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Çevlik T, Haklar G. Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data. J Med Biochem 2024; 43:43-49. [PMID: 38496028 PMCID: PMC10943459 DOI: 10.5937/jomb0-43052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/16/2023] [Indexed: 03/19/2024] Open
Abstract
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.
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Affiliation(s)
- Tülay Çevlik
- Marmara University Pendik E&R Hospital, Biochemistry Laboratory, Istanbul, Turkey
| | - Goncagül Haklar
- Marmara University Pendik E&R Hospital, Biochemistry Laboratory, Istanbul, Turkey
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2
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Coskun A. Bias in Laboratory Medicine: The Dark Side of the Moon. Ann Lab Med 2024; 44:6-20. [PMID: 37665281 PMCID: PMC10485854 DOI: 10.3343/alm.2024.44.1.6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 04/15/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.
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Affiliation(s)
- Abdurrahman Coskun
- Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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3
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Panda CR, Kumari S, Mangaraj M, Nayak S. The Evaluation of the Quality Performance of Biochemical Analytes in Clinical Biochemistry Laboratory Using Six Sigma Matrices. Cureus 2023; 15:e51386. [PMID: 38292960 PMCID: PMC10826247 DOI: 10.7759/cureus.51386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/31/2023] [Indexed: 02/01/2024] Open
Abstract
Introduction This study was conducted to assess the analytical performance of biochemical tests using Six Sigma methodology and to assess the underlying causes of unsatisfied performance of analytes with a sigma value of less than 4 using quality goal index (QGI) and root cause analysis (RCA). Methodology Daily data for internal quality control (IQC) for both level 1 (L1) and level 2 (L2) and monthly data for external quality assessment for a period of six months were recorded. The coefficient of variation (CV), bias, and total allowable error (TEa) were calculated to analyze the sigma (σ) values for 19 biochemical analytes. Quality goal index (QGI) analysis was done to analyze impressions and inaccuracies in analyte performance having a sigma value of less than 4. Root cause analysis (RCA) was done to evaluate the possible causes that can improve quality performance. Results Creatinine and high-density lipoprotein (HDL) had sigma metrics of ≤2.0, and chloride, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) had sigma values between 2 and 3. Glucose, total protein (TP), phosphate (Phos), and potassium had sigma values between 4 and 5 in level 1 QC. Sigma grading for level 2 quality control (QC) also gave similar results. For analytes with σ < 4, QGI analysis exposed inaccuracy or imprecision issues and identified errors such as the reconstitution of IQC, storage temperature, and air bubbles while processing the QC, being common causes of poor performance. Conclusion Six Sigma approach is helpful for quality assurance and identifying areas for improvement. Assessing Six Sigma metrics should be a routine practice to decide the frequency of QC run and to detect errors in analysis.
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Affiliation(s)
- Chhabi R Panda
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
| | - Suchitra Kumari
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
| | | | - Saurav Nayak
- Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, IND
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Li M, Li X, Lu X, Zhong M, Wang L, Song M, Xue F. Sigma metric used to evaluate the performance of haematology analysers: choosing an internal reference analyser for the laboratory. Hematology 2023; 28:2277498. [PMID: 37916652 DOI: 10.1080/16078454.2023.2277498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 10/25/2023] [Indexed: 11/03/2023] Open
Abstract
INTRODUCTION The sigma metric offers a quantitative framework for evaluating process performance in clinical laboratories. This study aimed to evaluate the analytical performance of automated analysers in haematology laboratories, using the sigma metric to choose the best analyser as an internal reference analyser. MATERIALS AND METHODS internal quality control (IQC) data were collected for 6 months from SNCS, and the sigma value was calculated for 9 haematology analysers in the laboratory. RESULTS For the normal control level, a satisfactory mean sigma value ≥3 was observed for all of the studied parameters of all automated analysers. For the low control level, platelet (PLT) count by Instrument (Inst.) G performed poorly, with a mean sigma value <3. Inst. H, with all parameters' sigma values >4, performed best and was chosen as the internal reference analyser. CONCLUSION The sigma metric can be used as a guide to choose the QC strategy and plan QC frequency. It can facilitate the comparison of the same assay performed by multiple systems.
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Affiliation(s)
- Min Li
- Department of Clinical Laboratory, Weifang People's Hospital, Weifang, People's Republic of China
| | - Xiaojuan Li
- Department of Clinical Laboratory, Weifang People's Hospital, Weifang, People's Republic of China
| | - Xiaohong Lu
- Department of Clinical Laboratory, Weifang People's Hospital, Weifang, People's Republic of China
| | - Mingqin Zhong
- Department of Clinical Laboratory, Weifang People's Hospital, Weifang, People's Republic of China
| | - Lin Wang
- Department of Clinical Laboratory, Weifang People's Hospital, Weifang, People's Republic of China
| | - Mingze Song
- Department of Clinical Laboratory, Weifang People's Hospital, Weifang, People's Republic of China
| | - Feng Xue
- Department of Clinical Laboratory, Weifang People's Hospital, Weifang, People's Republic of China
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Loh TP, Lim CY, Sethi SK, Tan RZ, Markus C. Advances in internal quality control. Crit Rev Clin Lab Sci 2023; 60:502-517. [PMID: 37194676 DOI: 10.1080/10408363.2023.2209174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/17/2023] [Accepted: 04/27/2023] [Indexed: 05/18/2023]
Abstract
Quality control practices in the modern laboratory are the result of significant advances over the many years of the profession. Major advance in conventional internal quality control has undergone a philosophical shift from a focus solely on the statistical assessment of the probability of error identification to more recent thinking on the capability of the measurement procedure (e.g. sigma metrics), and most recently, the risk of harm to the patient (the probability of patient results being affected by an error or the number of patient results with unacceptable analytical quality). Nonetheless, conventional internal quality control strategies still face significant limitations, such as the lack of (proven) commutability of the material with patient samples, the frequency of episodic testing, and the impact of operational and financial costs, that cannot be overcome by statistical advances. In contrast, patient-based quality control has seen significant developments including algorithms that improve the detection of specific errors, parameter optimization approaches, systematic validation protocols, and advanced algorithms that require very low numbers of patient results while retaining sensitive error detection. Patient-based quality control will continue to improve with the development of new algorithms that reduce biological noise and improve analytical error detection. Patient-based quality control provides continuous and commutable information about the measurement procedure that cannot be easily replicated by conventional internal quality control. Most importantly, the use of patient-based quality control helps laboratories to improve their appreciation of the clinical impact of the laboratory results produced, bringing them closer to the patients.Laboratories are encouraged to implement patient-based quality control processes to overcome the limitations of conventional internal quality control practices. Regulatory changes to recognize the capability of patient-based quality approaches, as well as laboratory informatics advances, are required for this tool to be adopted more widely.
