Interpreting two TSH results from the same patient.

OBJECTIVES: When the patient's mean (setpoint) concentration of an analyte is unknown and the physician tries to judge the clinical condition from the analyte concentration in two separate specimens taken a time apart, we believe that the two values should be judged against a bivariate reference interval derived from clinically healthy and stable individuals, rather than using univariate reference limits and comparing the difference between the values against reference change values (RCVs). In this work we compared the two models, using s-TSH as an example. METHODS: We simulated two s-TSH measurement values for 100,000 euthyreot subjects, and plotted the second value against the first, along with a markup of the central 50, 60, 70, 80, 90, and 95 % of the bivariate distribution, in addition to the 2.5 and 97.5 percentile univariate reference limits and the 2.5 and 97.5 percentile RCVs. We also estimated the diagnostic accuracy of the combination of the 2.5 and 97.5 univariate percentile reference limits and the 2.5 and 97.5 percentile RCVs against the central 95 % of the bivariate distribution. RESULTS: Graphically, the combination of the 2.5 and 97.5 univariate reference limits and the 2.5 and 97.5 percentile RCVs did not accurately delineate the central 95 % of the bivariate distribution. Numerically, the sensitivity and specificity of the combination were 80.2 and 92.2 %, respectively. CONCLUSIONS: The concentrations of s-TSH measured in two samples taken at separate times from a clinically healthy and stable individual cannot be accurately interpreted using the combination of univariate reference limits and RCVs.

Reference change values of FIB-4.

When comparing two analytical results for the same analyte, the clinicians may benefit from knowing the reference change values (RCVs) of the analyte. For Fibrosis-4 Index (FIB-4), a noninvasive test used for assessing the risk of liver fibrosis, no RCVs have been published for non-cirrhotic individuals. Therefore, we estimated RCVs for adults, using retrospectively collected data from outpatients with AST, ALT, and thrombocytes within the respective reference intervals. FIB-4 was calculated as (age × AST)/(thrombocytes × ALT0.5). From two FIB-4 values in each patient we calculated the RCVs parametrically and non-parametrically. For both methods, we estimated the limits of the central 90% of the distribution of the ratio between the second and the first measurement. We obtained data on 599 outpatients with two blood tests taken 3 - 972 (median 258) days apart. The RCVs were 0.72 - 1.40 and 0.72 - 1.43, respectively, using the parametric and non-parametric methods. The 5 and 95 percentiles were not statistically significantly associated with sex, age, level of analyte, or the time between the measurements. The within-subject biological variation of FIB-4 was estimated to be 13.9%. Conclusion: In 90% of the patients the ratio between the second and the first FIB-4 result was approximately 0.7 - 1.4.

Treadmill running intensity and post-exercise increase in plasma cardiac troponin I and T-A pilot study in healthy volunteers.

BACKGROUND: Plasma concentrations of cardiac troponins increase in healthy individuals after strenuous training, but the response to lower exercise intensities has not been characterized. AIM: To determine whether exercise at moderate intensity significantly increases plasma cardiac troponins measured with different assays in healthy recreational athletes. METHODS: Twenty-four self-reported healthy volunteers were instructed to complete three 60-min bouts of treadmill running at variable intensities: High-intensity training (HIT) including a maximal exercise test and an anaerobic threshold test followed by training at 80%-95% of maximum heart rate (HRmax ), Moderate-intensity training (MIT) at 60%-75% of HRmax , and Low-intensity training (LIT) at 45%-55% of HRmax . Blood samples were collected before and at 2, 4, and 6 h after HIT and 4 h after MIT and LIT. Troponin I and T were measured in plasma samples with assays from Abbot, Siemens, and Roche. RESULTS: Plasma troponins measured with all assays were significantly increased compared to baseline after HIT but not after LIT. After HIT, the fraction of all participants with one or more values above the assay-specific 99th percentiles ranged from 13% to 61%. The biomarker criteria for acute myocardial injury were met after HIT for troponin T in 75% of female participants having no clinical evidence of coronary artery disease. CONCLUSION: High-intensity, but not moderate- or low-intensity, training for 60 min induced a potentially clinically significant increase in plasma cardiac troponins in healthy volunteers. Results exceeding the population 99th percentiles were most frequent with the troponin T assay.

Testing the limits: the diagnostic accuracy of reference change values.

