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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

Simultaneous trend monitoring by commercial controls and duplicate analysis. a (normal CD4 count), b (low CD4 count) show representative graphs for simultaneous trend monitoring by duplicate analysis method and by commercial controls by plotting LJ charts. CD4 counts for commercial controls are plotted on left Y axis and % variation as obtained by duplicate analysis method is plotted on right Y axis against the no. of days on X axis. Green and red colour lines indicate % variation of samples with normal and low CD4 count, respectively. Black coloured solid line and two dotted lines on either side indicate 20% limits of percent variation, respectively. Blue line indicate CD4 counts of IMMUNO-TROL controls with blue coloured solid line and two dotted lines on either side indicating mean and 2 SD, respectively.
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Fig6: Simultaneous trend monitoring by commercial controls and duplicate analysis. a (normal CD4 count), b (low CD4 count) show representative graphs for simultaneous trend monitoring by duplicate analysis method and by commercial controls by plotting LJ charts. CD4 counts for commercial controls are plotted on left Y axis and % variation as obtained by duplicate analysis method is plotted on right Y axis against the no. of days on X axis. Green and red colour lines indicate % variation of samples with normal and low CD4 count, respectively. Black coloured solid line and two dotted lines on either side indicate 20% limits of percent variation, respectively. Blue line indicate CD4 counts of IMMUNO-TROL controls with blue coloured solid line and two dotted lines on either side indicating mean and 2 SD, respectively.

Mentions: One more advantage of the commercially available controls is their ability to monitor trend or shifts in the results, which can give the earliest identification of the problems related to changes in reagent lots, technical staff, instrument settings, environmental condition, etc. [12]. However, since most of the laboratories didn’t establish their own ranges because of short expiry of multi-check controls, it was not possible for them to monitor the trend as it is not recommended to use manufacturer’s range for this purpose. Trend monitoring was done by only one laboratory using IMMUNO-TROL control which has a longer shelf life. A representative LJ plot for the laboratory is shown in Fig. 6 along with the simultaneous trend monitoring by duplicate analysis method. Trends and shifts in duplicate analysis method were monitored by deviations of percent variation around 0%. The effective trend monitoring by this method was also stressed previously using continuous method of duplicate analysis [11]. For trend monitoring across CD4 testing laboratories, mean and standard error of daily percent variations was calculated and plotted against the days of the month as shown in Fig. 7. The graph demonstrates that the mean and standard error (SE) of percent variations across the laboratories also fluctuate around 0 %. However, the drawback of duplicate analysis method is that it detects changes only between two successive runs as against the commercial controls which monitor trend over the time. But since all the laboratories implement QC measures for controlling variations because of changes in reagent batches (parallel testing), equipment settings (equipment validation and calibration), staff (training and competency), environment (temperature and environment monitoring), the possible factors leading to trends or shifts in QC data are additionally controlled. Hence the duplicate analysis might be sufficient to monitor the trend if these additional measures are implemented.Fig. 6


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)

Simultaneous trend monitoring by commercial controls and duplicate analysis. a (normal CD4 count), b (low CD4 count) show representative graphs for simultaneous trend monitoring by duplicate analysis method and by commercial controls by plotting LJ charts. CD4 counts for commercial controls are plotted on left Y axis and % variation as obtained by duplicate analysis method is plotted on right Y axis against the no. of days on X axis. Green and red colour lines indicate % variation of samples with normal and low CD4 count, respectively. Black coloured solid line and two dotted lines on either side indicate 20% limits of percent variation, respectively. Blue line indicate CD4 counts of IMMUNO-TROL controls with blue coloured solid line and two dotted lines on either side indicating mean and 2 SD, respectively.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Simultaneous trend monitoring by commercial controls and duplicate analysis. a (normal CD4 count), b (low CD4 count) show representative graphs for simultaneous trend monitoring by duplicate analysis method and by commercial controls by plotting LJ charts. CD4 counts for commercial controls are plotted on left Y axis and % variation as obtained by duplicate analysis method is plotted on right Y axis against the no. of days on X axis. Green and red colour lines indicate % variation of samples with normal and low CD4 count, respectively. Black coloured solid line and two dotted lines on either side indicate 20% limits of percent variation, respectively. Blue line indicate CD4 counts of IMMUNO-TROL controls with blue coloured solid line and two dotted lines on either side indicating mean and 2 SD, respectively.
Mentions: One more advantage of the commercially available controls is their ability to monitor trend or shifts in the results, which can give the earliest identification of the problems related to changes in reagent lots, technical staff, instrument settings, environmental condition, etc. [12]. However, since most of the laboratories didn’t establish their own ranges because of short expiry of multi-check controls, it was not possible for them to monitor the trend as it is not recommended to use manufacturer’s range for this purpose. Trend monitoring was done by only one laboratory using IMMUNO-TROL control which has a longer shelf life. A representative LJ plot for the laboratory is shown in Fig. 6 along with the simultaneous trend monitoring by duplicate analysis method. Trends and shifts in duplicate analysis method were monitored by deviations of percent variation around 0%. The effective trend monitoring by this method was also stressed previously using continuous method of duplicate analysis [11]. For trend monitoring across CD4 testing laboratories, mean and standard error of daily percent variations was calculated and plotted against the days of the month as shown in Fig. 7. The graph demonstrates that the mean and standard error (SE) of percent variations across the laboratories also fluctuate around 0 %. However, the drawback of duplicate analysis method is that it detects changes only between two successive runs as against the commercial controls which monitor trend over the time. But since all the laboratories implement QC measures for controlling variations because of changes in reagent batches (parallel testing), equipment settings (equipment validation and calibration), staff (training and competency), environment (temperature and environment monitoring), the possible factors leading to trends or shifts in QC data are additionally controlled. Hence the duplicate analysis might be sufficient to monitor the trend if these additional measures are implemented.Fig. 6

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