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Ростовский Государственный Медицинский Университет

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M. Vidali

Pre-analytical conditions, particularly subject preparation, sample collection, processing, and transport, must be identied and carefully controlled to minimize their effect on the validity of results by introducing bias or increasing variability.

Analytical aspects shall also be carefully considered and described, including the method and instrumentation used, the analytical performance and, where possible, demonstration of “traceability” of the method used to higher metrological levels. In particular, this last aspect is crucial to enable the transfer of reference intervals between different laboratories.

In the denition of reference intervals, statistical methods are used to identify aberrant data, to stratify or partition reference individuals into homogeneous groups, and to calculate the limits of the reference interval and the associated condence limits. Aberrant results can signicantly affect the calculation of reference intervals. For their identication, and eventual elimination, it is necessary to observe the distribution of the collected data, to verify that the presence of apparently extreme data does not depend on pre-analytical, analytical, or gross errors and to apply appropriate statistical methods. The techniques used are the same as those described above. Several approaches have been described in the literature to assess the need to stratify or partition the reference population into homogeneous groups, including hierarchical ANOVA (or nested ANOVA), Sinton’s method, Harrys and Boyd’s method, and Lahti’s method. According to the latter approach, we rst calculate the limits that include 95% of all results (common distribution without subgroups) and then evaluate the number of observations in the subgroup distributions that fall outside these limits, that is, those that have values less than the lower limit or greater than the upper limit. Denoting with p the highest percentage of observations that in the distributions of the subgroups fall outside the limits (or in the right or left tail), if p>4.1% it is necessary to stratify, if <3.2% it is not necessary to stratify. For intermediate percentages, it is necessary to decide on the basis of nonstatistical criteria. Also for the calculation of the reference limits and the relative condence intervals, there are numerous methods in the literature, including the non-parametric method and the robust method of Horn and Pesce. With the non-parametric method, traditionally preferred by the IFCC, the reference interval corresponds to the central portion of the reference distribution comprising 95% of the values, delimited by the quantiles 0.025 and 0.975. Their calculation requires at least 1/0.025=40 data. With such size (n=40), the limits of the interval correspond to the values of the rst and last observation, but it is not possible to estimate the relative condence intervals and therefore the uncertainty. For 90% condence intervals, a minimum number of 120

individuals is required: in this situation, the limits of the reference interval correspond to observations 3 and 118 and the respective condence intervals to observations 1 and 7 and 114 and 120, respectively. For larger numbers, it is necessary to use tabulated values available in literature or statistical software. The main advantages of this method are the simplicity of calculation and the robustness; however, the high numerosity (even more if one considers that n=120 is the minimum numerosity for each possible subgroup) often limits its applicability, in particular in small laboratories. In the robust method of Horn and Pesce, which can be used in samples of lower numerosity, the reference limits are estimated by iteratively applying robust indicators, after Box-Cox normalizing transformation. In the case of reference intervals determined by the robust method, the relative condence intervals must be calculated by the bootstrap method, a statistical technique of sampling with re-entry; in short, from the sample of n reference individuals, N samples with replacement are extracted and of each one, by the Horn and Fish robust method, the two reference limits are calculated. Of the N estimates of the lower limit and N estimates of the upper limit of the reference interval, the percentiles 5 and 95 are calculated, which will constitute the limits of the two 90% condence intervals of the reference interval.