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Affiliation(s)
- Tze Ping Loh
- Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
| | - Chun Yee Lim
- Engineering Cluster, Singapore Institute of Technology, Singapore, Singapore
| | - Sunil Kumar Sethi
- Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
| | - Rui Zhen Tan
- Engineering Cluster, Singapore Institute of Technology, Singapore, Singapore
| | - Corey Markus
- Flinders University International Centre for Point-of-Care Testing, Flinders Health and Medical Research Institute, Flinders University, Adelaide, Australia
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6
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Ercan Ş. Comparison of Sigma metrics computed by three bias estimation approaches for 33 chemistry and 26 immunoassay analytes. ADVANCES IN LABORATORY MEDICINE 2023; 4:236-245. [PMID: 38162416 PMCID: PMC10756147 DOI: 10.1515/almed-2022-0095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 06/05/2023] [Indexed: 01/03/2024]
Abstract
Objectives Sigma metric can be calculated using a simple equation. However, there are multiple sources for the elements in the equation that may produce different Sigma values. This study aimed to investigate the importance of different bias estimation approaches for Sigma metric calculation. Methods Sigma metrics were computed for 33 chemistry and 26 immunoassay analytes on the Roche Cobas 6000 analyzer. Bias was estimated by three approaches: (1) averaging the monthly bias values obtained from the external quality assurance (EQA) studies; (2) calculating the bias values from the regression equation derived from the EQA data; and (3) averaging the monthly bias values from the internal quality control (IQC) events. Sigma metrics were separately calculated for the two levels of the IQC samples using three bias estimation approaches. The resulting Sigma values were classified into five categories considering Westgard Sigma Rules as ≥6, <6 and ≥5, <5 and ≥4, <4 and ≥3, and <3. Results When classifying Sigma metrics estimated by three bias estimation approaches for each assay, 16 chemistry assays at the IQC level 1 and 2 were observed to fall into different Sigma categories under at least one bias estimation approach. Similarly, for 12 immunoassays at the IQC level 1 and 2, Sigma category was different depending on bias estimation approach. Conclusions Sigma metrics may differ depending on bias estimation approaches. This should be considered when using Six Sigma for assessing analytical performance or scheduling the IQC events.
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Affiliation(s)
- Şerif Ercan
- Department of Medical Biochemistry, Lüleburgaz State Hospital, Lüleburgaz Devlet Hastanesi İstiklal Mah, Kırklareli, Türkiye
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Ercan Ş. Comparación de la métrica Sigma calculada con tres métodos de estimación del sesgo en 33 magnitudes químicas y 26 de inmunoensayo. ADVANCES IN LABORATORY MEDICINE 2023; 4:246-257. [PMID: 38162415 PMCID: PMC10756148 DOI: 10.1515/almed-2023-0095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 06/05/2023] [Indexed: 01/03/2024]
Abstract
Objetivos Aunque la métrica Sigma se puede calcular mediante una sencilla ecuación, la diversidad de fuentes de las que se extraen los elementos de la ecuación pueden arrojar diferentes valores Sigma. El objetivo de este estudio era investigar la importancia de las distintas estrategias de estimación del sesgo para el cálculo de la métrica Sigma. Métodos Se calculó la métrica Sigma de 33 magnitudes químicas y 26 magnitudes de inmunoensayo en un analizador Roche Cobas 6,000. El sesgo se calculó mediante tres métodos: a) calculando la media del sesgo mensual obtenida en los estudios de control de calidad externo (EQA, por sus siglas en inglés); 2) calculando los valores de sesgo mediante una ecuación de regresión a partir de datos obtenidos del EQA; y 3) calculando la media de los valores de sesgo mensual de los eventos de control de calidad internos (IQC, por sus siglas en inglés). Se realizó una métrica Sigma para cada uno de los dos niveles de muestras de IQC empleando tres métodos para calcular el sesgo. Los valores Sigma obtenidos se clasificaron en cinco categorías, en función de las reglas Sigma de Westgard, siendo ≥6, <6 y ≥5, <5 y ≥4, <4 y ≥3, y <3. Resultados Al clasificar la métrica Sigma, calculada aplicando tres métodos de estimación del sesgo para cada magnitud, se observó que 16 magnitudes químicas en los niveles 1 y 2 de IQC fueron clasificadas en categorías Sigma diferentes por al menos uno de los métodos de estimación de la desviación. Del mismo modo, dependiendo del método de estimación del sesgo empleado, se clasificaba en diferentes categorías a 12 magnitudes de inmunoensayo con niveles 1 y 2 de IQC. Conclusiones La métrica Sigma puede variar dependiendo del método empleado para calcular el sesgo, lo cual debe ser tenido en cuenta a la hora de evaluar el rendimiento analítico o programar eventos de IQC aplicando el método Seis Sigma.
