Patient-based Real-time Quality Control Research Articles

Regression-Adjusted Real-Time Quality Control.

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. 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. 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. 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.

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

IntroductionIt 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 methodsFour 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.ResultsWhen 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.DiscussionWhile 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.

Benefits, limitations, and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice.

In recent years, there has been renewed interest in the "old" average of normals concept, now generally referred to as moving average quality control (MA QC) or patient-based real-time quality control (PBRTQC). However, there are some controversies regarding PBRTQC which this review aims to address while also indicating the current status of PBRTQC. This review gives the background of certain newly described optimization and validation methods. It also indicates how QC plans incorporating PBRTQC can be designed for greater effectiveness and/or (cost) efficiency. Furthermore, it discusses controversies regarding the complexity of obtaining PBRTQC settings, the replacement of iQC, and software functionality requirements. Finally, it presents evidence of the added value and practicability of PBRTQC. Recent developments in, and availability of, simulation methods to optimize and validate laboratory-specific PBRTQC procedures have enabled medical laboratories to implement PBRTQC in their daily practice. Furthermore, these methods have made it possible to demonstrate the practicability and added value of PBRTQC by means of two prospective "clinical" studies and other investigations. Although internal QC will remain an essential part of any QC plan, applying PBRTQC can now significantly improve its performance and (cost) efficiency.

Assessment of patient-based real-time quality control algorithm performance on different types of analytical error

BackgroundPatient-based real-time quality control (PBRTQC) has gained attention because of its potential to detect analytical errors in situations wherein internal quality control is less effective. Multiple PBRTQC algorithms have been proposed. However, there is a lack of comprehensive comparison of the performance of PBRTQC algorithms on different types of analytical errors. Thus, a comparative study was conducted. MethodsThe performance of six different PBRTQC algorithms was evaluated on three types of analytical errors using 906,552 test results for outpatient serum sodium, chloride, alanine aminotransferase, and creatinine at the Department of Laboratory Medicine at Zhongshan Hospital, Fudan University in 2019. The performance results were compared and assessed. ResultsThe moving average, moving median, exponentially weighted moving average, and moving quartiles performed similarly for effectively detecting constant errors (CE) and proportional errors (PE) but not random errors (RE). The moving sum of positive patients and moving standard deviation could detect RE for serum sodium and chlorides but performed poorly on detecting the CE and PE. ConclusionsThis study demonstrated the importance of assessing the potential source of error of a particular analyte and the corresponding type of analytical error before choosing a quality control algorithm for implementation.

Understanding Patient-Based Real-Time Quality Control Using Simulation Modeling.

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. 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. 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. Computer simulations can optimize PBRTQC but some parameters are generally superior and can be taken as default.

Ten-Month Evaluation of the Routine Application of Patient Moving Average for Real-Time Quality Control in a Hospital Setting.

In recent years there has been renewed interest in patient-based real-time quality control (PBRTQC) techniques. This interest has been stimulated by the availability of new optimization and validation methods. Only a limited amount of research has focused on investigating the true operational value of PBRTQC. Therefore, we have evaluated the performance and value of recently implemented patient moving average quality control (MA QC) procedures. The MA QC settings and protocols were as previously described (Clin Chem Lab Med 2019;57:1329-38) and included MA QCs for 10 chemistry and 6 hematological tests, all performed on duplicate analyzer systems. All MA QC alarms that occurred during the first 10 months of routine clinical application were investigated for assay-specific alarm rate and occurrence in time. Furthermore, the causes of these MA QC alarms were investigated, and alarm relevance was determined on the basis of total allowable bias (TBa) and error (TEa) derived from biological variations. During the 10-month period, 202 individual MA QC alarms occurred, resulting in an overall MA QC alarm rate of 0.030% and a frequency of 4.67 per week. Most alarms were triggered by sodium MA QC. Based on all available fully executed and documented MA QC alarm work-ups, MA QC detected errors that in 26.0% of the alarms exceeded the TBa and in 13.7% the TEa. In 9.2% of the alarms, MA QC alarming triggered instant (technical) corrections. Routine clinical application of MA QC is feasible with maintaining a manageable number of alarms and enabling detection of relevant analytical errors.

Optimization and validation of patient-based real-time quality control procedure using moving average and average of normals with multi-rules for TT3, TT4, FT3, FT3, and TSH on three analyzers.

BackgroundWe have designed a patient‐based real‐time quality control (PBRTQC) procedure to detect analytical shifts and review analytical trends of measurement procedures.MethodsAll the nine months' patient results of total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), free triiodothyronine (FT3), and thyrotropin (TSH) measured by three identical analyzers were divided into three groups according to the source of inpatient patients, outpatient patients, and healthy people. The data in each group were truncated by optimized Box‐Plot method and normalized by Box‐Cox method if necessary. The z‐score charts of internal quality control (IQC) samples' results and PBRTQC data were drawn by IQC levels and groups, respectively. The analytical shifts and analytical trends were detected by multi‐rules of 2‐2S rules and moving average rules. The performances of PBRTQC were compared with the BIQC in which IQC samples were measurand only once per day at the beginning of the analytical batch. Twelve quality control cases were listed to validate the performances.ResultsAll the five analytes presented normal distributions when the parameter n of Box‐Plot method was 1.2. The percentages of excluded data ranged from 2.9% to 11.6%. 31 and 14 rejections triggered in PBRTQC and BIQC, respectively. 96.8% of the shift rejections in PBRTQC were trend‐related shifts and calibration‐related shifts, while the proportion was 85.7% in BIQC but 78.6% of the shift rejections in TSH. 25.7% and 8.6% of 105 calibration events which caused analytical shifts were detected by PBRTQC and BIQC, respectively. However, the performance of PBRTQC was not well in TSH because of its large coefficient of variation.ConclusionsThe optimized PBRTQC is high efficiency than BIQC in detecting analytical shifts, trends, and calibration events. The PBRTQC can be used as a low‐cost supplementary procedure to IQC every day, especially at the end of the analytical batch on that day when the within‐individual biological variation of analyte is not larger than its coefficient of variation in IQC. Further optimization and validation of PBRTQC are still needed.

Recommendation for performance verification of patient-based real-time quality control.

Patient-based real-time quality control (PBRTQC) is a laboratory tool for monitoring the performance of the testing process. It includes well-established procedures like Bull's algorithm, average of nomals, moving median, moving average (MA) and exponentially (weighted) MAs. Following the setup and optimization processes, a key step prior to the routine implementation of PBRTQC is the verification and documentation of the performance of the PBRTQC as part of the laboratory quality system. This verification process should provide a realistic representation of the performance of the PBRTQC in the environment it is being implemented in, to allow proper risk assessment by laboratory practitioners. This document focuses on the recommendation on performance verification of PBRTQC prior to implementation.

Patient-Based Real-Time Quality Control: Review and Recommendations.

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.