the immuassay handbook parte46

441© 2013 David G. Wild. Published by Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/B978-0-08-097037-0.00030-0

In the context of laboratory medicine, the aim of total quality assurance is to ensure that necessary diagnostic tests are requested and that results are analytically valid, interpreted correctly, and acted upon appropriately. Broadly, this requires suitable organizational infrastruc-ture together with the establishment of and adherence to approved policies relating to staff selection, training, equipment, facilities, etc. The adequacy of these policies is assessed formally through laboratory accreditation pro-grams. Essential requirements that must be met by a labo-ratory seeking accreditation include provision for robust internal quality control (IQC) procedures and participa-tion in external quality assessment (EQA) programs, which are known as proficiency testing programs in some countries including the United States. IQC and EQA are both fundamental to provision of a high-class laboratory service and are considered in detail in this chapter.

The main aims of quality assessment are to minimize variability arising from analytical, biological, and clinical sources so as to guarantee the following for any requested biochemical test:

Correct specimen from the correct patient, collected under appropriate conditions;

Accurate, reliable, and reproducible biochemical analyses;

Correct interpretation of results using appropriate reference data; and

Timely reporting of results to the clinician.

Pre-Analytical Quality RequirementsClinical results are only helpful if the appropriate tests are requested and samples are correctly labeled, collected, and stored under optimal conditions. Particularly careful attention should be given to what happens in the pre-analytical phase, as it is well accepted that many more errors occur then than in the analytical phase.

APPROPRIATE TEST SELECTIONLaboratories should work proactively to ensure ready availability of clear and helpful information to health pro-fessionals (doctors, nurses, and/or phlebotomists) about appropriate test selection, sample collection, and frequency of requesting. Many laboratories already have excellent handbooks summarizing these requirements available on their hospital intranets. The increasing implementation of

computerized test requesting in both primary and second-ary care settings provides an additional opportunity to ensure that relevant information is readily accessible at the time a test is requested.

SAMPLE COLLECTION, CENTRIFUGATION, AND STORAGEIt is essential that samples are labeled with the correct patient’s name or barcode label and that sufficient infor-mation (sometimes termed the “minimum dataset”) is pro-vided to ensure that laboratory results are linked with the appropriate patient record. Errors made at the time of sampling may be very difficult to identify subsequently.

Manufacturers’ advice regarding suitable tube types and sample stability should always be followed. The increasing use of serum gel tubes means that in many laboratories serum is not removed from the clot prior to analysis of stable analytes on automated analyzers. Depending on the analyte, samples can generally be stored at +4 °C for at least a week.

Some labile analytes, particularly peptide hormones, may rapidly degrade. Ideally, samples for such analyses should be centrifuged at 4 °C as soon as they arrive in the laboratory and then immediately frozen at temperatures below −30 °C. After being stored frozen, thawed samples should be mixed gently but thoroughly by inversion.

Some analytes need to be measured in plasma, but not every anticoagulant is suitable, e.g., heparin interferes in some analyses. In addition, in patients treated with heparin, small almost invisible clots may form due to conversion of fibrinogen to fibrin, and if unnoticed, may lead to inaccurate and imprecise results. This problem can be avoided by centrifuging serum samples prior to testing.

Blood samples should never be frozen before separation of the blood cells as when frozen whole blood is thawed, hemolysis occurs and cell contents are released. Hemolysis may lead to falsely low results (due to dilution by the cell contents) or to falsely high results (if the analyte is present in higher concentration in the blood cells). In addition, the cell contents or membranes can cause interference in some immunoassays.

While temporary storage in polystyrene tubes is prob-ably appropriate, if long-term storage is intended (e.g., for research studies), polypropylene tubes with a small capacity are preferred. Storage should be at tem-peratures below −30 °C (ideally at −80 °C). Since multiple freezing and thawing may lead to erroneous results due to molecular degradation or protein aggregation, it may be desirable to dispense the plasma or serum in aliquots of at least 0.5 mL.

For further information about sample management, see SAMPLE COLLECTION, INCLUDING PARTICIPANT PREPARA-TION and SAMPLE HANDLING.

Quality AssuranceCatharine M. Sturgeon1 ([email protected])

C H A P T E R

6.2

1 This chapter contains figures and tables from the previous version of this chapter, by Pierre Blockx and Manuella Martin.

442 The Immunoassay Handbook

IQC and EQA as Tools for Assessing QualityThe primary focus of well-designed IQC and EQA proce-dures is to assess analytical performance, but they can also provide some indication of how well other objectives listed above (including those most relevant in the pre-analytical phase) are being achieved. There are several key differences between IQC and EQA (Table 1). The most important is that IQC results control the release of clinical results in real time, enabling corrective action to be taken before results are released to the clinician. EQA data are usually available much later, and any remedial action required is necessarily retro-spective, so EQA complements, but cannot replace, IQC.

QUALITY SPECIFICATIONS TO MEET CLINICAL NEEDBoth IQC and EQA provide tools to achieve and maintain analytical performance that satisfactorily meets quality requirements for specific clinical decision making. Quality specifications based on biological variation are widely accepted as being most appropriate, with the most fre-quently applied requirement for desirable performance defined by an analytical variation (coefficient of variation (CVA)) that is less than half the within-subject biological variation (CVi), i.e.,

. .

More stringent quality specifications may be appropri-ate if desirable performance is readily achievable using current technology or, more critically, when there are ana-lytical concentrations (cutoff levels or decision points) that determine clinical decision making and have major impli-cations for the patient (e.g., when screening for Down’s syndrome or using troponin in the emergency room). Optimum performance may then be defined as

. .

Conversely, if current technology is not sufficient to achieve desirable performance for a particular analyte, less stringent minimum performance standards such as

.

may be implemented. Having established the performance to be achieved, this figure represents the total allowable analytical error and will inform decisions about perfor-mance limits for both IQC and EQA.

Internal Quality ControlThe essential requirements for an effective IQC program are outlined in Table 2 and are considered in more detail below.

IQC SAMPLESIQC samples are specimens with previously designated concentrations. Used appropriately, they enable indepen-dent detection of systematic (and occasionally random) errors associated with changes in instrument performance, laboratory procedures, and/or reagent changes. Controls are not used to prepare standard curves, and they should not be used to validate assays.

Sources of IQC SamplesIn practice, IQC samples are usually kit controls supplied by the assay manufacturer, commercially available controls from the third-party suppliers, and/or controls prepared in-house from pools of patient samples. They should resemble patient specimens as closely as possible but may need to con-tain added preservatives or may be frozen or lyophilized.

