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Duplicate analysis method: a cheaper alternative to commercial IQC materials in limited resource settings for monitoring CD4 testing.

Shete A, Singh DP, Mahajan B, Kokare A, Paranjape R, Thakar M - AIDS Res Ther (2015)

Bottom Line: There was good match between r(2) values and % CV of the laboratories performing both the types of QC methods.Commercially available controls showed limitations such as altered specimen quality leading to difficulties in manual gating and issues with the establishment of laboratory range.Duplicate analysis can serve as a cheaper alternative to commercially available controls for IQC of CD4 testing especially when supplemented with other QC measures for controlling variations caused by reagent, equipment, staff and environment in addition to the successful participation in External Quality Assurance programme.

View Article: PubMed Central - PubMed

Affiliation: National AIDS Research Institute, 73 G Block MIDC Bhosari, Pune, 411026 India.

ABSTRACT

Background: India has a large number of HIV infected patients being followed up at anti-retroviral therapy (ART) centers. The patients are regularly offered CD4 count estimation for deciding their eligibility for ART initiation as well as for monitoring response to ART, making CD4 count estimation a very critical test. Hence, quality control of CD4 testing is utmost important for ultimate success of ART program. As the commercial controls are very expensive, internal quality control (IQC), at present, is being done by duplicate analysis method using previous day samples in most of the laboratories. Hence the study was undertaken to review performance of duplicate analysis method for monitoring daily IQC.

Methods: Quality control (QC) data from 11 Indian laboratories using duplicate analysis and/or commercial controls for IQC of CD4 testing was collected for reviewing information on QC parameters such as precision, accuracy and trend monitoring. Precision was determined by r(2) values and mean % variation for duplicate analysis and coefficient of variation (% CV) for commercial controls. Accuracy was monitored by rate of QC failures for both the types of control and trend monitoring was done by plotting LJ charts for commercial controls and by plotting daily % variation for duplicate analysis.

Results: The laboratories using duplicate analysis for IQC showed good precision with mean % variation ranging from 0.5 to 7.2. There was good match between r(2) values and % CV of the laboratories performing both the types of QC methods. Rates of QC failures were 2.3 for duplicate analysis and 3 per laboratory-year for IMMUNO-TROL controls. Daily trend monitoring showed fluctuation of daily counts around mean in LJ charts and of percent variation around 0% in duplicate analysis method. Commercially available controls showed limitations such as altered specimen quality leading to difficulties in manual gating and issues with the establishment of laboratory range.

Conclusion: Duplicate analysis can serve as a cheaper alternative to commercially available controls for IQC of CD4 testing especially when supplemented with other QC measures for controlling variations caused by reagent, equipment, staff and environment in addition to the successful participation in External Quality Assurance programme.

No MeSH data available.


Related in: MedlinePlus

Regression analysis of the CD4+ T cell counts in duplicate analysis method. Regression plots for duplicate analysis method plotted for samples with normal CD4+ T cell count (a), and low CD4+ T cell counts (b). Day 1 CD4 counts are plotted on X axis and day 2 CD4 counts are plotted on Y axis. Data from different laboratories are presented using different colours.
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Fig2: Regression analysis of the CD4+ T cell counts in duplicate analysis method. Regression plots for duplicate analysis method plotted for samples with normal CD4+ T cell count (a), and low CD4+ T cell counts (b). Day 1 CD4 counts are plotted on X axis and day 2 CD4 counts are plotted on Y axis. Data from different laboratories are presented using different colours.

Mentions: The main objective of IQC is the monitoring of precision [4]. For commercially available controls, precision is determined by calculating % CV. The % CV for the commercial controls ranged from 2.2 to 12.5 (Table 1) for different laboratories. In case of duplicate analysis method, % variation was used for monitoring daily precision. The mean % variation for different laboratories ranged from 0.5 to 7.2 (Table 1). This was well within the acceptable limit mentioned in the NACO and other guidelines. Long term precision in case of the duplicate analyses was determined by calculating r2 values and mean of % variation over a period of time. All the laboratories using previous day sample as IQC showed good long term precision as evident from r2 value of more than 0.8 when data over 3 months was compared (Fig. 2). Bland–Altman analyses also demonstrated close agreement between day 1 and day 2 CD4 counts, with biases of 8.28 ± 51.4 and 2.38 ± 16.1, for the normal and low level absolute CD4 count controls, respectively (Fig. 3). Where the parallel data on both the controls was available during the same time (N = 6 laboratories), the r2 values were found to follow the same trend as the % CVs of commercial controls for the respective laboratories (Fig. 4). The laboratories showing lower % CV values also showed higher r2 values and vice a versa showing that the precision of duplicate analysis was comparable to that obtained using commercially available controls. Thus, r2 values should also be calculated for duplicate analysis method in addition to % variation on periodic basis to estimate the overall long term precision for the laboratories which will give information similar to that obtained using commercial controls.Table 1


Duplicate analysis method: a cheaper alternative to commercial IQC materials in limited resource settings for monitoring CD4 testing.

Shete A, Singh DP, Mahajan B, Kokare A, Paranjape R, Thakar M - AIDS Res Ther (2015)

Regression analysis of the CD4+ T cell counts in duplicate analysis method. Regression plots for duplicate analysis method plotted for samples with normal CD4+ T cell count (a), and low CD4+ T cell counts (b). Day 1 CD4 counts are plotted on X axis and day 2 CD4 counts are plotted on Y axis. Data from different laboratories are presented using different colours.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4536604&req=5

Fig2: Regression analysis of the CD4+ T cell counts in duplicate analysis method. Regression plots for duplicate analysis method plotted for samples with normal CD4+ T cell count (a), and low CD4+ T cell counts (b). Day 1 CD4 counts are plotted on X axis and day 2 CD4 counts are plotted on Y axis. Data from different laboratories are presented using different colours.
Mentions: The main objective of IQC is the monitoring of precision [4]. For commercially available controls, precision is determined by calculating % CV. The % CV for the commercial controls ranged from 2.2 to 12.5 (Table 1) for different laboratories. In case of duplicate analysis method, % variation was used for monitoring daily precision. The mean % variation for different laboratories ranged from 0.5 to 7.2 (Table 1). This was well within the acceptable limit mentioned in the NACO and other guidelines. Long term precision in case of the duplicate analyses was determined by calculating r2 values and mean of % variation over a period of time. All the laboratories using previous day sample as IQC showed good long term precision as evident from r2 value of more than 0.8 when data over 3 months was compared (Fig. 2). Bland–Altman analyses also demonstrated close agreement between day 1 and day 2 CD4 counts, with biases of 8.28 ± 51.4 and 2.38 ± 16.1, for the normal and low level absolute CD4 count controls, respectively (Fig. 3). Where the parallel data on both the controls was available during the same time (N = 6 laboratories), the r2 values were found to follow the same trend as the % CVs of commercial controls for the respective laboratories (Fig. 4). The laboratories showing lower % CV values also showed higher r2 values and vice a versa showing that the precision of duplicate analysis was comparable to that obtained using commercially available controls. Thus, r2 values should also be calculated for duplicate analysis method in addition to % variation on periodic basis to estimate the overall long term precision for the laboratories which will give information similar to that obtained using commercial controls.Table 1

Bottom Line: There was good match between r(2) values and % CV of the laboratories performing both the types of QC methods.Commercially available controls showed limitations such as altered specimen quality leading to difficulties in manual gating and issues with the establishment of laboratory range.Duplicate analysis can serve as a cheaper alternative to commercially available controls for IQC of CD4 testing especially when supplemented with other QC measures for controlling variations caused by reagent, equipment, staff and environment in addition to the successful participation in External Quality Assurance programme.

View Article: PubMed Central - PubMed

Affiliation: National AIDS Research Institute, 73 G Block MIDC Bhosari, Pune, 411026 India.

ABSTRACT

Background: India has a large number of HIV infected patients being followed up at anti-retroviral therapy (ART) centers. The patients are regularly offered CD4 count estimation for deciding their eligibility for ART initiation as well as for monitoring response to ART, making CD4 count estimation a very critical test. Hence, quality control of CD4 testing is utmost important for ultimate success of ART program. As the commercial controls are very expensive, internal quality control (IQC), at present, is being done by duplicate analysis method using previous day samples in most of the laboratories. Hence the study was undertaken to review performance of duplicate analysis method for monitoring daily IQC.

Methods: Quality control (QC) data from 11 Indian laboratories using duplicate analysis and/or commercial controls for IQC of CD4 testing was collected for reviewing information on QC parameters such as precision, accuracy and trend monitoring. Precision was determined by r(2) values and mean % variation for duplicate analysis and coefficient of variation (% CV) for commercial controls. Accuracy was monitored by rate of QC failures for both the types of control and trend monitoring was done by plotting LJ charts for commercial controls and by plotting daily % variation for duplicate analysis.

Results: The laboratories using duplicate analysis for IQC showed good precision with mean % variation ranging from 0.5 to 7.2. There was good match between r(2) values and % CV of the laboratories performing both the types of QC methods. Rates of QC failures were 2.3 for duplicate analysis and 3 per laboratory-year for IMMUNO-TROL controls. Daily trend monitoring showed fluctuation of daily counts around mean in LJ charts and of percent variation around 0% in duplicate analysis method. Commercially available controls showed limitations such as altered specimen quality leading to difficulties in manual gating and issues with the establishment of laboratory range.

Conclusion: Duplicate analysis can serve as a cheaper alternative to commercially available controls for IQC of CD4 testing especially when supplemented with other QC measures for controlling variations caused by reagent, equipment, staff and environment in addition to the successful participation in External Quality Assurance programme.

No MeSH data available.


Related in: MedlinePlus