An alternative to the fresh determination of reference intervals is the transfer of existing reference intervals. If the laboratory has already determined its own reference interval, and needs to change the analytical method, the new reference interval can be calculated from the previous one using its own samples and the equation of the regression model obtained by comparing the method currently in use with the new method (provided that the two methods have similar analytical performance). Alternatively, the laboratory, after verication, may implement a reference interval determined by a third party, provided that the pre-analytical and analytical conditions (method traceable to the same reference system) used to determine the interval are well documented and comparable with those of the laboratory and the two populations are similar. To verify the range, the laboratory selects 20 normal individuals (with characteristics that overlap with those of the reference population of the laboratory that determined the range), eliminates extreme values (by testing for aberrant values) and, if necessary, analyzes new samples until 20 results are obtained, and veries how many results are outside the considered reference range: if 2 or less values fall outside the interval, it can be implemented; if 3 or 4, the verication experiment is redone; and if still more than 2 values fall outside the interval or if 5 or more in the rst experiment, then the interval cannot be implemented and any pre-analytical or analytical differences or between the two populations must be checked.

4 The Role ofStatistics inLaboratory Medicine

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Individuality Index

A problem with using reference intervals to interpret laboratory data is that of so-called individuality, that is, for some analytes the values of each individual occupy, or extend, only a small portion of the interval. In other words, the intraindividual biological variability (CVI) is much smaller than the interindividual one (CVG). This concept is well expressed

by the individuality index:

I =

CVA<CVI, can be simplied to

+CV CV

= . A low individual-

, which when

ity index (II<0.6) means that the analyte has a marked individuality, while a high individuality index (II>1.4) indicates an analyte with little individuality. In the rst case, reference values are not very useful and it is preferable to compare the current results with the previous results of the same subject, while in the second case, when the values of the individual extend to the whole range, the comparison with a reference range is preferable. The individuality index can be increased by stratication or partition, thus dening different reference intervals for different subgroups.

Decision Levels

Reference intervals are quite different from decision levels or limits. In fact, while the former are determined in apparently healthy subjects using statistical methods, and therefore the comparison with the result presented by the patient can at least suggest whether the latter is probably healthy or unhealthy, the latter are determined by expert consensus and serve to identify a risk threshold, a classication threshold for a given pathology or a threshold for a clinical decision or treatment.

Performance ofaDiagnostic Test

Table 4.1 Contingency table

Gold standard Total

Sick Healthy

Tests under examination

Total Total sick

Pos True

positives (TP)

Neg False

negatives (FN)

people

False positives (FP)

True negatives (TN)

TotalHealthy

Total positives

Total negarives

tion. Hence, the attribution, on the basis of an analytical test, of an individual to a group or to another can be expressed only in probabilistic terms and the performance of the test in correctly classifying a subject is given by some indices such as clinical sensitivity, clinical specicity, positive predictive value, and negative predictive value, calculated by comparing in the so-called 2×2 contingency table the test whose performance is to be evaluated with a test of certain reliability (gold standard) (Table4.1).

Clinical sensitivity (Se) is the probability of a test to give a positive result if the subject is sick [Se=P(pos | sick)] and answers the question “how many sick individuals tested positive?” Sensitivity can also be understood as the proportion of sick individuals who test positive. In other words, the sensitivity of a test is its ability to correctly identify sick indi-

viduals. Thus,

sickTPTP FN

Clinical specicity (Sp) is the probability of a test to give a negative result if the subject is healthy [Sp = P(neg | healthy)] and answers the question “how many healthy subjects tested negative?” Specicity can also be understood as the proportion of healthy individuals who test negative. In other words, the specicity of a test is its ability to correctly

identify healthy individuals. Thus,

healthyTNTN FP

Sometimes the clinician nds himself in the situation in which it is necessary to classify an individual on the basis of an analytical result expressed by a continuous quantitative variable. The simplest classication is represented by a dichotomous qualitative variable that can assume only two values: positive or negative, sick or healthy, or high or low risk. In order to classify individuals into two subgroups, the clinician uses a threshold value (cut-off). However, the choice of this cut-off value is not always easy as often the distribution of results of the healthy population is partly overlapped with that of the sick popula-

From the observation of the two ratios, it is clear that the sensitivity is greater the lower the number of false negatives; equally the specicity is greater the lower the number of false positives. This means that a test with high sensitivity will rarely fail to identify sick patients and can therefore be useful to exclude a diagnosis (rule out); vice versa, a test with high specicity will rarely classify a healthy individual as sick and can therefore be useful to conrm a diagnosis (rule in).

