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Enhancing laboratory test consistency through linear transformation: A multi-center study.

Shitong Cheng^1,2, Dongliang Man^1,2, Zhiwei Zhou³

¹National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, 110001, PR China.

Practical Laboratory Medicine

|February 3, 2025

Summary

This summary is machine-generated.

A linear transformation method improved clinical laboratory test result consistency across five ISO 15189 accredited labs. This enhances mutual recognition of biochemical parameters, reducing redundant testing and healthcare costs.

Keywords:

Cloud-based platform Inter-laboratory comparability Laboratory result consistency Linear transformation Quality control (QC)Real-time mutual recognition

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Area of Science:

Clinical chemistry
Laboratory medicine
Medical diagnostics

Background:

Mutual recognition of clinical laboratory test results is promoted in China to reduce redundancy, improve convenience, and lower costs.
Ensuring consistency of test results across different laboratories is crucial for reliable medical services.

Purpose of the Study:

To enhance the consistency of clinical laboratory test results across laboratories using a linear transformation method.
To facilitate the mutual recognition of biochemical test results, focusing on ALP, CA, TBIL, TC, and TG.

Main Methods:

Five ISO 15189 accredited laboratories participated in the study.
Inter-laboratory and intra-laboratory conversion relationships were established using patient samples and quality control (QC) data.
A web-based tool was developed for real-time conversion and mutual recognition of laboratory test results.

Main Results:

The linear transformation method significantly improved the consistency of test results.
After three conversion stages, most results showed deviations within ±1/2 TEa compared to a reference laboratory.
Less significant conversion effects were observed for some low-value parameters due to measurement sensitivity.

Conclusions:

The developed linear transformation approach and web tool can enhance result consistency and facilitate mutual recognition.
The method shows potential for broader application in clinical laboratory settings.
Further research is needed due to limitations such as small sample size and focus on limited parameters.