Original Article

Ann Lab Med 2024; 44(3): 245-252

Published online May 1, 2024

Copyright © Korean Society for Laboratory Medicine.

Evaluation of Coefficients of Variation for Clinical Chemistry Tests Based on Internal Quality Control Data Across 5,425 Laboratories in China From 2013 to 2022

Wei Wang, M.S.1 , Zhixin Zhang, B.S.1 , Chuanbao Zhang, Ph.D.1 , Haijian Zhao, M.S.1 , Shuai Yuan, Ph.D.1 , Jiali Liu, B.S.1 , Na Dong, B.S.1 , Zhiguo Wang, M.S.1 , and Fengfeng Kang, M.S.2

1National Center for Clinical Laboratories, Beijing Engineering Research Center of Laboratory Medicine, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology, Beijing, China; 2Laboratory Medicine Center, Zhejiang Center for Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, China

Correspondence to: Fengfeng Kang, M.S.
Laboratory Medicine Center, Zhejiang Center for Clinical Laboratory, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, No.158 Shangtang Road, Hangzhou 310014, China

Received: June 5, 2023; Revised: September 25, 2023; Accepted: November 7, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Clinical chemistry tests are most widely used in clinical laboratories, and diverse measurement systems for these analyses are available in China. We evaluated the imprecision of clinical chemistry measurement systems based on internal QC (IQC) data.
Methods: IQC data for 27 general chemistry analytes were collected in February each year from 2013 to 2022. Four performance specifications were used to calculate pass rates for CVs of IQC data in 2022. Boxplots were drawn to analyze trends of CVs, and differences in CVs among different groups were assessed using the Mann–Whitney U-test or Kruskal–Wallis test.
Results: The number of participating laboratories increased significantly from 1,777 in 2013 to 5,425 in 2022. CVs significantly decreased for all 27 analytes, except creatine kinase and lipase. Triglycerides, total bilirubin, direct bilirubin, iron, and γ-glutamyl transferase achieved pass rates >80% for all goals. Nine analytes with pass rates <80% based on 1/3 allowable total error were further analyzed; the results indicated that closed systems exhibited lower CVs than open systems for all analytes, except total protein. For all nine analytes, differences were significant between tertiary hospitals and non-tertiary hospitals and between accredited and non-accredited laboratories.
Conclusions: The CVs of IQC data for clinical chemistry have seen a continuous overall improvement in China. However, there is ample room for imprecision improvement for several analytes, with stricter performance specifications.

Keywords: Clinical chemistry, Imprecision, Performance specification, Quality control

Clinical chemistry tests are most widely used in clinical laboratories. Numerous measurement systems are available in China, and laboratories often use open systems with unknown performance. Performance evaluation of clinical chemistry tests is of concern for laboratories and clinicians, and some relevant studies have been reported [1-3]. ISO 15189:2022 states that each laboratory should have an internal QC (IQC) procedure for monitoring the ongoing validity of examination results according to specified criteria that verifies the attainment of the intended quality and ensures validity pertinent to clinical decision making [4]. The main aim of IQC is to detect clinically important errors and evaluate imprecision in an analytical process, which is expressed as CV. In 2011, the National Center for Clinical Laboratories (NCCL) launched an IQC monitoring program for clinical chemistry in China to evaluate IQC practices of quantitative measurements in laboratories. This program provides the overall imprecision level in comparison with those of other participating laboratories.

Tests with inappropriate IQC performance, which is calculated as the number of tests with a CV higher than the selected target divided by the total number of quantitative tests [5], are a good quality indicator to monitor measurement imprecision in the intra-analytical phase of clinical testing. There are several performance specifications to evaluate the CV. Westgard and Burnett recommended that long-term imprecision should be <0.33 allowable total error (TEa) [6]. One-third of TEa, as suggested in external quality assessment criteria, is often used for imprecision evaluation in clinical laboratories in China [7]. According to the Consensus Statement of the International Federation of Clinical Chemistry, quality criteria based on biological variation (BV) are divided into three performance levels: minimum, desirable, and optimum [8].