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Affiliation(s)
- Şerif Ercan
- Departamento de Bioquímica Médica, Lüleburgaz State Hospital, Kırklareli, Türkiye
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8
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Sulthana P.K. S, U. R, Yassir S, Prasad V. G, Ansar M. A comparative evaluation of six sigma metrics and quality goal index ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during lockdown in a NABL accredited central laboratory. Biomedicine (Taipei) 2022. [DOI: 10.51248/.v42i5.2038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Introduction and Aim: Sigma represents Standard Deviation (SD) which indicates the degree of variation in a process, where the higher sigma value implies that less likely the laboratory reports false test results. Using a newer parameter called Quality Goal Index (QGI) we can find the reason behind the lower sigma value. Our study aimed to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown.
Methodology: A retrospective study was used to compare the six-sigma metric and QGI ratio 3 months prior to first lockdown due to COVID-19 pandemic and 3 months during the first lockdown for the selected ten analytes from 1st of January 2020 to 30th of June 2020 from the clinical biochemistry section of Yenepoya Medical College Hospital, Deralakatte, Mangalore.
Results: The sigma metrics from January to March (level 1) indicated that urea, TSH, beta-HCG fell short of meeting Six Sigma quality performance and from April to June, glucose, creatinine, urea and ALT had metrics less than 3 at both the Internal Quality Control levels. QGI ratio indicated that from January to March, the problem was imprecision for urea, TSH and beta-HCG (QGI < 0.8). From April to June, urea and creatinine showed imprecision, glucose and ALT showed inaccuracy, urea and ALT showed both imprecision and inaccuracy.
Conclusion: This study highlights the necessity for stringent Internal Quality Control and External Quality Assurance monitoring even during the lockdown period of the pandemic. By implementing six sigma and finding QGI ratio, quality of laboratory services can be improved immensely.
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Martínez-Morillo E, Elena-Pérez S, Cembrero-Fuciños D, García-Codesal MF, Contreras-Sanfeliciano T. Verification of examination procedures for 72 biochemical parameters on the atellica ® clinical chemistry and immunoassay analyzers. Scandinavian Journal of Clinical and Laboratory Investigation 2022; 82:419-431. [PMID: 35921081 DOI: 10.1080/00365513.2022.2102541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
The verification of examination procedures is a responsibility for clinical laboratories in order to guarantee that their performance characteristics comply with the specifications obtained during the validation process and are congruent with the intended scope of the assay. The aim was to perform an evaluation of precision, bias, linearity, linear drift, sample carry-over, and comparability of 73 assays from Siemens Healthineers, by following the CLSI EP10-A3 guidelines. The verification was performed by measuring 72 biochemical parameters in quality control (QC) materials from Bio-Rad (except for IL6) with 73 assays installed on eight measuring systems (five Atellica® CH 930 and three IM 1600 analyzers from Siemens Healthcare Diagnostics). The following information was collected: validation data from manufacturer, biological variation data from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) database, and specifications for fβhCG and PAPP-A assays to meet the Fetal Medicine Foundation standards. A total of 17550 results were obtained during EP10 verification process. Out of the 73 methods, only Cl-S, Mg-S, and Na-S failed the criteria for adequate precision, trueness, and comparability. The assays did not show significant loss of linearity, linear drift, or sample carry-over. This study allowed the initial training and familiarization with the instruments and the identification of operational issues. It also represented an opportunity to evaluate the QCs and to obtain analytical performance information for application of sigma six metrics for quality assurance. Professionals are advised to adequately standardize and protocolize their verification processes to ensure laboratory competence and patient safety.
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Affiliation(s)
| | - Sandra Elena-Pérez
- Department of Laboratory Medicine, University Hospital of Salamanca, Salamanca, Spain
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10
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Wauthier L, Di Chiaro L, Favresse J. Sigma Metrics in Laboratory Medicine: A Call for Harmonization. Clin Chim Acta 2022; 532:13-20. [PMID: 35594921 DOI: 10.1016/j.cca.2022.05.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/27/2022] [Accepted: 05/13/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND AIM Sigma metrics are applied in clinical laboratories to assess the quality of analytical processes. A parameter associated to a Sigma >6 is considered "world class" whereas a Sigma <3 is "poor" or "unacceptable". The aim of this retrospective study was to quantify the impact of different approaches for Sigma metrics calculation. MATERIAL AND METHODS Two IQC levels of 20 different parameters were evaluated for a 12-month period. Sigma metrics were calculated using the formula: (allowable total error (TEa) (%) - bias (%))/(coefficient of variation (CV) (%)). Method precision was calculated monthly or annually. The bias was obtained from peer comparison program (PCP) or external quality assessment program (EQAP), and 9 different TEa sources were included. RESULTS There was a substantial monthly variation of Sigma metrics for all combinations, with a median variation of 32% (IQR, 25.6-41.3%). Variation across multiple analyzers and IQC levels were also observed. Furthermore, TEa source had the highest impact on Sigma calculation with proportions of Sigma >6 ranging from 17.5% to 84.4%. The nature of bias was less decisive. CONCLUSION In absence of a clear consensus, we recommend that laboratories calculate Sigma metrics on a sufficiently long period of time (>6 months) and carefully evaluate the choice of TEa source.
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Affiliation(s)
- Loris Wauthier
- Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium
| | - Laura Di Chiaro
- Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium
| | - Julien Favresse
- Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium; Department of Pharmacy, Namur Research Institute for LIfe Sciences, University of Namur, Namur, Belgium.
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Koeder C, Kranz RM, Anand C, Husain S, Alzughayyar D, Schoch N, Hahn A, Englert H. Effect of a 1-Year Controlled Lifestyle Intervention on Body Weight and Other Risk Markers (the Healthy Lifestyle Community Programme, Cohort 2). Obes Facts 2022; 15:228-239. [PMID: 34923493 PMCID: PMC9021650 DOI: 10.1159/000521164] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/23/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION The prevalence of obesity is high and increasing worldwide. Obesity is generally associated with an increased risk of chronic disease and mortality. The objective of the study was to test the effect of a lifestyle intervention on body weight and other chronic disease risk markers. METHODS A non-randomized controlled trial was conducted, including mostly middle-aged and elderly participants recruited from the general population in rural northwest Germany (intervention: n = 114; control: n = 87). The intervention consisted of a 1-year lifestyle programme, focussing on four key areas: a largely plant-based diet (strongest emphasis), physical activity, stress management, and community support. Parameters were assessed at baseline, 10 weeks, 6 months, and 1 year. The control group received no intervention. RESULTS Compared to the control, in the intervention group, significantly lower 1-year trajectories were observed for body weight, body mass index (BMI), waist circumference (WC), total cholesterol, calculated LDL cholesterol, non-HDL cholesterol, remnant cholesterol (REM-C), glucose, HbA1c, and resting heart rate (RHR). However, between-group differences at 1 year were small for glucose, HbA1c, and cholesterol (apart from REM-C). No significant between-group differences were found for 1-year trajectories of measured LDL cholesterol, HDL cholesterol, triglycerides, insulin, blood pressure, and pulse pressure. CONCLUSION The intervention successfully reduced body weight, BMI, WC, REM-C, and RHR. However, at 1 year, effectiveness of the intervention regarding other risk markers was either very modest or could not be shown.
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Affiliation(s)
- Christian Koeder
- Institute of Food Science and Human Nutrition, Leibniz University Hannover, Hannover, Germany
- Department of Nutrition, University of Applied Sciences Münster, Münster, Germany
- *Christian Koeder,
| | - Ragna-Marie Kranz
- Department of Nutrition, University of Applied Sciences Münster, Münster, Germany
| | - Corinna Anand
- Department of Nutrition, University of Applied Sciences Münster, Münster, Germany
| | - Sarah Husain
- Department of Nutrition, University of Applied Sciences Münster, Münster, Germany
| | - Dima Alzughayyar
- Department of Nutrition, University of Applied Sciences Münster, Münster, Germany
| | - Nora Schoch
- Department of Nutrition, University of Applied Sciences Münster, Münster, Germany
| | - Andreas Hahn
- Institute of Food Science and Human Nutrition, Leibniz University Hannover, Hannover, Germany
| | - Heike Englert
- Department of Nutrition, University of Applied Sciences Münster, Münster, Germany
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12
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Pašić A, Šeherčehajić E. "Six Sigma" standard as a level of quality of biochemical laboratories. SANAMED 2022. [DOI: 10.5937/sanamed0-40408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The principal role of biochemical laboratories is responsibility for reliable, reproducible, accurate, timely, and accurately interpreted analysis results that help in making clinical decisions, while ensuring the desired clinical outcomes. To achieve this goal, the laboratory should introduce and maintain quality control in all phases of work. The importance of applying the Six SIGMA quality model has been analyzed in a large number of scientific studies. The purpose of this review is to highlight the importance of using six SIGMA metrics in biochemical laboratories and the current application of six SIGMA metrics in all laboratory work procedures. It has been shown that the six SIGMA model can be very useful in improving all phases of laboratory work, as well as that a detailed assessment of all procedures of the phases of work and improvement of the laboratory's quality control system is crucial for the laboratory to have the highest level of six SIGMA. Clinical laboratories should use SIGMA metrics to monitor their performance, as it makes it easier to identify gaps in their performance, thereby improving their efficiency and patient safety. Medical laboratory quality managers should provide a systematic methodology for analyzing and correcting quality assurance systems to achieve Six SIGMA quality-level standards.
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13
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Luo Y, Yan X, Xiao Q, Long Y, Pu J, Li Q, Cai Y, Chen Y, Zhang H, Chen C, Ou S. Application of Sigma metrics in the quality control strategies of immunology and protein analytes. J Clin Lab Anal 2021; 35:e24041. [PMID: 34606652 PMCID: PMC8605144 DOI: 10.1002/jcla.24041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/19/2022] Open
Abstract
Background Six Sigma (6σ) is an efficient laboratory management method. We aimed to analyze the performance of immunology and protein analytes in terms of Six Sigma. Methods Assays were evaluated for these 10 immunology and protein analytes: Immunoglobulin G (IgG), Immunoglobulin A (IgA), Immunoglobulin M (IgM), Complement 3 (C3), Complement 4 (C4), Prealbumin (PA), Rheumatoid factor (RF), Anti streptolysin O (ASO), C‐reactive protein (CRP), and Cystatin C (Cys C). The Sigma values were evaluated based on bias, four different allowable total error (TEa) and coefficient of variation (CV) at QC materials levels 1 and 2 in 2020. Sigma Method Decision Charts were established. Improvement measures of analytes with poor performance were recommended according to the quality goal index (QGI), and appropriate quality control rules were given according to the Sigma values. Results While using the TEaNCCL, 90% analytes had a world‐class performance with σ>6, Cys C showed marginal performance with σ<4. While using minimum, desirable, and optimal biological variation of TEa, only three (IgG, IgM, and CRP), one (CRP), and one (CRP) analytes reached 6σ level, respectively. Based on σNCCL that is calculated from TEaNCCL, Sigma Method Decision Charts were constructed. For Cys C, five multi‐rules (13s/22s/R4s/41s/6X, N = 6, R = 1, Batch length: 45) were adopted for QC management. The remaining analytes required only one QC rule (13s, N = 2, R = 1, Batch length: 1000). Cys C need to improve precision (QGI = 0.12). Conclusions The laboratories should choose appropriate TEa goals and make judicious use of Sigma metrics as a quality improvement tool.
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Affiliation(s)
- Yanfen Luo
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Xingxing Yan
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Qian Xiao
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yifei Long
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Jieying Pu
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Qiwei Li
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yimei Cai
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yushun Chen
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Hongyuan Zhang
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Cha Chen
- Department of Medicine Laboratory, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.,Department of Medicine Laboratory, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Songbang Ou
- Reproductive center, Department of Obstetrics and Gynecology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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14
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Dong P, Wang Y, Peng D, Wang J, Cheng Y, Deng X, Zheng B, Tao R. Utility of process capability indices in assessment of quality control processes at a clinical laboratory chain. J Clin Lab Anal 2021; 35:e23878. [PMID: 34165837 PMCID: PMC8373361 DOI: 10.1002/jcla.23878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND To evaluate the utility of the process capability indices Cp and Cpk for assessing the quality control processes at chain laboratory facilities. METHODS In April 2020, the minimum Cp and Cpk values for 33 assays of a laboratory chain with 19 facilities were collected for further analysis and a total of 627 datasets (Cp and Cpk ) were compared. In addition, standard values for Cp and Cpk , defined as the lowest of the top 20%, were obtained for comparison and the indices were used to determine whether precision or trueness improvements were required for the corresponding assay. RESULTS A total of 627 datasets of 33 assays from 19 laboratory facilities were collected for further analysis. Based on the Cp results, 329 (52.5%), 211 (33.7%), 65 (10.3%), and 22 (3.5%) were rated as excellent, good, marginal, and poor, respectively. While the corresponding results for Cpk were 300 (47.8%), 216 (34.4%), 79 (12.6%), and 32 (5.1%). In addition, it was noteworthy that eight (Cp criteria) and six assays (Cpk criteria) were rated as excellent or good at all 19 facilities. Comparison of the process capability indices at the Jinan KingMed Center with the standard values revealed that total protein, albumin, and urea showed trueness individual improvement, precision individual improvement, and precision common improvement, respectively, while the results of other assays were stable. CONCLUSION Process capability indices are useful for evaluating the quality control procedures in laboratory facilities and can help improve the precision and trueness of laboratory tests.
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Affiliation(s)
- Ping Dong
- Laboratory Diagnosis DepartmentJinan KingMed Center for Clinical LaboratoryJinanChina
| | - Yong‐Bo Wang
- Laboratory Diagnosis DepartmentQingdao KingMed Center for Clinical LaboratoryQingdaoChina
| | - De‐Zhi Peng
- Laboratory Diagnosis DepartmentJinan KingMed Center for Clinical LaboratoryJinanChina
| | - Jia‐Jia Wang
- Laboratory Diagnosis DepartmentJinan KingMed Center for Clinical LaboratoryJinanChina
| | - Ya‐Ting Cheng
- Laboratory Diagnosis DepartmentGuangzhou KingMed Center for Clinical LaboratoryGuangzhouChina
- KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhouChina
| | - Xiao‐Yan Deng
- KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhouChina
| | - Biao Zheng
- KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhouChina
| | - Ran Tao
- Laboratory Diagnosis DepartmentGuangzhou KingMed Center for Clinical LaboratoryGuangzhouChina
- KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhouChina
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15
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Sharma LK, Datta RR, Sharma N. Sigma Metric Evaluation of Drugs in a Clinical Laboratory: Importance of Choosing Appropriate Total Allowable Error and a Troubleshooting Roadmap. J Lab Physicians 2021; 13:44-49. [PMID: 34103878 PMCID: PMC8159660 DOI: 10.1055/s-0041-1726572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objectives
Stringent quality control is an essential requisite of diagnostic laboratories to deliver consistent results. Measures used to assess the performance of a clinical chemistry laboratory are internal quality control and external quality assurance scheme (EQAS). However, the number of errors cannot be measured by the above but can be quantified by sigma metrics. The sigma scale varies from 0 to 6 with “6” being the ideal goal, which is calculated by using total allowable error (TEa), bias, and precision. However, there is no proper consensus for setting a TEa goal, and influence of this limiting factor during routine laboratory practice and sigma calculation has not been adequately determined. The study evaluates the impact of the choice of TEa value on sigma score derivation and also describes a detailed structured approach (followed by the study laboratory) to determine the potential causes of errors causing poor sigma score.
Materials and Methods
The study was conducted at a clinical biochemistry laboratory of a central government tertiary care hospital. Internal and external quality control data were evaluated for a period of 5 months from October 2019 to February 2020. Three drugs (carbamazepine, phenytoin, and valproate) were evaluated on the sigma scale using two different TEa values to determine significant difference, if any.
Statistical Analysis
Bias was calculated using the following formula: Bias% = (laboratory EQAS result − peer group mean) × 100 / peer group mean Peer group mean sigma metric was calculated using the standard equation: Sigma value = TEa − bias / coefficient of variation (CV)%.
Results
Impressive sigma scores (> 3 sigma) for two out of three drugs were obtained with TEa value 25, while with TEa value 15, sigma score was distinctly dissimilar and warranted root cause analysis and corrective action plans to be implemented for both valproate and carbamazepine.
Conclusions
The current study evidently recognizes that distinctly different sigma values can be obtained, depending on the TEa values selected, and using the same bias and precision values in the sigma equation. The laboratories should thereby choose appropriate TEa goals and make judicious use of sigma metric as a quality improvement tool.
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Affiliation(s)
- Lokesh Kumar Sharma
- Department of Biochemistry, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS), Dr. Ram Manohar Lohia Hospital, New Delhi, India
| | - Rashmi Rasi Datta
- Department of Biochemistry, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS), Dr. Ram Manohar Lohia Hospital, New Delhi, India
| | - Neera Sharma
- Department of Biochemistry, Atal Bihari Vajpayee Institute of Medical Sciences (ABVIMS), Dr. Ram Manohar Lohia Hospital, New Delhi, India
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16
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Yu CW, He XY, Wan KX, Yuan ZJ, Liu H, Zhang J, Liu S, Yang J, Zou L. Improving quality management of newborn screening in southwest China. J Int Med Res 2021; 49:3000605211002999. [PMID: 33823629 PMCID: PMC8033469 DOI: 10.1177/03000605211002999] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Objective Newborn screening (NBS) programs benefit tens of millions of infants
worldwide each year. However, the extremely large screening populations and
number of laboratories involved pose great challenges to maintaining high
screening quality. To achieve continuous quality improvement, we established
a comprehensive quality management system (CQMS) in southwest China. Methods External quality assessment (EQA) and internal quality control were carried
out for basic quality management. We used 16 quality indicators (QIs) to
monitor the entire screening process, with external supervision from the
China National Accreditation Service for Conformity Assessment. All
retrospective data for quality assessment were collected consecutively from
laboratory management and patient follow-up systems. Results From 2015 to 2019, satisfactory EQA performance was achieved, with an average
score greater than 97 for each screening item. QI monitoring showed that NBS
quality improved continuously. The rate of health education provision
increased from 90.9% to 100% and the recall rate after a positive primary
screening increased from 85.4% to 99.2%. The unsatisfactory specimen rate
and rate of newborns lost to follow-up decreased to 0.38% and 0.08%,
respectively. Conclusions Implementing a CQMS and monitoring the whole screening process using QIs may
yield continuous quality improvement of NBS.
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Affiliation(s)
- Chao-Wen Yu
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xiao-Yan He
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ke-Xing Wan
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Zhao-Jian Yuan
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Hao Liu
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Juan Zhang
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Shan Liu
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Jing Yang
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Lin Zou
- Center for Clinical Molecular Medicine & Newborn Screening, Children's Hospital of Chongqing Medical University; National Clinical Research Center for Child Health and Disorders; Ministry of Education Key Laboratory of Child Development and Disorders; China International Science and Technology Cooperation Base of Child Development and Critical Disorders; Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China.,Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
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17
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Smit NPM, Ruhaak LR, Romijn FPHTM, Pieterse MM, van der Burgt YEM, Cobbaert CM. The Time Has Come for Quantitative Protein Mass Spectrometry Tests That Target Unmet Clinical Needs. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:636-647. [PMID: 33522792 PMCID: PMC7944566 DOI: 10.1021/jasms.0c00379] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/22/2020] [Accepted: 01/19/2021] [Indexed: 05/04/2023]
Abstract
Protein mass spectrometry (MS) is an enabling technology that is ideally suited for precision diagnostics. In contrast to immunoassays with indirect readouts, MS quantifications are multiplexed and include identification of proteoforms in a direct manner. Although widely used for routine measurements of drugs and metabolites, the number of clinical MS-based protein applications is limited. In this paper, we share our experience and aim to take away the concerns that have kept laboratory medicine from implementing quantitative protein MS. To ensure added value of new medical tests and guarantee accurate test results, five key elements of test evaluation have been established by a working group within the European Federation for Clinical Chemistry and Laboratory Medicine. Moreover, it is emphasized to identify clinical gaps in the contemporary clinical pathways before test development is started. We demonstrate that quantitative protein MS tests that provide an additional layer of clinical information have robust performance and meet long-term desirable analytical performance specifications as exemplified by our own experience. Yet, the adoption of quantitative protein MS tests into medical laboratories is seriously hampered due to its complexity, lack of robotization and high initial investment costs. Successful and widespread implementation in medical laboratories requires uptake and automation of this next generation protein technology by the In-Vitro Diagnostics industry. Also, training curricula of lab workers and lab specialists should include education on enabling technologies for transitioning to precision medicine by quantitative protein MS tests.
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Affiliation(s)
- Nico P. M. Smit
- Department of Clinical Chemistry and
Laboratory Medicine, Leiden University Medical
Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - L. Renee Ruhaak
- Department of Clinical Chemistry and
Laboratory Medicine, Leiden University Medical
Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Fred P. H. T. M. Romijn
- Department of Clinical Chemistry and
Laboratory Medicine, Leiden University Medical
Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Mervin M. Pieterse
- Department of Clinical Chemistry and
Laboratory Medicine, Leiden University Medical
Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Yuri E. M. van der Burgt
- Department of Clinical Chemistry and
Laboratory Medicine, Leiden University Medical
Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Christa M. Cobbaert
- Department of Clinical Chemistry and
Laboratory Medicine, Leiden University Medical
Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
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18
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Peng S, Zhang J, Zhou W, Mao W, Han Z. Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings. J Clin Lab Anal 2021; 35:e23665. [PMID: 33270940 PMCID: PMC7957980 DOI: 10.1002/jcla.23665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 11/07/2020] [Accepted: 11/10/2020] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The performance of 18 routine chemical detection methods was evaluated by the sigma (σ) metric, and Westgard Sigma rules with run size were used to establish internal quality control (IQC) standards to reduce patient risks. MATERIALS AND METHODS External quality assessment (EQA) and internal quality control data from 18 assays in a biochemical laboratory were collected from January to June 2020. The sigma values of each assay were calculated, based on the bias, total error allowable, and coefficient of variation, appropriate quality control rules were selected. According to the quality goal index, the main causes of poor performance were determined to guide quality improvement. RESULTS At IQC material level 1, seven of the 18 assays achieved five sigma (excellent), and five assays (UA, Crea, AMY, TC and Na) showed world-class performance. At IQC material level 2, 14 of the 18 assays achieved 5 sigma (excellent), and thirteen assays (UA, ALT, CK, Crea, AMY, K, AST, ALP, Na, LDH, Mg, TC and GGT) showed world-class performance. The quality goal index (QGI) was calculated for items with analysis performance <5 sigma, and the main causes of poor performance were determined to guide quality improvement. CONCLUSIONS Westgard sigma rules with run size are an effective tool for evaluating the performance of biochemical assays. These rules can be used to more simply and intuitively select the quality control strategy of related items and reduce the risk to patients.
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Affiliation(s)
- SongQing Peng
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
| | - JinFei Zhang
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
| | - WuQiong Zhou
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
| | - WeiLin Mao
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
- Key laboratory of digestive system diseases of ShengzhouShengzhou People’s HospitalShengzhouChina
| | - Zhong Han
- Department of Clinical LaboratoryShengzhou People's HospitalShengzhou Branch of the First Affiliated Hospital of Zhejiang UniversityShengzhouChina
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19
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Teshome M, Worede A, Asmelash D. Total Clinical Chemistry Laboratory Errors and Evaluation of the Analytical Quality Control Using Sigma Metric for Routine Clinical Chemistry Tests. J Multidiscip Healthc 2021; 14:125-136. [PMID: 33488088 PMCID: PMC7815085 DOI: 10.2147/jmdh.s286679] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/06/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Currently, the use of clinical laboratory tests is growing at a promising rate and about 80% of the clinical decisions made are based on the laboratory test results. Therefore, it is a major task to achieve quality service. This study was conducted to assess the magnitude of errors in the total testing process of Clinical Chemistry Laboratory and to evaluate analytical quality control using sigma metrics. METHODS A cross-sectional study was conducted at Dessie Comprehensive Specialized Hospital Clinical Chemistry Laboratory, Northeast Ethiopia, from 10 February 2020 to 10 June 2020. All Clinical Chemistry Laboratory test requests with their respective samples, external quality control and all daily internal quality control data during the study period were included in the study. Data were collected using a prepared checklist and analyzed using SPSS version 21. RESULTS A total of 4719 blood samples with their test requests were included in the study. Out of 145,383 quality indicators, an error rate of 22,301 (15.3%) was identified in the total testing process. Of the total errors, 76.3% were pre-analytical, 2.1% were analytical and 21.6% were post-analytical errors (p<0.0001). Of the total 14 analytes in the sigma metric evaluation, except ALP, all routine clinical chemistry tests were below the standard (<3). In multivariate logistic regression, the location of patients in the inpatient department was significantly associated with the specimen rejection ((AOR=1.837, 95% CI (1.288-2.618), p=0.001). CONCLUSION The study found a higher frequency of errors in the total testing process in the Clinical Chemistry Laboratory and almost all test parameters had an unsatisfactory sigma metric value.
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Affiliation(s)
- Mulugeta Teshome
- Department of Medical Laboratory, Dessie Comprehensive Specialized Hospital, Dessie, Ethiopia
| | - Abebaw Worede
- Department of Clinical Chemistry, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Daniel Asmelash
- Department of Clinical Chemistry, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
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20
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Goel P, Malik G, Prasad S, Rani I, Manhas S, Goel K. Analysis of performance of clinical biochemistry laboratory using Sigma metrics and Quality Goal Index. Pract Lab Med 2021; 23:e00195. [PMID: 33392370 PMCID: PMC7773579 DOI: 10.1016/j.plabm.2020.e00195] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 12/11/2020] [Indexed: 02/07/2023] Open
Abstract
Background Unreliable and ingenuine results issued by clinical laboratories have serious consequences for the patients. Sigma metrics is a standardized tool for Quality assessment for test performance in a laboratory. Objective To evaluate the performance of routine biochemistry laboratory at MMIMSR, Mullana in terms of Sigma metrics and Quality Goal Index. Material and methods This cross sectional study evaluated performance of 14 routine chemistry parameters using retrospective Internal Quality Control data of two levels on Siemens Dimension Rxl from Feb to Jul 2019 for CV% and EQAS reports from CMC, Vellore for Bias%. Sigma metrics was calculated using total allowable error targets as per CLIA and Biological Variability database guidelines. Results For level-2 IQC; TG, Chol, ALP showed excellent performance with σ > 6 while σ < 3 was observed for AST, Total Protein, Glucose, BUN and ALT using CLIA guidelines while in IQC Level-3 poor performers were only BUN and ALT with Ca, TG and Chol showing σ > 6. Further by using Biological Variability data guidelines; 10 parameters of IQC Level-2 and 5 of IQC level-3 were poor performers with σ < 3. Conclusion Sigma metrics is an excellent tool for performance analysis of tests performed in a clinical laboratory. Lack of precision in terms of CV% was seen for majority of the poor performers. Total allowable error targets using Biological Variability data revealed σ < 3 for 10 parameters while using CLIA guidelines σ < 3 was seen for only 5 parameters of IQC level-2.
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Affiliation(s)
- Parul Goel
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
- Corresponding author.
| | - Gagandeep Malik
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Suvarna Prasad
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Isha Rani
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Sunita Manhas
- Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, MMDU, Mullana, India
| | - Kapil Goel
- Department of Community Medicine & School of Public Health, Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh, 160012, India
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21
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Yang F, Wang W, Liu Q, Wang X, Bian G, Teng S, Liang W. The application of Six Sigma to perform quality analyses of plasma proteins. Ann Clin Biochem 2019; 57:121-127. [PMID: 31726847 DOI: 10.1177/0004563219892023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background The Six Sigma theory is an important tool for laboratory quality management. It has been widely used in clinical chemistry, haematology and other disciplines. The aim of our study was to evaluate the analytical performance of plasma proteins by application of Sigma metric and to compare the differences among three different allowable total errors in evaluating the analytical performance of plasma proteins. Methods Three different allowable total error values were used as quality goals. Data from an external quality assessment were used as bias, and the cumulative coefficient of variation in internal quality control data was used to represent the amount of imprecision during the same period. Sigma metric of analytes was calculated using the above data. The quality goal index was calculated to provide corrected measures for continuous improvements in analytical quality. Results The Sigma metric was highest using the external quality assessment standards of China: it was sigma ≥6 or higher in 57.1% of plasma proteins. But Sigma metric was lower by using RiliBÄK or biological variation standards. IgG, C3 and C-reactive protein all required quality improvements in imprecision. A single-rule 13s for internal quality control was recommended for IgA, IgM, C4 and rheumatoid factor, whereas multiple rules (13s/22s/R4s) were recommended for IgG, C3 and C-reactive protein, according to the external quality assessment standards of China. Conclusions Different quality goals can lead to different Sigma metric for the same analyte. As the lowest acceptable standard in clinical practice, the external quality assessment standard of China can guide laboratories to formulate reasonable quality improvement programmes.
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Affiliation(s)
- Fumeng Yang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Wenjun Wang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Qian Liu
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Xizhen Wang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Guangrong Bian
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Shijie Teng
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
| | - Wei Liang
- Department of Laboratory Medicine, The Second People's Hospital of Lianyungang, Lianyungang, PR China
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22
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Zhou B, Wu Y, He H, Li C, Tan L, Cao Y. Practical application of Six Sigma management in analytical biochemistry processes in clinical settings. J Clin Lab Anal 2019; 34:e23126. [PMID: 31774217 PMCID: PMC6977137 DOI: 10.1002/jcla.23126] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 10/31/2019] [Accepted: 11/08/2019] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Six Sigma methodology with a zero-defect goal has long been applied in commercial settings and was utilized in this study to assure/improve the quality of various analytes. METHODS Daily internal quality control (QC) and external quality assessment data were collected and analyzed by calculating the sigma (σ) values for 19 analytes based on the coefficient of variation, bias, and total error allowable. Standardized QC sigma charts were established with these parameters. Quality goal index (QGI) analysis and root cause analysis (RCA) were used to discover potential problems for the analytes. RESULTS Five analytes with σ ≥ 6 achieved world-class performance, and only the Westgard rule (13s ) with one control measurement at two QC material levels (N2) per QC event and a run size of 1000 patient samples between QC events (R1000) was needed for QC. In contrast, more control rules (22s /R4s /41s ) along with high N values and low R values were needed for quality assurance for five analytes with 4 ≤ σ < 6. However, the sigma levels of nine analytes were σ < 4 at one or more QC levels, and a more rigorous QC procedure (13s /22s /R4s /41s /8x with N4 and R45) was implemented. The combination of QGI analysis and RCA further revealed inaccuracy or imprecision problems for these analytes with σ < 4 and discovered five aspects of potential causes considered for quality improvement. CONCLUSIONS Six Sigma methodology is an effective tool for evaluating the performance of biochemical analytes and is conducive to quality assurance and improvement.
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Affiliation(s)
- Bingfei Zhou
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Yi Wu
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Hanlin He
- Department of Medical laboratory of Hunan Normal University School of Medicine, Changsha, China
| | - Cunyan Li
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Liming Tan
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Youde Cao
- Clinical Laboratory of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.,Research Office of Clinical Laboratory, Clinical Translational Medicine Research Institute of Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
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23
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Tzortzopoulos A, Raftopoulos V, Talias MA. Performance characteristics of automated clinical chemistry analyzers using commercial assay reagents contributing to quality assurance and clinical decision in a hospital laboratory. Scandinavian Journal of Clinical and Laboratory Investigation 2019; 80:46-54. [PMID: 31766906 DOI: 10.1080/00365513.2019.1695282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Background: Clinical laboratories provide essential diagnostic services that are essential in clinical decision making, contributing to the quality of healthcare. The performance of two Siemens ADVIA 1800 analyzers was characterized in a hospital Biochemistry laboratory in order to evaluate the analytical characteristics of such automated analyzer systems using nonoriginal assay reagents attempting to support laboratory quality service and crucial clinical decision making. Methods: We independently completed performance validation studies including trueness, precision, sensitivity as well as measurement of uncertainty and sigma metrics calculation for 25 biochemical parameters. Results: Trueness expressed as bias was less than 20% for both ADVIA 1800 analyzers. Within run and total precisions expressed as CV% were ≤9.85% on both analyzers for most parameters studied with few exceptions (Mg, TB, DB, Cl, HDL and UA) observed either in low or in high level samples and between the two analyzers. LoB, LoD and LoQ values produced by the two analyzers were comparable except Cl. Uncertainty values produced by the two analyzers were comparable with no significant differences. Quality performance of reagent assays was studied using the sigma metrics system. The sigma values were plotted on normalized method decision charts for graphical representation of assay performances for each analyzer. Conclusions: The two ADVIA systems, independently evaluated, showed consistent performance characteristics with certain discrepancies by several reagents. Sigma analysis was helpful for revealing the quality performance of non-original reagents supporting the need for strict assessment of quality assurance and in some instances optimization/improvement of assay methods.
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Affiliation(s)
- Athanasios Tzortzopoulos
- Biochemistry Laboratory, General Hospital of Agrinio, Agrinio, Greece.,Department of Clinical Biochemistry, Aghia Sophia' Children's Hospital, Athens, Greece
| | | | - Michael A Talias
- Department of Healthcare Management, Faculty of Economics and Management, Open University of Cyprus, Nicosia, Cyprus
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