Chapter 3 Levey-Jennings Charts & Westgard Rules

Summary

Chapter 3 introduces Levey-Jennings Charts and Westgard Rules for quality control in laboratories. It explains how to calculate decision limits using standard deviation and illustrates their application on control charts. The script clarifies that only 0.3% of QC values should fall outside ±3s limits, indicating significant errors. It warns against prematurely invalidating patient results due to a single value outside ±2s limits. The importance of documenting QC results through logs and charts is emphasized, along with the detection of systematic and random errors. Westgard's rules for evaluating analytical run quality are outlined, providing a statistical basis for quality control in medical laboratories.

Takeaways

• 📊 Levey-Jennings (L-J) charts are used for plotting quality control (QC) values to monitor the performance of analytical processes.
• 📈 Standard deviation is key in calculating the decision limits (±1s, ±2s, and ±3s) for L-J charts, which help in assessing the control of test results.
• 🔍 A well-controlled analytical process will have approximately 68% of QC values within ±1s, 95.5% within ±2s, and 99.7% within ±3s of the mean.
• ⚠️ Values outside ±3s are indicative of significant error and should not be reported for patient results.
• 🚫 Laboratories should not dismiss an entire analytical run based on a single QC value outside ±2s but within ±3s, as this could lead to unnecessary repetition and delays.
• 📝 Maintaining a QC log is essential for documenting the assay of quality control materials and inspection of results, ensuring the quality of analytical runs.
• 🔍 Systematic errors in QC can manifest as trends or shifts in control values, suggesting issues like instrument deterioration or calibration inaccuracies.
• 🔎 Random errors are deviations from expected results, with acceptable random error defined by standard deviation; unacceptable error is any point outside the ±3s limits.
• 📚 Dr. James Westgard's rules provide a statistical framework for evaluating the quality of analytical runs, with six basic rules to identify random or systematic errors.
• 📝 The Westgard rules include 1-3s, 2-2s, R-4s, 3-1s, and 4-1s, each with specific criteria for identifying different types of errors in QC results.

Q & A

What is the primary purpose of a Levey-Jennings chart?

The primary purpose of a Levey-Jennings chart is to graph successive quality control values for each test and level of control, allowing for the monitoring and evaluation of an analytical process's performance over time.

What are the decision limits used in Levey-Jennings charts?

The decision limits used in Levey-Jennings charts are ±1s, ±2s, and ±3s from the mean, which represent one, two, and three standard deviations respectively.

What percentage of QC values are expected to fall within ±1 standard deviation when a process is in control?

Approximately 68% of all QC values are expected to fall within ±1 standard deviation (1s) when the analytical process is in control.

How does the percentage of QC values within control limits change for ±2s and ±3s?

About 95.5% of all QC values fall within ±2 standard deviations (2s), and approximately 99.7% of all QC values are within ±3 standard deviations (3s) when the process is in control.

What is the significance of a QC value falling outside the ±3s limits?

Any value outside of ±3s is considered to be associated with a significant error condition, and patient results should not be reported.

Why might a laboratory incorrectly decide that patient specimens and QC values are invalid?

Some laboratories might incorrectly decide that patient specimens and QC values are invalid if any quality control value is outside its ±2s limits, not realizing that approximately 4.5% of all valid QC values can fall between ±2 and ±3 standard deviation limits.

What is the importance of maintaining a QC Log in a laboratory?

Maintaining a QC Log is important for documenting that quality control materials are assayed and that the quality control results have been inspected to assure the quality of the analytical run.

What are the two types of errors that can be identified through the analysis of QC results?

The two types of errors that can be identified through the analysis of QC results are systematic error, which is evidenced by a change in the mean of the control values, and random error, which is any deviation away from the expected result.

What does a trend in control values indicate about the test system?

A trend in control values indicates a gradual loss of reliability in the test system, which is usually subtle and may be caused by factors such as deterioration of the instrument light source or gradual accumulation of debris.

What is the difference between a trend and a shift in QC data?

A trend represents a gradual change in the mean of the control values, while a shift indicates an abrupt and dramatic positive or negative change in test system performance.

What are the six basic rules in the Westgard scheme for evaluating the quality of analytical runs?