To our knowledge, studies on CV assessment of measurement systems based on IQC data are limited. We evaluated CVs from IQC data of 27 clinical chemistry analytes between 2013 to 2022 and provided the pass rates against different imprecision specifications. Our aim was to observe the overall trends in the imprecision of various clinical chemistry measurement systems in China and to establish appropriate BV specifications for laboratories.

Study design

Clinical laboratories from various provinces in China who voluntarily participated in the IQC monitoring program for clinical chemistry were included in this study. The number of participating laboratories increased from 1,777 in 2013 to 5,425 in 2022.

IQC data for clinical chemistry tests, including mean and SD of monthly IQC data, method, the manufacturer of instrument, reagent, and calibrator, were collected in February each year via the website developed by the NCCL. Each laboratory was instructed to exclude out-of-control QC results from further analysis according to in-house QC rules and ranges. Measurement systems using the original instrument, reagents, and calibrators from the same manufacturer were defined as closed systems, whereas those employing components from different manufacturers were categorized as open systems. The following 27 analytes were analyzed: potassium (K), sodium (Na), chloride (Cl), calcium (Ca), phosphorus (P), glucose (Glu), urea nitrogen (Urea), uric acid (UA), creatinine (Cre), total protein (TP), albumin (Alb), total cholesterol (TC), triglycerides (TG), total bilirubin (TBil), direct bilirubin (DBil), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), amylase (AMY), creatine kinase (CK), lactate dehydrogenase (LDH), cholinesterase (CHE), lipase (LPS), iron (Fe), magnesium (Mg), γ-glutamyl transferase (GGT), and α-hydroxybutyrate dehydrogenase (α-HBDH).

Performance specifications for imprecision

The latest BV data of the 27 analytes were obtained from the website of the European Federation of Clinical Chemistry and Laboratory Medicine [9]. Three levels of allowable imprecision of each analyte were defined according to the formula [10]: CVA <f1×CVI (CVA: analytical variation; CVI: intra-individual variation; minimum level, f1=0.75; desirable level, f1=0.5; optimum level, f1=0.25). 1/3 TEa goals were calculated based on the latest external quality assessment criteria in China established by the NCCL [7].

Statistical analysis

Microsoft Excel 2010 and GraphPad Prism 9 were used for statistical analysis. The CV for each QC level was calculated by dividing the SD by the mean. If multiple QC levels were utilized, a pooled CV was calculated using the following equation:


(where n is the number of the QC level). The Shapiro–Wilk test was used to check the normality of the CV data for each analyte, and non-parametric statistics were employed for the analysis of data with a non-normal distribution.

A boxplot was drawn to visually represent the CV distribution from 2013 to 2022. Differences in the CV between 2013 and 2022 for each analyte were evaluated using the Mann–Whitney U-test. Additionally, CVs of IQC data from 2022 were evaluated based on four performance specifications. For analytes with pass rates <80% based on the 1/3 TEa goal, differences in the CV among different groups were analyzed using the Mann–Whitney U-test or Kruskal–Wallis test. Statistical significance was further determined by post-hoc pairwise analysis by applying Bonferroni’s correction. P<0.05 was considered statistically significant.

From 2013 to 2022, 1,777, 2,049, 2,223, 2,371, 2,499, 2,799, 3,274, 4,617, 4,958, and 5,425 laboratories, respectively, participated in the IQC monitoring program for clinical chemistry. The number of participating laboratories varied across assays, with the highest participation observed for ALT, which increased from 684 to 4,459 over a decade, and the lowest observed for LPS, with an increase from 95 to 1,318. General information on the participants in 2013 and 2022 is provided in Table 1.

Table 1 . General information on laboratories participating in the IQC monitoring program for clinical chemistry by the NCCL in 2013 and 2022

Participating laboratories1,7775,425
Type of medical institution
Hospital laboratory1,501844,72487
Commercial laboratory7543076
Health examination center1412254
Hospital grade
Type of hospital
General hospital1,058602,80352
Specialized hospital1981180715
Traditional Chinese medicine hospital150871213
Women’s and children’s hospital4633266
ISO 15189 accreditation
CAP accreditation

*Including the Entry-Exit Inspection and Quarantine Bureau and the Center for Disease Control and Prevention.

Including minority, prison, and community hospitals.

Abbreviations: NCCL, National Center for Clinical Laboratories; IQC, internal QC; CAP, College of American Pathologists.

IQC CVs of participating laboratories from 2013 to 2022

Changes in CV distributions for the 27 analytes from 2013 to 2022 are shown in Supplemental Data Fig. S1. The graphs reveal a gradual decline in the median and interquartile range (IQR) of CVs. In comparison to 2013, CVs significantly (P<0.05, Mann–Whitney U-test) decreased by 2022 for all analytes except CK and LPS (Supplemental Data Fig. S1).

Imprecision analysis of IQC data from 2022

Four performance specifications for precision were defined for each analyte, as listed in Table 2. The median CV values for the 27 analytes in 2022 ranged from 0.9% to 3.7%. For TG, TBil, DBil, Fe, and GGT, >80% of laboratories achieved acceptable performance across all four specifications. In contrast, pass rates for Na, Cl, Ca, TP, and Alb were all <80%. According to BV goals, the percentages of laboratories achieving acceptable performance based on the three levels of specification were all <80% for seven analytes: Na, Cl, Ca, Cre, TP, Alb, and Mg. None of the laboratories met the optimum BV goals for Na, Cl, Ca, and Mg. Based on the 1/3 TEa goal, the proportion of laboratories with acceptable performance was <80% for nine analytes: K, Na, Cl, Ca, P, Glu, Urea, TP, and Alb. These nine analytes were further analyzed and compared among different groups.

Table 2 . Performance specifications for imprecision and pass rates of CVs for IQC data of participating laboratories in 2022

AnalyteNMedian CV (Q1, Q3), %Allowable imprecision (pass rate), %
OptimumDesirableMinimum1/3 TEa
K3,9501.2 (0.8, 1.9)1.0 (43.1)2.1 (80.7)3.1 (92.0)2.0 (79.4)
Na3,8760.9 (0.7, 1.4)0.1 (0)0.3 (1.5)0.4 (5.7)1.3 (74.5)
Cl3,6591.1 (0.8, 1.5)0.3 (0)0.6 (12.5)0.8 (30.4)1.3 (67.8)
Ca3,6141.6 (1.2, 2.2)0.5 (0)0.9 (14.8)1.4 (45.0)1.7 (58.9)
P3,3192.0 (1.5, 3.1)2.0 (50.4)3.9 (84.9)5.9 (94.3)3.3 (78.4)
Glu4,3051.6 (1.1, 2.3)1.3 (37.9)2.5 (79.2)3.8 (92.3)2.3 (76.1)
Urea4,3752.4 (1.8, 3.2)3.5 (79.5)7.0 (96.8)10.4 (100)2.7 (63.6)
UA4,2241.5 (1.1, 2.5)2.1 (69.4)4.2 (89.2)6.2 (97.8)4.0 (88.4)
Cre4,3202.0 (1.4, 3.2)1.1 (13.8)2.3 (59.1)3.4 (78.4)4.0 (85.4)
TP4,1421.5 (1.1, 2.0)0.7 (7.2)1.3 (40.4)2.0 (76.7)1.7 (63.5)
Alb4,2531.6 (1.2, 2.3)0.6 (2.8)1.3 (36.0)1.9 (65.0)2.0 (68.8)
TC4,2841.7 (1.2, 2.6)1.3 (33.9)2.7 (77.9)4.0 (89.3)3.0 (83.0)
TG4,1242.1 (1.5, 3.6)5.0 (88.3)10.0 (99.2)15.0 (100)4.7 (85.7)
TBil4,2632.6 (1.8, 4.2)5.0 (82.8)10.0 (96.9)15.0 (100)5.0 (82.8)
DBil*4,1473.5 (2.4, 5.1)9.2 (93.8)18.4 (100)27.6 (100)6.7 (85.6)
ALT4,4593.1 (2.1, 4.5)2.5 (38.1)5.1 (82.6)7.6 (93.9)5.3 (84.6)
AST4,3532.7 (1.8, 4.0)2.4 (43.1)4.8 (82.9)7.2 (94.0)5.0 (85.5)
ALP*4,2972.8 (2.0, 4.2)1.6 (18.5)3.2 (58.8)4.8 (81.5)6.0 (91.4)
AMY3,3621.8 (1.3, 2.8)1.7 (47.9)3.3 (80.9)5.0 (91.9)5.0 (91.9)
CK3,9782.3 (1.6, 3.6)3.8 (78.0)7.5 (95.0)11.3 (99.7)5.0 (88.4)
LDH4,1262.2 (1.6, 3.3)1.3 (15.1)2.6 (62.1)3.9 (83.3)3.7 (81.2)
CHE*2,9801.7 (1.2, 2.8)1.5 (43.2)3.1 (79.1)4.6 (88.1)6.7 (95.0)
LPS1,3183.7 (2.5, 5.7)2.3 (22.8)4.6 (63.4)6.9 (85.1)6.7 (83.8)
Fe1,6242.2 (1.5, 3.5)5.2 (88.2)10.4 (99.1)15.5 (100)5.0 (87.4)
Mg3,0692.8 (1.9, 4.3)0.7 (0)1.5 (14.4)2.2 (36.6)5.0 (83.6)
GGT4,2572.0 (1.4, 3.0)3.4 (80.4)6.7 (95.2)10.1 (99.9)3.7 (83.9)
α-HBDH3,0452.7 (1.9, 4.1)Not availableNot availableNot available10.0 (96.3)