Kit controlsA major disadvantage of the use of kit controls is that they are often prepared in the same way as the calibrators and may be in a different matrix from clinical samples or may contain purified analyte. Consequently, they may not behave in the same way as patient samples. In addition, as

TABLE 1 Key Differences between IQC and EQA

IQC EQA

Assesses whether the run meets quality standards set by the laboratory

Assesses whether laboratory results meet quality standards set by an independent organization

Controls release of results in real time

Provides retrospective information so does not control immediate release of results (but may stimulate improvement)

Provides a measure of the stability of test results

Demonstrates how well results compare to those obtained elsewhere

Can be applied to all tests, including research tests

May not be available for all tests

TABLE 2 Requirements for Effective IQC for Immunoassay

Design of IQC Protocol Suitable number of IQC specimens per run or batch Specimens suitably positioned and included more than once per

run to check for assay drift IQC specimens to be treated exactly as if they were patient

specimens Results examined and acted upon before reporting clinical results

Characteristics of IQC Samples Matrix identical to or resembling as closely as possible that of the

samples to be tested (e.g., human serum for clinical samples) Analyte concentrations in the clinically relevant range, and close

to decision levels of the test Stable (before and after reconstitution if lyophilized) and

homogeneous Free from avoidable infectious hazards (e.g., negative for hepatitis

B, hepatitis C, and HIV antigens) Single batches available in sufficient volume Economically viable

Definition of Target Values Well-established target values, preassigned or determined

in-house using appropriate statisticsAssessment of IQC Performance

Objective rules for acceptance or rejection of assay runs Highlight gross errors (outliers) and investigate causes

443CHAPTER 6.2 Quality Assurance

kit control reagent lots may themselves change relatively frequently, their use in monitoring kit lot-to-lot variation over significant time periods can be limited.

Commercial controlsCommercial control preparations have several advantages. They are supplied by a different manufacturer and are generally freeze-dried, stable, and available in large quan-tities so that the same lot may be used for a long time. A number of different analytes can be determined in each control, and the concentrations are at different levels. However, although the controls are usually based on defi-brinated human plasma, which is similar to serum, the plasma may be stripped of most of its contents before the analytes are added back to give the required concentra-tions. Although manufacturers adjust protein and salt con-centrations back to normal levels, commercial controls are not the same as unprocessed serum. Additionally, many different analytes are present in the same control prepara-tion (often at high concentrations), which can cause a dif-ference in control values between two methods that use different antisera, due to cross-reactivity. Control values can also alter if a manufacturer changes the antibodies used in a kit, so commercial control preparations may not always reflect how an assay is performing with clinical samples. This is more likely to occur for heterogeneous analytes such as those frequently measured by immu no-assay. For these reasons, although these controls are gen-erally suitable for routine IQC purposes and are superior to kit controls, they should not be used for comparisons of different assays for the same analyte.

In-house controlsIQC samples can also be prepared “in-house,” using pools of patient samples. Pools have the advantages of being cheap, being similar in composition to routine patient sam-ples, and being available for analytes for which no commer-cial preparations exist. However, they have some important disadvantages, which should not be overlooked. They may be positive for hepatitis B or C, or HIV, they may not be very stable (unless lyophilized), they are available in rela-tively limited volumes, and their concentrations may be difficult to predict. Since some analytes may dissociate fol-lowing the repeat cycles of freezing and thawing that are necessary for their preparation, in-house IQC samples should not be used for dilution and correlation studies.

Concentrations and Characteristics of IQC SamplesIt is essential that providers of IQC samples demonstrate that their samples are stable, homogeneous, and noninfec-tious. Users require them to be economically priced and available in sufficient volume to enable the use of the same lot number over relatively long periods of time (ideally several years). If control sera are lyophilized (as is neces-sary for some unstable analytes such as adrenocortico-tropic hormone), accurate reconstitution with the correct diluent is critical. Liquid control sera should be stored at −30 °C or below (preferably at −80 °C). Storage at −20 °C should be avoided as this is close to the eutectic point of serum and may cause protein breakdown.

Availability of IQC samples at clinically relevant con-centrations, especially those close to clinical decision lim-its, is essential. This is particularly important for analytes used in screening programs (e.g., α-fetoprotein and human chorionic gonadotrophin (hCG) in maternal serum screen-ing for Down’s syndrome) and for those where decision limits determine clinical action (e.g., use of prostate-spe-cific antigen results in selecting patients for prostatic biopsy or troponin to diagnose myocardial infarction).

Assignment of target values to IQC samplesMethod means and ranges of acceptable values are usually provided with commercially available IQC samples, but the ranges are often wide, and many laboratories prefer to establish their own target values. This is most conveniently achieved by assaying a fresh aliquot of each IQC sample in different runs. Where assay performance is good, as is fre-quently the case for automated analyzers, single rather than duplicate measurements may be appropriate. The means and standard deviations (SDs) can then be calcu-lated, and the means are taken as the target values. The minimum number of assays required to estimate the SD is 20. Where the same analyte is run on multiple systems (i.e., more than one analyzer), some laboratories undertake a minimum of 15 determinations on each system and col-late the data to determine the target. Single rather than duplicate determinations are sufficient. Ideally, assign-ment of target values should involve the use of more than one reagent and calibrator lot over the period to obtain more representative data, but this is not always possible.

Number and positioning of IQC samplesIQC samples should be included at regular intervals, whether assays are performed using automated random-access systems (where a run may be defined as a 12 or 24h period) or in batches (most frequently for manual assays). Here the term “run” will include batch analyses unless otherwise specified. Inclusion of QC specimens of at least two concentrations is desirable (more may be required for some analytes), and these may be supplemented by inclu-sion of previously assayed clinical specimens (sometimes termed “repeat analytical controls”).

IQC samples are often conveniently placed at the begin-ning and the end of the run (and frequently also in the mid-dle of the run) or may be randomly positioned throughout the run. Where runs are continuous, at least three controls are typically run per shift, e.g., at 8 am, 1 pm, and 9 pm.

IQC PROGRAMSThe purpose of IQC is to enable the assessment of

Within-run precision, through the evaluation of replicates;

Between-run precision, from the variation in the values of IQC samples over time;

Within-run bias, by comparing average values of con-trol samples with their target values; and

Trends, cycles, and patterns, by careful monitoring of the data from each run using control charts. In some cases, this approach may also be applied to other parameters, e.g., percent binding at zero concentration (nonspecific binding in immunometric assays).


These checks all involve comparing the run under evalua-tion with previous runs, with the assumption that consis-tency of controls, calibration curve shape, and within-assay precision provides assurance that clinical results are simi-larly consistent. The basic performance statistics required are summarized in Table 3.

Assessing Within-Run PrecisionWithin-run precision is evaluated by calculating the CV of the concentrations of replicates of IQC samples. These CVs are best interpreted by plotting them against concentration in a precision profile. Generally, when drawing a precision profile, a reasonable criterion for acceptable performance is a CV of 5% for at least 90% of all the results between the lowest concentration calibrator (excluding the zero calibra-tor) and the highest concentration calibrator. The remain-ing 10% of the results may have a slightly higher CV but should be at either end of the calibration range (see Fig. 1).

Within-run CVs of ≤5% are generally readily achievable using automated analyzers. They may be more difficult to achieve for manual assays and for some analytes that are inherently difficult to measure reproducibly, e.g., because current assays are not sufficiently sensitive to measure low enough concentrations of a particular analyte in biological fluids.

Assessing Between-Run PrecisionAssessment of between-run precision requires a method for recording and comparing (in both tabular and graphi-cal form) key parameters (including IQC results) from each run together with a system of objective rules for deciding whether to accept or reject results and when to investigate changes in performance.

Computerized methods are now available that plot the charts required, both speeding up the process and flagging warnings of changes in performance automatically. Graphs can of course be plotted manually if wished.

Several options for assessing between-run precision are described below.

Control SheetsA simple but practical approach for a basic quality control program involves testing of IQC samples at three different concentrations in each run. The control values obtained

are recorded on a control sheet, and after every 20 runs (or a defined time period), the mean, SD, and % CV are calculated. Results that fall outside the predetermined lim-its, such as ±2 SDs of the mean, require further investiga-tion (this represents the 95% confidence interval).

The control sheet provides information about the between-run performance of the method used and may also indicate the changes associated with the between-lot variation provided the lot numbers used are recorded on the sheet (see Table 4).

Levey–Jennings Control ChartsProblems associated with drift or changes of reagent are easier to recognize and interpret if QC data are graphed, most often on a Levey–Jennings (or Shewhart) control chart (Fig. 2).

These charts are constructed by plotting the control data on a graph in which the mean values and the SDs for each IQC sample are indicated on the y-axis and the run identifier (date or run number) is recorded under the x-axis. Additional information about kit lot numbers or reagent changes may also be recorded.

TABLE 3 Basic Performance Statistics for IQC

Precision Bias Comments Relating to EQA

Within run Within-sample, within-run CV (precision profile)

Mean differences between results on IQC pool and target value

For individual participants, EQA schemes provide information about within-run bias but not about precision

Between run Within-sample, between-run CV of QC pools across runs

Mean differences between results on QC pools and target value across several runs

The mean (cumulated) bias for QC pools across several distributions and the precision (variability) of that bias provide the performance parameters used by most EQA schemes

Table after that in Seth, J. Quality assurance. In: Principles and Practice of Immunoassay, 2nd edn. (eds Price, C.P. and Newman, D.J.), 209–242 (MacMillan Reference Ltd, Bath, 1997).

FIGURE 1 Precision profile for a typical immunoassay. The working range (CV <5%) is between (approximately) 1 and 25 ng/mL.


TABLE 4 Control Sheet

Quality ControlControl preparation: UVWParameter: Human growth hormoneExpiry date: October 2000Kit: XYZMaster lot: 2785 VUnit: mU/L

Low Medium High Lot No. Expiry Date

N, SD, % CV 20, 0.12, 3.7 20, 0.54, 3.7 20, 2.2, 5.1

Mean 3.26 14.6 42.5

± 2 SD 3.02–3.50 13.5–15.7 38.2–46.8

Date Technician

10 April 1999 CC 3.33 14.7 45.4 32,051 9 May 199917 April 1999 KC 3.40 14.2 45.3 32,051 9 May 199924 April 1999 CC 3.40 14.6 46.7 32,051 9 May 199930 April 1999 MK 3.30 15.3 48.3 32,051 9 May 19998 May 1999 AL 3.43 14.8 45.1 32,051 9 May 199914 May 1999 SvA 3.48 14.6 45.5 32,463 30 May 199922 May 1999 SvA 3.33 14.9 46.2 32,463 30 May 199929 May 1999 AL 3.46 14.3 44.1 32,463 30 May 19995 June 1999 RS 3.11 14.9 44.2 33,064 25 July 199912 June 1999 GV 3.47 15.4 46.7 33,064 25 July 199919 June 1999 MV 3.44 15.0 41.7 33,064 25 July 199926 June 1999 LT 3.49 15.4 42.7 33,064 25 July 19993 July 1999 LT 3.33 14.2 38.9 33,064 25 July 199910 July 1999 LH 3.42 14.6 42.4 33,064 25 July 199917 July 1999 GV 3.36 15.5 45.0 33,560 06 September

1999

FIGURE 2 Levey–Jennings plot for a typical immunoassay. The result on 30th April indicates a run that is out of control.


Control charts require an estimate of the mean and SD for each IQC sample, i.e., the same information that is acquired when assigning values to IQC samples prior to use. Acceptable limits of performance are most often expressed in terms of multiples of the SD, with warning limits gener-ally considered to be ±1, 2, and 3 SDs. The ±2 SD limit approximates the ±95% confidence intervals, which means that about 95% of the IQC values should be within this limit if the run is under control. About 99.7% of the values should be within the 3 SD limit, and for this reason, if a control lies outside this limit, the run is normally rejected.

Decision rules may be set to suit particular requirements of the laboratory. For example, exceeding the 2 SD limit may require an experienced person to check the run results, while if two IQC samples in the same run both exceed the 2 SD limit, the assay may be rejected.

Cusum ChartsAlthough the graphical presentation of Shewhart/Levey–Jennings control charts provides information superior to that provided by simple tabulation of IQC results, it is not very sensitive to small changes in mean values. Consistent

bias from the target values is evident more quickly and clearly if Cusum or cumulative sum charts are plotted (Table 5 and Fig. 3). These are similar to the Shewhart/Levey–Jennings charts, but instead of plotting individual values for a control, the difference of each new value from the target value is added to the cumulative sum of all the previous differences.

In a Cusum chart, a horizontal line signifies zero bias while any systematic bias results in a slope away from the horizontal, i.e., upward for positive bias and downward for negative bias. The steepness of the slope is proportional to the magnitude of the bias. Most Cusum programs use a threshold value for the deviation, initiating the Cusum cal-culation only if the deviation has exceeded a threshold value, typically set at 0.5 or 1 SD. If the difference from the target value changes its sign (e.g., from positive to neg-ative), the Cusum line is interrupted. A new plot is started when another observation exceeds the threshold value. The user can choose to set warning limits, e.g., at ±2 or 3 SDs. When the Cusum line crosses these warning limits, the run is said to be out of control.

Cusum charts can pinpoint when a change in mean value occurred because such changes will lead to an inflection point in the plotted line as the slope changes. This is help-ful when troubleshooting the cause of bias.

Criteria for Run Acceptance or RejectionThe widely adopted Westgard rules (see SAMPLE STATIS-TICAL PACKAGES FOR IQC) provide a statistically sound framework that enables objective decision making about whether or not a run should be rejected. Accepting that some background statistical variation is unavoidable, the Westgard rules have been designed to provide a low probability that normal assays are rejected and a high probability that errors are detected. At least two decision rules are required, one to detect the random analytical error and another to detect the systematic analytical error. Seven different Westgard decision rules can be applied to two or more control samples assayed in each run as outlined in Table 6. A decision to accept the ana-lytical run without further review requires that there is no violation of any of the rules. The 12s rule can be used to prompt more detailed inspection of the data using the other rules. If any of these are also violated, the run should be rejected.

QC SOFTWARE PROGRAMSThere are a number of commercially available QC soft-ware programs (a few of which are listed in Sample Statis-tical Packages for IQC) that provide a convenient means of implementing rigorous IQC decision limits, usually based on the Westgard rules. Such programs are also installed on most automated analyzers, providing continuous monitor-ing of the accuracy and the precision of the individual ana-lyzer. Multiple control samples can be defined, with control results saved in QC files by control sample and/or by analyte. These may be archived to convenient storage media (e.g., CDs and disk drives) to provide a permanent record that complies with accreditation standards, which may require data storage for up to 30 years. Data reports

TABLE 5 Calculations for Cusum Chart

Date

Control Concen-tration (mU/L)

Bias (Value − )

Bias − 0.5sd Cusum

10 April 45.6 0.6 – 0.017 April 45.2 0.2 – 0.024 April 46.8 1.8 0.9 0.930 April 48.4 3.4 2.5 3.48 May 45.6 0.6 – 3.414 May 45.4 0.4 – 3.422 May 46.6 1.6 0.7 4.129 May 45.0 0 – 4.15 June 44.8 −0.2 – 0.0 Reset12 June 46.6 1.6 0.7 0.7 Reset19 June 45.4 0.4 – 0.726 June 45.0 0 – 0.73 July 43.4 −1.6 −0.7 −0.7 Reset10 July 44.8 −0.2 – −0.717 July 46.8 1.8 0.9 0.9 Reset24 July 45.8 0.8 – 0.931 July 45.5 0.5 – 0.97 August 46.2 1.2 0.3 1.214 August 45.7 0.7 – 1.221 August 45.9 0.9 0.0 1.2

Control mean ( ) = 45.0

SD = 1.8% CV = 4.0

Threshold (0.5 SD) = 0.9

To calculate the values to be plotted on a Cusum chart, subtract the target mean for the control from each individual result and then subtract the threshold value. Then add each number to the “cumulative sum” of the previous numbers. The Cusum is reset to zero if the difference from the target value changes its sign.


can be generated for different time intervals (daily or other), by analyte or by control sample. Levey–Jennings and Cusum graphs can readily be obtained with flags high-lighting results that do not meet set criteria.

Many automated systems have what is termed “mid-dleware” that acts as an interface between the analyzer and the main laboratory information system (see Chap-ters 7.9–7.19). The software has a multitude of applica-tions for the data management (e.g., trapping results

outwith pathological limits (for telephoning or further verification) and rules to convert results below limits of detection) and can also contribute to the management of IQC data. Middleware software enables automated appli-cation of user-defined QC rules. When these are breached, the software will trap and prevent the release of results while the reasons for the breach are being investigated and resolved. Clinical results may be selected for release or rerun depending on the nature of the investigation. This

FIGURE 3 Cusum chart for a typical immunoassay.

TABLE 6 Characteristics of Some Commonly Applied Westgard Rules

Rule

Type of Error Likely to Be Detected Explanation Action Comment

12s One IQC result more than ±2 SDs from the mean

Additional inspection of data using the other rules required

This is the usual warning rule in a classical Levey–Jennings procedure

22s Systematic Two IQC results more than ±2 SDs from the mean in the same run or (for the same IQC sample) in two consecutive runs

Additional inspection of data using the other rules required

This is a usual rejection limit in a classical Levey–Jennings procedure

13s Random One IQC result more than ±3 SDs from the mean

Assay results held until error identified

This is a usual action or rejection limit in a classical Levey–Jennings procedure

41s Systematic Four consecutive IQC results more than ±2 SDs from the mean


This can occur for one IQC sample in four consecutive runs or across two controls in two consecutive runs

R4s Random Two consecutive IQC results are >4 SDs apart (i.e., one outside + 2 SDs and one outside − 2 SDs)


Shift Systematic A series of ≥10 IQC results consistently above or below the mean but remain within the limits

Assay suspended until cause found

Cause often a constant systematic error (bias)

Trend Trend A series of ≥10 IQC results change in a consistent direction

Assay suspended until cause found

Cause often a constant proportional error (bias). This error is readily identified from a Cusum chart

Table after that in Fritsma, G.A. and McGlasson, D. Quick Guide to Laboratory Statistics and Quality Control (AACC Press, Washington, DC, 2012).


provides the individual laboratory with convenient and up-to-date information in a readily accessible form.

In a further extension of IQC provision, some diagnos-tic manufacturers of automated instruments now offer arrangements by which IQC data from all users of a par-ticular method are uploaded to a central repository. This allows comparison of results from all laboratories using the same method. Assuming data are available for the same IQC material (often the kit controls), this may help to identify possible reasons for laboratory-related differences in performance (e.g., use of different reagent lots) and can enhance conventional IQC practice. Further develop-ments in such provision are likely, particularly as the skill mix in laboratories changes with increasing automation. However, it should be noted that unless information about acceptability of a run is returned from the manufacturer to the user immediately (i.e., in real time), this approach—which resembles but does not replace EQA provision (see later)—complements rather than replaces proactive exami-nation of IQC data in the individual laboratory prior to the issue of results.

REQUIRED ACTION FOR AN OUT-OF-CONTROL ASSAY RUNIQC protocols are only of value if the data are examined in real time, and immediate action is taken when any IQC results fail agreed rules. Although it may be tempting to reanalyze the IQC sample repeatedly until the result falls within acceptable limits, this is not appropriate: the action that should be taken is as outlined in Table 7.

Further troubleshooting may be necessary if it is still not possible to resolve the problem. Some factors that may contribute to poor performance and should be excluded as possible causes are listed in Table 8. It is particularly important that the performance characteristics of labora-tory equipment such as pipettes, dispensers, diluters, auto-mated devices, and detection systems are checked, e.g., when a new instrument or an accessory such as an auto-mated pipette is introduced, when a repaired instrument is brought back into service, or when a particular instrument is suspected of poor performance. IQC data should be reviewed regularly with failures explored in a constructive and collaborative manner and remedial action taken to avoid recurrence. Documentation of such discussions is often required by accreditation bodies and can be conve-niently effected, for example, by preparing corrective and preventive action reports.

IQC FOR POINT-OF-CARE DEVICESThe above discussion has focused on IQC practice for quan-titative immunoassay methods. However, with the increas-ing use of point-of-care testing (POCT), attention also needs to be paid to monitor the use of these devices in the clinical setting. Helpful guidance is available from the UK Medi-cines and Healthcare products Regulatory Agency, which states that managers of POCT services must have arrange-ments in place for training, management, quality assurance, and quality control of POCT devices. Assessment of the ser-vice by an external accreditation body is recommended.

Many hospitals now have a dedicated POCT coordinator who is responsible for arranging occasional distribution of appropriately constituted IQC samples to relevant POCT sites, for assessing the results and for implementing immedi-ate remedial or corrective action (often additional training) when required. These activities can helpfully be undertaken in collaboration with the relevant diagnostic manufacturers who may also provide formal training to users.

External Quality AssessmentAs outlined in Table 1, EQA complements but cannot replace IQC although many of the fundamental require-ments are similar. It is important to note that while partici-pation in relevant EQA programs (where available) is an essential requirement for laboratory accreditation, how EQA performance is viewed by regulatory authorities var-ies internationally. In some countries (e.g., Germany and the United States), demonstration of satisfactory EQA performance is essential for reimbursement, while in oth-ers (e.g., the Netherlands and the United Kingdom), the ethos is less regulatory and more collaborative and educa-tional. However, in all countries, there are established pro-cedures by which higher authorities can take action (up to and including stopping the laboratory from performing the test) if performance of an individual laboratory is unsatisfactory for one or more analytes.

The general principles underpinning EQA for immuno-assay tests are similar to those for other EQA in clinical chemistry, although there are some aspects (e.g., choice of sample material for distribution and definition of target values) that present special challenges. Basic requirements are presented in Table 9 and discussed further below.

DESIGN OF EQA SCHEMEThe frequency of sample distributions or surveys and the number of EQA samples per survey should be sufficient to allow an assessment of performance that is both practically relevant and statistically valid. Infrequent surveys with

TABLE 7 Action to be Undertaken Following an IQC Failure

Action Comments

Reanalyze the IQC sample Five percent of the results will be outside the limits when using ±2 SD from the mean as the limits of acceptability

Prepare fresh IQC sample and reanalyze

IQC sample may have been reconstituted incorrectly, deteriorated (especially at high temperature), or partially evaporated

Prepare fresh reagents and reanalyze

Reagents may have been contaminated or partially evaporated

Recalibrate instrument Instrument maintenance may be required


only a few samples are likely to provide little information of real value. The UK National External Quality Assess-ment Service (UK NEQAS) distributes three to five sam-ples monthly for many immunoassay analytes. This provides an adequate statistical basis for assessing cumula-tive performance over a 6 month period (18–30 samples). Participants return their results to the EQA center within 3 weeks of receipt and usually receive their report a week later. This allows remedial action to be taken in a realistic time scale if untoward trends in performance are detected.

It is essential for appropriate interpretation of results that the EQA center maintains accurate and up-to-date records of the method currently used by each participant. Failure to do so may lead to inappropriate conclusions about method performance, particularly for heterogeneous analytes. Similarly, the center must be aware of the units in which each participant is reporting results. It is essential for the meaningful analysis of EQA data to adopt a single scheme unit—preferably that of the relevant World Health Organization International Standard or the unit most com-monly used by participants to report clinical results. It may sometimes be necessary, particularly for schemes with par-ticipants from different countries, for the EQA center to use appropriately established conversion factors to convert results reported in non-scheme units prior to data analysis. As laboratory data are increasingly downloaded into elec-tronic patient records held on centralized computer sys-tems, achieving national (and preferably international)

TABLE 8 Possible Causes of Poor IQC Performance

Possible Causes Comments

Pipette calibration Precision and accuracy of fixed volume and repeating pipettes should be evaluated by weighing volumes from 25 to 1000 µL. Precision should be better than 1% CV for fixed volume and 0.5% CV for repeating pipettes and accuracy between 100 ± 1% (commercially available pipette calibrating services are used by some laboratories)

Inaccurate dilutions Manual dilutions can be checked by making serial dilutions (e.g., consecutive 1 in 2 dilutions of a stock solution up to a 1 in 32 dilution) or a dilution series (e.g., preparing 1 in 2, 1 in 3, and 1 in 5 dilutions and then further diluting each of these to obtain 1 in 4, 1 in 8, 1 in 6, 1 in 9, and 1 in 10 dilutions). Duplicates of 100 µL of each dilution are then pipetted. Precision on the mean of the duplicates after multiplication by the corresponding dilution factors should be <2% CV. If appropriate, manual results obtained can be compared with dilutions made on the automated analyzer

Inadequate analyzer maintenance It is essential to ensure that manufacturers’ instructions for maintenance and performance checking are carried out carefully. Depending on the system, required procedures may include:Daily: Emptying waste containers, removing empty or outdated reagent packs, verifying inventory, inspecting and cleaning sample trays, and ensuring that QC samples have been processedWeekly: Cleaning probes, performing system cleaning procedures, and undertaking accuracy and precision testsMonthly: Backing up QC calibration files, replacing components

Preparation of reagents and controls

Instructions in the package insert must be followed carefully, particularly in relation to storage tempera-tures and reconstitution. Vials of lyophilized materials should be opened carefully and reconstitution carried out using a calibrated pipette, with the correct diluent at the appropriate temperature. Mixing should be carried out cautiously (e.g., by inversion or on a roller) to avoid foaming. Extended contact between the solution and the vial stopper is best avoided. Polypropylene tubes should be used when samples are aliquoted for storage. Thawed reagents should not be refrozen

Temperature control The temperatures of refrigerators and freezers should be checked daily. Refrigerators should maintain a temperature of between 2 and 8 °C and freezers should ideally be less than −30 °C

Water quality Water should be as free from impurities as possible. This is usually achieved using deionization and/or reverse osmosis to remove impurities. Most immunoassays can be performed with water with resistivity not lower than 5 MΩ/cm at 25 °C and an organic content of less than 2 parts per million

Staff training Ensuring that staff are adequately and appropriately trained is critically important, with regular compe-tency assessment in place.

TABLE 9 Requirements for Effective EQA for Immunoassay

Design of EQA Scheme Frequency of sample distribution sufficient to provide practically

relevant and statistically valid data Rapid return of EQA reports to participants Accurate documentation of the methods used Common units for reporting results EQA samples assayed by participants in exactly the same way as

routine clinical specimens Clear and concise reports

Characteristics of EQA Samples Should behave identically to patient samples in all assay systems Analyte concentrations appropriate to the clinical use of test Stable under conditions of sample distribution Free from avoidable infections hazards (e.g., negative for hepatitis

B, hepatitis C, and HIV antigens)Definition of Target Values

Reference method values wherever possible Consensus values validated for accuracy and stability

Assessment of EQA Performance Use of appropriate statistics Estimation of overall, method group and individual laboratory

performance Highlighting of gross errors (outliers) National or international parameters defining acceptable

performance limits Assesses other aspects of performance (e.g., interpretation,

reference intervals, and effect of interferences) as appropriate

Table after that in Seth, J. Quality assurance. In: Principles and Practice of Immunoassay, 2nd edn. (eds Price, C.P. and Newman, D.J.), 209–242 (MacMillan Reference Ltd, Bath, 1997).


agreement on units for reporting is a high priority that can be facilitated and encouraged by EQA providers.

Participants should treat EQA samples exactly as they would clinical samples. Failure to do so is likely to give an overly optimistic impression of performance that is clearly undesirable. Anecdotal data suggest that preferential treat-ment of EQA samples is more likely to occur when EQA data are used for accreditation in a more regulatory and legislative manner. This tendency is probably reduced by promoting the educational role of EQA, enabling good communication between EQA providers and participants, and encouraging the view that EQA is part of a collabora-tive effort among professionals (including those in the diagnostics industry) to improve laboratory services rather than police them.

CHARACTERISTICS OF EQA SAMPLESEQA for immunoassay requires the use of human body fluids (serum, plasma, or urine) so as to resemble the char-acteristics of the relevant clinical samples. This should ensure that the protein matrix and the immunological nature of the samples are similar. In practice, it is rarely possible to prepare an EQA sample using material from a single donor, and material obtained from different donors must be pooled. Blood transfusion services may be able to provide outdated or otherwise clinically unsuitable serum or plasma donations for the use in the preparation of EQA samples. Advantageously, such material will already have been tested for possible infectious hazards. In some cases (e.g., in order to achieve high concentrations of a given analyte such as a tumor markers), small volumes of patient serum containing high concentrations of the required ana-lyte must be added to a pool of normal serum or plasma to achieve measurable concentrations. Clinical samples sur-plus to diagnostic testing may be used for this purpose pro-vided this is permitted by relevant legislation. This does not require ethical approval in the United Kingdom, pro-vided the specimens are anonymized. Testing of pools of anonymized specimens for infectious hazards is also per-mitted although testing of individual anonymized samples is not. Donations specifically taken for EQA purposes require informed patient consent.

It is now generally accepted that EQA providers should be able to demonstrate that the pooled material behaves similarly to single-donor material, i.e., that the specimens are commutable. Commutability is defined as a property of the EQA sample, which means that the sample reacts in different analytical methods in a similar manner (i.e., that the same numerical relationship is observed) as do repre-sentative clinical patient samples. A commutable EQA sample will give the same result in a given system as would a patient sample containing the same quantity of analyte. This is a difficult concept, particularly for complex ana-lytes such as those usually measured by immunoassay, and methods for assessing commutability are still being refined. However, an EQA sample that is not commutable for dif-ferent methods may not provide information that reflects method performance for clinical samples.

Ensuring the commutability of standards is also impor-tant. Addition of known amounts of standard analytes (where possible the relevant International Standard) to

serum is valuable in checking method recoveries. How-ever, as has been recognized for many years for analytes known to exist in serum in heterogeneous forms, the pat-tern of results obtained for samples containing added puri-fied (exogenous) hormone may differ significantly from that seen with endogenous hormone. Samples containing exogenous analyte are, therefore, routinely excluded from the calculations of cumulative performance in many UK NEQAS schemes.

In order to prepare commutable samples, it is necessary to minimize the number of manipulations to which the matrix is subjected. Nevertheless, it is usually desirable to filter pools of liquid serum or plasma down to at least 0.2 µm prior to use. Addition of preservative may also be required, particularly if samples are sent in liquid form. Kathon® (0.5% v/v) has been found to be satisfactory for many analytes and is less likely than sodium azide to inhibit signal detection systems.

As for IQC, samples for EQA should be of clinically relevant concentrations, with most of the working and/or clinically relevant range assessed each year when feasible. Stability of the samples should be established, both dur-ing storage (at −30 °C or below) at the EQA center and during transit. A convenient means of assessing the latter is by issuing samples prepared from portions of the same pool and which have been pre-aged at room temperature prior to dispatch, e.g., for 0, 3, and 7 days. As this does not take account of additional time in transit, it is likely to provide a “worst-case” indication of stability and should identify difficulties with particular analytes. This approach is helpful as an analyte that is stable when measured in some immunoassays may not be stable in others. Differ-ent samples for the same analyte may not be equally stable either in the freezer or at ambient temperature (as docu-mented for urine specimens containing hCG) introduc-ing additional complexity when assessing EQA specimen stability. While lyophilization should reduce errors due to instability, it may introduce others including loss of immunoreactivity, denaturation, and/or errors in recon-stitution. Although best avoided, lyophilization is essen-tial for some unstable analytes such as parathyroid hormone, calcitonin, and adrenocorticotropic hormone. Conditions for lyophilization should be optimized for each analyte.

Homogeneity can be conveniently assessed by assaying an appropriate number of replicates of the same specimen and confirming that results show satisfactory agreement (e.g., within ±5% of the mean).

As well as ensuring that samples are negative for infec-tious hazards to protect laboratory staff, the safety of oth-ers who may come into contact with the specimens must be considered. Packages must be appropriately labeled, and EQA providers should adhere to postal regulations on safe packaging.

DEFINING THE TARGET VALUETarget values in EQA schemes should represent the clos-est estimate of the “true” or correct value that can be achieved and be both accurate and reproducible. These aims are difficult to achieve in immunoassay, particularly for complex or heterogeneous analytes, but failure to do so


may encourage continued use of inaccurate assays. As sum-marized in Table 10, there are several candidate target values.

Ideally, target values should be assigned using a refer-ence method procedure (or well-established surrogate method traceable to such a procedure) that enables trace-ability of the result to a reference system and requires that the EQA samples are commutable. When this is feasible, traceability should be related to the database held by the Joint Committee for Traceability in Laboratory Medicine, which details approved reference methods, reference materials, and reference laboratories. The same approach can be used for EQA samples that may not be commutable, but it will not be possible to assess whether deviation from the target reflects calibration error or matrix effects. As yet this general approach has been applied most successfully in

the EQA of cortisol and a few other steroid hormones, with further development highly desirable.

Use of consensus mean or all-laboratory trimmed mean (ALTM) target values is convenient. However, even if quantitative recovery can be demonstrated, this does not guarantee accuracy, particularly if isoforms present in patient specimens differ from or are more varied than those present in the purified standard used. Validity of the ALTM also depends on the assumption that matrix-related bias is similar in all methods. The extent to which this is the case will depend on how closely the EQA specimens resemble clinical samples. When EQA data suggest that a particular method is producing results that differ signifi-cantly from those of other methods and if there is no sig-nificant evidence of calibration error, further work should be undertaken by the EQA provider to establish whether the same effect is observed for patient specimens. Avail-ability of well-validated panels of reference sera facilitates such investigation.

