single analyte test example

The cost of the test and/or the technical difficulty in performing the test may be so high that its availability is limited. 18 tools, free-to-use, and easy-to-use. RW Examples of Common Causes of Preanalytical Error. (b) Complete a linear regression analysis for each data set and verify that the results from part (a) are correct. analyte: [ an´ah-līt ] a substance or material determined by a chemical analysis. 3. Although this is a common approach for comparing two analytical methods, it does violate one of the requirements for an unweighted linear regression—that indeterminate errors affect y only. Baskin It is important to note that any test with a sensitivity = 50% and a specificity = 50% is no better than a coin toss in deciding whether or not a disease may be present. 11. A more precise quantitative estimate of the superiority of one test over another can be obtained by comparing the area-under-the-curve (AUC) for each test and applying statistics to determine the significance of the difference between AUC values. Sufficient internal standard is added to a sample to make its concentration 15.0 mg/L Analysis of the sample yields signals for the analyte and internal standard of 0.274 and 0.198, respectively. (a) Determine the standardization relationship using a linear regression, and report confidence intervals for the slope and the y-intercept. Hypothesis deduction involves establishing a differential diagnosis based on the patient’s history, including family, social, and drug history, and physical exam findings, followed by the selection of laboratory tests that are the most likely to confirm (ie, allow the clinician to deduce) a diagnosis on the list of differential diagnoses. Substituting the sample’s absorbance into the calibration equation and solving for CA give, \[S_\ce{samp} = 0.114 =29.59\: \ce{M}^{–1} × C_\ce{A} + 0.015\], \[C_\ce{A} = \mathrm{3.35 × 10^{-3}\: M}\], For the one-point standardization, we first solve for kA, \[k_\ce{A}= \dfrac{S_\ce{std}}{C_\ce{std}} = \mathrm{\dfrac{0.0931}{3.16×10^{−3}\: M} = 29.46\: M^{−1}}\]. ; Wiley: New York, 1998. recommended sample number and characteristics for assays detecting a single analyte, multiple analytes reported as a single result, or multiple individually detected and reported analytes. The LibreTexts libraries are Powered by MindTouch® and are supported by the Department of Education Open Textbook Pilot Project, the UC Davis Office of the Provost, the UC Davis Library, the California State University Affordable Learning Solutions Program, and Merlot. This discussion will begin with some general comments about approaches to ordering clinical laboratory tests, followed by “real-world” examples to illustrate these approaches. To determine the concentration of analyte in a sample, a standard additions was performed. For example, alkaline phosphatase, an enzyme produced by osteoblasts (bone-forming cells), would be expected to be higher in a healthy 10- to 12-year-old during puberty and the growth spurt (ie, increased bone formation during lengthening of the long bones) that normally accompanies puberty in adolescent males and females than those observed in a prepubertal or elderly individual. ASRs Cannot Be Sold with Valida- To create a residual plot, we need to calculate the residual error for each standard. Our study lays the conceptual and practical foundation for a single, multi-analyte blood test for cancers of many types. This example convincingly demonstrates how small changes in conditions can strongly influence an analyte stability. P Because each channel has three Glass Nano Reactors (GNRs) coated with a capture antibody, you get triplicate answers for each sample. Such an understanding is critical to good laboratory practice and patient outcomes. Since physicians frequently order the same test at multiple time points during the course of the patients’ management, they are faced with the challenge of interpreting when the magnitude of the change in values for an analyte constitutes a significant change (or critical difference [CD]) that may (or should) affect medical decision making (eg, trigger a change in therapy, such as increasing or decreasing a drug dosage). A major misconception among clinicians is the feeling that a laboratory test is more objective than a patient’s history and physical examination. Why is the laboratory an afterthought for managed care organizations? 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). We also acknowledge previous National Science Foundation support under grant numbers 1246120, 1525057, and 1413739. For example, the analyte cortisol, a glucocorticoid produced by the adrenal cortex that is important in glucose homeostasis, normally displays diurnal variation. The following table contains the relevant information. If uncorrected, the presence of the interference will result in biased estimates of analyte concentration. 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 acc… Reflectance test strips utilize an enzymatic reaction to produce a color change that is detected by the analyzer after whole blood is applied . Integrated care pathway for breast cancer: A relational and geographical approach. Examples of MLL FISH signal pattern results from a test set of 4 normal cases, 3 cases with 11q23 rearrangements, 2 with loss of chromosome 11, and 1 with gain of chromosome 11 are presented in Table 3. Multi-channel methods can detect, and sometimes correct for, the presence of a number of types of interferences in the sample. ), determined on a defined population of “healthy” individuals, that lie between the lower and the upper limits that constitute 95% of all values. Limit of Quanititation . AOZ LC is a quantitative method for the simultaneous detection of aflatoxin, ochratoxin A (OTA) and zearalenone (ZEA) in several commodities. A second 50 mL aliquot of the sample, which is spiked with 1.00 mL of a 10.0-ppm standard solution of the analyte, gives a signal of 23.1. We can use a t-test to compare the slope and the y-intercept from a linear regression to the expected values. Calculate the weight percent of analyte in the original sample. From this data, the values for prevalence, sensitivity, specificity, efficiency, positive predictive value (PPV), and negative predictive value (NPV) can be determined: Prevalence (p) = No. The principal advantage of ROC curves is their ability to provide information on test performance at all decision thresholds.3,6. A 50.00 mL sample containing an analyte gives a signal of 11.5 (arbitrary units). Adopted a LibreTexts for your class? Gras 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. 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. The “laboratory testing cycle” (Figure 1) consists of all steps between the time when a clinician thinks about and orders a laboratory test and the time the appropriate patient’s sample for testing is obtained (eg, a blood specimen taken from an antecubital vein) and the results of the testing are returned to the clinician (often called the “vein-to-brain” turnaround time [TAT] of test results). Dramatic representation of diagnostic sensitivity and specificity using the analyte prostate-specific antigen (PSA) as an example. The degree of imprecision (ie, lack of reproducibility) in the quantitative measurement of any analyte is given by the magnitude of the coefficient of variation (CV), expressed usually as a percent, obtained from multiple measurements of the analyte using the formula: %CV = (SD/mean) × 100; where mean and SD are the mean and standard deviation of the values obtained from the multiple measurements of an analyte. More specifically, the regression underestimates the slope, b1, and overestimates the y-intercept, b0. Alternative approaches are found in Hartman, C.; Smeyers-Verbeke, J.; Penninckx, W.; Massart, D. L. Anal. 9. Troost and Olavsesn investigated the application of an internal standardization to the quantitative analysis of polynuclear aromatic hydrocarbons.15 The following results were obtained for the analysis of phenanthrene using isotopically labeled phenanthrene as an internal standard. Estimate the precision and reproducibility of serological assays that test human plasma from donations of whole blood and blood components with the Thermo Scientific AcroMetrix Serology Single Analyte … Diagnosis (to rule in or rule out a diagnosis). This is okay because the calculation depends on the average signal and the number of replicates, and not on the individual measurements. . A “perfect” test would have both 100% diagnostic sensitivity and specificity, which seldom occurs in practice and if it does, the population of diseased and non-diseased patients studied was probably not large and varied enough to demonstrate that the test was not perfect. The appropriate test statistic for the y-intercept is found by rearranging equation 5.23. For example, when determining a reference interval for TSH (also known as thyrotropin), it is critically important that the population of individuals tested be free of any pituitary or thyroid disease likely to affect the pituitary-hypothalamic-thyroid axis, which, under the action of the thyroid hormones tri- (T3) and tetraiodothyronine (T4), exert regulatory control over circulating levels of TSH. To three significant figures, each data set has a slope of 0.500 and a y-intercept of 3.00. \[s_{b_1}= \sqrt{\dfrac{6×(1.997×10^{−3})^2}{6×(1.378×10^{−4}) − (2.371×10^{−2})^2}} = 0.3007\], \[s_{b_0}= \sqrt{\dfrac{(1.997×10^{−3})^2×(1.378×10^{−4})}{6×(1.378×10^{−4}) − (2.371×10^{−2})^2}} = 1.441×10^{−3}\], \[β_1 = b_1 ± ts_{b_1} = \mathrm{29.57 ± (2.78×0.3007) = 29.57\: M^{-1} ± 0.85\: M^{-1}}\], \[β_0 = b_0 ± ts_{b_0}= 0.0015 ± \left\{2.78 × (1.441×10^{−3})\right\} = 0.0015 ± 0.0040\], 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} Thus, reference intervals are intended to serve as a guideline for evaluating individual values and, for many analytes, information on the limits of an analyte for a population of individuals with the disease or diseases the test was designed to detect is even more informative. In addition, however, the number of false-positives increases, which causes the specificity of this test to worsen since specificity = TN/(TN + FP), because any increase in the number of false-positives, a term in the denominator of this equation, results in a decrease in the value given by this equation. 5. In addition, derive equations for the slope and y-intercept, and explain how you can determine the amount of analyte in a sample from the calibration curve. Inaccurate quantification due to poor analyte recovery can be overcome by matrix matching, which involves the preparation of calibration standards in a sample similar in composition to the sample under study (Akkaya et al., 2017). The standard deviation about the regression, therefore, is, \[s_r= \sqrt{\dfrac{1.596×10^{−6}}{6− 2}} = 1.997×10^{−3}\]. Lastly, to accurately interpret test results, it may be necessary to know gender-specific and/or age-stratified reference intervals since the values for many analytes vary with developmental stage or age. Miller Visual inspection of Figure 2.2 reveals that, if the cutoff value, denoted by the dotted line at 4.0 ng/mL, is lowered to 2.0 ng/mL, the sensitivity of the PSA test improves from 80% at a cutoff of 4.0 ng/mL to 100% at a cutoff of 2.0 ng/mL since there are no false-negatives (ie, in this example, all individuals with prostate cancer have PSA values greater than 2.0 ng/mL). 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. Many types equation 5.22 gives the test height of 100, LibreTexts content is by... 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