Total Clinical Chemistry Laboratory Errors and Evaluation of the Analytical Quality Control Using Sigma Metric for Routine Clinical Chemistry Tests

Evaluation of Laboratory Performance in Consideration with Pre analytical and Post analytical Quality Indicators

Implementation of Quality indicators (QIs) plays an imperative role in improving the total testing process, as it provides a quantitative basis for evaluating the laboratory performance. Besides monitoring of analytical quality specifications, several lines of experimental and clinical evidence have alluded a pivotal role of extra-analytical phases in improving the quality of laboratory services and therefore a relevance of pre- and post-analytical steps have been speculated on the overall quality in the total testing process and consequently on clinical decision-making. This was a retrospective study designed to evaluate and review different extra-analytical quality indicators in NABL accredited clinical biochemistry laboratory at BJ Medical College and Civil Hospital, Ahmedabad, Gujarat in an endeavour to ameliorate the performance of the laboratory. All Clinical Chemistry Laboratory test requests with their respective samples from January 2018 to December 2021 were included in the study. A total of 1,439,011samples were processed, and were evaluated for seven QIs [(% of number of suitable samples not received; QI-8), (% of number of samples received in inappropriate container; QI-9), (% of number of samples hemolysed; QI-10), (% of number of samples with inadequate sample volume; QI 12) (% of number of samples received mismatched; QI 15), (% of number of samples reported after turnaround time; QI 21) and (% of number of samples with critical values informed; QI 22)] based on defined criteria of Quality Specification given by International Federation of Clinical Chemistry. Total number of preanalytical errors was 53,669 (3.72%). Among the preanalytical errors, inadequate sample volume (2.37% of total samples; 63.49% of total pre-analytical errors) was the most common anomaly followed by Not received samples (24.18%) hemolysis (8.26%) mismatched (3.91%) and 0.14% samples were received in Inappropriate container; manifesting that the error frequency was unacceptable for QI 21 and QI 8, acceptable for QI 10, minimally acceptable for QI 15 and optimum for QI QI 9. Furthermore, there was year-wise progressive decline in error rate of inadequate sample volume, hemolysed sample received and mismatched samples. Total number of post analytical errors were 19,002 (1.32%). TAT outlier and critical values communicated were the two QIs evaluated for this phase and results of both QI were within acceptable limits. Quality indicators serve as a tool to monitor process performance and consequently derived error rates warrant active intervention to improve the laboratory services and patient health care. Dissemination of certified documents, regular staff training and evaluation needs to be conducted.

Assessment of hematology laboratory performance in the total testing process using quality indicators and sigma metrics in the northwest of Ethiopia: A cross-sectional study

Background and Aims

Assuring laboratory quality by minimizing the magnitude of errors is essential. Therefore, this study aimed to assess hematology laboratory performance in the total testing process using quality indicators and sigma metrics.

Methods

A cross-sectional study was conducted from April to June 2022. The study included a total of 13,546 samples. Data on included variables were collected using a checklist. Descriptive statistics were used to present the overall distribution of errors. Binary logistic regression models were applied. Furthermore, using a Sigma scale, the percentage of errors was converted to defects per million opportunities to assess laboratory performance. Finally, the defect per million opportunities was converted to a sigma value using a sigma calculator.

Results

Of the 13,546 samples and corresponding requests, the overall error rate was 123,296/474,234 (26%): 93,412/47,234 (19.7%) pre-analytical, 2364/474,234 (0.5%) analytical, and 27,520/474,234 (5.8%) post-analytical. Of the overall errors, 93,412/123,296 (75.8%), 2364/123,296 (1.9%), and 27,520/123,296 (22.3%) were pre-analytical, analytical, and post-analytical errors, respectively. The overall sigma value of the laboratory was 2.2. The sigma values of the pre-analytical, analytical, and post-analytical phases were 2.4, 4.1, and 3.1, respectively. The sample from the inpatient department and collected without adherence to the standard operating procedures (SOPs) had a significantly higher (p < 0.05) rejection rate as compared to the outpatient department and collected with adherence to SOPs, respectively. In addition, an association between prolonged turnaround times and manual recording, inpatient departments, and morning work shifts was observed.

Conclusion

The current study found that the overall performance of the laboratory was very poor (less than three sigma). Therefore, the hospital leadership should change the manual system of ordering tests and release of results to a computerized system and give need-based training for all professionals involved in hematology laboratory sample collection and processing.

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Sigma metric analysis of quality indicators across the testing process as an effective tool for the evaluation of laboratory performance

Background

Laboratories across the world are successfully using quality indicators (QIs) to monitor their performance. We aimed to analyze the effectiveness of using the peer group comparison and statistical tools such as sigma metrics for periodic evaluation of QIs and identify potential errors in the preanalytical, analytical, and postanalytical phases.

Methods

We evaluated the monthly QIs for 1 year. A total of 11 QIs were evaluated across the three phases of the total testing process, using percentage variance, and sigma metric analysis.

Results

Our study observed that based on sigma metric analysis, the performance was good for all the QIs except for the number of samples with the inappropriate specimen hemolyzed samples, clotted samples, and turnaround time (Sigma value < 3). The percentage variance of QIs in all the phases was plotted in a Pareto chart, which helped us in identifying turnaround time and internal quality control performance are the key areas that contribute to almost 80% of the errors among all the QIs.

Conclusion

Laboratory performance evaluation using QIs and sigma metric analysis helped us in identifying and prioritizing the corrective actions in the key areas of the total testing process.

