Laboratory quality control: using patient data to assess analytical performance

Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data

Background

Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal quality control (IQC) and external quality assurance (EQA) data in the calculation of sigma metrics.

Methods

This retrospective study was conducted in Marmara University Pendik E&R Hospital Biochemistry Laboratory. Sigma metrics calculation was performed as (TEa-bias)/CV). CV was calculated from IQC data from June 2018 - February 2019. EQA bias was calculated as the mean of % deviation from the peer group means in the last seven surveys, and IQC bias was calculated as (laboratory control result mean-manufacturer control mean)/ manufacturer control mean) x100. In parameters where sigma metrics were <5; QGI=bias/1.5 CV) score of <0.8 indicated imprecision, >1.2 pointed inaccuracy, and 0.8-1.2 showed both imprecision and inaccuracy.

Results

Creatine kinase (both levels), iron and magnesium (pathologic levels) showed an ideal performance with ≥6 sigma level for both bias determinations. Eight of the 17 parameters had different sigma levels when we compared sigma values calculated from EQA and IQC derived bias% while the rest were grouped at the same levels.

Conclusions

Sigma metrics is a good quality tool to assess a laboratory's analytical performance and facilitate the comparison of the assay performances in the same manner across multiple systems. However, we might need to design a tight internal quality control protocol for analytes showing poor assay performance.

Development and clinical evaluation of an online automated quality control system for improving laboratory quality management

BACKGROUND

We developed an efficient online automated quality control (AUTO QC) system and tested its feasibility on automatic laboratory assembly lines.

METHODS

AUTO QC is based on developed quality control software (Smart QC) and designed adaptable consumables before. We applied the system to two assembly lines in our laboratory. Using third-party quality control samples, we evaluated the impact of the online AUTO QC system on out-of-control rate, biosecurity risk, turnaround time (TAT) and cost.

RESULTS

AUTO QC significantly decreased the occurrence rate of the Westgard quality control rules 1_3S/2_2S/R_4s and 1_2S, representing out-of-control and warning, respectively. The out-of-control rates were reduced by 58%, and the potential biosecurity risk of the samples decreased by 90%. The AUTO QC implementation also reduced the median TAT (by 7 min), the number of full-time employees and the cost of the quality control samples (by 45%).

CONCLUSIONS

The total laboratory AUTO QC system can improve the quality and stability of QC testing and reduce cost.

Technical quality assurance and quality control for medical laboratories: a review and proposal of a new concept to obtain integrated and validated QA/QC plans

Technical quality assurance (QA) and quality control (QA/QC) are important activities within medical laboratories to ensure the adequate quality of obtained test results. QA/QC tools available at medical laboratories include external QC and internal QC, patient-based real-time quality control (PBRTQC) tools such as moving average quality control (MAQC), limit checks, delta checks, and multivariate checks, and finally, analyzer flagging. Recently, for PBRTQC tools, new optimization and validation methods based on error detection simulation have been developed to obtain laboratory-specific insights into PBRTQC error detection. These developments have enabled implementation and application of these individual tools in routine clinical practice. As a next step, they also enable performance comparison of the individual QA/QC tools and integration of all the individual QA/QC tools in order to obtain the most powerful and efficient QA/QC plans. In this review, a brief overview of the individual QA/QC tools and their characteristics is provided and the error detection simulation approaches are explained. Finally, a new concept entitled integrated quality assurance and control (IQAC) is presented. To enable IQAC, a conceptual framework is suggested and demonstrated for sodium, based on available published data. The proposed IQAC framework provides ways and tools by which the performance of different QA/QC tools can be compared in a so-called QA/QC error detection table to enable optimization and validation of the overall QA/QC plan in terms of alarm rate as well as pre-analytical, analytical, and post-analytical error detection performance.