ASSESSMENT OF PERFORMANCEEssentially, the same two statistical parameters are used in all EQA schemes to assess individual laboratory perfor-mance, although specifics of terminology and mode of cal-culation may differ. Choice of target, whether and/or how results are log transformed, and methods for identifying and excluding outliers are most likely to vary. However, the parameters usually include a component (sometimes termed BIAS) that estimates the cumulated deviation of results for individual samples from the target values over a given time period or number of samples and a component (sometimes termed consistency or variability of the BIAS (VAR)) that represents the variability of that BIAS over the same time period. These data are complementary to those from IQC, the major difference being that IQC assesses these parameters within samples whereas EQA assesses across samples.

An additional estimate (sometimes based on the “chosen CV”), relating to the degree of difficulty of measuring the given analyte and representing the current “state-of-the-art”, is sometimes also introduced. The statistical validity of this approach may be questioned, especially for complex heterogeneous analytes.

Limits of acceptable performance are usually defined in terms of the above parameters, e.g., laboratories may be required to maintain cumulative BIAS within ±20% of the ideal figure of 0% and cumulative VAR <20%. These per-formance criteria are frequently set by EQA staff in col-laboration with the members of the relevant scientific advisory group and then (in the United Kingdom) submit-ted for review and approval to the National Quality Advi-sory Panel for Chemical Pathology. Every 4 months, the panel receives reports from all EQA centers operating in the United Kingdom. These list those laboratories with persistent poor performance, i.e., with BIAS and/or VAR outwith the limits of acceptable performance for three con-secutive months and those who have failed to return results regularly to the EQA center. In practice, particularly for well-established analytes, few laboratories are referred to the panel as most problems can be resolved through dialog between staff in the laboratory and the EQA center.

TABLE 10 Target Values that may be used in EQA for Immunoassays

Target Value Comments

Reference method value

The ideal target value but available only for analytes for which a reference method based on isotope dilution mass spectrometry (or similar technique) is established, e.g., well-defined analytes such as steroid hormones. Likely to become feasible for more complex analytes (e.g., parathyroid hormone) as technology improves but requires consider-able investment of both time and finance

Mean of all laboratory results

The consensus mean of all results (trimmed to remove outliers) or ALTM is the most widely-used target in immunoassay EQA schemes. Provided results are available from at least 15 participating laboratories, the ALTM is reasonably robust, easily calculated, and applicable to a wide range of analytes. However, its accuracy cannot be assumed and should be continuously tested, e.g., through recovery experiments in which the observed mean is compared with the calculated value on adding a known amount of the analyte to the sample. Further evidence to support the validity of the ALTM can be obtained by demonstrating linearity of the ALTM on distribution of different concentrations of the same pool and assessing the stability of the ALTM on repeat distribution of the same pool

Mean of results from laboratories using a particular type of procedure

This is similar to the ALTM but includes results from a subset of methods and may be appropriate if methods for the same analyte differ fundamentally, e.g., “intact” and “total” method group means for hCG methods, depending on whether the hCG beta subunit is or is not recognized

Mean of results for a single method

May be used when results obtained in different methods vary substantially but can provide an over-optimistic impression of overall performance and should be regarded as a temporary measure. Its validity may be in doubt if there are too few users (five is the minimum)

Mean of reference laboratory group

May be helpful for an analyte under development for which a few laboratories have special expertise, but has little applica-tion for established assays


Overall PerformanceProvided EQA samples are appropriate and target values valid, EQA data can provide an excellent indication of “state-of-the-art” performance. The scatter of results (expressed as SD or CV) around the ALTM (Fig. 4) indi-cates how well methods agree and enable ready assessment of which analytes would most benefit from efforts to improve method comparability. For methods where a ref-erence method procedure exists or where there is a reliable recovery-validated target, the goal for individual manufac-turers is clearly to achieve accurate calibration of their methods.

Method-Related PerformanceSimilar examination of EQA data for individual methods provides a powerful means for assessing method perfor-mance, provided there are sufficient users (ideally a min-imum of 10) and that the target value can be validated. Method-related reports can be prepared to enable ready assessment of the cumulative bias from the target and its variability, which is a measure of between-laboratory precision. Uniquely, these data represent performance in many laboratories and under routine “field” condi-tions. They can be particularly helpful to diagnostic manufacturers in highlighting methods that require improvement.

Individual Laboratory PerformanceThe prime concern of the individual participant is whether their own EQA results are acceptable. Provision of EQA reports that readily indicate whether this is the case is, therefore, desirable. Some EQA schemes present “traffic light” reports in which colored symbols represent desir-able, acceptable, and unacceptable performance. However, this approach may pose difficulties for some complex het-erogeneous analytes measured by immunoassay since there may be no “correct” answer and—even where an ALTM target is used—there may be significant between-method differences reflecting differing antibody specificities and/or method design. For such analytes, a more pragmatic approach is desirable. It may be appropriate to compare the individual laboratory’s results with those of others using the same method and to take action only if results are out of consensus. This requires flexibility that is more likely to be feasible in EQA schemes with an educational rather than a regulatory ethos.

Informative Presentation of EQA ResultsEQA schemes differ in the frequency with which they pre-pare reports, with some presenting data once or twice a year, as a cycle summarizing data from a number of speci-men distributions, and others presenting data following each distribution, as is the case in the UK NEQAS schemes. Whatever the frequency or format, the report should include:

an estimate of performance with respect to the accepted criteria (e.g., BIAS and VAR);

the mean bias for each distribution, i.e., averaged over the specimens distributed;

the stability of the target values across distributions for pools issued (as different specimens) more than once;

the reproducibility of the laboratory’s results on a sin-gle sample across distributions;

bias from the target related to sample type and concen-tration; and

the laboratory error on each sample (i.e., percent devia-tion from the target value).

An example of a page from an EQA report that presents all the above information is shown in Fig. 5.

Computerized reports from some EQA providers may be accompanied by additional commentary relevant to the specimens issued (e.g., the results of recovery or interfer-ence studies) or other information of interest. Brief sur-veys of recent literature reports related to the analytes and their clinical use may also be provided.

