Quality control in the medical laboratory is a statistical process used to monitor and evaluate the analytical process that produces patient results. Show When a diagnostic test is performed in the medical laboratory, the outcome of the test is a result. The result may be a patient result or it may be a quality control (QC) result. The result may be quantitative (a number) or qualitative (positive or negative) or semi-quantitative (limited to a few different values).1 QC results are used to validate whether the instrument is operating within pre-defined specifications, inferring that patient test results are reliable. Once the test system is validated, patient results can then be used for diagnosis, prognosis, or treatment planning. For example, when a patient’s serum is assayed (tested) for potassium, the test result tells us how much potassium (concentration) is present in the blood. This result is then used by the physician to determine whether the patient has a low, normal or high potassium. Let’s assume the measured value of potassium in a patient’s serum is 2.8 mmol/L (a unit of measure, millimoles per liter). This result is abnormally low and indicates an inappropriate loss of potassium. But how does the person performing the test know that this result is truly reliable? It could be possible that the instrument is out of calibration and the patient’s true potassium value is 4.2 mmol/L – a normal result. The question of reliability for most testing can be resolved by regular use of quality control materials and statistical process control.
A quality control product is a patient-like material ideally made from human serum, urine or spinal fluid. A control product can be a liquid or freezedried (lyophilized) material and is composed of one or more constituents (analytes) of known concentration. Control products should be tested in the same manner as patient samples. A quality control product usually contains many different analytes. For example, a general chemistry control can contain any number of chemistry analytes including potassium, glucose, albumin and calcium. A normal control product contains normal levels for the analyte being tested. An abnormal control product contains the analyte at a concentration above or below the normal range for the analyte. For example, the normal range for a potassium level is about 3.5 – 5.0 mmol/L. A normal control would contain potassium at a level within this range. An abnormal control would contain potassium at a level below 3.5 mmol/L or above 5.0 mmol/L. Good laboratory practice requires testing normal and abnormal controls for each test at least daily to monitor the analytical process. If the test is stable for less than 24 hours or some change has occurred which could potentially affect the test stability, controls should be assayed more frequently Regular testing of quality control products creates a QC database that the laboratory uses to validate the test system. Validation occurs by comparing daily QC results to a laboratory-defined range of QC values. The lab-defined range is calculated from QC data collected from testing of normal and abnormal controls.
QC statistics for each test performed in the laboratory are calculated from the QC database collected by regular testing of control products. The data collected is specific for each level of control. Consequently, the statistics and ranges calculated from this data are also specific for each level of control and reflect the behavior of the test at specific concentrations. The most fundamental statistics used by the laboratory are the mean [x] and standard deviation [s].
The mean (or average) is the laboratory’s best estimate of the analyte’s true value for a specific level of control. To calculate a mean for a specific level of control, first, add all the values collected for that control. Then divide the sum of these values by the total number of values.
Standard deviation is a statistic that quantifies how close numerical values (i.e., QC values) are in relation to each other. The term precision is often used interchangeably with standard deviation. Another term, imprecision, is used to express how far apart numerical values are from each other. Standard deviation is calculated for control products from the same data used to calculate the mean. It provides the laboratory an estimate of test consistency at specific concentrations. The repeatability of a test may be consistent (low standard deviation, low imprecision) or inconsistent (high standard deviation, high imprecision). Inconsistent repeatability may be due to the chemistry involved or to a malfunction. If it is a malfunction, the laboratory must correct the problem. It is desirable to get repeated measurements of the same specimen as close as possible. Good precision is especially needed for tests that are repeated regularly on the same patient to track treatment or disease progress. For example, a diabetic patient in a critical care situation may have glucose levels run every 2 to 4 hours. In this case, it is important for the glucose test to be precise because lack of precision can cause loss of test reliability. If there is a lot of variability in the test performance (high imprecision, high standard deviation), the glucose result at different times may not be true. Standard deviation may also be used to monitor on-going day-to-day performance.
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. 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. But 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:
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:
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 13s represents a control rule that is violated when one control observation exceeds the ±3s control limits.
This 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.
This rule identifies unacceptable random error or possibly the beginning of a large systematic error. Any QC result outside ±3s violates this rule.
This rule identifies systematic error only. The criteria for violation of this rule are:
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
This 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)
The criteria which must be met to violate this rule are:
The criteria which must be met to violate this rule are:
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:
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. The maintenance of a quality management system is crucial to a laboratory for providing the correct test results every time. Quality assurance Important elements of a quality management system include:
Are procedures used in each assay to assure a test run is valid and results are reliable:
External quality assessment schemes (EQAS) Aims to analyse the accuracy of the entire testing process from receipt of sample and testing of sample to reporting of results What Westgard rule is violated?A 41s violation occurs whenever 4 consecutive points exceed the same 1s limit. These 4 may be from one control material or they may also be the last 2 points from a high level control material and the last 2 points from a normal level control material, thus the rule may also be applied across materials.
Which Westgard violation is most likely due to a random error?Run 7 The high control result exceeds its +3s limit, therefore there is a 13s control rule violation. This most likely indicates random error.
What is R 4s rule?The R4s rule applies to controls within a run. If two controls exceed 4SD, that is, if one control exceeds +2SD and the other control (or another control, if more than 2 controls are tested) exceeds -2SD, the run should be rejected.
What are the 5 Westgard rules?Westgard rules. |
Which Westgard rule violation is illustrated by these normal and abnormal control charts
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