Since sensitivity and specicity are calculated on a sample, the respective condence intervals must also be associ-

−

()

PPV

−−

[]

()()11

[]

()

−

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ated with the two statistics. For 95% condence, the

Se Se

95 196

respective intervals are

95 196

=±

%.=±

PPV PPV

sitivity and

In real situations, where the distributions of healthy and sick populations are partially overlapping, as the selected threshold value varies, an increase in sensitivity of a test is associated with a decrease in specicity and vice versa. The preference for one or the other index depends on the particular situation: a high sensitivity test is preferable for rare pathologies, when the missed diagnosis may have serious consequences (fatal but potentially curable pathologies) or to exclude a diagnosis (rule out); a high specicity test is preferable for high prevalence pathologies in order to avoid waste of resources, and in situations where a false positive diagnosis may have serious consequences on the subject (economic, psychological, invasiveness of further tests).

Not all individuals who test positive or negative are sick or healthy, respectively. To answer this question, two other calculated indices are useful: the positive predictive value (PPV) and the negative predictive value (NPV).

The positive predictive value is the probability that an individual testing positive is ill [PPV= P(sick | pos)] and indicates the proportion of test-positive individuals who are

ill. It is calculated as PPV

The negative predictive value is the probability that a testnegative individual is healthy [NPV=P(healthy | negative)] and indicates the proportion of test-negative individuals who

are healthy. It is calculated as NPV

Note that if PPV is the probability that a test-positive individual is ill, then 1-PPV is the probability that a test-positive individual is healthy. Equally, if NPV is the probability that a test-negative individual is healthy, then 1-NPV is the probability that a test-negative individual is sick.

Condence intervals can also be calculated for the positive and negative predictive value:

CI %

95 196

=±

CI %

95 196

One must pay close attention that the PPV and the NPV depend closely on the prevalence of the disease. This dependence is easily understood by applying Bayes’ theorem:

%.=±

Sp Sp

−

healthy

positivesTPTP FP

−

positives

PPV NPV

negativ

for the PPV while

1−

()

for sen-

sick

for specicity.

negativesTNTN FN

for theNPV

prevalence Se prevalence Sp

()

prevalence Sp prevalence Se

Thus, under conditions of low prevalence and specicity of a test, the positive predictive value drastically decreases and the negative predictive value increases.

From the formula of the PPV, it is also understood that, as the specificity tends to 0 (and therefore the sensitivity to 1), the PPV tends to the prevalence of the disease.

One approach to increasing PPV is obviously to operate in high prevalence situations, such as applying the test to high-risk subgroups.

Another index aimed at assessing the performance of a diagnostic test is the likelihood ratio (LR). The positive likelihood ratio (LR+) indicates how many times a positive test is more likely in a sick individual than in a healthy one, that is, the ratio of the probability of having a positive result among those with the disease divided by the probability of having a positive result among those without the disease:

lihood ratio (LR−) indicates how many times a negative test is more likely in a sick individual than in a healthy one, that is, the ratio of the probability of having a negative result among those with the disease divided by the probability of having a positive result among those without the disease:

−

if LR>1 the test supports the presence of the disease (particularly if LR>10 the test is very effective in conrming), if LR<1 the test excludes the presence of the disease (particularly if LR<0.1 the test is very effective in excluding), if LR=1 the usefulness of the test in the diagnostic process is null. Likelihood ratios are very useful to the clinician to understand how much the probability of a diagnosis hypothesized before the test increases in case of a positive or negative result. In other words, from the “a priori” or “pre-test” probability of a certain diagnosis (probability deduced from the literature for the general population, from the symptoms and signs presented by the subject or in any case from the available information), we pass to the “a posteriori” or “post- test” probability, that is, the probability of having a certain pathology after having obtained the results of a test. The calculation is much simplied if the odds are used instead of the pre- and post-test probabilities. In fact, in the case of odds, the effect of the likelihood ratio is multiplicative.