Reference change values (RCVs) are used by the physician to judge whether a change in analyte concentration from one sample to the next may represent a clinically significant change. Published RCVs are usually given as fixed percentages of the analyte concentration in the first sample. The accuracy of published RCVs is not well known. We obtained public-use data from the US National Health and Nutrition Examination Survey (NHANES) 2001-2002 to study the distribution of changes in the concentration of eight commonly used analytes. Specimens were obtained on two occasions 7-47 days apart from 279 to 411 individuals with an analyte concentration within the reference interval in both samples. The analytes were albumin, calcium, cholesterol, phosphate, potassium, sodium, hemoglobin and thrombocytes. For each analyte, normal within-subject biological coefficient of variation from the EFLM Working Group on Biological Variation and the NHANES analytical coefficient of variation were used to calculate the 5 and 95 percentile RCVs. These RCVs were calculated as fixed percentages of the analyte concentrations in the first sample and compared to the empirical 5 and 95 percentiles. The sensitivity of the RCVs in detecting changes outside the empirical percentiles ranged from 0.35 for sodium to 0.80 for albumin. The specificity of the RCVs in detecting changes inside the empirical percentiles ranged from 0.85 for potassium to 0.97 for thrombocytes. Calculating RCVs as fixed percentages of the analyte concentration in the first sample lessened the diagnostic accuracy. RCVs given as a function of the first result would perform better.

Establishing the 99th percentile of a novel assay for high-sensitivity troponin I in a healthy blood donor population.

Background: The recommended cut-off of cardiac troponin (cTn) for the diagnosis of acute myocardial infarction (AMI) is the 99th percentile in a healthy reference population. We aimed to determine the 99th percentile of the novel ADVIA Centaur® High Sensitivity Troponin I assay (Siemens Healthcare Diagnostics) in fresh lithium heparin plasma samples from healthy blood donors. Methods: A total of 1000 apparently healthy blood donors were included. High-sensitivity (hs) cTnI, hs-cTnT, creatinine and N-terminal pro b-type natriuretic peptide (NT-proBNP) were measured in fresh lithium heparin plasma samples, and glycated hemoglobin (HbA1c) was measured in ethylenediaminetetraacetic acid (EDTA)-blood. The 99th percentile was estimated for the whole population, as well as for males and females separately. Results: For the total population the 99th percentile of ADVIA Centaur® High Sensitivity Troponin I was 96 (65-149) ng/L. The estimated value differed significantly from results published by others and was highly dependent on which values were considered statistical outliers. Conclusions: The estimated 99th percentile for hs-cTnI in the population studied differed significantly from previously published results. There is a need for further specifications regarding how subjects used for estimating the 99th percentile of cTns in healthy populations should be recruited and how outlier values should be identified, as this can highly influence the diagnostic cut-off applied for AMI.

A new index of clinical utility for diagnostic tests.

Clinical utility of a diagnostic test depends on its diagnostic accuracy, the pretest probability of disease and the clinical consequences of the test results. Tools for evaluating clinical utility are scarce. We propose a new clinical utility index (CUI), which is the expected gain in utility (EGU) of the test divided by the EGU of an ideal test, both adjusted for EGU of the optimal clinical action without testing. The index expresses the relative benefit of using the test compared to using an optimal test when making a clinical decision. To illustrate how the index may be used, we estimated CUI for fasting glucose, both as a continuous and as a dichotomous test, at several values of pretest probability of diabetes mellitus and at two levels of cost/benefit-ratio. In the same clinical situations we also estimated CUI for the 2 h glucose tolerance test. Hemoglobin A1c ≥ 48 mmol/mol was used as a reference standard for diabetes mellitus. In this model, fasting glucose was clinically more useful as a continuous test than as a dichotomous one, based on CUIs. At pretest probability above the treatment threshold, fasting glucose as a continuous test was even more useful than the complete glucose tolerance test. These results are not necessarily generalizable; however, they show how the CUI can be used to select the most useful test in certain clinical situations.

New reference intervals for cortisol, cortisol binding globulin and free cortisol index in women using ethinyl estradiol.