The six basic rules in the Westgard scheme are 1-3s, 2-2s, R-4s, 10x, 4-1s, and 3-1s rules, which are used individually or in combination to evaluate the quality of analytical runs.

Outlines

00:00

📊 Introduction to Levey-Jennings Charts and Westgard Rules

This paragraph introduces the concept of Levey-Jennings (L-J) charts, which are used for plotting quality control values in a laboratory setting. It explains the use of standard deviation in creating these charts and the calculation of decision limits at ±1s, ±2s, and ±3s from the mean. The paragraph provides an example using Level I potassium control data, with a mean of 4.1 mmol/L and a standard deviation of 0.1 mmol/L. It discusses the statistical distribution of QC values in relation to these limits, highlighting that 68% of values should fall within ±1s, 95.5% within ±2s, and 99.7% within ±3s. The paragraph also addresses the implications of values falling outside these limits, the potential for laboratories to incorrectly reject valid runs based on ±2s limits, and the importance of maintaining a QC Log to document and assess the quality of analytical runs.

05:05

🔍 Detecting Systematic and Random Error in QC

Paragraph 2 delves into the detection of systematic and random errors in quality control. Systematic errors are indicated by a change in the mean of control values, which can manifest as either a trend or a shift. Trends are gradual changes, often subtle, and may be caused by factors such as instrument light source deterioration or reagent aging. Shifts, on the other hand, are abrupt changes that signify a sudden performance alteration in the test system. The paragraph also discusses the causes of shifts, which can include instrument maintenance or changes in reagent formulation. Random error is any deviation from the expected result, with acceptable random error defined by standard deviation. The Westgard rules, introduced by Dr. James Westgard, are a set of statistical process control principles used to evaluate the quality of analytical runs. The paragraph outlines the six basic rules and their shorthand notation, explaining how they are used to identify and respond to errors in laboratory testing.

10:07

📉 Westgard Rules for Evaluating Analytical Runs

Paragraph 3 continues the discussion on Westgard rules, focusing on their application in evaluating the quality of analytical runs. It explains the 2-2s rule, which identifies systematic error when two consecutive QC results are greater than 2s on the same side of the mean. The paragraph differentiates between within-run and across run applications of this rule, highlighting their implications for the analytical curve. The R-4s rule is introduced as a method for identifying random error within a single run when there is at least a 4s difference between control values. The paragraph also describes the 3-1s and 4-1s rules, which detect smaller systematic errors or analytical bias. It explains the criteria for rule violations and the applications of these rules within control material or across control materials. The paragraph concludes by stating that violations of these rules do not necessarily require the rejection of an analytical run and may be addressed through calibration or instrument maintenance.

15:11

🔚 Conclusion and Review of Key Points

The final paragraph summarizes the key points covered in the module. It reiterates the purpose of the Levey-Jennings chart for plotting quality control values and the process of calculating decision limits. The paragraph emphasizes the importance of not incorrectly rejecting valid analytical runs based on ±2s limits and the significance of documenting QC results through a QC Log. It also reviews the concept of systematic error, which can be indicated by trends or shifts in control values, and the importance of addressing these errors to maintain the reliability of the test system. The paragraph concludes with a call to action for laboratory professionals to visit www.qcnet.com for their QC needs, suggesting further resources for quality control in laboratory settings.

Keywords

💡Levey-Jennings Charts

Levey-Jennings Charts, also known as L-J or LJ charts, are graphical tools used in clinical laboratories to monitor the quality of test results over time. They plot successive quality control values, allowing technicians to visually assess the stability and reliability of the testing process. In the video, the creation of these charts is emphasized as a critical first step, with decision limits calculated based on the mean and standard deviation of the control values.

💡Standard Deviation

Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of values. In the context of the video, standard deviation is used to calculate the decision limits for the Levey-Jennings charts, which help determine the acceptable range of variation for quality control values. The script mentions that the mean for Level I potassium control is 4.1 mmol/L with a standard deviation of 0.1 mmol/L, illustrating how these values are used.

💡Decision Limits

Decision limits are the boundaries set on control charts to determine whether a process is in a state of statistical control. In the video, these limits are described as ±1s, ±2s, and ±3s from the mean, which correspond to different probabilities of a process being in control. These limits are crucial for identifying when a testing process may be experiencing issues that require attention.

