As with all measurement procedures, quantitative analyses in clinical laboratories have inherent errors. The professional must keep in mind that the error is such an intrinsic possibility, becoming a probability. Gauss's law, defined by the normal distribution of errors and with a bell-shaped curve, is well known and should be part of professionals' concepts. It represents the distribution of measurements, including errors, of analyzes in the same sample.

Therefore, whenever measurements are made in replicates of a sample, or in control material, the value found will never be the same and its position will be within a predictable distance from the central point (the mean), in a random distribution. This will always highlight an “error”.

According to the normal distribution of Gauss's law, the results will be 68.2%, between +- 1, of the standard deviation (SD) around the mean, 95.4% of the time in the range of +- 2SD, around the mean and 99.5% of the time between +- 3SD oscillating around the mean.

This behavior is characterized as a distribution of errors in replicate measurements and the sizes of the errors are a consequence of the variability, which is the SD, or even better, the variability index, the Coefficient of Variation (CV). Measurements that result in values greater than expected within an acceptable range increase the random error, simply called random error.

Systematic error

If the distribution of points stops showing oscillation around the mean, then there is a deviation from what is expected in the normal distribution and is systematic, sometimes characterizing an error of this nature. There may be a systematic error when:

  • control values exceed some threshold in a specific number of consecutive observations;
  • control values are on the same side of the average for seven consecutive days, without necessarily exceeding the limits.
  • control values on seven consecutive days will show an increasing or decreasing trend, not necessarily exceeding any limit.

The systematic error is the most frequent type, in general, resulting from persistent problems that are easier to discover and solve. They have a certain direction (more or less) and relate to variations in the average.

Random error

The random error, on the other hand, points to the greater variability of the system, meaning the widening of the distribution curve of control results, the Gauss Curve. This occurrence leads to increased inprecision of the analytical method and can be expressed in terms of SD or CV. The direction and size of the error cannot be predicted, which leads to a wide distribution of points, which is why the base of the Gaussian curve increases. Random errors are related to imprecision (SD and CV) and their causes are more difficult to clarify.

For a good state of stability, it is desirable that the data oscillate around an average, without trends and without exaggerated distance from the central line, the so-calledGaussian distribution.

Therefore, the Internal Quality Control (IQC) procedure in clinical laboratories must be able to detect measurement errors, appropriately, in the control material, with the aim of increasing the predictability of the quality of results from patient samples. To this end, the Levey-Jennings chart analysis contributes to understanding the variability of the results obtained from the materials, pointing out random and systematic errors in carrying out the process.

Multiple control rules and error types

When violated, the rules point to the error type , which contributes to understanding the problem and finding the root cause.
It is necessary to understand the meaning of the violated rule and the extent of involvement, whether a problem affects only 1 level of control, more than 1 level, only 1 analyte and more than 1 analyte.

Classifying errors into systematic and random, we can tell which type relates to a specific rule.

Systematic errors – are highlighted by most of the rules, such as 2:2s, 4:1s, 5x, 7x, 7T and 10x.

Because they have the right direction, they are errors whose causes are more easily perceived. Other analytes from the same system may present the same problem.

Random errors – highlighted by 1:3s and R:4s. The 1:3s rule may eventually point out a systematic error of great proportion, or magnitude.

They can be caused by different factors and, as they are random, it becomes more difficult to find the root cause. Always evaluate the error history of the analyte.

Analyzing possible causes of errors

Identified the type of error, if random or  systematic, go in search of the root cause. The systematic errors are more frequent, caused by persistent problems and are easier to resolve. The random errors does not have a defined meaning and direction, making it more difficult to find the root cause.

Remember to check the last movement in relation to the device (recent maintenance), reagent (new reagent, new batch), calibration, etc. Use information about types of errors, in a list, to remind you of the possibilities. Carry out an accurate review of the causes of error. Spend a little time on the analysis, to save on time and materials, avoiding fruitless repetitions that increase your costs. Mainly evaluate two aspects:

  • If the problem affects other tests that are performed on the same device. This is what we can call a “common denominator” for the different analytical systems.
  • If there has been recent intervention on the equipment, from preventative maintenance, any damage, to changing reagents.

Internal Quality Control – learn how to deal with errors

There are error situations in clinical laboratories that require permanent attention from professionals. Carrying out daily checks by Internal Quality Control and knowing how to adopt best practices to address non-conformities is an important step towards carrying out a good job, safely. Some professionals just repeat control runs when non-conformities are pointed out, which is not correct. The clinical laboratory must establish strategies to deal with these situations and incorporate them into daily life.

If the quality control is well planned and the Quality Manager expands his knowledge test by test, his vision of the analytical systems will be so consistent that it will enable clarity and greater accuracy in the search for solutions to problems that arise.

Knowing how to deal with loss of control and what actions should be taken is essential to guarantee test results.

To understand in more detail how random and systematic errors happen, access our free eBook:  7 FUNDAMENTAL attitudes to deal with non-conformities.