11. Rapid polymerase chain reaction (PCR) assay for a meningococcus-specific insertion sequence (IS). For example, the analyte cortisol, a glucocorticoid produced by the adrenal cortex that is important in glucose homeostasis, normally displays diurnal variation. Acta 1997, 338, 19–40, and Zwanziger, H. W.; Sârbu, C. Anal. Despite this ever-expanding plethora of useful and reliable clinical laboratory tests for diagnosing and monitoring the myriad of diseases effecting mankind, the recent emphasis on reducing health care costs and the emergence of managed care organizations led to efforts to reduce the abuse (over-ordering) and misuse (eg, ordering the right test for the wrong purpose or vice versa) of these tests. (b) Complete a linear regression analysis for each data set and verify that the results from part (a) are correct.
relevant to the patient. of true-negatives + No. Quantitative values for all analytes are affected by both imprecision (ie, lack of reproducibility) in the measurement of the analyte and intra-individual variation over time in the concentration of the analyte due to normal physiologic mechanisms (ie, biological variation) that are independent of any disease process. Remove this apparent outlier and reanalyze the data using a linear regression. Figure 5.29 shows an R session for this problem, including loading the chemCal package, creating objects to hold the values for Cstd, Sstd, and Ssamp. The more challenging question facing clinicians, however, is: What is the probability of this patient having disease X if the test result is positive (or negative)?5 The reader is referred to reference 5 for a statistical briefing on how to estimate the probability of disease using likelihood ratios. In these assays, the intensity of the test line is inversely proportional to the analyte concentration. Thus, in qualitatively comparing 2 or more tests in their ability to discriminate between 2 alternative states of health using ROC curves, the test associated with the curve that is displaced further toward the upper left-hand corner of the ROC curve has better discriminating ability (ie, a cutoff value for the test can be chosen that yields higher sensitivity and/or specificity) than tests associated with curves that lie below this curve. Post-analytical errors include such mistakes as transcription errors (eg, an accurate and reliable result reported on the wrong patient, using the wrong value, and/or with the wrong units [eg, mg/L instead of mg/day]). Typically, a reference interval or a decision level is used, against which the patient’s test value is compared. Since reference intervals for all analytes are based typically on the limits for the analyte that include 95% of all values obtained on healthy individuals with the assumption that the distribution of these values is Gaussian (or “bell-shaped”), it is important to recognize that 5% (or 1 out of 20; ie, the 2.5% of healthy individuals with analyte values in the left tail of the data distribution and the 2.5% of healthy individuals with analyte values in the right tail of the distribution when the reference interval is defined as the limits of the 2.5th and 97.5th percentiles of the distribution of all analyte values obtained on healthy individuals) of healthy individuals will have values outside these limits, either low or high (Figure 2.4). Before considering this aspect of the selection of laboratory tests, we must first understand the terms that describe their diagnostic performance. (e) Briefly comment on the importance of visually examining your data. Ordering the following 20 laboratory (and other) tests on a 4-year-old child with signs and symptoms of an upper respiratory tract infection, fever (102.2 °F), and generalized seizure lasting 2 min represents a shotgun—and very expensive—approach to arriving at a diagnosis: Quantitative immunoglobulins (IgG, IgA, IgM), Quantitative alpha-1-antitrypsin (AAT) level, Nasopharyngeal culture for pertussis infection, Purified protein derivative (tuberculin) (PPD)/trichophyton/cocci skin tests. Note that for Ssamp, we do not have the actual values for the three replicate measurements. Laboratory test results may influence up to 70 percent of medical decision making.9 However, one must wonder whether the test results are being interpreted correctly, and—if not—what the impact is of incorrect or inappropriate interpretation on the accuracy of diagnostic decision making based, in part, on laboratory test results. For obvious reasons, it is difficult, if not impossible, to obtain such reference intervals. 13. Thus, in this situation, a laboratory test with high specificity is desirable. Acceptance Criteria ≥3 for each replicate . NA
Figure 5.27 Plot of the residual errors for the data in Practice Exercise 5.5.
This is especially useful for sorting single samples. In Chapter 4 we used a paired t-test to compare two analytical methods used to independently analyze a series of samples of variable composition. You will find tolerances for different types of volumetric glassware and digital pipets in Table 4.2 and Table 4.3. By appropriately selecting patients on whom the test is performed (ie, by maximizing the prevalence of disease in the population sampled). We can minimize the effect of this bias by placing the more precise analytical method on the x-axis, by using more samples to increase the degrees of freedom, and by using samples that uniformly cover the range of concentrations.
Specimen collection tube not filled properly. concentrations of more than one analyte in a single sample may be determined. Search for other works by this author on: Errors in a stat laboratory: Types and frequencies 10 years later, Application of Six Sigma concept in clinical laboratories: A review, Luminaries in laboratory medicine: Otto Folin, Chapter 2: The Use of Clinical Laboratory Tests in Diagnostic Decision-Making, Statistical briefing: Estimating the probability of disease, ROC curves in clinical chemistry: Uses, misuses, and possible solutions, Evaluation of laboratory data by conventional statistics and by three types of neural networks, Comparison of logistic regression and neural net modeling for prediction of prostate cancer pathologic stage. (d) A single sample is buffered to a pH of 3.7 and analyzed for cadmium, yielding a signal of 66.3. How good is the test in discriminating between health versus disease? LB
ML
Most clinicians can achieve this total with a good history and physical examination! The results of a relatively recent article on the sources of laboratory errors in stat testing, which should be very gratifying to laboratorians, has shown that analytical sources of error occurred least frequently (15%) while preanalytical errors occurred most frequently (62%) (Figure 1A).1. A standard sample contains 10.0 mg/L of analyte and 15.0 mg/L of internal standard. For each of the pair of calibration curves shown in Figure 5.26, select the calibration curve using the more appropriate set of standards. MC
The standard deviations in the slope and the y-intercept are 0.118 and 1.125 for each data set. For any given test, there is always a trade-off between sensitivity and specificity, such that choosing a cutoff value (decision threshold) for a particular test that maximizes sensitivity occurs at the expense of specificity. 3 . Analysis of the sample gives signals for the analyte and internal standard of 0.155 and 0.233 (arbitrary units), respectively. In this example "glucose" is the component and "concentration" is the measurable property. Chem. S
Thus, it is incumbent upon physicians and laboratorians to understand which laboratory tests are appropriate to order in the diagnosis and follow up of a patient’s medical condition. M
chemical sample analytical technique analyte concentration measurement data additional data classical or instrumental single … Provides a visual statement of this limit and may represent one of the calibration curves. For details about curvilinear regression, see (a) Sharaf, M. A.; Illman, D. L.; Kowalski, B. R. Franke, J. P.; de Zeeuw, R. A.; Hakkert, R. All results for these tests were either “normal,” “negative,” or “no growth” (cultures), supporting a diagnosis of febrile seizure over bacterial, viral, or fungal meningitis. To determine the concentration of analyte in a sample, a standard additions was performed. and are available as single- and multiple-analyte test strips . Using this information, a 2 x 2 table can be constructed as shown below: Positive PSA test = men with a serum PSA concentration ≥ 4.0 ng/mL, Negative PSA test = men with a serum PSA concentration < 4.0 ng/mL. Receiver- (or relative-) operator characteristic (ROC) curves provide another useful tool in assessing the diagnostic accuracy of a laboratory test, because all (specificity, sensitivity) pairs for a test are plotted. With an Ssamp. of 0.114, CA, \[C_\ce{A} = \dfrac{S_\ce{samp} − b_0}{b_1} = \mathrm{\dfrac{0.114− 0.0014}{29.59\: M^{-1}} = 3.80×10^{−3}\: M}\], \[s_{C_\ce{A}}= \dfrac{1.996×10^{−3}}{29.59} \sqrt{\dfrac{1}{3} + \dfrac{1}{6} + \dfrac{(0.114−0.1183)^2}{(29.59)^2 × (4.408×10^{-5})}} = 4.772×10^{−5}\], \[\begin{align} For example, if you were testing mercury levels in blood then the blood and its components would be the matrix and mercury would be the analyte. The x-intercept is the value of X when Y is zero, or, \[0 = \dfrac{k_\ce{A}C_\ce{A}V_\ce{o}}{V_\ce{f}} + k_\ce{A} × \left\{x\textrm{-intercept}\right\}\], \[x\textrm{-intercept} = −\dfrac{\dfrac{k_\ce{A}C_\ce{A}V_\ce{o}}{V_\ce{f}}}{k_\ce{A}} = −\dfrac{C_\ce{A}V_\ce{o}}{V_\ce{f}}\], Using the calibration equation from Figure 5.7a, we find that the x-intercept is, \[x\textrm{-intercept} = -\mathrm{\dfrac{0.1478}{0.0854\: mL^{-1}} = -1.731\: mL}\], Plugging this into the equation for the x-intercept and solving for CA gives the concentration of Mn2+ as, \[x\textrm{-intercept} = \mathrm{−3.478\: mL = - \dfrac{\mathit{C}_A × 25.00\: mL}{100.6\: mg/L} = 6.96\: mg/L}\], \[x\textrm{-intercept} = \mathrm{−\dfrac{0.1478}{0.0425\: mL^{-1}} = −3.478\: mL}\], \[x\textrm{-intercept} = \mathrm{−3.478\: mL = − \dfrac{\mathit{C}_A × 25.00\: mL}{50.00\: L} = 6.96\: mg/L}\]. Examples of Common Causes of Preanalytical Error.
(c) How much more or less sensitive is this method at the lower pH?
