1. Introduction

The role of clinical laboratory is to measure and test patient samples. These measurements are a central part of modern clinical management; they are used by clinicians to diagnose disease states, to guide treatment course and to determine prognosis. The modern clinical laboratory uses a plethora of instruments to quantify and measure different analytes and reports results that are used by clinicians. The most important metrics that a test must possess to be used in clinical laboratory are technical accuracy and precision [1].

A test is technically accurate if it produces valid information. A precise test will produce similar results when the test is repeated multiple times. Accuracy (or rather trueness) is a measure of the

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

proximity of the test results to the true value. Precision measures reliability and reproducibility. These metrics are complementary and a good clinical test needs to be both accurate and precise [2]. Some have suggested that trueness should be used to refer to the agreement of the measurement to the true value and accuracy to encompass both trueness and precision.

error. All tests need to be checked continuously for presence of error and identifying systematic error is part of the function of a clinical laboratory. The measurement error can be regarded as a noise that can obscure the signal or true test value. In the presence of noise, drawing conclusions from the signal that may change the true value in a clinically significant manner risks jeopardizing the patient's health. As a result, the lab should strive to identify noise, minimize it or reduce its impact on patient outcomes. In this regard, systematic error is especially dangerous since it will skew the test results in a manner that cannot be corrected by repeat measurements. Unfortunately, systematic error can be very difficult to identify and/ or quantify. In this chapter we focus on approaches for identification of systematic error using

Systematic Error Detection in Laboratory Medicine http://dx.doi.org/10.5772/intechopen.72311 51

2. Systematic error detection using quality control experiments

Simply stated, the aim of quality control experiments is to determine the performance of the laboratory tests with measuring of known samples or references, that is, samples in which the true value of the analyte being tested is known. These methods are mainly set up to detect random error and check instrument precision. However, the same results can be used to detect

The laboratories can use certified reference materials to measure and identify systematic error. If the reference sample is measured with each analytical run, you would expect the results of the reference sample measurements to show a random distribution around the true value, yet if the results are consistently lower or higher than the reference value then you would suspect

For systematic error measurement, a method comparison method is needed to identify systematic error. Any systematic error found needs to be corrected using a recovery experiment

The first step in identification of systematic error is to visually inspect the quality control process. Levey-Jennings plot shows the fluctuation of reference sample measurements around the mean against time. The chart's reference lines include control limits, 2 standard deviation

The mean, standard deviation and the control limits are calculated by a replication study where the certified reference material is repeatedly measured. The repeated measurements allow for calculation of mean and standard deviation of the control sample levels. The trial limits are mean 3 standard deviations. The next step is to eliminate the replication study results that are beyond the 3 standard deviations. Then the mean and standard deviation are recalculated and the trial limits are again set. Again, results beyond the trial limits are excluded. The process continues until all the remaining results are within the trial limits. These final trial limits, mean and standard deviation are set as the reference measures for that

lines, 1 standard deviation lines and the mean reference line.

within-laboratory comparisons [5, 6].

bias and systematic error [7].

that a bias exists [2, 8].

2.1. Levey-Jennings plots

and calibration.

reference sample.

Accuracy and precision are related to a concept called measurement error: every measurement is associated with a degree of error or uncertainty. The goal in laboratory medicine is to minimize the measurement error so that it does not adversely affect the clinical decision-making process. Measurement error can never be truly nullified, but it can be decreased to a scale that is acceptable by clinicians, laboratory directors and laboratory regulatory agencies [2, 3].

Measurement errors can be random, i.e. they can be unpredictable. All measurements have random error. Random errors are due to unpredictable variations in sample, instrument, measurement process or analysis and it can be said to follow a Gaussian distribution, i.e. random error follows randomness and chance and thus laws of probability apply to random error. As the instruments get more precise the Gaussian distribution of the random error gets narrower and the random error decreases. At the same time, if we repeat an experiment or test multiple times we can average out random error from our measurements. i.e. the mean of multiple repeated measurements gets closer to the true value as the number of repeats increases. This forms the basis of reporting confidence intervals for measurements [2, 4].

Bias or systematic error is a form of measurement error that skews the results to one side. Repeating the measurements cannot eliminate bias. In other words, bias is a non-zero error which will consistently affect the results and can show a problem with the measurement process often requiring corrective action. The corrective action can be in form of calibration by introducing a correction factor or by changing components of measurement. Systematic error can be short-term or long-term, with very short-term systematic error often manifesting as random error.

Systematic error and random error have a cumulative effect on the measurement results (Figure 1). Thus, measurement error is often considered as total error with both bias and random error contributing. Laboratories often have limits for total error, bias and random

Figure 1. This figure depicts the cumulative effect of systematic error and random error. The X-axis represents the value determined and the Y-axis plots the frequency of occurrence of each value.

error. All tests need to be checked continuously for presence of error and identifying systematic error is part of the function of a clinical laboratory. The measurement error can be regarded as a noise that can obscure the signal or true test value. In the presence of noise, drawing conclusions from the signal that may change the true value in a clinically significant manner risks jeopardizing the patient's health. As a result, the lab should strive to identify noise, minimize it or reduce its impact on patient outcomes. In this regard, systematic error is especially dangerous since it will skew the test results in a manner that cannot be corrected by repeat measurements. Unfortunately, systematic error can be very difficult to identify and/ or quantify. In this chapter we focus on approaches for identification of systematic error using within-laboratory comparisons [5, 6].
