AIM To study sigma metrics and quality goal index ratio(QGI). METHODS The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board ...AIM To study sigma metrics and quality goal index ratio(QGI). METHODS The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board for Testing and Calibration of Laboratories accredited lab as per the International Organization for Standardization 15189:2012 and provides service to a > 1700-bed tertiary care hospital. Data of 16 analytes was extracted over a period of one year from January 2017 to December 2017 for calculation of precision, accuracy, sigma metrics, total error, and QGI. RESULTS The average coefficient of variation ranged from 2.12%(albumin) to 5.42%(creatinine) for level 2 internal quality control and 2%(albumin) to 3.62%(high density lipoprotein-cholesterol) for level 3 internal quality control. Average coefficient of variation of all the parameters was below 5%, reflecting very good precision. The sigma metrics for level 2 indicated that 11(68.5%) of the 16 parameters fall short of meeting Six Sigma quality performance. Of these, five failed to meet minimum sigma quality performance with metrics less than 3, and another six just met minimal acceptable performance with sigma metrics between 3 and 6. For level 3, the data collected indicated eight(50%) of the parameters did not achieve Six Sigma quality performance, out of which three had metrics less than 3, and five had metrics between 3 and 6. QGI ratio indicated that the main problem was inaccuracy in the case of total cholesterol, aspartate transaminase, and alanine transaminase(QGI > 1.2), imprecision in the case of urea(QGI < 0.8), and both imprecision and inaccuracy for glucose.CONCLUSION On the basis of sigma metrics and QGI, it may be concluded that the Clinical Biochemistry Laboratory, PGIMS, Rohtak was able to achieve satisfactory results with world class performance for many analytes one year preceding the accreditation by the National Accreditation Board for Testing and Calibration of Laboratories. Aspartate transaminase and alanine transaminase required strict external quality assurance scheme monitoring and modification in quality control procedure as their QGI ratio showed inaccuracy.展开更多
目的对急诊生化分析项目进行Westgard西格玛性能评价,设定个性化的质控规则,确定合适的质控频率,明确改进方向,规范室内质控的开展。方法收集本院急诊生化检测系统21个分析项目共24个月的质控数据所得到的实际累积不精密度(CV)、与对等...目的对急诊生化分析项目进行Westgard西格玛性能评价,设定个性化的质控规则,确定合适的质控频率,明确改进方向,规范室内质控的开展。方法收集本院急诊生化检测系统21个分析项目共24个月的质控数据所得到的实际累积不精密度(CV)、与对等组的偏倚(BIAS)以及本院设定的允许总误差(TEa),使用Unity Real Time(简称URT)质控管理平台,得到分析项目的σ值、推荐的质控规则、假失控率和误差检出率等信息;并应用新推出的分析批长度Westgard Sigma规则流程图这一工具,来确定各分析项目合适的分析批长度(质控频率);而对于σ<6的分析项目,通过计算其质量目标指数(QGI),查找方法性能不佳的原因进行改进。结果(1)21个生化分析项目的实际累积CV、BIAS和总误差TE均在质量目标设定范围之内,不同项目的σ值不同,采用的质控规则从单规则到多规则,实现了个性化质控规则的设定;(2)21个生化分析项目的分析批长度不尽相同,结合该院平均每日急诊生化类样本数量选择合适的质控检测频率;(3)对于σ<6的17个分析项目中,QGI<0.8有16个项目,提示优先改进精密度;QGI>1.2有1个项目,提示优先改进正确度。结论Westgard西格玛规则是一种科学,实用的质量控制规则选取、分析批长度评估工具,可有效地应用于定量分析项目的质量管理。展开更多
目的运用分析批长度Westgard西格玛规则流程图优化实验室肿瘤标志物检测项目质控策略以及质量目标指数查找质量下降的原因,降低实验室成本和风险,提升实验室检测水平。方法收集本院核医学科2020年5月和6月两个月的肿瘤标志物检测项目室...目的运用分析批长度Westgard西格玛规则流程图优化实验室肿瘤标志物检测项目质控策略以及质量目标指数查找质量下降的原因,降低实验室成本和风险,提升实验室检测水平。方法收集本院核医学科2020年5月和6月两个月的肿瘤标志物检测项目室内质控在控数据的变异系数(coefficient of variation,CV)及2020年6月卫生部室间质评实验数据中的偏倚(Bias)值,采用2020年卫生部临床检验中心性能规范中的总允许误差(TEa),计算各项目的西格玛(σ)值,然后运用分析批长度Westgard西格玛规则图选择合适的质控规则,最后计算质量目标指数(quality goal index,QGI),分析影响质量性能的主要因素,通过3个月的改进,利用2020年9月和10月在控数据的CV值及2020年9月卫生部第二次的Bias值重新计算西格玛(σ)值,最后通过配对t检验验证差异有无统计学意义。结果σ值>6的项目有FPSA,应选择单规则13S,分析批长度R1=1000个;σ值介于5和6之间的项目有NSE,应选用多规则13s/22s/R4s,分析批长度R1=450个;σ值介于4和5之间的项目有CA125、CA19-9、TPSA,应选择多规则13s/22s/R4s/41s,分析批长度R1=200个;σ值小于4的项目有AFP、CEA、CA15-3、CA72-4、CYFRA21-1,应采用多规则13s/22s/R4s/41s/6x,分析批长度R1=45个。QGI小于0.8的项目有AFP、CEA、CA125、CA15-3、CA19-9、CA72-4、NSE、CYFRA21-1共计8个项目,优先改进精密度,大于1.2的项目只有TPSA,优先改进正确度。结论分析批长度Westgard西格玛规则图和质量目标指数两者相结合,可以为质控策略提供依据,使肿瘤标志物检测质量得到改进。展开更多
文摘AIM To study sigma metrics and quality goal index ratio(QGI). METHODS The retrospective study was conducted at the Clinical Biochemistry Laboratory, PGIMS, Rohtak, which recently became a National Accreditation Board for Testing and Calibration of Laboratories accredited lab as per the International Organization for Standardization 15189:2012 and provides service to a > 1700-bed tertiary care hospital. Data of 16 analytes was extracted over a period of one year from January 2017 to December 2017 for calculation of precision, accuracy, sigma metrics, total error, and QGI. RESULTS The average coefficient of variation ranged from 2.12%(albumin) to 5.42%(creatinine) for level 2 internal quality control and 2%(albumin) to 3.62%(high density lipoprotein-cholesterol) for level 3 internal quality control. Average coefficient of variation of all the parameters was below 5%, reflecting very good precision. The sigma metrics for level 2 indicated that 11(68.5%) of the 16 parameters fall short of meeting Six Sigma quality performance. Of these, five failed to meet minimum sigma quality performance with metrics less than 3, and another six just met minimal acceptable performance with sigma metrics between 3 and 6. For level 3, the data collected indicated eight(50%) of the parameters did not achieve Six Sigma quality performance, out of which three had metrics less than 3, and five had metrics between 3 and 6. QGI ratio indicated that the main problem was inaccuracy in the case of total cholesterol, aspartate transaminase, and alanine transaminase(QGI > 1.2), imprecision in the case of urea(QGI < 0.8), and both imprecision and inaccuracy for glucose.CONCLUSION On the basis of sigma metrics and QGI, it may be concluded that the Clinical Biochemistry Laboratory, PGIMS, Rohtak was able to achieve satisfactory results with world class performance for many analytes one year preceding the accreditation by the National Accreditation Board for Testing and Calibration of Laboratories. Aspartate transaminase and alanine transaminase required strict external quality assurance scheme monitoring and modification in quality control procedure as their QGI ratio showed inaccuracy.
文摘目的对急诊生化分析项目进行Westgard西格玛性能评价,设定个性化的质控规则,确定合适的质控频率,明确改进方向,规范室内质控的开展。方法收集本院急诊生化检测系统21个分析项目共24个月的质控数据所得到的实际累积不精密度(CV)、与对等组的偏倚(BIAS)以及本院设定的允许总误差(TEa),使用Unity Real Time(简称URT)质控管理平台,得到分析项目的σ值、推荐的质控规则、假失控率和误差检出率等信息;并应用新推出的分析批长度Westgard Sigma规则流程图这一工具,来确定各分析项目合适的分析批长度(质控频率);而对于σ<6的分析项目,通过计算其质量目标指数(QGI),查找方法性能不佳的原因进行改进。结果(1)21个生化分析项目的实际累积CV、BIAS和总误差TE均在质量目标设定范围之内,不同项目的σ值不同,采用的质控规则从单规则到多规则,实现了个性化质控规则的设定;(2)21个生化分析项目的分析批长度不尽相同,结合该院平均每日急诊生化类样本数量选择合适的质控检测频率;(3)对于σ<6的17个分析项目中,QGI<0.8有16个项目,提示优先改进精密度;QGI>1.2有1个项目,提示优先改进正确度。结论Westgard西格玛规则是一种科学,实用的质量控制规则选取、分析批长度评估工具,可有效地应用于定量分析项目的质量管理。
文摘目的运用分析批长度Westgard西格玛规则流程图优化实验室肿瘤标志物检测项目质控策略以及质量目标指数查找质量下降的原因,降低实验室成本和风险,提升实验室检测水平。方法收集本院核医学科2020年5月和6月两个月的肿瘤标志物检测项目室内质控在控数据的变异系数(coefficient of variation,CV)及2020年6月卫生部室间质评实验数据中的偏倚(Bias)值,采用2020年卫生部临床检验中心性能规范中的总允许误差(TEa),计算各项目的西格玛(σ)值,然后运用分析批长度Westgard西格玛规则图选择合适的质控规则,最后计算质量目标指数(quality goal index,QGI),分析影响质量性能的主要因素,通过3个月的改进,利用2020年9月和10月在控数据的CV值及2020年9月卫生部第二次的Bias值重新计算西格玛(σ)值,最后通过配对t检验验证差异有无统计学意义。结果σ值>6的项目有FPSA,应选择单规则13S,分析批长度R1=1000个;σ值介于5和6之间的项目有NSE,应选用多规则13s/22s/R4s,分析批长度R1=450个;σ值介于4和5之间的项目有CA125、CA19-9、TPSA,应选择多规则13s/22s/R4s/41s,分析批长度R1=200个;σ值小于4的项目有AFP、CEA、CA15-3、CA72-4、CYFRA21-1,应采用多规则13s/22s/R4s/41s/6x,分析批长度R1=45个。QGI小于0.8的项目有AFP、CEA、CA125、CA15-3、CA19-9、CA72-4、NSE、CYFRA21-1共计8个项目,优先改进精密度,大于1.2的项目只有TPSA,优先改进正确度。结论分析批长度Westgard西格玛规则图和质量目标指数两者相结合,可以为质控策略提供依据,使肿瘤标志物检测质量得到改进。