What does analyte mean? We will then review the important diagnostic performance characteristics of laboratory tests, how they are calculated, and a principal tool (ie, receiver-operator characteristic [ROC] curves) used to assess the diagnostic accuracy of a laboratory test at specific cutoff values for the test. For many of your sample testing and screening needs, PBL can help you measure your analyte(s) of interest. Analyte concentrations in the spiked samples are compared to analyte … The analyte-coated microparticles are rapidly agglutinated in the presence of antibody reactive to the analyte and without competing analyte in the test sample. After reading this paper, readers should be able to describe the “laboratory testing cycle” and discuss the potential sources of error that can occur in each phase of this cycle. In some cases, it may be desirable to use a laboratory test with high sensitivity while sacrificing “some” specificity or vice versa. of true-positives + No. Pass/Fail . The principal advantage of ROC curves is their ability to provide information on test performance at all decision thresholds.3,6. 2002;27:2). Random errors can be caused by timing, temperature, or pipetting variations that occur randomly during the measurement process and are independent of the operator performing the measurement. The single analyte test system entails using an organic solvent to extract the analytes from soil and employs various combinations of solid phase extraction (SPE), liquid-liquid transfer and acid-base cleanup techniques to separate the analytes into an organic solvent. The clinical usefulness or practical value of the information provided by ROC curves in patient care may vary, however, even for tests that have good discriminating ability (ie, high sensitivity and specificity at a particular decision threshold). Ideally, the best reference interval for an analyte would be individual-specific such that the value for the analyte, determined when the individual is ill, could be compared with the limits for this analyte, established on this same individual, when he or she was healthy or without the illness. A “run” is a single processing of a sample, from sample prep to test result. \[s_{b_1}= \sqrt{\dfrac{6×(1.997×10^{−3})^2}{6×(1.378×10^{−4}) − (2.371×10^{−2})^2}} = 0.3007\], \[s_{b_0}= \sqrt{\dfrac{(1.997×10^{−3})^2×(1.378×10^{−4})}{6×(1.378×10^{−4}) − (2.371×10^{−2})^2}} = 1.441×10^{−3}\], \[β_1 = b_1 ± ts_{b_1} = \mathrm{29.57 ± (2.78×0.3007) = 29.57\: M^{-1} ± 0.85\: M^{-1}}\], \[β_0 = b_0 ± ts_{b_0}= 0.0015 ± \left\{2.78 × (1.441×10^{−3})\right\} = 0.0015 ± 0.0040\], With an average Ssamp of 0.114, the concentration of analyte, CA, is, \[C_\ce{A} = \dfrac{S_\ce{samp} − b_0}{b_1} = \mathrm{\dfrac{0.114− 0.0015}{29.57\: M^{-1}} = 3.80×10^{−3}\: M}\], \[s_{C_\ce{A}}= \dfrac{1.997×10^{−3}}{29.57} \sqrt{\dfrac{1}{3} + \dfrac{1}{6} + \dfrac{(0.114−0.1183)^2}{(29.57)^2 × (4.408×10^{-5})}} = 4.778×10^{−5}\], \[\begin{align}
Moreover, the diagnostic accuracy (ie, sensitivity and specificity) of PSA in providing this discrimination is higher (AUC = 0.86) in Figure 2.3B than in Figure 2.3A (AUC = 0.66), probably due to differences in the study designs represented by the data shown in each panel of Figure 2.3. JM
For example, the product could be coloured or could be a solid that precipitates from… Example of a Gaussian (or bell-shaped) distribution of test values in which ~68% of the values are between the mean (μ) ± 1 standard deviation (σ); ~95% are between μ ± 2σ; and, ~99% are between μ ± 3σ. Immunoassays measure the formation of antibody-antigen complexes and detect them via an i… of true-positives/(No. Thus, a laboratory test with 95% sensitivity and 95% specificity (sum = 190) is an excellent test. The top 5 causes of preanalytical errors were:1. A test with 100% sensitivity and 100% specificity in discriminating prostatic cancer from benign prostatic hyperplasia (BPH) and prostatitis at all decision thresholds would be represented by the y-axis and the line perpendicular to the y-axis at a sensitivity of 1.0 = 100% in a square plot of FPR versus TPR (Figure 2.3A). Plebani
and then use this value of kA to solve for CA. There is a direct relationship between the magnitude of the CV and the degree of imprecision (ie, the lower the CV, the lower the imprecision [or the higher the degree of precision]). When a 10.00 mL aliquot of the resulting solution is diluted to 25.00 mL it gives signal of 0.235 (arbitrary units). The pattern of laboratory test results shown for the pregnant “Patient” in Table 2 most closely match those consistent with a diagnosis of idiopathic thrombocytopenic purpura (ITP), rather than other possible causes of pregnancy-associated thrombocytopenia: gestational thrombocytopenic (GTP); thrombotic thrombocytopenia (TTP); hemolytic uremic syndrome (HUS); disseminated intravascular coagulation (DIC); or, (syndrome of) hemolysis, elevated liver enzymes, and low platelet count (HELLP). Dramatic representation of diagnostic sensitivity and specificity using the analyte prostate-specific antigen (PSA) as an example. 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