Related Experiment Videos
Patient-Based Real-Time Quality Control for Semiquantitative Tests Based on Hierarchical Differences
Yuanyuan Li1,2,3, Xiaoling Chen1,2,3, Ying Zhao1,2,3
1Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Annals of Laboratory Medicine
|August 6, 2026
Summary
New algorithms improve patient-based real-time quality control (PBRTQC) for semiquantitative tests. Hierarchical difference (HD)-based methods offer superior error detection and stability over traditional approaches.
Area of Science:
- Clinical chemistry
- Medical diagnostics
- Artificial intelligence in healthcare
Background:
- Patient-based real-time quality control (PBRTQC) has advanced with AI, but its use in semiquantitative tests is underexplored.
- Urine protein (URP) is a common semiquantitative parameter in urinalysis.
- This study investigates PBRTQC for semiquantitative tests using URP as a model.
Purpose of the Study:
- To develop and evaluate novel PBRTQC algorithms for semiquantitative tests.
- To assess the performance of hierarchical difference (HD)-based algorithms against existing methods.
- To determine the anti-interference capabilities of the new algorithms.
Main Methods:
- Assessed correlation between semiquantitative urine protein (URP) and quantitative urine total protein (UTP).
- Developed three HD-based algorithms (MRI, MAHD, MAAD) using graded measurements.
- Compared HD algorithms with the moving rate of positive results (MRP) via simulations for error detection (SEs, REs) and anti-interference.
Main Results:
- HD-based algorithms significantly reduced patient impact during error detection (83.1% for SEs, 94.2% for REs) compared to MRP.
- MRI, MAHD, and MAAD demonstrated enhanced detection of both systematic errors (SEs) and random errors (REs).
- HD algorithms maintained stable performance when sample sequences were rearranged, unlike MRP.
Conclusions:
- Novel HD-based algorithms offer superior error detection and anti-interference for PBRTQC in semiquantitative tests.
- This work presents an effective PBRTQC strategy for semiquantitative assays by utilizing related quantitative data.
- The findings advance quality control methodologies in routine urinalysis.
Related Concept Videos
01:05Quality Control
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
01:24Automated Microbial Diagnostics
Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
01:15Introduction to Statistical Process Control
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
01:17Cochran's Q Test
Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
01:00Detection of Gross Error: The Q Test
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...