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7 Levey–Jennings Chart and Westgard Rules

7 Levey–Jennings Chart and Westgard Rules

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4.7 LeveyÀJennings Chart and Westgard Rules







are commonly used for calibration. One calibrator must be a zero

calibrator and the highest calibrator must contain a concentration of the

analyte at the upper end of the analytical measurement range. Other

calibrators usually have concentrations in between the zero calibrator and

the highest calibrator, and represent normal values of the analyte as well

as values expected in a disease state (for drugs, values below therapeutic

range, between therapeutic ranges, and then toxic range).

Controls are materials that contain a known amount of the analyte. The

matrix of the control must be similar to the matrix of the patient’s

sample; for example, matrix of the control must resemble serum for

assays conducted in serum or plasma.



A LeveyÀJennings chart is commonly used for recording observed values of

controls during daily operation of a clinical laboratory. A LeveyÀJennings

chart is a graphical representation of all control values for an assay during an

extended period of laboratory operation. In this graphical representation,

values are plotted with respect to the calculated mean and standard deviation, and if all controls are within the mean and 6 2 SD, then all control

values are within acceptable limits and all runs during that period will have

acceptable performance (Figure 4.3). In this figure, all glucose low controls

were within acceptable limits for the entire month. The LeveyÀJennings chart

must be constructed for each control (low and high control or low, medium,

and high control) for each assay the laboratory offers. For example, if the laboratory runs two controls (low and high) for each test and offers 100 tests,

then there will be 100 3 2, or 200 LeveyÀJennings charts each month.

Usually a LeveyÀJennings chart is constructed for one control for one month.

The laboratory director or designee must review all LeveyÀJennings charts

each month and sign them for compliance with an accrediting agency.

93.6



+ 3 SD



90.4



+ 2 SD



87.2



+ 1 SD



84



Mean



80.8



– 1 SD



77.6



– 2 SD

– 3 SD



74.4

0



5



FIGURE 4.3

LeveyÀJennings chart with no violation.



10



15

Days



20



25



30



55



56



CHAPTER 4:



Laboratory Statistics and Quality Control



Table 4.1 Westgard Rules

Violation



Comments



Accept/Reject

Run



Error

Type



12s



One control value is outside 6 2 SD limit, but other control within

6 2 SD limit

One control exceeds 6 3 SD

Both controls outside 6 2 SD limit, or two consecutive controls

outside limit

One control 12 SD and another 22 SD

Four consecutive control exceeding 11 SD or 21 SD

Ten consecutive control values falling on one side of the mean



Accept run



Random



Reject run

Reject run



Random

Systematic



Reject run

Reject run*

Reject run*



Random

Systematic

Systematic



13s

22s

R4s

41S

103



*Although these are rejection rules, a laboratory may consider these violations as warnings and may accept the runs and take steps

to correct such systematic errors.



However, if technologists review results of the control during a run and

accept the run if the value of the control is within an acceptable range established by the laboratory (usually a mean of 6 2 SD), then the laboratory

supervisor can review all control data on a daily basis; usually the supervisor

reviews all control data weekly.

Usually Westgard rules are used for interpreting a LeveyÀJennings chart, and

for certain violations a run must be rejected and the problem resolved prior

to resuming testing of a patient’s samples. Various errors can occur in

LeveyÀJennings charts, including shift, trend, and other violations

(Table 4.1). The basic principle is that control values must fall within

6 2 SD of the mean, but there are some situations when violation of

Westgard rules occurs despite control values that are within the 6 2 SD limits of the mean. Usually 12s is a warning rule and occurs due to random error

(Figure 4.4), and other rules are rejection rules. In addition, shift (Figure 4.5)

and trend (Figure 4.6) may be observed in LeveyÀJennings charts, indicating

systematic errors where corrective actions must be taken. When 10 or more

consecutive control values are falling on one side of the mean, a shift is

observed (103 rule). In addition, when a 10 3 violation is observed, it may

also indicate a trend when control values indicate an upward trend.



4.8 DELTA CHECKS

Delta checks are an additional quality control measure adopted by the computer of an automated analyzer or the laboratory information system (LIS)

where a value is flagged if the value deviates more than a predetermined

limit from the previous value in the same patient. The limit of deviation for



4.8 Delta Checks



96.8

12S violation



Glucose control,mg/dL



93.6



+ 3 SD



22S violation



90.4



+ 2 SD



87.2

84



+ 1 SD

Mean



80.8



– 1 SD



77.6



– 2 SD

41S violation



74.4



– 3 SD



13S violation



71.2

0



5



10



20



15

Days



25



30



FIGURE 4.4

LeveyÀJennings chart showing certain violations.

