-term variation and those based on long-term variation. We have observed that clinical laboratory science (CLS) tends to estimate variation using long-term variation but that most other fields use short-term variation. The objective of this study is to compare these two methods of estimating process variation, compare the accuracy of control limits generated by each method, and explore whether it would be useful for clinical laboratories to adopt methods used in other fields. METHODS: We conducted a literature review to compare recommendations for methods for estimation of variation in CLS with other fields. We searched textbooks for suggested methods and also searched the primary literature for references to methods associated with short-term and long-term variation. We provide theoretical results from statistics to show that, in theory, short-term estimates can differ from long-term estimates of variation. We used simulation studies to show that one can construct examples where short-term and long-term estimates of variation lead to significant differences in control limits. Finally, we show laboratory data comparing short-term and long-term estimates of variation. RESULTS: We found that practice in CLS differs from other fields. We found no references to methods based on short-term variation in CLS textbooks and only one reference in the primary literature. In contrast, standard quality control (QC) texts recommend methods based on short-term variation and the primary literature makes frequent reference to such methods. We found statistical papers that show that, in theory, estimates based on long-term variation can produce inflated estimates of process variation. We used simulation to show that such examples can be constructed. We examined 95 QC charts and found that in 93 cases, there were significant differences between short-term and long-term estimates of variation. The ratio of long-term to short-term variation was greater than 1.5 in 18% of cases. CONCLUSION: Estimates of variation based on short-term and long-term variation can lead to significant differences in estimates. Estimates based on long-term variation are frequently larger than estimates based on short-term variation.

Selecting a Risk-Based SQC Procedure for a HbA1c Total QC Plan.

BACKGROUND: Recent US practice guidelines and laboratory regulations for quality control (QC) emphasize the development of QC plans and the application of risk management principles. The US Clinical Laboratory Improvement Amendments (CLIA) now includes an option to comply with QC regulations by developing an individualized QC plan (IQCP) based on a risk assessment of the total testing process. The Clinical and Laboratory Standards Institute (CLSI) has provided new practice guidelines for application of risk management to QC plans and statistical QC (SQC). METHODS: We describe an alternative approach for developing a total QC plan (TQCP) that includes a risk-based SQC procedure. CLIA compliance is maintained by analyzing at least 2 levels of controls per day. A Sigma-Metric SQC Run Size nomogram provides a graphical tool to simplify the selection of risk-based SQC procedures. APPLICATIONS: Current HbA1c method performance, as demonstrated by published method validation studies, is estimated to be 4-Sigma quality at best. Optimal SQC strategies require more QC than the CLIA minimum requirement of 2 levels per day. More complex control algorithms, more control measurements, and a bracketed mode of operation are needed to assure the intended quality of results. CONCLUSIONS: A total QC plan with a risk-based SQC procedure provides a simpler alternative to an individualized QC plan. A Sigma-Metric SQC Run Size nomogram provides a practical tool for selecting appropriate control rules, numbers of control measurements, and run size (or frequency of SQC). Applications demonstrate the need for continued improvement of analytical performance of HbA1c laboratory methods.

Selecting multi-rule quality control procedures based on patient risk.

BACKGROUND: Traditionally, statistical quality control (SQC) planning is aimed at preventing the error rate from exceeding a pre-defined acceptable rate (Westgard JO. Basic QC Practices, 4th ed. Westgard QC, 2016). A pivotal characteristic for planning a QC procedure with the traditional approach is the probability of rejecting an analytical run that contains critical size errors (Pedc). Multi-rule QC procedures, with fully documented power curves, are important tools for SQC. In addition, it has been recommended (Parvin CA, Gronowski AM. Effect of analytical run length on quality-control (QC) performance and the QC planning process. Clin Chem 1997;43:2149-54) to optimize the frequency of QC on the basis of 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)]. The relationship between Pedc and Max E(Nuf) has been studied for single rule QC procedures (Yago M, Alcover S. Selecting statistical procedures for quality control planning based on risk management. Clin Chem 2016;62:959-65), but corresponding information for multi-rule QC is lacking. METHODS: We used a statistical model to investigate the relationship between Pedc and Max E(Nuf) for multi-rules commonly used in clinical laboratories, and constructed charts relating the Max E(Nuf) and the sigma capability of the examination procedure for multi-rules which can be used as practical tools for planning SQC. RESULTS: There is a close relationship between Pedc and Max E(Nuf) for commonly used multi-rules. Common multi-rule SQC procedures traditionally designed for high Pedc will also provide low Max E(Nuf) values. CONCLUSIONS: Multi-rule SQC procedures can be used for controlling intermediate and low sigma capability method to attain a low Max E(Nuf) so that the probability of patient harm is mitigated to acceptable levels.

Sigma Metrics Across the Total Testing Process.

Laboratory quality control has been developed for several decades to ensure patients' safety, from a statistical quality control focus on the analytical phase to total laboratory processes. The sigma concept provides a convenient way to quantify the number of errors in extra-analytical and analytical phases through the defect per million and sigma metric equation. Participation in a sigma verification program can be a convenient way to monitor analytical performance continuous quality improvement. Improvement of sigma-scale performance has been shown from our data. New tools and techniques for integration are needed.