−+−

pos sick

pos healthy

negsick

neghealthy

prevalence Se

×+

()

prevalence Sp

−

. Similarly, the negative like-

−

. For interpretation purposes,

odds

pretest

−

odds

posttest

−

1-Specificity

Sensitivity

1.0

4 The Role ofStatistics inLaboratory Medicine

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For this purpose, rst we transform the pre-test probabil-

prob

pretest

ity into odds,

pretest

−

−1

prob

−

(when the pre-test

probability is equal to the prevalence of the pathology in the population or in that particular subgroup or class of individu-

als we will have

odds are calculated odds

pretest−

post−test

prevalence

−1

prevalence

=LR×odds

), the post-test

and, apply-

pre−test

ing the inverse formula, the post-test probability is calculated

odds

posttest

rob

posttest

−

odds

−

. Alternatively, particularly in the

clinical setting, it is possible to use Fagan’s nomogram. The main advantages of using the likelihood ratio are represented by the fact that it is an easily understandable tool and that it can be used according to a sequential approach (the post-test probability of a given diagnostic test can be used as a pre-test probability for the next one). However, its use is limited in all those situations where a pre-test probability is not available.

ROC Curves

ROC (Receiver Operating Characteristic) curves are an important statistical tool to study the diagnostic performance of a test; in particular, they allow to evaluate how sensitivity and specicity change when the cut-off selected for a continuous quantitative result is changed.

In short, given a test with results described by a quantitative variable, various cut-offs are selected and for each of them the sensitivity and specicity of the test are calculated; in a Cartesian diagram, the curve given by the union of the points with the abscissa 1-specicity, that is, the proportion of false positives, and the ordinate sensitivity, that is, the proportion of true positives, is then drawn (Fig.4.9). The slope of the line joining the origin of the axes with every single point of the ROC curve, that is, with every selected cut-off, is equal to the likelihood ratio at that point, that is, for that cutoff. The diagnostic power of the test, that is, its ability to correctly classify sick and healthy people, is expressed by the area under the ROC curve or AUC (Area Under the Curve) and is proportional to it. For AUC=1, the test discriminates perfectly sick and healthy while for AUC =0.5 the test is not informative. For intermediate values, between

0.5 and 1, the test will show poor, moderate, or high accuracy. The condence interval for AUC can be calculated by different methods, including that of Hanley & McNeil, DeLong, or the bootstrap method, which are generally available in major statistical software. ROC curves, and their respective AUCs, can also be used to compare two or more different diagnostic tests in the same graph.

0.8

0.6

0.4

0.2

0.08

0.0 0.2 0.4 0.6 0.8 1.0

Fig. 4.9 ROC curve

Recommended Readings

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parison studies. Stat Methods Med Res 8:135–160

Bock BJ, Dolan CT, Miller GC etal (2003) The data warehouse as

a foundation for population-based reference intervals. Am J Clin Pathol 120:662–670

Box G, Cox D (1964) An analysis of transformations. J Royal Stat Soc

B26:211–252

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valli di riferimento riferibili. Biochim Clin 36:171–176. Clinical and Laboratory Standards Institute. Dening, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline-Third Edition. CLSI document EP28-A3c. 2010

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precision and estimation of bias; Approved guideline-Third Edition. CLSI document EP15-A3

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reference limits estimated from patients’ results by three mathematical procedures. Clin Chim Acta 279:97–105

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to produce separate reference ranges. Clin Chem 36:265–270

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Elements ofMetrology

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OrazioRuzzenente andClaudioBrentegani

Introduction

Omnia in mensura et in numero et in pondere disposuisti (You

have arranged everything by measure, number, and weight) (St.

Augustine).

Measurement is the highest degree of knowledge of a thing or phenomenon.