*BV data of DBil, ALP, and CHE were taken from the Westgard QC database at, which is an update of Ricos, et al. [11], as no related information was found on the European Federation of Clinical Chemistry and Laboratory Medicine website.

No BV data were obtained for α-HBDH.

Abbreviations: IQC, internal QC; Q1, lower quartile; Q3, upper quartile; TEa, total allowable error; K, potassium; Na, sodium; Cl, chloride; Ca, calcium; P, phosphorus; Glu, glucose; Urea, urea nitrogen; UA, uric acid; Cre, creatinine; TP, total protein; Alb, albumin; TC, total cholesterol; TG, triglycerides; TBil, total bilirubin; DBil, direct bilirubin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; AMY, amylase; CK, creatine kinase; LDH, lactate dehydrogenase; CHE, cholinesterase; LPS, lipase; Fe, iron; Mg, magnesium; GGT, γ-glutamyltransferase; α-HBDH, α-hydroxybutyrate dehydrogenase; BV, biological variation.

Imprecision analysis among different groups

As shown in Table 3, the CVs for the nine analytes were smaller in ISO 15189-accredited laboratories than in non-accredited laboratories, and the differences were statistically significant (P<0.05, Mann–Whitney U-test). The pass rates based on the 1/3 TEa for accredited laboratories were all >80%, except for Ca. Similar results were found in the comparison between tertiary and non-tertiary hospitals: CVs in laboratories of tertiary hospitals were significantly smaller than those in non-tertiary hospitals for all nine analytes.

Table 3 . CV distribution and pass rates based on the 1/3 TEa goal for IQC data from participating laboratories in 2022

AnalyteHospital grade, %ISO 15189 accreditation, %
Median CV (Q1, Q3)Pass rateMedian CV (Q1, Q3)Pass rateMedian CV (Q1, Q3)Pass rateMedian CV (Q1, Q3)Pass rate
K1.1 (0.8, 1.7)81.41.2 (0.8, 2.0)75.5<0.0010.9 (0.7, 1.2)96.01.2 (0.8, 2.0)77.1<0.001
Na0.9 (0.7, 1.3)76.81.0 (0.7, 1.5)69.9<0.0010.8 (0.6, 0.9)95.21.0 (0.7, 1.4)71.7<0.001
Cl1.0 (0.8, 1.5)69.71.1 (0.8, 1.7)61.7<0.0010.9 (0.7, 1.1)88.21.1 (0.8, 1.6)64.2<0.001
Ca1.5 (1.2, 2.1)61.31.7 (1.2, 2.4)53.0<0.0011.3 (1.1, 1.7)79.71.6 (1.2, 2.3)55.6<0.001
P2.0 (1.5, 3.0)81.12.1 (1.5, 3.5)73.00.0011.8 (1.4, 2.4)93.12.1 (1.5, 3.3)76.3<0.001
Glu1.5 (1.1, 2.1)79.81.7 (1.2, 2.6)70.5<0.0011.4 (1.0, 1.8)93.01.6 (1.1, 2.4)74.1<0.001
Urea2.3 (1.8, 3.0)67.32.5 (1.8, 3.6)58.2<0.0012.1 (1.7, 2.6)83.82.4 (1.8, 3.3)61.3<0.001
TP1.5 (1.1, 1.9)65.71.6 (1.2, 2.2)57.8<0.0011.4 (1.1, 1.6)83.01.5 (1.1, 2.1)60.2<0.001
Alb1.6 (1.1, 2.2)71.71.7 (1.2, 2.5)64.2<0.0011.4 (1.1, 1.8)84.51.7 (1.2, 2.4)66.8<0.001