💡Quality Control (QC) Values

Quality control values are the results obtained from analyzing control samples, which are used to monitor the performance of analytical methods in a laboratory. The video explains that these values are plotted on Levey-Jennings charts to assess the day-to-day performance of tests. The script emphasizes that approximately 68% of QC values should fall within ±1 standard deviation of the mean when a process is in control.

💡Systematic Error

Systematic error refers to a consistent偏差 in measurements that is not random and can be traced to a specific cause. In the video, it is discussed in the context of changes in the mean of control values, which can be indicated by a trend or a shift. The script provides examples such as deterioration of the instrument light source or gradual accumulation of debris in sample/reagent tubing, which could lead to such errors.

💡Random Error

Random error is the unpredictable and variable偏差 that occurs in measurements, which is expected to some extent in any analytical process. The video explains that random error is defined as any deviation from the calculated mean, with acceptable random error being quantified by standard deviation. Unacceptable random error is indicated when a data point falls outside the ±3s limits.

💡Westgard Rules

Westgard Rules are a set of statistical rules developed by Dr. James Westgard to evaluate the quality of analytical runs in medical laboratories. The video outlines six basic rules that can be used individually or in combination to assess the quality of test results. These rules help laboratories identify when a process may be out of control and require corrective action.

💡Trend

A trend, as discussed in the video, refers to a gradual and consistent change in the mean of control values over time. This could indicate a slow degradation of the testing system's reliability. The script warns that trends are often subtle and may be caused by factors such as the deterioration of the instrument light source or the gradual accumulation of debris on electrode surfaces.

💡Shift

A shift, as mentioned in the video, is an abrupt change in the mean of control values, indicating a sudden and significant alteration in the performance of the testing system. Shifts can be caused by events such as sudden failure of the light source, changes in reagent formulation, or major instrument maintenance. Identifying shifts is crucial for maintaining the accuracy and reliability of test results.

💡QC Log

The QC Log is a record-keeping system used in laboratories to document the assay of quality control materials and the inspection of quality control results. The video emphasizes the importance of maintaining a QC Log, which can be on a computer or paper, to ensure the quality of analytical runs. The log should include details such as the test name, instrument, date, and results for each control level, as well as actions taken to resolve any out-of-control situations.

Highlights

Standard deviation is commonly used for preparing Levey-Jennings charts.

Levey-Jennings chart is used to graph successive quality control values.

A chart is created for each test and level of control.

Decision limits are calculated as ±1s, ±2s, and ±3s from the mean.

Approximately 68% of QC values fall within ±1 standard deviation when the process is in control.

95.5% of QC values fall within ±2 standard deviations of the mean under control conditions.

About 4.5% of data will be outside the ±2s limits when the process is in control.

99.7% of QC values are within ±3 standard deviations of the mean.

Any value outside of ±3s is considered to be associated with a significant error condition.

Some labs incorrectly consider values outside ±2s limits as out of control.

Approximately 4.5% of valid QC values will fall between ±2 and ±3 standard deviation limits.

Laboratories should document the assay of quality control materials and inspection of QC results.

Systematic error is indicated by a change in the mean of control values, which can be gradual (trend) or abrupt (shift).

Random error is any deviation from the expected result, with acceptable and unacceptable types.

Westgard Rules are used to evaluate the quality of analytical runs based on statistical process control principles.

The 1-2s rule warns of potential random or systematic error when a single control observation is outside ±2s limits.

The 1-3s rule identifies unacceptable random error or the start of a large systematic error.

The 2-2s rule identifies systematic error when two consecutive QC results are greater than 2s on the same side of the mean.

The R-4s rule identifies random error when there is at least a 4s difference between control values within a single run.

The 3-1s and 4-1s rules identify smaller systematic errors or analytical bias within control materials.

The 10x rule identifies systematic bias when 10 or more control results are on the same side of the mean.

Transcripts

Welcome to Chapter 3: Levey-Jennings Charts & Westgard Rules.

Standard deviation is commonly used for preparing Levey-Jennings (L-J or LJ) charts.

The Levey-Jennings chart is used to graph successive (run-to-run or day-to-day) quality

control values.

A chart is created for each test and level of control.

The first step is to calculate decision limits.

These limits are ±1s, ±2s and ±3s from the mean.

