Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation

Real world feasibility of patient-based real time quality control (PBRTQC) using five analytes in a South African laboratory

BACKGROUND

Patient-based real-time quality control (PBRTQC) identifies possible bias in methods by utilising shifts in trend of statistical measures in laboratory results. In this study we aimed to compare and optimize various PBRTQC procedures for serum alanine aminotransferase, albumin, calcium, ferritin and sodium.

METHODS

In a bias simulation study, we added artificial bias to intervals of patient data and then evaluated the efficiency with which various PBRTQC procedures were able to detect this bias. PBRTQC procedures used included block size, moving statistic calculation, control limits as well as truncation limits. The number of patients till error detection, the false alarm rate as well as validation charts were utilised to select the optimal PBRTQC procedure for each analyte.

RESULTS

The optimal PBRTQC procedures identified for each analyte were: ALT - MA T0 50 MaxMin; Albumin - MA T5 100 Perc; Calcium - MM T5 50 Perc; Ferritin - MA T0 100 MaxMin; and Sodium - MA T5 75 MaxMin. (T, Truncation limits; Control limits- MA, Moving Average, MM, Moving median; Perc, percentile) CONCLUSIONS: The use of large, real patient datasets allows for the reliable determination of laboratory-specific PBRTQC procedures. This study demonstrates that the moving average calculation excels in both normal and transformed analyte distributions. Whilst, the benefits of PBRTQC procedures are proven, the complex and time-consuming optimisation process may be a barrier to the rapid implementation in under-resourced countries.

Development and Evaluation of "The Delta Plus-Minus Even Distribution Check": A Novel Patient-Based Real-Time Quality Control Method for Laboratory Tests

BACKGROUND

Laboratory testing of large sample numbers necessitates high-volume rapid processing, and these test results require immediate validation and a high level of quality assurance. Therefore, real-time quality control including delta checking is an important issue. Delta checking is a process of identifying errors in individual patient results by reviewing differences from previous results of the same patient (Δ value). Under stable analytical conditions, Δ values are equally positively and negatively distributed.

METHODS

The previous 20 Δ values from 3 tests (cholesterol, albumin, and urea nitrogen) were analyzed by calculating the R-value: "the positive Δ value ratio minus 0.5." This method of monitoring optimized R-values is referred to as the even-check method (ECM) and was compared with quality control (QC) testing in terms of error detection.

RESULTS

Bias was observed on 4 of the 120 days for the 3 analytes measured. When QC detected errors, the ECM captured the same systematic errors and more rapidly. In contrast, the ECM did not generate an alarm for the one random error that occurred in QC. While QC did not detect any errors, the percentage of R-values exceeding the acceptable range was under 2%, the number of days generating alarms was between 16 and 21 days, with short alarm periods, and a median number of samples per alarm period between 7 and 9 samples.

CONCLUSIONS

The ECM is a practical real-time QC method, controlled by setting R-value conditions, that quickly detects bias values.

Technical quality assurance and quality control for medical laboratories: a review and proposal of a new concept to obtain integrated and validated QA/QC plans

Technical quality assurance (QA) and quality control (QA/QC) are important activities within medical laboratories to ensure the adequate quality of obtained test results. QA/QC tools available at medical laboratories include external QC and internal QC, patient-based real-time quality control (PBRTQC) tools such as moving average quality control (MAQC), limit checks, delta checks, and multivariate checks, and finally, analyzer flagging. Recently, for PBRTQC tools, new optimization and validation methods based on error detection simulation have been developed to obtain laboratory-specific insights into PBRTQC error detection. These developments have enabled implementation and application of these individual tools in routine clinical practice. As a next step, they also enable performance comparison of the individual QA/QC tools and integration of all the individual QA/QC tools in order to obtain the most powerful and efficient QA/QC plans. In this review, a brief overview of the individual QA/QC tools and their characteristics is provided and the error detection simulation approaches are explained. Finally, a new concept entitled integrated quality assurance and control (IQAC) is presented. To enable IQAC, a conceptual framework is suggested and demonstrated for sodium, based on available published data. The proposed IQAC framework provides ways and tools by which the performance of different QA/QC tools can be compared in a so-called QA/QC error detection table to enable optimization and validation of the overall QA/QC plan in terms of alarm rate as well as pre-analytical, analytical, and post-analytical error detection performance.

Internal quality control - past, present and future trends

Objectives

This paper offers an historical view, through a summary of the internal quality control (IQC) models used from second half of twentyth century to those performed today and wants to give a projection on how the future should be addressed.

Methods

The material used in this work study are all papers collected referring IQC procedures. The method used is the critical analysis of the different IQC models with a discussion on the weak and the strong points of each model.

Results

First models were based on testing control materials and using multiples of the analytical procedure standard deviation as control limits. Later, these limits were substituted by values related with the intended use of test, mainly derived from biological variation. For measurands with no available control material methods based on replicate analysis of patient' samples were developed and have been improved recently; also, the sigma metrics that relates the quality desired with the laboratory performance has resulted in a highly efficient quality control model. Present tendency is to modulate IQC considering the workload and the impact of analytical failure in the patent harm.

Conclusions

This paper remarks the strong points of IQC models, indicates the weak points that should be eliminated from practice and gives a future projection on how to promote patient safety through laboratory examinations.

A study on quality control using delta data with machine learning technique

Background

In the big data era, patient-based real-time quality control (PBRTQC), as an emerging quality control (QC) method, is expanding within the clinical laboratory industry. However, the main issue of current PBRTQC methodology is data stability. Our study is aimed to explore a novel protocol for data stability by combining delta data with machine learning (ML) technique to improve the capacity of QC event detection.

Methods

A data set of 423,290 laboratory results from Beijing Chao-yang Hospital 2019 patient results were used as a training set (n = 380960, 90%) and internal validation set (n = 42330, 10%). A further 22,460 results from Beijing Long-fu Hospital 2019 patient results were used as a test set. Three-type data (1) Single-type data processed by truncation limits; (2) delta-type data processed by truncation limits and (3)delta-type data processed by Isolated Forest (IF) algorithm were evaluated with accuracy, sensitivity, NPed, etc., and compared with previously published statistical methods.

Results

The optimal model was based on Random Forest (RF) algorithm by using delta-type data processed by IF algorithm. The model had a better accuracy (0.99), sensitivity (0.99) specificity (0.99) and AUC (0.99) with the dependent test set, surpassing the critical bias of PBRTQC by over 50%. For the LYMPH#, HGB, and PLT, the cumulative MNPed of MLQC were reduced by 95.43%, 97.39%, and 97.97% respectively when compared to the best of the PBRTQC.

Conclusion

Final results indicate that by integrating an innovative ML algorithm with the overall data processing protocol the detection of QC events is improved.

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A protocol for data processing by using delta data together with machine learning algorithm, enables to improve data stability.

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After data processing, the performance of QC event prediction surpassed over 50% clinical recognized PBRTQC method, especially for the hard-to-detect error in QC event prediction.

Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values

BACKGROUND

Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2.

METHODS

We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants.

RESULTS

The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation.

CONCLUSIONS

Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.