Preanalytical Errors in the Central Laboratory of a University Hospital Based on the Analysis of Year-Round Data

Delta check limits for thyroid function tests adjusted for clinical settings

BACKGROUND

This study aimed to determine practical delta check limits (DCLs) for thyroid function tests (TFTs) to detect sample misidentifications across various clinical settings.

METHODS

Between 2020 and 2022, 610,437 paired TFT results were collected from six university hospitals. The absolute DCL (absDCL) was determined using the 95th percentile for each clinical setting from a random 60 % of the total data. These absDCLs were then tested within and across different settings using the remaining 40 % of the data, alongside mix-up datasets for result and sample comparisons. The sensitivities of absDCL were calculated within and across groups in the mix-up datasets.

RESULTS

Health screening absDCLs were notably lower than in other settings (2.58 vs. 5.93-7.08 for thyroid-stimulating hormone; 4.12 vs. 8.24-10.04 for free thyroxine; 0.49 vs. 0.82-0.91 for total triiodothyronine). The proportion of results exceeding absDCL of health screening differed from those of other clinical settings. Furthermore, sensitivity between health screening and other clinical settings was significantly different in both the result mix-up and sample mix-up datasets.

CONCLUSIONS

This study determined practical DCLs for TFTs and highlighted differences in absDCLs between health screening and other settings. These findings emphasize the importance of tailored DCLs in improving the accurate reporting of TFTs.

Machine learning-based delta check method for detecting misidentification errors in tumor marker tests

OBJECTIVES

Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods.

METHODS

We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV).

RESULTS

The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV.

CONCLUSIONS

Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods.

Development and Application of Computerized Risk Registry and Management Tool Based on FMEA and FRACAS for Total Testing Process

Background and Objectives: Risk management is considered an integral part of laboratory medicine to assure laboratory quality and patient safety. However, the concept of risk management is philosophical, so actually performing risk management in a clinical laboratory can be challenging. Therefore, we would like to develop a sustainable, practical system for continuous total laboratory risk management. Materials and Methods: This study was composed of two phases: the development phase in 2019 and the application phase in 2020. A concept flow diagram for the computerized risk registry and management tool (RRMT) was designed using the failure mode and effects analysis (FMEA) and the failure reporting, analysis, and corrective action system (FRACAS) methods. The failure stage was divided into six according to the testing sequence. We applied laboratory errors to this system over one year in 2020. The risk priority number (RPN) score was calculated by multiplying the severity of the failure mode, frequency (or probability) of occurrence, and detection difficulty. Results: 103 cases were reported to RRMT during one year. Among them, 32 cases (31.1%) were summarized using the FMEA method, and the remaining 71 cases (68.9%) were evaluated using the FRACAS method. There was no failure in the patient registration phase. Chemistry units accounted for the highest proportion of failure with 18 cases (17.5%), while urine test units accounted for the lowest portion of failure with two cases (1.9%). Conclusion: We developed and applied a practical computerized risk-management tool based on FMEA and FRACAS methods for the entire testing process. RRMT was useful to detect, evaluate, and report failures. This system might be a great example of a risk management system optimized for clinical laboratories.