Let’s begin with the data set from the previous module.

The mean for the Level I potassium control is 4.1 mmol/L and the standard deviation is

0.1 mmol/L.

This is an illustration of how ±1s, ±2s and ±3s quality control limits are calculated

using that mean and standard deviation.

The Levey-Jennings chart we have developed can be overlaid onto a bell-shaped curve to

illustrate the overall distribution of quality control values.

When an analytical process is within control, approximately 68% of all QC values fall within

±1 standard deviation (1s).

Likewise 95.5% of all QC values fall within ±2 standard deviations (2s) of the mean.

About 4.5% of all data will be outside the ±2s limits when the analytical process is

in control.

Approximately 99.7% of all QC values are found to be within ±3 standard deviations (3s)

of the mean.

As only 0.3%, or 3 out of 1000 points, will fall outside the ±3s limits, any value outside

of ±3s is considered to be associated with a significant error condition and patient

results should not be reported.

Some laboratories consider any quality control value outside its ±2s limits to be out of

control.

They incorrectly decide that the patient specimens and QC values are invalid.

An analytical run should not be rejected if a single quality control value is outside

the ±2s QC limits but within the ±3s QC limits.

Approximately 4.5% of all valid QC values will fall somewhere between ±2 and ±3 standard

deviation limits.

Laboratories that use a ±2s limit frequently reject good runs.

That means patient samples are repeated unnecessarily, labor and materials are wasted, and patient

results are unnecessarily delayed.

The laboratory needs to document that quality control materials are assayed and that the

quality control results have been inspected to assure the quality of the analytical run.

This documentation is accomplished by maintaining a QC Log and using the Levey-Jennings chart

on a regular basis.

The QC Log can be maintained on a computer or on paper.

The log should identify the name of the test, the instrument, units, the date the test is

performed, the initials of the person performing the test, and the results for each level of

control assayed.

Optional items for the log include: method and the assay temperature (usually included

for enzymes).

There should be room to write in actions taken to resolve any situation which is identified

as “out-of-control” or unacceptable and a place for documentation of supervisory review.

Once the QC results are entered into the QC Log, they should be plotted on the Levey-Jennings

chart.

When the results are plotted, an assessment can be made about the quality of the run.

The technologist/technician performing the test should look for systematic error and

random error.

Systematic error is evidenced by a change in the mean of the control values.

The change in the mean may be gradual and demonstrated as a trend in control values

or it may be abrupt and demonstrated as a shift in control values.

A trend indicates a gradual loss of reliability in the test system.

Trends are usually subtle.

Causes of trending may include: Deterioration of the instrument light source.

Gradual accumulation of debris in sample/reagent tubing.

Gradual accumulation of debris on electrode surfaces Aging of reagents.

Gradual deterioration of control materials.

Gradual deterioration of incubation chamber temperature (enzymes only).

Gradual deterioration of light filter integrity.

Gradual deterioration of calibration.

Abrupt changes in the control mean are defined as shifts.

Shifts in QC data represent a sudden and dramatic positive or negative change in test system

performance.

Shifts may be caused by: Sudden failure or change in the light source.

Change in reagent formulation.

Change of reagent lot.

Major instrument maintenance.

Sudden change in incubation temperature (enzymes only).

Change in room temperature or humidity.

Failure in the sampling system.

Failure in reagent dispense system.

Inaccurate calibration/recalibration.

Technically, random error is any deviation away from an expected result.

For QC results, any positive or negative deviation away from the calculated mean is defined as

random error.

There is acceptable (or expected) random error as defined and quantified by standard deviation.

There is unacceptable (unexpected) random error that is any data point outside the expected

population of data (e.g., a data point outside the ±3s limits).

In 1981, Dr. James Westgard of the University of Wisconsin published an article on laboratory

quality control that set the basis for evaluating analytical run quality for medical laboratories.

The elements of the Westgard system are based on principles of statistical process control

used in industry nationwide since the 1950s.

There are six basic rules in the Westgard scheme.

These rules are used individually or in combination to evaluate the quality of analytical runs.

Westgard devised a shorthand notation for expressing quality control rules.

Most of the quality control rules can be expressed as NL where N represents the number of control

observations to be evaluated and L represents the statistical limit for evaluating the control

observations.

