Effectiveness of a Quality Control Center in Enhancing National Antimicrobial Resistance Monitoring
Overview
The study evaluates the performance of a centralized quality control center (QCC) in maintaining high standards of antimicrobial resistance (AMR) surveillance in South Korea's Kor-GLASS system. Results indicate that interlaboratory proficiency testing (IPT) consistently exceeded 97% categorical agreement, with no external quality assessment (EQA) failures observed.
Background
Antimicrobial resistance (AMR) poses a significant global health threat, with millions of infections and deaths attributed to resistant pathogens annually. Reliable AMR surveillance data is crucial for public health responses, necessitating robust quality assurance frameworks. The Kor-GLASS system in South Korea aims to provide standardized AMR data, supported by a QCC to ensure data quality and comparability.
Data Highlights
Assessment Type
Performance Metric
Result
Interlaboratory Proficiency Testing (IPT)
Categorical Agreement (CA)
Exceeds 97%
External Quality Assessment (EQA)
Failures
None
Key Findings
Overall CA in IPT consistently exceeded 97% across the study period.
No EQA failures were observed among participating centers.
Major errors in Phase II were primarily due to AST reading and near-breakpoint discrepancies.
Frequency of major errors decreased in Phase III following corrective actions and educational interventions.
High concordance in ceftazidime-avibactam susceptibility testing was noted, with rare discrepancies.
Interlaboratory validation confirmed acceptable performance for AST of Haemophilus spp.
Clinical Implications
The findings support the importance of a centralized QCC in maintaining high-quality AMR surveillance. Continuous quality assurance activities are essential for ensuring the reliability of laboratory data and enhancing public health responses to AMR.
Conclusion
The study demonstrates that a QCC can effectively sustain reliable AMR surveillance performance during system expansion, reinforcing the credibility of AMR data generation.
by Dong Woo Shin, Hyunji Kim, Jeong Su Park, Kyoung Un Park, Min Hyuk Choi, Dokyun Kim, Seok Hoon Jeong, Hee Jung Kim, Young Ah Kim, Kwangjin Ahn, Young Uh, Yong Jun Kwon, Jong Hee Shin, Soo Hyun Kim, Jeong Hwan Shin, Hee Young Kang, Dong Chan Moon, Sung Young Lee, Songmee Bae
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