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

BACKGROUND: 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. CONTENT: 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. OUTLOOK: 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.

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

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.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Routine clinical application of MA QC is feasible with maintaining a manageable number of alarms and enabling detection of relevant analytical errors.

A primer on patient-based quality control techniques.

BACKGROUND: Patient-based Quality Control techniques have been described for more than fifty years and have been widely used routinely in hematology for forty. However, because of practical issues they have not been widely utilised in clinical chemistry laboratories. But recently because of the availability of middleware and a greater appreciation of the benefits of these processes there has been a willingness to investigate their use as a QC tool. CONTENT: This Review describes the development of various patient-based quality control techniques from the earliest Average of Normals approach including the Moving Average, Moving Median and Moving Sum of Outliers. Variations such as weighting, transformation and annealing are discussed. Integrating patient-based QC and conventional QC is discussed as the problem of sub-populations. SUMMARY: Patient-based QC methods will become more widespread as their benefits are more fully understood and middleware becomes available that allows laboratories to implement these techniques with their patient populations.

Comparison of traditional statistical quality control using commercially available control materials and two patient-based quality control procedures for the ADVIA 120 Hematology System.

BACKGROUND: Quality control procedures are an important part of the overall quality assurance for production of accurate and reliable hematologic results. OBJECTIVES: This study aimed to validate a quality control material-based procedure and assess two patient-based quality control procedures (repeat patient testing [RPT] and average of normals [AoN]) with the ADVIA 120 Hematology System. METHODS: Requirements for quality control procedures were obtained with the computerized statistical and quality program, EZRules3. The procedures were evaluated comparing the probability of error detection (Ped), probability of false rejection (Pfr), and sigma metrics. RESULTS: All three of the quality control procedures could be applied with 1-3s control rules, achieving the desired quality requirements. Validation of the quality control materials achieved values for Ped and Pfr of ≥90% and 0%, respectively. Patient-based procedures obtained a ≥85% Ped and a 0% Pfr, except for platelets in the AoN procedure, which achieved a 77% Ped. The RPT achievable total errors were similar to those of the traditional quality control materials and the AoN procedures, except for platelets, which had an achievable total error of 75%. CONCLUSIONS: Patient-based procedures are suitable for veterinary laboratories. The RPT approach may benefit laboratories with limited budgets and low hematology caseloads. The AoN procedure may benefit laboratories with higher hematology caseloads.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

OBJECTIVE: The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. METHOD: Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. RESULTS: The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. CONCLUSION: A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors.