Journal Information
Journal ID (publisher-id): BM
Journal ID (nlm-ta): Biochem Med (Zagreb)
Title: Biochemia Medica
Abbreviated Title: Biochem. Med. (Zagreb)
ISSN (print): 1330-0962
ISSN (electronic): 1846-7482
Publisher: Croatian Society of Medical Biochemistry and Laboratory Medicine
Article Information
Copyright statement: Copyright Croatian Society of Medical Biochemistry and Laboratory Medicine
Copyright: 2024, Copyright Croatian Society of Medical Biochemistry and Laboratory Medicine
License (https://creativecommons.org/licenses/by/4.0/):
This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Date received: 22 September 2023
Date accepted: 08 March 2024
Publication date: 15 June 2024
Publication date: 15 June 2024
Volume: 34
Issue: 2
Electronic Location Identifier: 020707
Publisher ID: bm-34-2-020707
DOI: 10.11613/BM.2024.020707
Comparative study on the quality control effectiveness of AI-PBRTQC and traditional PBRTQC model in identifying quality risks
Xucai Dong[1]
Xi Meng[1]
Bin Li[1]
Dongmei Wen[2]
Author notes:
[*] Corresponding author: mumufly@126.com
• Establish optimal patient-based real-time quality control models for different analytes based on patient-based real-time quality control real-time intelligent monitoring platform
• Clinical application effect of artificial intelligence patient-based real-time quality control real-time intelligent monitoring platform in identifying real-world quality risks
Introduction
We compared the quality control efficiency of artificial intelligence-patient-based real-time quality control (AI-PBRTQC) and traditional PBRTQC in laboratories to create favorable conditions for the broader application of PBRTQC in clinical laboratories.
Materials and methods
In the present study, the data of patients with total thyroxine (TT4), anti-Müllerian hormone (AMH), alanine aminotransferase (ALT), total cholesterol (TC), urea, and albumin (ALB) over five months were categorized into two groups: AI-PBRTQC group and traditional PBRTQC group. The Box-Cox transformation method estimated truncation ranges in the conventional PBRTQC group. In contrast, in the AI-PBRTQC group, the PBRTQC software platform intelligently selected the truncation ranges. We developed various validation models by incorporating different weighting factors, denoted as λ. Error detection, false positive rate, false negative rate, average number of the patient sample until error detection, and area under the curve were employed to evaluate the optimal PBRTQC model in this study. This study provides evidence of the effectiveness of AI-PBRTQC in identifying quality risks by analyzing quality risk cases.
Results
The optimal parameter setting scheme for PBRTQC is TT4 (78-186), λ = 0.03; AMH (0.02-2.96), λ = 0.02; ALT (10-25), λ = 0.02; TC (2.84-5.87), λ = 0.02; urea (3.5-6.6), λ = 0.02; ALB (43-52), λ = 0.05.
Conclusions
The AI-PBRTQC group was more efficient in identifying quality risks than the conventional PBRTQC. AI-PBRTQC can also effectively identify quality risks in a small number of samples. AI-PBRTQC can be used to determine quality risks in both biochemistry and immunology analytes. AI-PBRTQC identifies quality risks such as reagent calibration, onboard time, and brand changes.
Keywords: patient-based real-time quality control; exponentially weighted moving average; quality risk