Only the measurement of a “thing” or phenomenon allows us to know it in depth and make qualied decisions about it. Metrology is the science of measuring processes and is particularly important for laboratory professionals.

We provide below some denitions regarding metrology taken from “International Vocabulary of Metrology– Basic and GeneralConcepts and Associated Terms”:

• Measurement: a process to quantitavely describe the

property (magnitude) of a thing or phenomenon

• Measure: the result of a measurement consisting of a set

of three data elements (numerical value, unit of measure,

measurement uncertainty)

• Magnitude: an attribute of a thing or phenomenon that can

be expressed qualitatively or quantitatively

• Measurand: quantity intended to be measured

• Measurement unit: real scalar individual quantity, dened

and adopted by convention, with which any other quan-

tityof the same kind canbe compared to express the ratio

of the two quantities as a number

• Measurement uncertainty: a parameter characterizing the

dispersion of the values being attributed to a measurand,

based on the information used

O. Ruzzenente Section of Clinical Biochemistry, University of Verona, Verona, Italy

C. Brentegani ( Section of Clinical Biochemistry, Department of Neurological, Biomedical and Movement Sciences, University Hospital of Verona, Verona, Italy e-mail: claudio.brentegani@univr.it

The measurement process is characteristically carried out in successive steps. The rst step is to identify the measurand and dene its state during the measurement process since the latter can affect the characteristics of the measurand itself. The next step is to establish the reason for the measurement (appropriateness of the request) because its knowledge can inuence the measurement procedure (choice of the measurement method or instrument) and the choice of acceptable limits of imprecision for that measurement.

Another step is to assess the suitability of the sample to be analyzed. The denition of a sample in biochemistry is different from that in metrology. In metrology, a sample is a medium that reproduces a known value of a quantity for use as a comparison. In biochemistry, a sample can be understood as a piece of material representing the whole. In this context, there is a fundamental part of clinical biochemistry that studies pre-analytical procedures that can also heavily inuence the representativeness of the sample. In order to provide reliable measurements, it is fundamental to evaluate the representativeness of the material being measured, particularly in this historical moment, because completely automatic instruments carry out a large part of the analyses (measurements). Interferences due to incorrect preanalytical procedures or the presence of interfering substances cause a systematic, and often clinically signicant, error in the determination of the concentration of an analyte.

Metrology inLaboratory

The choice of measurement method and instrumentation is the next step. Measurement methods can be direct or indirect. In biochemistry, the two direct methods still used are potentiometry, which measures the potential difference due to electrical charges carried by ions in solution (ions selective electrodes, blood gas analysis), and the lowering of the cryogenic point, which measures the osmolality of a sample. The direct methods usually allow for obtaining comparable results and are characterized by low measurement

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O. Ruzzenente and C. Brentegani

uncertainties. All other methods are indirect and often involve several steps to obtain the signal to be measured. It is, therefore, essential to know exactly the relationships among the products obtained in the various steps of the reactions during measuring procedures. Each step has a certain degree of uncertainty, and it is, therefore, important when studying a method to calculate the total inaccuracy by adding up the inaccuracies of the various steps. These methods are the most common way to determine the concentration of a substance. This quantity is not directly measurable but requires specic reagents to form with the substance under examination known products with a signal measurable by laboratory instruments.

The choice of instrumentation to perform a measurement must be weighted according to the measuring principle, and all the mechanical activities of the instrument must have a negligible inuence (better not inuence at all) on the result of the measurement. Once the signal has been detected, it must be transformed into a measurement by a logical or practical process known as calibration. The calibration of an instrument is a mechanical operation that prepares the instrument to provide measurement of a certain quantity equal to those of the reference material. Calibration, on the other hand, is a logical–practical operation that establishes the signal-tosize ratio by comparing, using indirect methods, known values of the same quantity of one or more reference materials.