Highlighting of Outliers and Other ErrorsEQA providers should review participants’ reports before returning them to ensure that results are generally in agreement with the expected and also to identify any out-liers that should be brought to the attention of the labo-ratory. In practice, most outliers reflect errors made when the EQA specimens were booked in at reception or when results were submitted to the EQA center (either via a web portal or by fax). Although inevitably reflecting lack of care and attention to detail, in most cases, the lat-ter may be regarded as artificial EQA- associated errors since most clinical results are now uploaded directly from automated analyzers to the laboratory information system. However, this is not always the case: pregnancy testing results obtained at POC are often manually tran-scribed, and transcription errors for EQA specimens will then reflect what is happening for clinical results. Errors

Specimen : M157 259 36.1 6.3 0

44 37.4 4.6 0 24 34.7 6.4 0 23 34.7 6.6 0 103 36.3 5.5 0 96 36.3 5.3 0 46 36.4 6.9 0 18 34.6 3.7 0 13 34.8 4.7 0

n Mean GCV Outl.

All methods

Abbott ArchitectBeckman Access

Beckman DxlRoche Elecsys

E170, e601, e602Siemens A CentaurSiemens 1200fam

Immulite 2000, XPi

FIGURE 4 Histogram showing a typical spread of results observed for a single sample in the UK NEQAS for alpha-fetoprotein (AFP). The arrow indicates where this laboratory’s result lies on the graph. (Data used with the permission of UK NEQAS (Edinburgh)).


made during the booking-in stage are also likely to occur with clinical samples, with a reasonable assumption being that their frequency is at least proportionate to that observed for EQA specimens. Logging of these errors by the EQA center should form part of performance moni-toring, with participants requested to investigate the cause, take remedial action, and provide written confir-mation of action taken. If laboratories make a significant number of such errors within a given time period (e.g., 6 months), this constitutes poor performance.

Reasons for outlier results for EQA specimens should always be carefully investigated as they may indicate an underlying analytical or reporting problem (e.g., a blocked probe, failure of level or bubble detection, or use of incorrect units when reporting results). Logging and regular review of errors reported to the EQA center can helpfully identify problems with particular methods or analyzers.

Other Aspects of PerformanceOccasional distribution of samples to assess the effect of clinically relevant cross-reactions (e.g., dehydroepi-androsterone sulfate in testosterone assays), interfering substances (e.g., human anti-mouse antibody in two-site immunoassays) or methodological pitfalls (e.g., high-dose hooking in prolactin or tumor marker assays), pro-vides a highly effective means of drawing these potential problems to the attention of participants. Assessment of

long-term stability of results, which is particularly important for tumor markers, can be achieved by distrib-uting samples of the same pool on more than one occasion.

Additional aspects of performance such as clinical refer-ence intervals and decision limits used, lowest concentra-tions reported, and post-analytical interpretative provision can also be conveniently assessed through carefully designed surveys and interpretative exercises. These may be provided with solely educational intent, without formal scoring and with the aim of encouraging improvement in practice. EQA schemes specifically designed to assess and score interpretative provision are also available and may in the future contribute to formal assessment of fitness to practice.

EQA FOR POCTWith increasing use of POCT, whether EQA of these tests is required and if so, how to implement this most effec-tively, is increasingly being discussed. A successful approach taken for hCG POCT pregnancy testing devices has been to encourage enrollment of hospital laboratories overseeing such services in national EQA schemes and then to enable that laboratory to enroll each of its POCT sites as associated participants. Sufficient sets of two liquid urine EQA specimens are then sent to the parent labora-tory monthly for the distribution to the POCT sites. All results are returned to the EQA center and analyzed

FIGURE 5 A typical “analysis of bias” chart from the monthly report of a participant in the UK NEQAS for AFP. The table summarizes laboratory performance over 6 months. Each column represents a single distribution of five samples, with the target, the laboratory’s own result for each sample, and the deviation of that result from the target shown. Stability of results for pools distributed on more than one occasion is readily apparent, as is any dose-dependent bias. Monthly and cumulative mean bias and variability of that bias are indicated at the bottom of the chart. (Data used with the permission of UK NEQAS (Edinburgh)).


using EQA software that can accept qualitative results (i.e., positive, negative, and equivocal). All reports are sent back to the parent laboratory, which has the responsibility for disseminating these to the POCT sites and implement-ing any remedial action required. Anecdotal evidence sug-gests that this hub and spoke model works well, with the arrival of the reports from the EQA center providing addi-tional opportunity for the POCT coordinator to interact with any POCT sites with unsatisfactory performance. In some cases, such EQA provision has been extended beyond the hospital setting to doctors’ offices and high street pharmacies.

LIMITATIONS OF EQAWhile EQA undoubtedly provides a powerful tool for improving analytical quality, the caveats described above need to be remembered. It is essential that EQA specimens are appropriately constituted and are commutable with patient samples, and that participants handle EQA speci-mens exactly as they would patient samples and report their results to the EQA center as they would patient results. Ensuring that target values are appropriate is also critical. Although the ALTM provides a convenient means of assessing method performance, if there are specificity differences between methods (as is the case for many com-plex analytes), method bias may appear to be high or low as a consequence. This makes it difficult to assess perfor-mance and also has commercial implications—particularly where data are used for licensing purposes—for in vitro diagnostics (IVD) manufacturers who clearly wish their results to be close to target. Use of the ALTM may at worst discourage adoption of an improved method because of out-of-consensus performance in an EQA scheme. International initiatives both to improve the traceability of results and to elucidate what is being measured in different immunoassays should help to improve the situation. Suc-cessful outcomes will require continued active dialog and collaboration among staff in clinical laboratories, EQA

centers, the IVD industry, professional bodies, and regula-tory agencies.

ConclusionsEffective IQC and EQA procedures are essential for suc-cessful total quality management in any laboratory provid-ing immunoassay testing. Ensuring that IQC and EQA specimens resemble clinical samples as closely as possible, that they are treated as if they are clinical samples, that data analysis is fit for purpose, and above all that IQC and EQA results are acted upon in an effective and timely manner, are all fundamental to improve the quality of immunoassay tests.

Sample Statistical Packages for IQC

Bio-RAD QC Net (http://www.qcnet.com/), QC Validator® (Westgard QC, Madison, WI, USA,

http://www.westgard.com/), and StatLIA CQA (Brendan Technologies, Carlsbad, CA,

USA, http://www.brendan.com/).

References and Further ReadingFritsma, G.A. and McGlasson, D. Quick Guide to Laboratory Statistics and Quality

Control. (AACC Press, Washington, DC, 2012).Fraser, C.G. Biological Variation: From Principles to Practice. (AACC Press,

Washington, DC, 2001).Miller, W.G., Jones, G.R.D., Horowitz, G.L. and Weykamp, C. Proficiency

testing/external quality assessment: current challenges and future directions. Clin. Chem. 57, 1670–1680 (2011).

Seth, J. Quality assurance. In: Principles and Practice of Immunoassay, 2nd edn (eds Price, C.P. and Newman, D.J.), 209–242 (MacMillan, Bath, 1997).

White, G.H. Metrological traceability in clinical biochemistry. Ann. Clin. Biochem. 48, 393–409 (2011).

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