Abbreviations: IQC, internal QC; Q1, lower quartile; Q3, upper quartile; TEa, total allowable error; K, potassium; Na, sodium; Cl, chloride; Ca, calcium; P, phosphorus; Glu, glucose; Urea, urea nitrogen; TP, total protein; Alb, albumin.

Next, we analyzed the difference in CVs among measurement systems. The number of laboratories using a closed measurement system was significantly higher than the number of laboratories using an open system for K, Na, and Cl, and vice versa for the other six analytes (Table 4). The median CV values were smaller in closed systems than in open systems for all analytes, except TP. The pass rates for K and P with closed measurement systems were >80%. CVs differed significantly between closed and open measurement systems for all analytes (P<0.05, Mann–Whitney U-test).

Table 4 . CV distribution of closed and open systems and pass rates based on the 1/3 TEa goal for IQC data from participating laboratories in 2022

AnalyteNClosed system, %Open system, %P
PercentageMedian CV (Q1, Q3)Pass ratePercentageMedian CV (Q1, Q3)Pass rate
K3,80685.01.1 (0.8, 1.7)81.815.01.4 (0.9, 2.2)72.6<0.001
Na3,77183.90.9 (0.7, 1.3)76.516.11.0 (0.7, 1.5)67.3<0.001
Cl3,60384.01.0 (0.8, 1.5) (0.9, 1.8)60.3<0.001
Ca3,70245.91.4 (1.1, 2.0)64.454.11.7 (1.3, 2.5)50.9<0.001
P3,31444.01.9 (1.5, 2.9) (1.5, 3.3)75.4<0.001
Glu4,30445.11.5 (1.1, 2.2)77.754.91.6 (1.2, 2.4)74.80.018
Urea4,37041.82.3 (1.8, 3.0)67.558.22.4 (1.8, 3.4)60.90.001
TP4,20244.11.6 (1.2, 2.1) (1.0, 2.0)63.7<0.001
Alb4,27442.91.6 (1.2, 2.3)68.857.11.6 (1.1, 2.3)68.60.010

Abbreviations: TEa, total allowable error; IQC, internal QC; Q1, lower quartile; Q3, upper quartile; TEa, total allowable error; K, potassium; Na, sodium; Cl, chloride; Ca, calcium; P, phosphorus; Glu, glucose; Urea, urea nitrogen; TP, total protein; Alb, albumin.

Finally, we analyzed the differences among closed systems. The top three closed measurement systems were Beckman (Beckman Coulter Life Sciences, Brea, CA, USA), Roche (F. Hoffmann-La Roche, Basel, Switzerland), and Hitachi (Tokyo, Japan) for K, Na, and Cl, and Beckman, Roche, and Mindray (Mindray Bio-Medical Electronics, Shenzhen, China) for Ca, P, Glu, Urea, TP, and Alb. As shown in Table 5, >80% of laboratories met the 1/3 TEa goal across all three measurement systems for K. The Roche systems demonstrated the highest pass rates for GLU and TP, whereas the Beckman systems exhibited superior performance for other analytes. In contrast, the lowest pass rates were found for the Roche systems for Na and Alb, the Hitachi system for K and Cl, and the Mindray system for Ca, P, Glu, Urea, and TP. CV differences among the three measurement systems were significant for all analytes, except Urea (P<0.05, Kruskal–Wallis test). As shown in Table 6, the difference was substantially significant in 18 out of 24 pairwise comparisons. The differences between Roche and Hitachi for K and Na were not significant. Similar results were observed in the comparison between Roche and Beckman for P and TP, between Roche and Mindray for Alb, and between Beckman and Mindray for Glu.