In some cases, the association between signal and magnitude is established by chemical or physical relationships (e.g., Lambert–Beer law in spectrophotometry). However,the direct application is impossible because, in a routine laboratory, the measurement conditions cannot be perfectly controlled. The relationship is therefore obtained experimentally by constructing calibration lines or curves. It is preferable, during calibration, to use several reference samples and construct a calibration curve or straight line to minimize the inaccuracies related to the measurement of every single point of the curve. The choice of reference samples for the construction of the calibration curve shall consider the matrix effect. The samples must therefore be as similar as possible to those under analysis because all the substances present may inuence the measurement even if they are not considered true interferents. Once all the steps described have been implemented, the expression of the measurement result is achieved. Three pieces of information should always be provided when communicating the results of a measurement: the numerical value, the unit of measurement, and the uncertainty of the measurement.

The unit of measure chosen to provide the result and the correct choice of the number of signicant digits of the

numerical value help to dene the quality of a measurement. In recent years, there has been an innovative process whereby the focus of analysis is no longer the actual value of the measurand, but rather the quality of the measurement. The term measurement uncertainty is more appropriate than the term error and well describes the indeterminacy of any experimental result.

The evaluation of the measurement uncertainty is statistical and is therefore based on a calculation procedure codied in an international standard (UNI CEI ENV 13005, July

2007).

An indispensable prerequisite for considering a measurement as qualied information is the traceability of the measurements, that is, the existence of a chain of comparisons that enables each result obtained to be linked to internationally recognized reference standards. In this case, an increase in uncertainty can be generated in the passages between one comparison and another, which must be accurately quantied.

To resume and widen the discussion, it is necessary to premise that it is quite easy in biochemistry to identify the measurand because it is almost always the concentration of a substance or analyte, even if sometimes it is not so easy to identify exactly (or completely) the substance itself whose concentration is to be measured. Furthermore, a straight line corresponding to the Lambert–Beer law often represents the marker-concentration relationship. Although it is difcult to apply the law directly in a routine laboratory, it is relatively easy to identify this relationship and the linearity limit, which also represents the concentration limit beyond which the relationship is no longer predictable. Even in the case of reactions involving enzymes, although the latter present some problems characteristic of enzymatic reactions (isoforms), they are oxidation–reduction reactions and therefore relatively easy to predict. For a more detailed discussion of this aspect, please refer to the chapter on enzymes. All the activities described above can therefore be summarized in the standardization of a method.

Standardization inLaboratory

The denition of standardization encompasses a set of specic rules that are the basis of guidelines aimed at achieving an optimal degree of uniformity in each discipline. The product of standardization must ensure the highest possible comparability in the standardization of measures, consistency, and quality in the various aspects of procedures, thus guaranteeing the producer and the consumer. Translating the deni-

5 Elements ofMetrology

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tion into good laboratory practice, the product of standardization is a good test with optimal results that contributes to quality diagnostics, followed by better patient care and improved public health. Standardization in the laboratory can therefore be explained by:

• Production of reference standards

• Control in the production of calibrators

• Method validation protocols and their instrumental applicability

Production ofReference Standards

iIt is necessary to distinguish two important areas of the laboratory: the strictly chemical area and the immunochemical one. Concerning the rst one, it is easy to nd pure substances and obtain primary standards by weighing them (e.g., glucose quantied by gravimetric procedure). Regarding the second area, standardization is more complex since the procedure represents the becoming of a strictly biological process, including all its variables, hence the wellknown problems of standardization of immunoassays. The Expert Committee on Biological Standardization (ECBS) of the World Health Organization (WHO) conrms international standards and other reference substances for biological uids. For some hormones and tumor markers, the National Institute for Biological Standards and Control (NIB- SC) is the reference. Other institutions, such as International Federation of Clinical Chemistry (IFCC), College of American Pathologists (CAP), and Clinical Laboratory Standard Institute (CLSI), collaborate in specic areas. The primary reference standards, characterized by puried substances and harmonization procedures between biological activity and concentration, are International Standard (IS) and International Reference Preparation (IRP).