Optimization and Validation of Limit Check Error-Detection Performance Using a Laboratory-Specific Data-Simulation Approach: A Prerequisite for an Evidence-Based Practice

BACKGROUND

Autoverification procedures based on limit checks (LCs) provide important support to preanalytical, analytical, and postanalytical quality assurance in medical laboratories. A recently described method, based on laboratory-specific error-detection performances, was used to determine LCs for all chemistry analytes performed on random-access chemistry analyzers prior to application.

METHODS

Using data sets of historical test results, error-detection simulations of limit checks were performed using the online MA Generator system (www.huvaros.com). Errors were introduced at various positions in the data set, and the number of tests required for an LC alarm to occur was plotted in bias detection curves. Random error detection was defined as an LC alarm occurring in 1 test result, whereas systematic error detection was defined as an LC alarm occurring within an analytical run, both with ≥97.5% probability. To enable the lower limit check (LLC) and the upper limit check (ULC) to be optimized, the simulation results and the LC alarm rates for specific LLCs and ULCs were presented in LC performance tables.

RESULTS

Optimal LLCs and ULCs were obtained for 31 analytes based on their random and systematic error-detection performances and the alarm rate. Reliable detection of random errors greater than 60% was only possible for analytes known to have a rather small variation of results. Furthermore, differences for negative and positive errors were observed.

CONCLUSIONS

The used method brings objectivity to the error-detection performance of LCs, thereby enabling laboratory-specific LCs to be optimized and validated prior to application.

A patient risk model to determine the optimal output constancy check frequency for a radiotherapy machine

PURPOSE

The output constancy check, a basic quality control (QC) item for radiotherapy machines, is performed daily according to suggestions in technical reports by experienced experts. In this study, a patient risk model was built to determine the optimal frequency of an output constancy check for a specific radiotherapy machine.

METHODS AND MATERIALS

The method was based on the patient risk model and comprised three steps: 1) the power function graph was used to select a proper QC rule and the average number of QC measurements per QC rule evaluation. 2) The optimal QC frequency was determined by the minimum integer value of expected patients treated between QC measurements. 3) The individual control chart (I-Chart) was used to evaluate the effectiveness of the model. The model was implemented on the output constancy check of a Tomotherapy machine.

RESULTS

The QC rule with the limits set to the mean ± 3 standard deviations and 5 measurements per QC were selected according to the power function graph. The optimal frequency was observed every 21 patients. The I-Chart showed that the optimal frequency detected the machine failure earlier compared to the conventional daily frequency. The model could monitor whether Tomotherapy machine was in good condition and predicted the time to adjust the machine.

CONCLUSIONS

The optimal output constancy check frequency of a radiotherapy machine is determined by the number of patients, which uses patient risk model. The optimal frequency is superior to the conventional daily frequency in identifying machine failure earlier.

Moving Rate of Positive Patient Results as a Quality Control Tool for High-Sensitivity Cardiac Troponin T Assays

Background

A small shift in high-sensitivity cardiac troponin T (hs-cTnT) assays can lead to different result interpretation and consequent patient management. We explored whether a small bias could be detected using conventional internal quality control (QC) procedures, evaluated the performance of moving average (MA)-based QC procedures, and proposed a new QC procedure based on the moving rate (MR) of positive patient results of hs-cTnT assays.

Methods

The ability of conventional QC to detect a 5 ng/L bias was examined using the1_3s/ 2_2s/R_4s multi-rule procedure as deviation rules.We developed MA and MR procedures for the hs-cTnT assay using eight months of patient data. The performance of different MA or MR procedures was investigated by calculating the median number of patient samples affected until a bias introduced into the dataset was detected (MNPed). After comparing the MNPed across different procedures, we selected an optimal MA or MR procedure for validation. Validation graphs were plotted using the minimum, median, and maximum number of results affected until bias detection.

Results

Our conventional QC procedures could not detect a positive bias of 5 ng/L. When a positive bias was introduced, MNPed was much higher using MA than using MR, with cut-off values of 5 ng/L and 14 ng/L, respectively. MR validation charts for optimal procedures provided insight into the MR performance.

Conclusions

The MR procedure could detect different errors with few false alarms. In the hs-cTnT assay, the MR procedure with a smaller cut-off value outperformed MA and conventional QC procedures for small bias detection.

Understanding Patient-Based Real-Time Quality Control Using Simulation Modeling

Background

Patient-based real-time quality control (PBRTQC) avoids limitations of traditional quality control methods based on the measurement of stabilized control samples. However, PBRTQC needs to be adapted to the individual laboratories with parameters such as algorithm, truncation, block size, and control limit.

Methods

In a computer simulation, biases were added to real patient results of 10 analytes with diverse properties. Different PBRTQC methods were assessed on their ability to detect these biases early.

Results

The simulation based on 460 000 historical patient measurements for each analyte revealed several recommendations for PBRTQC. Control limit calculation with “percentiles of daily extremes” led to effective limits and allowed specification of the percentage of days with false alarms. However, changes in measurement distribution easily increased false alarms. Box–Cox but not logarithmic transformation improved error detection. Winsorization of outlying values often led to a better performance than simple outlier removal. For medians and Harrell–Davis 50 percentile estimators (HD50s), no truncation was necessary. Block size influenced medians substantially and HD50s to a lesser extent. Conversely, a change of truncation limits affected means and exponentially moving averages more than a change of block sizes. A large spread of patient measurements impeded error detection. PBRTQC methods were not always able to detect an allowable bias within the simulated 1000 erroneous measurements. A web application was developed to estimate PBRTQC performance.

Conclusions

Computer simulations can optimize PBRTQC but some parameters are generally superior and can be taken as default.

An approach to selecting auto-verification limits and validating their error detection performance independently for pre-analytical and analytical errors

BACKGROUND

Auto-verification limits are widely used to trigger confirmatory actions to enable detection of pre-analytical, analytical and post-analytical errors. An approach is presented for validating auto-verification limit performance in a laboratory-specific manner, independently for pre-analytical and analytical error detection.

METHODS

To evaluate this approach, MA Generator (www.huvaros.com) was used to run error-detection simulations using various upper-limit checks (ULC) and lower-limit checks (LLC). Pre-analytical error detection was defined as triggering of a limit check alarm within one erroneous result. Analytical error detection was defined as triggering a limit check alarm within the scheduled internal QC measurement interval, both with ≥97.5% probability. Furthermore, the limit check alarm rates were obtained.

RESULTS

Pre-analytical error detection and rapid detection of larger analytical errors by limit checks outperformed moving average quality control at the cost of a significantly larger number of alarms. A pre-analytical error detection by LLC and ULC of ≥-55% and >60%, ≥-10% and ≥20%, and ≥-40% and ≥50% and an analytical error detection of ≥-4% and ≥15%, ≥-3% and ≥4% and ≥-30% and ≥25% were obtained for hemoglobin, sodium and calcium, respectively.

CONCLUSIONS

The obtained ULC and LLC alarm rate and error detection performance, enabled substantiated selection of optimal auto-verification limits and validation thereof.

Assessing subsets of analytes in context of detecting laboratory errors

Laboratory error detection is a hard task yet plays an important role in efficient care of the patients. Quality controls are inadequate in detecting pre-analytic errors and are not frequent enough. Hence population- and patient-based detectors are developed. However, it is not clear what set of analytes leads to the most efficient error detectors. Here, we use three different scoring functions that can be used in detecting errors, to rank a set of analytes in terms of their strength in distinguishing erroneous measurements. We also observe that using evaluations of larger subsets of analytes in our analysis does not necessarily lead to a more accurate error detector. In our data set obtained from renal kidney disease inpatients, calcium, potassium, and sodium, emerged as the top-3 indicators of an erroneous measurement. Using the joint likelihood of these three analytes, we obtain an estimated AUC of 0.73 in error detection.

Assessment of average of normals (AON) procedure for outlier-free datasets including qualitative values below limit of detection (LoD): an application within tumor markers such as CA 15-3, CA 125, and CA 19-9

Average of normals (AON) is a quality control procedure that is sensitive only to systematic errors that can occur in an analytical process in which patient test results are used. The aim of this study was to develop an alternative model in order to apply the AON quality control procedure to datasets that include qualitative values below limit of detection (LoD). The reported patient test results for tumor markers, such as CA 15-3, CA 125, and CA 19-9, analyzed by two instruments, were retrieved from the information system over a period of 5 months, using the calibrator and control materials with the same lot numbers. The median as a measure of central tendency and the median absolute deviation (MAD) as a measure of dispersion were used for the complementary model of AON quality control procedure. The u_bias values, which were determined for the bias component of the measurement uncertainty, were partially linked to the percentages of the daily median values of the test results that fall within the control limits. The results for these tumor markers, in which lower limits of reference intervals are not medically important for clinical diagnosis and management, showed that the AON quality control procedure, using the MAD around the median, can be applied for datasets including qualitative values below LoD.

Blood collection tube-related alterations in analyte concentrations in quality control material and serum specimens

OBJECTIVES

Several previous studies have described the effects of interfering substances on clinical assay results; however, the effects of exogenous substances, particularly additives from blood collection tubes on quality control (QC) specimens and serum specimens have not been well examined. This study examines the effects of blood-collection tube additives on total triiodothyronine (TT3), and thyroxine (TT4), cortisol, and routine clinical chemistry tests in QC and serum specimens from apparently healthy volunteers.

METHODS

QC and serum specimens were poured or collected into different blood collection tubes. TT3 and TT4, cortisol, and routine chemistry tests were analyzed from the different blood-collection tube types.

RESULTS

The findings of this study demonstrate statistically and/or clinically significant blood collection tube-related alterations in the TT3, TT4, and cortisol concentrations of QC specimens and TT4 concentrations from serum specimens.

CONCLUSIONS

These findings have important implications for clinical laboratories, demonstrating that QC specimens should ideally, like patients' specimens, be poured into blood collection tubes. This strategy would reveal any adverse effects caused by blood collection tubes, which otherwise would not likely be detected by most routine QC practices. The results of this study also show the importance of producing blood collection tubes that contain additives that are truly inert and do not adversely affect clinical laboratory testing.

Internal quality control: best practice

There is a wide variation in laboratory practice with regard to implementation and review of internal quality control (IQC). A poor approach can lead to a spectrum of scenarios from validation of incorrect patient results to over investigation of falsely rejected analytical runs. This article will provide a practical approach for the routine clinical biochemistry laboratory to introduce an efficient quality control system that will optimise error detection and reduce the rate of false rejection. Each stage of the IQC system is considered, from selection of IQC material to selection of IQC rules, and finally the appropriate action to follow when a rejection signal has been obtained. The main objective of IQC is to ensure day-to-day consistency of an analytical process and thus help to determine whether patient results are reliable enough to be released. The required quality and assay performance varies between analytes as does the definition of a clinically significant error. Unfortunately many laboratories currently decide what is clinically significant at the troubleshooting stage. Assay-specific IQC systems will reduce the number of inappropriate sample-run rejections compared with the blanket use of one IQC rule. In practice, only three or four different IQC rules are required for the whole of the routine biochemistry repertoire as assays are assigned into groups based on performance. The tools to categorise performance and assign IQC rules based on that performance are presented. Although significant investment of time and education is required prior to implementation, laboratories have shown that such systems achieve considerable reductions in cost and labour.

Long-term stability of laboratory tests and practical implications for quality management

Long-term stability of analytical performance is required for adequate patient management. We investigated the use of patient data to document test stability, and the relevance of observed instabilities on a surrogate medical outcome. We used multiyear patient and internal quality control (IQC) data from two laboratories for tests to monitor chronic kidney and thyroid disease.

We plotted moving means of the 50th percentiles of stratified patient data and of the daily IQC means. We evaluated observed instabilities based on goals inferred from the analytes’ biological variation and investigated their effect on classification of results against reference intervals.

Patient and IQC data generally matched well, except for analytes, for which other than analytical variation sources prevailed. Analytical instabilities were predominantly due to reagent/calibrator lot changes, however, for immunoassays also to within-lot instabilities, urging frequent recalibrations. The relevance of biased results on medical decisions ranged from negligible to very pronounced, indicating the need for assessment of analytical performance in relation to quality goals inferred from biological variation.

Patient percentiles offer great potential to assess/monitor the medium- to long-term analytical stability of a test within certain constraints. Differences in analytical quality between assays can significantly affect medical outcome.

Contributions of CCLM to advances in quality control

Abstract The discipline of laboratory medicine is relatively young when considered in the context of the history of medicine itself. The history of quality control, within the context of laboratory medicine, also enjoys a relatively brief, but rich history. Laboratory quality control continues to evolve along with advances in automation, measurement techniques and information technology. Clinical Chemistry and Laboratory Medicine (CCLM) has played a key role in helping disseminate information about the proper use and utility of quality control. Publication of important advances in quality control techniques and dissemination of guidelines concerned with laboratory quality control has undoubtedly helped readers of this journal keep up to date on the most recent developments in this field.

How useful are delta checks in the 21 century? A stochastic-dynamic model of specimen mix-up and detection

Introduction:

Delta checks use two specimen test results taken in succession in order to detect test result changes greater than expected physiological variation. One of the most common and serious errors detected by delta checks is specimen mix-up errors. The positive and negative predictive values of delta checks for detecting specimen mix-up errors, however, are largely unknown.

Materials and Methods:

We addressed this question by first constructing a stochastic dynamic model using repeat test values for five analytes from approximately 8000 inpatients in Calgary, Alberta, Canada. The analytes examined were sodium, potassium, chloride, bicarbonate, and creatinine. The model simulated specimen mix-up errors by randomly switching a set number of pairs of second test results. Sensitivities and specificities were then calculated for each analyte for six combinations of delta check equations and cut-off values from the published literature.

Results:

Delta check specificities obtained from this model ranged from 50% to 99%; however the sensitivities were generally below 20% with the exception of creatinine for which the best performing delta check had a sensitivity of 82.8%. Within a plausible incidence range of specimen mix-ups the positive predictive values of even the best performing delta check equation and analyte became negligible.

Conclusion:

This finding casts doubt on the ongoing clinical utility of delta checks in the setting of low rates of specimen mix-ups.

Statistical error detection for clinical laboratory tests

Errors in clinical laboratory tests lead to increased costs and patient risks. Such errors are relatively rare, affecting ∼0.5% of samples. Existing techniques for detecting errors have either far too low sensitivity or specificity to be useful. This preliminary study develops statistical sample selection criteria that capture faults upwards of fifty times more efficiently than expected from random sampling. Although this is only the first step towards an integrated discriminant system for reliable detection of laboratory errors, the statistical detection scheme demonstrated here outperforms existing methods.

Quality assessment of an interferon-gamma release assay for tuberculosis infection in a resource-limited setting

Background

When a test for diagnosis of infectious diseases is introduced in a resource-limited setting, monitoring quality is a major concern. An optimized design of experiment and statistical models are required for this assessment.

Methods

Interferon-gamma release assay to detect tuberculosis (TB) infection from whole blood was tested in Hanoi, Viet Nam. Balanced incomplete block design (BIBD) was planned and fixed-effect models with heterogeneous error variance were used for analysis. In the first trial, the whole blood from 12 donors was incubated with nil, TB-specific antigens or mitogen. In 72 measurements, two laboratory members exchanged their roles in harvesting plasma and testing for interferon-gamma release using enzyme linked immunosorbent assay (ELISA) technique. After intervention including checkup of all steps and standard operation procedures, the second trial was implemented in a similar manner.

Results

The lack of precision in the first trial was clearly demonstrated. Large within-individual error was significantly affected by both harvester and ELISA operator, indicating that both of the steps had problems. After the intervention, overall within-individual error was significantly reduced (P < 0.0001) and error variance was no longer affected by laboratory personnel in charge, indicating that a marked improvement could be objectively observed.

Conclusion

BIBD and analysis of fixed-effect models with heterogeneous variance are suitable and useful for objective and individualized assessment of proficiency in a multistep diagnostic test for infectious diseases in a resource-constrained laboratory. The action plan based on our findings would be worth considering when monitoring for internal quality control is difficult on site.

Lower confidence limits for critical systematic errors

OBJECTIVES

The critical systematic error (DeltaSE(C)) can be used to select a quality control scheme. It is just an estimate of the true DeltaSE(C), however, and its precision depends on the sample size. Thus, the lower confidence limit (LCL) of DeltaSE(C) should be adopted instead to guard against false optimism.

DESIGN AND METHODS

LCLs are calculated as a function of DeltaSE(C) and sample size using a modified version of Bissell's formula.

RESULTS

The uncertainty in DeltaSE(C) may be significant. The largest relative difference between DeltaSE(C) and its LCL is 52.2%, for DeltaSE(C)=2 and n=20. This difference decreases with increasing DeltaSE(C) and sample size.

CONCLUSIONS

It is shown that taking into account the uncertainty in DeltaSE(C) can greatly influence quality control planning decisions. Caution should thus be exercised when interpreting control data and choosing an appropriate quality control strategy. The derived equation for LCL is simple, and can be immediately applied to practical solutions.

Reduction of multi-dimensional laboratory data to a two-dimensional plot: a novel technique for the identification of laboratory error

BACKGROUND

The clinical laboratory generates large amounts of patient-specific data. Detection of errors that arise during pre-analytical, analytical, and post-analytical processes is difficult. We performed a pilot study, utilizing a multidimensional data reduction technique, to assess the utility of this method for identifying errors in laboratory data.

METHODS

We evaluated 13,670 individual patient records collected over a 2-month period from hospital inpatients and outpatients. We utilized those patient records that contained a complete set of 14 different biochemical analytes. We used two-dimensional generative topographic mapping to project the 14-dimensional record to a two-dimensional space.

RESULTS AND CONCLUSIONS

The use of a two-dimensional generative topographic mapping technique to plot multi-analyte patient data as a two-dimensional graph allows for the rapid identification of potentially anomalous data. Although we performed a retrospective analysis, this technique has the benefit of being able to assess laboratory-generated data in real time, allowing for the rapid identification and correction of anomalous data before they are released to the physician. In addition, serial laboratory multi-analyte data for an individual patient can also be plotted as a two-dimensional plot. This tool might also be useful for assessing patient wellbeing and prognosis.

Differential effect of blood collection tubes on total free fatty acids (FFA) and total triiodothyronine (TT3) concentration: a model for studying interference from tube constituents

BACKGROUND

Besides total triiodothyronine (TT3), total free fatty acids (FFA) concentrations were higher with serum separator tube (SST) than Vacuette tubes.

METHODS

The effects of surfactant, rubber stopper, and separator gel from various tubes were investigated on FFA, beta-hydroxybutyrate (beta-HB), and TT3 with 8 different tube types in blood specimens of apparently healthy volunteers.

RESULTS

Compared to Vacuette tubes, serum FFA and TT3 concentrations were significantly higher in SST than glass tubes. Reformulated SST eliminated the increase in TT3 but not FFA. No significant difference was observed for beta-HB concentration among tube types. Surfactant and rubber stoppers from the different tube types significantly increased TT3 but not FFA and beta-HB concentrations. Agitation of whole blood but not serum or plasma specimens with separator gel from SST, reformulated SST and plasma preparation tube (PPT) tubes compared to Vacuette tubes gave higher FFA but not beta-HB levels.

CONCLUSIONS

Unidentified component(s) from the separator gel in SST, reformulated SST and PPT tubes cause falsely high FFA concentration. In contrast to TT3, falsely high FFA results require exposure of whole blood and not serum to tube constituent(s). The approach employed here may serve as a model for assessing interference(s) from tube constituent(s).

Preanalytical variability: the dark side of the moon in laboratory testing

Remarkable advances in instrument technology, automation and computer science have greatly simplified many aspects of previously tedious tasks in laboratory diagnostics, creating a greater volume of routine work, and significantly improving the quality of results of laboratory testing. Following the development and successful implementation of high-quality analytical standards, analytical errors are no longer the main factor influencing the reliability and clinical utilization of laboratory diagnostics. Therefore, additional sources of variation in the entire laboratory testing process should become the focus for further and necessary quality improvements. Errors occurring within the extra-analytical phases are still the prevailing source of concern. Accordingly, lack of standardized procedures for sample collection, including patient preparation, specimen acquisition, handling and storage, account for up to 93% of the errors currently encountered within the entire diagnostic process. The profound awareness that complete elimination of laboratory testing errors is unrealistic, especially those relating to extra-analytical phases that are harder to control, highlights the importance of good laboratory practice and compliance with the new accreditation standards, which encompass the adoption of suitable strategies for error prevention, tracking and reduction, including process redesign, the use of extra-analytical specifications and improved communication among caregivers.

The clinical chemistry laboratory: current status, problems and diagnostic prospects

Although increased automation, advanced analytical techniques and sophisticated information technology have greatly improved the performance and quality in medical laboratory testing, several studies show that significant amounts of errors occur. Detailed analysis revealed that most of the errors occur in the preanalytical phase, while fewer errors occur in the intra- and post-analytical phase. The majority of errors are caused by wrong sampling or occur during transport to the laboratory. This review focuses on the analytical procedures in a large central laboratory. Possible problems are described by following samples from the patient to the laboratory and back. Finally, the advantages and disadvantages of point-of-care testing versus central laboratory are compared.

Effect of blood collection tubes on total triiodothyronine and other laboratory assays

BACKGROUND

Increased total triiodothyronine (TT(3)) assay results in apparently euthyroid patients triggered an investigation of the effect of blood collection tubes on serum TT(3) and other laboratory assays.

METHODS

We examined potential assay interference for three types of tubes: plastic Greiner Bio-One Vacuette; glass Becton Dickinson (BD) Vacutainer; and plastic BD Vacutainer SST tubes. Serum samples from apparently healthy volunteers (age range, 30-60 years; 15 males and 34 females) were collected in different tube types and analyzed in 17 immunoassays (n = 49), 30 clinical chemistry tests (n = 20), and 33 immunology assays (n = 15). Tube effects were also examined by adding pooled serum to different tube types.

RESULTS

TT(3) values, when measured by the IMMULITE 2000 but not the AxSYM analyzer, were significantly higher (P <0.0001) for SST (2.81 nmol/L) than either glass (2.15 nmol/L) or Vacuette (2.24 nmol/L) tubes. The effect was large enough to substantially shift the distribution of patient values, increasing the percentage of values above the reference interval from 11.3% to 35.8%. The degree of interference from SST tubes on TT(3) differed among various tube lots and could be attributed to a tube additive shared by other plastic tubes. Results from several other tests statistically differed among tube types, but differences were not considered to be clinically significant.

CONCLUSIONS

Assay interferences from blood collection tubes represent challenges to clinical laboratories because they are not detected by the usual quality-control or proficiency testing programs. Laboratories can, however, address this problem by monitoring distribution of patients' results.