Table 5 . CV distribution of three closed systems and pass rates based on the 1/3 TEa goal for IQA data from participating laboratories in 2022

AnalyteNBeckman, %Roche, %Hitachi, %Mindray, %P
PercentageMedian CV (Q1, Q3)Pass ratePercentageMedian CV (Q1, Q3)Pass ratePercentageMedian CV (Q1, Q3)Pass ratePercentageMedian CV (Q1, Q3)Pass rate
K3,23635.01.0 (0.7, 1.5)86.517.71.1 (0.7, 1.7)84.414.11.2 (0.8, 1.7)82.4///<0.001
Na3,16534.60.8 (0.6, 1.2) (0.7, 1.4)74.513.90.9 (0.7, 1.3)77.6///<0.001
Cl3,02834.20.9 (0.7, 1.2)79.918.21.1 (0.8, 1.5)66.814.21.2 (0.9, 1.5)59.8///<0.001
Ca1,70033.81.3 (1.0, 1.8) (1.1, 2.0)67.7///17.41.7 (1.3, 2.5)52.2<0.001
P1,45735.91.9 (1.5, 2.8)86.433.61.8 (1.4, 2.7)83.0///16.72.2 (1.6, 3.3)76.2<0.001
Glu1,94130.41.7 (1.2, 2.2)78.329.31.4 (1.0, 1.9)82.8///23.31.6 (1.2, 2.5)70.6<0.001
Urea1,82531.12.3 (1.8, 2.9) (1.8, 3.0)66.6///24.72.3 (1.8, 3.3)64.70.274
TP1,85132.21.6 (1.3, 2.0)62.431.31.5 (1.2, 2.0)62.4///23.81.7 (1.3, 2.4)53.2<0.001
Alb1,83432.01.5 (1.1, 2.0)77.630.51.9 (1.4, 2.4)62.4///24.51.7 (1.2, 2.6)63.3<0.001

Abbreviations: TEa, total allowable error; IQC, internal QC; Q1, lower quartile; Q3, upper quartile; TEa, total allowable error; K, potassium; Na, sodium; Cl, chloride; Ca, calcium; P, phosphorus; Glu, glucose; Urea, urea nitrogen; TP, total protein; Alb, albumin.

Table 6 . Pairwise analysis with Bonferroni’s correction

Beckman – RocheBeckman – HitachiBeckman – MindrayRoche – HitachiRoche – Mindray

Abbreviations: K, potassium; NA, not available; Na, sodium; Cl, chloride; Ca, calcium; P, phosphorus; Glu, glucose; TP, total protein; Alb, albumin.

As part of performance verification, imprecision is often evaluated before introducing a new measurement procedure or conducting a major maintenance in the laboratory. IQC plays an important role in the clinical practice of clinical laboratories for monitoring the analytical performance of measurement systems and explaining the errors of test results. Clinical laboratories can evaluate the imprecision of their measurement systems by monitoring monthly and cumulative CVs from IQC data. We evaluated monthly CVs for 27 clinical chemistry analytes to observe the trend from 2013 to 2022. The CVs of most analytes exhibited a gradual decrease in terms of median and IQR. By 2022, CVs significantly decreased for 25 analytes, reflecting the overall improvement of imprecision performance. In 2013, the participants in the IQC monitoring program primarily consisted of tertiary hospital laboratories. By 2022, the number of participants had significantly increased, whereas the percentage of tertiary hospital laboratories decreased from 71% to 56%. CVs in tertiary hospital laboratories were significantly smaller than those in non-tertiary hospital laboratories. Therefore, we believe that precision performance actually did improve and was not a result of changes in the participating laboratories.