• IS (lot/year) is always identied bya lot and year of production and is approved after a long process. They are available in small quantities, freeze-dried in protein “medium,” and can satisfy requests for a period of 10–20years.

• IRP (batch/year) is proposed by WHO despite a route that has not been fully rened. These standards are generally used to dene the concentrations of secondary standards,represented by sufciently pure substances. They are generally used to perform calibration curves and are stored to control them over time.

Control intheProduction ofCalibrators

The above procedures give rise to the certication of the calibrator values accompanying the various diagnostic kits. A signicant element in the calibration procedure is instrumentation (immunochemistry is affected by non-dedicated instrumentation). Calibrators for immunochemistry kits are provided either to generate multipoint calibration curves (in ELISA and RIA procedures) or, in the presence of dedicated instrumentation, to transfer the new calibration to the one obtained “in-house” using only two calibration points. The evaluation of the latter procedure cannot disregard the knowledge of mathematical elements that are at the basis of the study of the curves. The two-point calibration procedure is generated on the following mathematical basis:

• The “in-house” curve, generated on more than one point, is often characterized by a sigmoid, characteristic of all biological phenomena, from which, through mathematical processes (tting study), is extrapolated the interval of greater linearity, which is represented by a straight line and, as such, characterized by two important parameters, i.e., the angular coefcient and the intercept.

• The adjustment curve obtained in the laboratory bytwo calibrators is a straight line easily comparable with the one obtained “in-house” using the same parameters. The best adjustment is obtained by well-dened calibrators concentrations, one of which should represent the part closest to the limit of detection (LoD) and the other a point close to 50–60% of the linearity range. The comparison of the parameters of the lines, whose knowledge allows for validation of the calibration, will transfer, together with specic algorithms, the obtained value to that of the curve obtained “in-house”.

Method Validation Protocols andTheir Instrumental Applicability

Instrumentation should never be thought of as an element to relieve the analyst’s responsibility for the reliability of measurements. This aspect allows a double evaluation of thetest limits and the instrumentation. The knowledge and verication of the analytical sensitivity and the measurement linearity range, performed on the instrumentation in use, can contribute to analytical choices in which the error can be reduced. Knowledge of these aspects leads to a better setting and interpretation of quality control.

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O. Ruzzenente and C. Brentegani

Problems inStandardization ofImmunochemistry

The complexity of standardization in immunochemistry is generated by numerous problems, including protein heterogeneity, matrix effects, cross-reactivity, the presence of heterophilic antibodies, and the need to measure concentrations close to the limit of detection. In addition, the measured immunoreactivity often represents a mixture of protein isoforms that differ in their degree of glycosylation, degradation, immune complex formation, and even activity.

For some proteins with purication difculties, IS and IRP standards report the concentration in U/L standardized in terms of bioactivity. This aspect is difcult to quantify mathematically and, therefore, more suitable for harmonization of the results than for standardization.

Good protein purication allows the expression of the mass concentration that most clearly reects what is being measured. However, even the determination of molar concentration presents difculties since it can be accurately determined by amino acid analysis even in the presence of heterogeneity concerningthe carbohydrate composition and the isoelectric point.In contrast,the gravimetric determination, especially for glycoproteins, is more inaccurate since the freeze-drying process fails to completely remove the water bound in the molecular structure.

Immunochemical reaction is based onan antigen, anantibody, and the “matrix” in which they are located, which exerts its action by maintaining their tertiary structure unaltered (immunoreactivity) through a suitable salt and protein concentration. The use of monoclonal antibodies has greatly improved specicity through the characterization of epitope mapping, knowledge of which would allow the immunoreactivity of dosage to be dened based on its similarity to the reference antibody.

Only validation protocols provide the means to understand their complexity and take action to improve their analytical performance.