Thus 1-3s represents a control rule that is violated when one control observation exceeds

the ±3s control limits.

The 1-2s rule is a warning rule that is violated when a single control observation is outside

the ±2s limits.

Remember that in the absence of added analytical error, about 4.5% of all quality control results

will fall between the 2s and 3s limits.

This rule merely warns that random error or systematic error may be present in the test

system.

The relationship between this value and other control results within the current and previous

analytical runs must be examined.

If no relationship can be found and no source of error can be identified, it must be assumed

that a single control value outside the ±2s limits is an acceptable random error.

Patient results can be reported.

The 1-3s rule identifies unacceptable random error or possibly the beginning of a large

systematic error.

Any QC result outside ±3s violates this rule.

The 2-2s rule identifies systematic error only.

The criteria for violation of this rule are: Two consecutive QC results.

Greater than 2s.

On the same side of the mean.

There are two applications to this rule: within-run and across runs.

The within-run application affects all control results obtained for the current analytical

run.

For example, if a normal (Level I) and abnormal (Level II) control are assayed in this run

and both levels of control are greater than 2s on the same side of the mean, this run

violates the within-run application for systematic error.

If however, Level I is -1s and Level II is +2.5s (a violation of the 12s rule), the Level

II result from the previous run must be examined.

If Level II in the previous run was at +2.0s or greater, then the across run application

for systematic error is violated.

Violation of the within-run application indicates that systematic error is present and that

it affects potentially the entire analytical curve.

Violation of the across run application indicates that only a single portion of the analytical

curve is affected by the error.

The R-4s rule identifies random error only, and is applied only within the current run.

If there is at least a 4s difference between control values within a single run, the rule

is violated for random error.

For example, assume both Level I and Level II have been assayed within the current run.

Level I is +2.8s above the mean and Level II is -1.3s below the mean.

The total difference between the two control levels is greater than 4s (e.g. [+2.8s – (-1.3s)]

= 4.1s).

Violation of any of the following rules does not necessarily require rejection of the analytical

run.

These violations typically identify smaller systematic error or analytical bias that is

not often clinically significant or relevant.

Analytical bias may be eliminated by performing calibration or instrument maintenance.

The following criteria must be met for a 3-1s rule violation: Three consecutive results.

Greater than 1s.

On the same side of the mean.

The following criteria must be met for a 4-1s rule violation: Four consecutive results.Greater

than 1s.

On the same side of the mean.

There are two applications to the 31s and 41s rule.

These are within control material (e.g. all Level I control results) or across control

materials (e.g., Level I, II, and III control results in combination).

Within control material violations indicate systematic bias in a single area of the method

curve while violation of the across control materials application indicates systematic

error over a broader concentration

These rules are violated when there are: 7 or 8, or 9, or 10, or 12 control results,

On the same side of the mean regardless of the specific standard deviation in which they

are located.

Each of these rules also has two applications: within control material (e.g., all Level I

control results) or across control materials (e.g. Level I, II, and III control results

in combination).

Within control material violations indicate systematic bias in a single area of the method

curve while violation of the across control materials application indicates systematic

bias over a broader concentration.

We have reached the end of this module.

Let’s review some basic points before we check your understanding.

The Levey-Jennings chart is used to graph successive (run-to-run or day-to-day) quality

control values.The first step in creating a Levey-Jennings chart is to calculate decision

limits.

These limits are ±1s, ±2s and ±3s from the mean.Some laboratories consider any quality

control value outside its ±2s limits to be out of control.

They incorrectly decide that the patient specimens and QC values are invalid.Approximately 4.5%

of all valid QC values will fall somewhere between ±2 and ±3 standard deviation limits.The

laboratory needs to document that quality control materials are assayed and that the

quality control results have been inspected to assure the quality of the analytical run.Systematic

error is evidenced by a change in the mean of the control values.

The change in the mean may be gradual and demonstrated as a trend in control values

or it may be abrupt and demonstrated as a shift in control

values.

A trend indicates a gradual loss of reliability in the test system.

Trends are usually subtle.Abrupt changes in the control mean are defined as shifts.

Shifts in QC data represent a sudden and dramatic positive or negative change in test system

performance.

For all your laboratory QC needs go to www.qcnet.com.