Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data
Background: Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal...
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| Format: | Article |
| Language: | English |
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Society of Medical Biochemists of Serbia, Belgrade
2024-01-01
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| Series: | Journal of Medical Biochemistry |
| Subjects: | |
| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/1452-8258/2024/1452-82582401043Q.pdf |
| _version_ | 1797319627076796416 |
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| author | Çevlik Tülay Haklar Goncagül |
| author_facet | Çevlik Tülay Haklar Goncagül |
| author_sort | Çevlik Tülay |
| collection | DOAJ |
| description | Background: Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal quality control (IQC) and external quality assurance (EQA) data in the calculation of sigma metrics. Methods: This retrospective study was conducted in Marmara University Pendik E&R Hospital Biochemistry Laboratory. Sigma metrics calculation was performed as (TEa-bias)/CV). CV was calculated from IQC data from June 2018 - February 2019. EQA bias was calculated as the mean of % deviation from the peer group means in the last seven surveys, and IQC bias was calculated as (laboratory control result mean-manufacturer control mean)/ manufacturer control mean) x100. In parameters where sigma metrics were <5; QGI=bias/1.5 CV) score of <0.8 indicated imprecision, >1.2 pointed inaccuracy, and 0.8-1.2 showed both imprecision and inaccuracy. Results: Creatine kinase (both levels), iron and magnesium (pathologic levels) showed an ideal performance with ≥6 sigma level for both bias determinations. Eight of the 17 parameters had different sigma levels when we compared sigma values calculated from EQA and IQC derived bias% while the rest were grouped at the same levels. Conclusions: Sigma metrics is a good quality tool to assess a laboratory's analytical performance and facilitate the comparison of the assay performances in the same manner across multiple systems. However, we might need to design a tight internal quality control protocol for analytes showing poor assay performance. |
| first_indexed | 2024-03-08T04:10:36Z |
| format | Article |
| id | doaj.art-7e245a4519ad42268217eb1cbd0e76de |
| institution | Directory Open Access Journal |
| issn | 1452-8258 1452-8266 |
| language | English |
| last_indexed | 2024-03-08T04:10:36Z |
| publishDate | 2024-01-01 |
| publisher | Society of Medical Biochemists of Serbia, Belgrade |
| record_format | Article |
| series | Journal of Medical Biochemistry |
| spelling | doaj.art-7e245a4519ad42268217eb1cbd0e76de2024-02-08T22:37:02ZengSociety of Medical Biochemists of Serbia, BelgradeJournal of Medical Biochemistry1452-82581452-82662024-01-01431434910.5937/jomb0-430521452-82582401043QSix SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance dataÇevlik Tülay0Haklar Goncagül1Marmara University Pendik E&R Hospital, Biochemistry Laboratory, Istanbul, TurkeyMarmara University Pendik E&R Hospital, Biochemistry Laboratory, Istanbul, TurkeyBackground: Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal quality control (IQC) and external quality assurance (EQA) data in the calculation of sigma metrics. Methods: This retrospective study was conducted in Marmara University Pendik E&R Hospital Biochemistry Laboratory. Sigma metrics calculation was performed as (TEa-bias)/CV). CV was calculated from IQC data from June 2018 - February 2019. EQA bias was calculated as the mean of % deviation from the peer group means in the last seven surveys, and IQC bias was calculated as (laboratory control result mean-manufacturer control mean)/ manufacturer control mean) x100. In parameters where sigma metrics were <5; QGI=bias/1.5 CV) score of <0.8 indicated imprecision, >1.2 pointed inaccuracy, and 0.8-1.2 showed both imprecision and inaccuracy. Results: Creatine kinase (both levels), iron and magnesium (pathologic levels) showed an ideal performance with ≥6 sigma level for both bias determinations. Eight of the 17 parameters had different sigma levels when we compared sigma values calculated from EQA and IQC derived bias% while the rest were grouped at the same levels. Conclusions: Sigma metrics is a good quality tool to assess a laboratory's analytical performance and facilitate the comparison of the assay performances in the same manner across multiple systems. However, we might need to design a tight internal quality control protocol for analytes showing poor assay performance.https://scindeks-clanci.ceon.rs/data/pdf/1452-8258/2024/1452-82582401043Q.pdfsix sigma methodquality goal indexquality managementimprecisionbias |
| spellingShingle | Çevlik Tülay Haklar Goncagül Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data Journal of Medical Biochemistry six sigma method quality goal index quality management imprecision bias |
| title | Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data |
| title_full | Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data |
| title_fullStr | Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data |
| title_full_unstemmed | Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data |
| title_short | Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data |
| title_sort | six sigma evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data |
| topic | six sigma method quality goal index quality management imprecision bias |
| url | https://scindeks-clanci.ceon.rs/data/pdf/1452-8258/2024/1452-82582401043Q.pdf |
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