93.6



+ 3 SD



90.4



+ 2 SD

+ 1 SD



87.2



Shift



84



Mean



80.8



– 1 SD



77.6



– 2 SD

– 3 SD



74.4

0



5



10



15



20



25



30



Days



FIGURE 4.5

LeveyÀJennings chart showing shift of control values.

+3 SD



Glucose control, mg/dL



93.6



+2 SD



90.4

Trend

87.2



+1 SD



84



Mean



80.8



–1 SD



77.6



–2 SD

–3 SD



74.4

0



5



FIGURE 4.6

LeveyÀJennings chart showing trend.



10



15

Days



20



25



30



57



58



CHAPTER 4:



Laboratory Statistics and Quality Control



each analyte is set by laboratory professionals. The basis of the delta check is

that the value of an analyte in a patient should not deviate significantly from

the previous value unless certain intervention is done; for example, a high

glucose value may decrease significantly following administration of insulin.

If a value is flagged as a failed delta check, then a further investigation

should be made. A phone call to the nurse may address issues such as erroneous results due to collection of a specimen from an IV line or collection of

the wrong specimen. Quality control of the assay must also be addressed to

ensure that the erroneous result is not due to instrument malfunction.

The value of a delta check is usually based on one of the following criteria:













Delta difference: current valueÀprevious value should be within a

predetermined limit.

Delta percent change: delta difference/current value.

Rate difference: delta difference/delta interval 3 100.

Rate percent change: delta percentage change/delta interval.



4.9 METHOD VALIDATION/EVALUATION

OF A NEW METHOD

Since April 2003, clinical laboratories must perform method validation for

each new test implemented in the laboratory even though such tests have

FDA approval. The following are steps for method validation as well as

implementation of a new method in the clinical laboratory:

















Within-run assay precision must be validated by running low, medium,

and high controls, or low and high controls 20 times each in a single

run. Then mean, standard deviation, and CV must be calculated

individually for low, medium, and high control.

Between-run assay precision must be established by running low,

medium, and high control, or low and high control once daily for

20 consecutive days. Then mean, standard deviation, and CV must be

calculated.

Although assay linearity is provided by the manufacturer, it must be

validated in the clinical laboratory prior to running patient specimens.

Linearity is essentially the calibration range of the assay (also called

“analytical measurement range”). In order to validate the linearity, a

high-end calibrator or standard can be selected and then diluted to

produce at least four to five dilutions that cover the entire analytical

measurement range. Then, if the observed value matches the expected

value, the assay can be considered linear over the stated range.

The detection limit should be traditionally determined by running a zero

calibrator or blank specimen 20 times and then determining the mean



4.10 How to Interpret the Regression Equation?







and standard deviation. The detection limit (also called the lower limit of

detection) is the mean 12 SD value. However, the guidelines of the

Clinical Laboratory Standard Institute (CLSI, E17 protocol) advise that a

specimen with no analyte (blank specimen) should be run; then the

Limit of Blank (LoB) 5 Mean 1 1.654 SD. This should be established by

running blank specimens 60 times, but if a company already established

a guideline, then 20 runs are enough. Limit of Quantification is usually

defined as a concentration where CV is 20% or less [4].

Comparison of a new method with an existing method is a very

important step in method validation. For this purpose, at least 100

patient specimens must be run in the laboratory at the same time with

both the existing method and the new method. It is advisable to batch

patient samples and then run these specimens by both methods on the

same day, and, if possible, at the same time (by splitting specimens).

Then results obtained by the existing method should be plotted in the

x-axis (reference method) and corresponding values obtained by the new

method should be plotted in the y-axis. Linear regression is the simplest

way of comparing results obtained by the existing method in the

laboratory and the new method. The linear regression equation is the line

of best fit with all data points. A computer can produce the linear

regression line as well as an equation called a linear regression equation,

which is the equation representing a straight line (regression line),

Equation 4.9:

y 5 mx 1 b







ð4:9Þ



Here, “m” is called the slope of the line and “b” is the intercept. The

computer calculates the equation of the regression line using a least

squares approach. The software also calculates “r,” the correlation

coefficient, using a complicated formula.



4.10 HOW TO INTERPRET THE REGRESSION

EQUATION?

The regression equation (y 5 mx 1 b) provides a lot of important information regarding how the new method (y) compares with the reference method

(x). Interpretations of a linear regression equation include:







Ideal value: m 5 1, b 5 0, and y 5 x. In reality this never happens.

If the value of m is less than 1.0, then the method shows negative bias

compared to the reference method. Bias can be calculated as 1 2 m; for

example, if the value of “m” is 0.95, then the negative bias is

1 2 0.95 5 0.05, or 0.05 3 100 5 5%.



59



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