Defining quality goals has been a continuing global effort, and the 1999 Stockholm Consensus Agreement was updated during a meeting in Milan in 2014 [12]. The agreement simplified the hierarchy to three models: model 1 based on the effect of analytical performance on clinical outcomes, model 2 based on components of BV of the measurand, and model 3 based on the state of the art [8]. In this study, the optimum, desirable, and minimum performance specifications of imprecision for 27 analytes were established based on the latest BV data. In comparison to the 1/3 TEa goal, which is the most commonly used performance specification for imprecision in China, specifications based on BV were stricter for most analytes, particularly for Na, Cl, and Mg. Even the minimum goal will be difficult to achieve for all three analytes. Recently, a performance specification for Na was suggested, with a desirable CV of 0.3% and minimum CV of 0.4%, levels that at present cannot be achieved with any routine method [13, 14]. The specification for Na measurement should not be based on BV as the present analytical quality seems to meet the present medical need [15]. Therefore, BV goals are challenging, and specifications based on clinical outcome or the state of the art may be an alternative. In contrast, for some analytes, such as DBil, TBil, and TG, BV goals were wide and easily achievable [16]. Quality standards should be set according to the specific purpose. For the goal of improvement, it would be more preferable that the specification is defined to a level that the majority of laboratories can achieve. Therefore, a quality level that ~80% of laboratories can achieve has been determined as an appropriate performance specification [17]. As shown in Table 2, the recommended imprecision criteria based on BV are as follows: optimum level as the goal for Urea, TG, TBil, DBil, CK, Fe, and GGT; desirable level as the goal for K, P, Glu, UA, TC, ALT, AST, AMY, and CHE; and minimum level as the goal for Cre, TP, ALP, LDH, and LPS.

A quality indicator is a good tool to inform laboratory managers on how well the laboratory performance meets the requirements. Laboratories can monitor CVs as a quality indicator by comparing CVs of IQC data with appropriate quality specifications to comprehensively evaluate the precision level of a measurement system. Five analytes, i.e., TG, TBil, DBil, Fe, and GGT, exhibited satisfactory performance for all four performance specifications, with the proportion of qualified laboratories reaching >80%. In contrast, the proportions of acceptable CVs were all <80% for Na, Cl, Ca, TP, and Alb, indicating that there is ample room for improvement in terms of imprecision for these analytes.

Numerous instruments and reagents are commercially available for clinical chemistry analytes in China, and most laboratories prefer to employ open systems. However, differences between closed and open systems were significant for nine analytes (K, Na, Cl, Ca, P, Glu, Urea, TP, and Alb; Table 3). According to the 1/3 TEa goal, higher pass rates were observed for closed system for all analytes, except TP. Further comparison of CVs for TP among the three closed systems revealed significant differences between Mindray and Beckman as well as Roche. The primary factor contributing to the higher CVs of TP in the closed systems may be attributed to the larger CVs observed in the Mindray system.

Laboratory medicine has seen great progress in technology and methods. Investigating the imprecision performance of clinical chemistry analytes over 10 yrs is instructive. This study revealed CVs change trends for 27 clinical chemistry analytes over 10 yrs, providing an opportunity for participants to make comparisons with other laboratories. The main limitation of this study is that only one month of IQC data were collected, which may not be representative of an entire year. Further investigation of the long-term performance of IQC in China is required. In addition, the IQC monitoring program was voluntary, and laboratories with good practices are often more willing to report their data. Hence, the results of this study may represent those laboratories with relatively better performance.

In conclusion, based on 10-yr CVs of data on 27 clinical chemistry analytes from an IQC monitoring program, imprecision performance has seen a continuous overall improvement in China. However, there is ample room for improvement for several analytes according to stricter performance specifications. Finally, we established preliminary performance specifications for imprecision based on BV data on clinical chemistry in China.

Wang W, Wang ZG, and Kang FF designed the study. Yuan S, Liu JL, and Dong N collected the data. Kang FF, Zhang ZX, Zhang CB, and Zhao HJ analyzed the results. Wang W, Zhang ZX, and Kang FF wrote the manuscript. Wang ZG and Kang FF reviewed the manuscript. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

This work was supported by the National Natural Science Foundation of China (Grant No. 81871737), Project for Science Technology Department of Zhejiang Province (Grant No. 2020C 35057), and Zhejiang Provincial Project for Medical and Health Science and Technology (Grant No. 2022KY526).

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