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

Quality control in the Netherlands; todays practices and starting points for guidance and future research

OBJECTIVES

Adequate analytical quality of reported results is primarily ensured by performing internal quality control (iQC). Currently, several different iQC practices are in use. As a prelude to the revision of a Dutch guidance document on analytical QC, a questionnaire was sent out to gain insights in the applied practices and the need for guidance.

METHODS

A questionnaire, containing 20 multiple-choice questions with possibilities for explanation and comment on iQC practices and aspects was distributed to all clinical chemistry laboratories within the Netherlands. Results were reported descriptively.

RESULTS

Responses were received from 27 clinical laboratories (response 43 %). In 30 % the iQC was based on the analytical characteristics only, while 30 % used a 6-Sigma method, 19 % risk-based beyond 6-Sigma and 22 % used an alternative approach. 89 % of laboratories used a virtual analyzer model for iQC setup within one or more laboratory sites. Practices for determining standard deviation (SD) values included determining SD for each new iQC material (35 %), using historical SD values for new materials (35 %), and incorporating clinical tolerances into the SD value (31 %). Furthermore, 44 % of laboratories used patient moving averages for one or more tests. Daily iQC management was based on either "traffic lights" indicating in or out of control status, and review of all QC charts, often using multiple software systems.

CONCLUSIONS

A large heterogeneity of iQC practices in clinical laboratories was observed in the Netherlands. Several starting points for further research and/or guidance were identified, particularly in relation to the determination of SD values, the virtual analyzer model and methods to ensure analyzer equivalence.

The value of Sigma-metrics in laboratory medicine

While Six Sigma is used in different disciplines to improve quality, Tony Badric and Elvar Theodorsson in a recent paper in CCLM have questioned Six Sigma application in medical laboratory concluding Six Sigma has provided no value to medical laboratory. In addition, the authors have expanded their criticism to Total Analytical Error (TAE) model and statistical quality control. To address their arguments, we have explained the basics of TAE model and Six Sigma and have shown the value of Six Sigma to medical laboratory.

Integrating Patient-Based Real-Time Quality Control (PBRTQC) in a New Field: Inter-Comparison between Biochemical Instrumentations with LDL-C

BACKGROUND

Patient-based real-time quality control (PBRTQC) can be a valuable tool in clinical laboratories due to its cost-effectiveness and constant monitoring. More focus is placed on discovering and improving algorithms that compliment conventional internal control techniques. The practical implementation of PBRTQC with a biochemical instrument comparison is lacking. We aim to evaluate PBRTQC's efficacy and practicality by comparing low-density lipoprotein cholesterol (LDL-C) test results to ensure consistent real-time monitoring across biochemical instrumentations in clinical laboratories.

METHOD

From 1 September 2021 to 30 August 2022, the First Affiliated Hospital of Xi'an Jiaotong University collected data from 158,259 both healthy and diseased patients, including 84,187 male and 74,072 female patients, and examined their LDL-C results. This dataset encompassed a group comprising 50,556 individuals undergoing health examinations, a group comprising 42,472 inpatients (IP), and a group comprising 75,490 outpatients (OP) for the PBRTQC intelligent monitoring platform to conduct daily tests, parameter configuration, program development, real-time execution, and performance validation of the patients' data. Moreover 40 patients' LDL-C levels were assessed using two biochemical analyzers, designated as the reference and comparator instruments. A total of 160 LDL-C results were obtained from 40 both healthy and diseased patients, including 14 OP, 16 IP, and 10 health examination attendees, who were selected to represent LDL-C levels broadly. Two biochemical instruments measured LDL-C measurements from the same individuals to investigate consistency and reproducibility across patient statuses and settings. We employed exponentially weighted moving average (EWMA) and moving median (MM) methods to calculate inter-instrument bias and ensure analytical accuracy. Inter-instrument bias for LDL-C measurements was determined by analyzing fresh serum samples, different concentrations of quality control (QC), and commercialized calibrators, employing both EWMA and MM within two assay systems. The assessment of inter-instrumental bias with five different methods adhered to the external quality assessment standards of the Clinical Laboratory Center of the Health Planning Commission, which mandates a bias within ±15.0%.

RESULT

We calculated inter-instrument comparison bias with each of the five methods based on patient big data. The comparison of fresh serum samples, different concentrations of QC, commercialized calibrators, and EWMA were all in the permissive range, except for MM. MM showed that the bias between two biochemical instruments in the concentration ranges of 1.5 mmoL/L-6.2 mmoL/L exceeded the permissible range. This was mainly due to the small number of specimens, affected by variations among individual patients, leading to increased false alarms and reduced effectiveness in monitoring the consistency of the inter-instrumental results. Moreover, the inter-comparison bias derived from EWMA was less than 3.01%, meeting the 15% range assessment criteria. The bias result for MM was lower than 24.66%, which was much higher than EWMA. Thus, EWMA is better than MM for monitoring inter-instrument comparability. PBRTQC can complement the use of inter-comparison bias between biochemical analyzers. EWMA has comparable inter-instrument comparability monitoring efficacy.

CONCLUSIONS

The utilization of AI-based PBRTQC enables the automated real-time comparison of test results across different biochemical instruments, leading to a reduction in laboratory operating costs, enhanced work efficiency, and improved QC. This advanced technology facilitates seamless data integration and analysis, ultimately contributing to a more streamlined and efficient laboratory workflow in the biomedical field.

Lung cancer tumor marker analysis: A clinical laboratory perspective

 Clinical laboratories are responsible for performing lung cancer tumor marker testing as part of routine clinical care. It is their responsibility to guarantee that the reported tumor marker results are reliable and meet the necessary quality standards for proper clinical use. During the different laboratory phases, pre-analytical, analytical and post-analytical, specific steps and processes can introduce errors and generate incorrect clinical interpretation. This editorial briefly outlines critical laboratory issues related to lung cancer tumor markers, specific for each of these three laboratory phases.