
Average of delta – a new concept in quality control grd jones

tarix  17.09.2018  ölçüsü  264,5 Kb.   #68935 

GRD Jones Department of Chemical Pathology, St Vincents Hospital, Sydney, Australia
Background The Average of Normals (AON) is an accepted QC process for clinical laboratories. AON is the average of a set number of patient results, usually within set limits (eg normal range). The AON rule “fires” when the function exceeds a preset limit (eg 2.5 x analytical CV). Delta checks are the comparison of a result with a previous result from the same patient. Delta checks are used to detect blunders or other errors I combine these concepts to produce the Average of Delta (AOD) a new QC tool for clinical laboratories.
Terminology A Delta Value is a recent patient result minus the preceding result for that patient. An AOD function is the average of a series of delta values. AODN is an AOD function averaging N delta values. NAOD is the number of samples included in an AOD function. N90 is the number of samples with valid previous result, required to detect a change in assay bias with 90% probability using an AOD function. CVwi is the withinindividual Biological Variation. CVa is the analytical variation expressed as a CV. SDAOD is the SD of the AOD function
Methods AOD functions were modelled in a spreadsheet application using Microsoft Excel. Variations in CVa and CVwi were modelled using the random number generator with a Normal distribution. Models were based on 100 data sets, each of 110 delta values. Factors adjusted in the model were:  the ratio of Cva/Cvwi
 NAOD
 Bias changes in assay performance.
Data sets were generated for various values of CVa and CVwi with the variation (CVdata set) in results described as follows  CVdata set = SQRT(CVa2 + CVwi2)
Second data sets were independently generated using the same values for CVa and CVwi Delta Values were were obtained by subtracting the data points from the second data set from those in the first to produce a series of delta values. Changes in bias were modelled by addition of fixed amounts to the delta values at a fixed point in the data set. AOD functions were set to trigger if a data point fell outside limits defines by +/ 2.5 SDAOD.
AOD Functions Figure 1 AOD functions for various values of NAOD. CVa = 0.1, CVwi = 0.2 The decrease in SD with increasing N is equal to dividing by the square root of N. (data not shown)
Effects of Bias on AOD Functions A fixed bias was added after delta value 10 in each data set. The AOD function followed the change in bias with the following features:  With smaller values of NAOD, the response occurred more rapidly, but was smaller relative to the scatter of the AOD function
 With higher values of NAOD, the response was slower, but was larger relative to the scatter of the AOD function.
Examples are shown in figure 2.
Error detection of bias can be measured as the number of delta values (samples with previous results) required to detect changes in bias with specified certainty. N90 and average first detection for a range of values for Cva/Cvwi and NAOD are shown in figure 3. The following conclusions can be drawn:  Earlier error detection occurs with lower values of Cva/Cvwi
 Error detection generally varies with NAOD in a “Ushape” with an optimal range of values depending on CVa/CVwi .
Examples of actual values for CVa/CVwi are in the table.
Figure 3 Figure 3 Number of samples required for average 1st firing (A) and N90 (B) for detection of a shift of 2.5 x CVa for various values of CVwi/CVa and NAOD. Earlier error detection with lower CVwi/CVa Optimal error detection with NAOD 520
Discussion The limitation of AON is the ratio of group biological variation to analytical CV. AOD may outperform AON if:  CVwi is small compared to betweenperson biological variation (a low Index of Individuality).
 The frequency of samples with previous results is high.
 Clinics or weekends affect AON results. Note than AOD should not be affected by change in patient mix as it uses patients as their own control.
AON may complement standard QC if it can:
Conclusions Average of Delta may allow improved error detection without additional QC testing. The process would most suit tests as follows:  A low withinindividual biological variation compared to the analytical variation.
 A high frequency of repeat testing.
Software programs must be written to further evaluate and this tool and allow for use in the routine environment.
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