Comparison of Liver Function Test Results between Architect C8000 and COBAS C501 Automatic Chemistry Analyzer

Liver function tests are frequently used to screen liver function, monitor therapy, and determine the severity of liver problems. The present study aimed to assess the consistency of the results of the liver function parameters between the two analyzers, Architect c8000 and Cobas C501. This laboratory-based analytical observational study was conducted in a cross-sectional manner. Sample collection was performed through a consecutive sampling procedure from June to December 2019 in the Clinical Pathology Laboratory, Dr. Mohammad Hoesin General Hospital, Palembang, South Sumatra, Indonesia. The research sample consisted of the liver function examination results of patients, carried out using the Architect c8000 and Roche Cobas c501 chemistry analyzers. Serum albumin, alanine transaminase, aspartate aminotransferase, and total protein were the studied variables. The Spearman, Mann-Whitney, and Bland-Altman tests were used to evaluate the comparison test. In total, 100 blood samples were obtained in this study. The results revealed a highly significant correlation (r>0.90, P=0<001) among the four liver function parameters. The results of the liver function parameters inspected by the two analyzers did not differ significantly (P>0.05). In addition, there was a solid agreement on all parameters, with a near-perfect level (concordance correlation coefficient>0.90) and more than 95% of data points falling within the acceptable range. The Architect c8000 and Cobas c501 analyzers produced similar results for liver function tests; hence, these devices can be used interchangeably.

Analysis of preanalytical errors in a clinical chemistry laboratory: A 2-year study

Patient safety and medical diagnosis of patients are mainly influenced by laboratory results. The present study aimed to evaluate the errors in the preanalytical phase of testing in a Clinical Chemistry diagnostic laboratory. A review was conducted at the Clinical Chemistry Laboratory of a hospital in Saudi Arabia from January 2019 to December 2020. Using the laboratory information system, the data of all canceled tests and requests were retrieved and evaluated for preanalytical errors. A total of 55,345 laboratory test requests and samples from different departments were evaluated for preanalytical errors. An overall rate of 12.1% (6705) was determined as preanalytical errors. The occurrence of these errors was found to be highest in the emergency department (21%). The leading preanalytical errors were nonreceived samples (3.7%) and hemolysis (3.5%). The annual preanalytical errors revealed an increasing rate in outpatient and inpatient departments, while a decreasing rate was observed in the emergency department. An increased rate of errors was also noted for the 2-year study period from 11.3% to 12.9%. The preanalytical phase has a significant impact on the quality of laboratory results. The rate of error in the study was high and the leading causes were nonreceived samples and hemolysis. An increased occurrence of hemolyzed samples in the outpatient department was noted. Enhanced educational efforts emphasizing specimen quality issues and training in sample collection among hospital staff must be carried out.

Sigma Metrics in Laboratory Medicine: A Call for Harmonization

BACKGROUND AND AIM

Sigma metrics are applied in clinical laboratories to assess the quality of analytical processes. A parameter associated to a Sigma >6 is considered "world class" whereas a Sigma <3 is "poor" or "unacceptable". The aim of this retrospective study was to quantify the impact of different approaches for Sigma metrics calculation.

MATERIAL AND METHODS

Two IQC levels of 20 different parameters were evaluated for a 12-month period. Sigma metrics were calculated using the formula: (allowable total error (TEa) (%) - bias (%))/(coefficient of variation (CV) (%)). Method precision was calculated monthly or annually. The bias was obtained from peer comparison program (PCP) or external quality assessment program (EQAP), and 9 different TEa sources were included.

RESULTS

There was a substantial monthly variation of Sigma metrics for all combinations, with a median variation of 32% (IQR, 25.6-41.3%). Variation across multiple analyzers and IQC levels were also observed. Furthermore, TEa source had the highest impact on Sigma calculation with proportions of Sigma >6 ranging from 17.5% to 84.4%. The nature of bias was less decisive.

CONCLUSION

In absence of a clear consensus, we recommend that laboratories calculate Sigma metrics on a sufficiently long period of time (>6 months) and carefully evaluate the choice of TEa source.

"Six Sigma" standard as a level of quality of biochemical laboratories

The principal role of biochemical laboratories is responsibility for reliable, reproducible, accurate, timely, and accurately interpreted analysis results that help in making clinical decisions, while ensuring the desired clinical outcomes. To achieve this goal, the laboratory should introduce and maintain quality control in all phases of work. The importance of applying the Six SIGMA quality model has been analyzed in a large number of scientific studies. The purpose of this review is to highlight the importance of using six SIGMA metrics in biochemical laboratories and the current application of six SIGMA metrics in all laboratory work procedures. It has been shown that the six SIGMA model can be very useful in improving all phases of laboratory work, as well as that a detailed assessment of all procedures of the phases of work and improvement of the laboratory's quality control system is crucial for the laboratory to have the highest level of six SIGMA. Clinical laboratories should use SIGMA metrics to monitor their performance, as it makes it easier to identify gaps in their performance, thereby improving their efficiency and patient safety. Medical laboratory quality managers should provide a systematic methodology for analyzing and correcting quality assurance systems to achieve Six SIGMA quality-level standards.

Application of Sigma metrics in the quality control strategies of immunology and protein analytes

Background

Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma.

Methods

Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C‐reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values.

Results

While using the TEa_NCCL, 90% analytes had a world‐class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σ_NCCL that is calculated from TEa_NCCL, Sigma Method Decision Charts were constructed. For Cys C, five multi‐rules (1_3s/2_2s/R_4s/4_1s/6_X, N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (1_3s, N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12).

Conclusions

The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool.