Steven C. Kazmierczak

Laboratory quality control: using patient data to assess analytical performance.

Abstract Quality control plays a vital role helping to ensure the reliability of laboratory test results. The application of statistical quality control has been a component of laboratory medicine for approximately 50 years. Many of the control rules based on the early applications of statistical quality control have remained essentially unchanged since their initial introduction. Optimization of quality control rules can vary depending on the application for which a test is to be used. This review explores the various applications of laboratory quality control procedures and their role in identifying laboratory error. The ubiquitous use of computers in todays laboratories has enabled the development of more sophisticated means of assessing laboratory quality. The use of the Six Sigma technique and its adoption by the laboratory community is one example. Other examples include the use of patient-derived quality control procedures as a means of assessing laboratory performance. Early examples of these types of applications include use of Bulls algorithm, anion gap measurements, and delta checking. More recent applications include the correlation of laboratory test results, the average of normals procedure, and the Bhattacharya method.

Quantum dots: heralding a brighter future for clinical diagnostics

Quantum dots (QDs) are semiconductor nanocrystals that possess unique optical properties including broad-range excitation, size-tunable narrow emission spectra and high photostability, giving them considerable value in various biomedical applications. The size and composition of QDs can be varied to obtain the desired emission properties and make them amenable to simultaneous detection of multiple targets. Furthermore, numerous surface functionalizations can be used to adapt QDs to the needed application. The successful use of QDs has been reported in the areas of in vitro diagnostics and imaging. There is also potential for multimodal applications for simultaneous imaging. Toxicity issues are still a prime concern with regards to in vivo applications on account of the toxic constituents of QDs.

Analytical interference. More than just a laboratory problem.

Specimen integrity is an important preanalytic factor that affects the accuracy and clinical utility of laboratory test results. Some common preanalytic factors that result in the rejection of specimens from analysis include the use of improper collection containers, excessive time delay from specimen collection to analysis, failure to store specimens at an appropriate temperature prior to analysis, failure to shield the specimen from direct light, and collection of the specimen at the wrong time of day or at an inappropriate time after administration of certain pharmacologic agents.1 While most laboratorians can readily identify these factors as responsible for some types of measurement error or leading to incorrect interpretation of test results, it should be recognized that they account for well less that half of the reasons for rejection of specimens by the clinical laboratory. The most common preanalytic factor affecting the acceptability of specimens for analysis is the presence of interfering substances within the specimen. The presence of interfering substances alters the correct value of the measured result and may lead to inappropriate clinical intervention and compromise patient outcome. The article by Tang et al2 in this issue of the Journal attempts to address one important aspect of point-of-care glucose monitoring devices, ie, interference effects from pharmacologic agents administered to patients in the critical care setting. Although the investigation of interference effects should be the responsibility of the instrument manufacturer, Tang et al note that information provided by the manufacturer concerning interference effects is often too vague to be of value. Manufacturers’ performance claims regarding interference effects often do not specify what the manufacturer considers “significant” interference, the concentration or activity of the analyte of interest within the specimen investigated for suspected interference, or the actual experimental protocol used for investigating interference effects. From the manufacturer’s perspective, this approach of providing simple statements with respect to interference claims is justified on the grounds that providing laboratorians more detailed information about assay interference is not helpful, because the actual concentration of the interfering substance is rarely known.3 While this may be true for certain drugs that are not routinely measured in patients, many medications that do interfere with analytical methods, eg, the interference from acetaminophen identified in the current study, are routinely measured in most clinical laboratories. In addition, interference effects due to hemolysis, bilirubinemia, and lipemia can be easily ascertained, since many of the automated clinical chemistry systems now are able to provide quantitative measurement of these common interfering substances. However, some instrument manufacturers that do offer hardware capable of performing quantitative measurements of these common interfering substances still provide nebulous interference claims. For example, although the Hitachi 747 system (Boehringer Mannheim, Indianapolis, IN) can accurately measure serum hemoglobin concentrations, the interference claim for hemolysis from the manufacturer’s alanine aminotransferase assay states that “RBC contamination may elevate results....”4 This information is clearly insufficient to allow the user to make an informed decision about the effect of a potential interferent, regardless of the instrument’s ability to provide quantitative measurement of serum hemoglobin concentrations. Another approach that manufacturers can take to provide interference claim information is to supply the user with quantitative statements such as, “Compound x, at a concentration of 100 mg, may decrease results by 10%.”3 This type

Correction Factors for Estimating Potassium Concentrations in Samples With In Vitro Hemolysis: A Detriment to Patient Safety

CONTEXT Correction factors have been proposed for estimating true potassium concentrations in blood samples with evidence of in vitro hemolysis. OBJECTIVE We used 2 different models of true (ie, nonsimulated) in vitro hemolysis to evaluate the clinical utility of correction factors for estimating potassium concentrations in samples with evidence of in vitro hemolysis. DESIGN Potassium correction factors were derived using 2 different models. In model 1, potassium and plasma hemoglobin were measured with the Hitachi 747 analyzer in 132 paired blood samples, with each pair consisting of 1 sample with evidence of hemolysis and 1 without, collected during the same phlebotomy procedure. The change in measured potassium concentration was plotted versus the change in plasma hemoglobin concentration for each pair of samples. In model 2, the potassium levels of 142 784 blood samples and the corresponding hemolytic index values were measured with the Beckman LX20 analyzer. Potassium concentrations at the 10th, 25th, 50th, 75th, and 90th percentiles were calculated for each hemolysis index category. RESULTS From our 2 models, we derived correction factors expressing an increase in potassium concentration of 0.51 and 0.40 mEq/L for every increase in plasma hemoglobin concentration of 0.1 g/dL. These correction factors are much higher than those reported in studies that simulated in vitro hemolysis by freeze-thaw lysis or osmotic disruption of whole blood. CONCLUSIONS Use of correction factors for estimating the true potassium concentration in samples with evidence of in vitro hemolysis is not recommended. Derivation of correction factors by using samples with nonsimulated in vitro hemolysis suggests that the actual increase in potassium in hemolyzed samples is much higher than that reported in previous studies that produced hemolysis with artificial means.

Application of spin label electron paramagnetic resonance in the diagnosis and prognosis of cancer and sepsis

Abstract Diagnostic medicine has seen significant changes during the past decade. The emergence of proteomics and genomics has significantly increased our understanding of disease. These fields have also revealed the vast array of proteins that are expressed in various disease processes, such as cancer. Measurement of these unique proteins expressed in certain diseases may offer diagnostic clues or allow patient prognosis to be assessed. Another approach is to measure the effects that these ligands have on the structure and function of albumin. Albumin is known to play an important role in modulating the serum concentrations of various proteins produced by tumor cells. In this review, we introduce the reader to the technique of spin labeling followed by electron paramagnetic resonance spectroscopy. This method is a powerful tool for evaluating the structural and functional changes that can occur to albumin following the binding of various ligands. We describe the utility of this technique for the diagnosis of cancer and sepsis, as well as some other novel potential applications. Clin Chem Lab Med 2008;46:1203–10.

Improving healthcare through advances in point-of-care technologies.

No Abstract available

Point-of-care testing quality: some positives but also some negatives.

Point-of-care testing (POCT)2 represents one of the fastest growing segments of the diagnostics market. Although major advances have been made in the instrumentation and methods used for POCT, further development and enhancements appear to be necessary to meet the challenges presented by the difficult testing environments in which these devices are used. In addition, the varied experience and training of users and their use of samples that may not be optimal for testing continue to present challenges in the implementation of POCT (1). One of the major advantages of POCT is that it provides much faster access to test results, allowing for more rapid clinical decision making and more-appropriate treatments and interventions. In addition, POCT can help minimize time-dependent changes in labile analytes such as lactate and glucose, which can be caused by delays in sample transport to the clinical laboratory. Finally, many POCT methods require much smaller sample volumes than those needed for testing in the central clinical laboratory. Preventing medical errors has become a major focus of quality improvement in healthcare. Most errors that occur in the clinical laboratory setting take place during the pre- and postanalytical phases of the testing process, and several studies have documented the types and frequencies of the errors that can occur (2). In addition to the benefits mentioned above, the use of POCT may help reduce the frequencies of some of these errors, such as the preanalytical errors associated with inappropriate sampling, inappropriate preparation or packaging of samples, and misidentification of patients (3). In addition, the …

Identification and quantitation of drugs of abuse in urine using the generalized rank annihilation method of curve resolution

A rapid analytical method which is of practical use for the identification and quantitation of drugs of abuse in urine using HPLC with a diode-array detection is described. Because the method utilizes mathematical resolution of partially resolved peaks, greatly simplified sample preparation procedures and very short run times can be used. The generalized rank annihilation method (GRAM) is used to eliminate response due to unknown background peaks and separate partially resolved peaks. An optimized gradient elution program was found for which morphine, phenylpropanolamine, ephedrine, benzoylecgonine, lidocaine, cocaine, diphenhydramine, nortriptyline, norpropoxyphene, nordiazepam, codeine, D-amphetamine, meperidine, and amitriptyline elute from the HPLC column in less than 8.5 min. A commercially available system for HPLC analysis of drugs of abuse is currently available, however, the commercially available system takes 21 min to complete its analysis. Two modified sample pre-treatment methods were also developed to simplify sample treatment procedures substantially. In this paper, The GRAM technique is shown to be extremely powerful in identifying drugs of abuse from large overlapping peaks.

Multiple regression analysis of interference effects from a hemoglobin-based oxygen carrier solution

Abstract The use of hemoglobin-based oxygen carrier solutions in patients requiring blood transfusion will necessitate that clinical laboratories have mechanisms in place to evaluate the potential interference effect of these substances on testing methods. Because these oxygen carrier solutions contain acellular hemoglobin, but do not contain many of the intracellular enzymes and ions present in erythrocytes, interference effects from blood substitutes may be quite different when compared to in vivo or in vitro lysis of erythrocytes. We evaluated the potential interference effect of Diaspirin Cross-linked Hemoglobin on 29 different clinical laboratory analytes. Various combinations of these analytes were tested using the Hitachi 747 and 911 systems, a Beckman CX3, an Abbott AxSym, a Bayer Immuno I, and a Dade ACA IV; a total of 60 analyte/instrument combinations. We used the method of multiple regression analysis to classify interferences as analyte-dependent, analyte-independent, or a combination of the first two types. The presence of clinically significant test interference was derived by using the criteria for maximum allowable error specified in the Clinical Laboratory Improvement Amendments of 1988. Using these criteria, we found significant interference from Diaspirin Cross-linked Hemoglobin with 13 of 29 analytes tested. Interference was noted with the Hitachi 747 and 911 methods for albumin, alkaline phosphatase, total and conjugated bilirubin, cholesterol, total carbon dioxide, iron, lactate dehydrogenase, magnesium, total protein, and triglyceride. In addition, Diaspirin Cross-linked Hemoglobin interfered with measurement of L-lactate using the ACA IV and minor interference was noted with glucose measured using the Beckman CX3. Data from the interference studies was graphically displayed in the form of interference plots. These plots show the maximum allowable test error, due to Diaspirin Cross-linked Hemoglobin, as a function of analyte and interferent concentrations. Evaluation of the potential interference effect of hemoglobin-based oxygen carrier solutions with use of multiple regression analysis and graphical display of the resultant data in the form of interference plots allows for more reliable reporting of test results from specimens containing these products.

Statistical techniques for evaluating the diagnostic utility of laboratory tests.

Abstract Clinical laboratory data is used to help classify patients into diagnostic disease categories so that appropriate therapy may be implemented and prognosis estimated. Unfortunately, the process of correctly classifying patients with respect to disease status is often difficult. Patients may have several concurrent disease processes and the clinical signs and symptoms of many diseases lack specificity. In addition, results of laboratory tests and other diagnostic procedures from healthy and diseased individuals often overlap. Finally, advances in computer technology and laboratory automation have resulted in an extraordinary increase in the amount of information produced by the clinical laboratory; information which must be correctly evaluated and acted upon so that appropriate treatment and additional testing, if necessary, can be implemented. Clinical informatics refers to a broad array of statistical methods used for the evaluation and management of diagnostic information necessary for appropriate patient care. Within the realm of clinical chemistry, clinical informatics may be used to indicate the acquisition, evaluation, representation and interpretation of clinical chemistry data. This review discusses some of the techniques that should be used for the evaluation of the diagnostic utility of clinical laboratory data. The major topics to be covered include probalistic approaches to data evaluation, and information theory. The latter topic will be discussed in some detail because it introduces important concepts useful in providing for cost-effective, quality patient care. In addition, an example illustrating how the informational value of diagnostic tests can be determined is shown.