Healthy women using contraceptives containing a low dose of an estrogen may have a higher serum concentration of cortisol (s-cortisol) and cortisol binding globulin (s-CBG) than the commonly used upper reference limits. There are no published reference intervals for s-cortisol, s-CBG, serum free cortisol index (s-FCI) or cortisol in saliva (sa-cortisol) for these women. The aim was to establish the above-mentioned reference intervals and document the differences in s-cortisol and s-CBG in one group of women using and another group not using ethinyl estradiol (EE). In this cross-sectional study, the reference limits presented were given as the 2.5 and 97.5 percentiles of the distribution of reference values in a population of 277 healthy volunteer women, aged 18-45 years. 157 women were not using any type of estrogen, while 120 women were using contraceptives containing a daily dose of 15-35 µg of EE. Serum and salivary cortisol, and serum CBG were measured using standard laboratory methods. S-FCI was calculated as s-cortisol/s-CBG. The reference intervals for s-cortisol in samples collected at 0800-1030 am in women using and not using EE contraception were: 284-994 nmol/L and 159-569 nmol/L respectively, and for s-CBG: 847-3366 nmol/L and 860-1940 nmol/L, respectively. For s-FCI and sa-cortisol, no clinically significant differences were found. Sa-cortisol may be the preferred measurand for evaluation of possible hypercortisolism in women using estrogens, since cortisol in saliva is not influenced by estrogen. If assessing morning s-cortisol and s-CBG in women using EE, we recommend using separate - and not the commonly used - reference intervals.

The effects of dry ice exposure on plasma pH and coagulation analyses.

BACKGROUND: We recently observed that exposure to dry ice lowered sample pH and increased clotting times in lupus anticoagulant analyses, and that such changes could be prevented by placing samples at -80°C after dry ice exposure. In the current study, we sought to evaluate the effects of dry ice exposure on pH and various commonly used coagulation analyses. METHODS: Citrated plasma from 30 healthy blood donors was allocated to four preanalytical regimes: (1) immediate analysis of fresh plasma or (2) storage at -20°C; (3) storage at -20°C followed by dry ice exposure for 24 h or (4) storage at -20°C followed by dry ice exposure for 24 h and storage at -80°C for 24 h before analysis. Analyses of pH, prothrombin time international normalized ratio (PT-INR), activated partial thromboplastin time (APTT), antithrombin, fibrinogen, protein C and protein S was performed. RESULTS: Samples exposed to dry ice had significantly lower pH, prolonged clotting times in PT-INR, APTT and fibrinogen analyses as well as lower levels of protein C, than samples not exposed to dry ice. These changes in coagulation analyses were not present if samples were stored at -80°C for 24 h after dry ice exposure. Antithrombin and protein S were not significantly affected by dry ice exposure. CONCLUSIONS: Dry ice exposure lowered sample pH and affected various coagulation analyses. These effects were avoided by storing samples at -80°C for 24 h after dry ice exposure.

Using the hazard ratio to evaluate allowable total error in predictive measurands.

BACKGROUND: Allowable total error is usually derived from data on biological variation or from state-of-the-art of measuring technology. Here we present a new principle for evaluating allowable total error when the concentration of the analyte (the measurand) is used for prediction: What are the predictive consequences of allowable total errors in terms of errors in the estimate of the hazard ratio (HR)? METHODS: We explored the effect of analytical measurement errors on Cox regression estimates of HR. Published data on Cox regression coefficients were used to illustrate the effect of measurement errors on predicting cardiovascular events or death based on serum concentration of cholesterol and on progression of chronic kidney disease to kidney failure based on serum concentrations of albumin, bicarbonate, calcium and phosphate, and urine albumin/creatinine-ratio. RESULTS: If the acceptable error in the estimate of the HR is 10%, allowable total errors in serum cholesterol, bicarbonate and phosphate are approximately the same as allowable total error based on biological variation, while allowable total error in serum albumin and calcium are a little larger than estimates based on biological variation. CONCLUSIONS: Evaluating allowable total error from its effect on the estimate of HR is universally applicable to measurands used for prediction.

Using probit regression to disclose the analytical performance of qualitative and semi-quantitative tests.

BACKGROUND: The analytical performance of qualitative and semi-quantitative tests is usually studied by calculating the fraction of positive results after replicate testing of a few specimens with known concentrations of the analyte. We propose using probit regression to model the probability of positive results as a function of the analyte concentration, based on testing many specimens once with a qualitative and a quantitative test. METHODS: We collected laboratory data where urine specimens had been analyzed by both a urine albumin ('protein') dipstick test (Combur-Test strips) and a quantitative test (BN ProSpec System). For each dipstick cut-off level probit regression was used to estimate the probability of positive results as a function of urine albumin concentration. We also used probit regression to estimate the standard deviation of the continuous measurement signal that lies behind the binary test response. Finally, we used probit regression to estimate the probability of reading a specific semi-quantitative dipstick result as a function of urine albumin concentration. RESULTS: Based on analyses of 3259 specimens, the concentration of urine albumin with a 0.5 (50%) probability of positive result was 57 mg/L at the lowest possible cut-off limit, and 246 and 750 mg/L at the next (higher) levels. The corresponding standard deviations were 29, 83, and 217 mg/L, respectively. Semi-quantitatively, the maximum probability of these three readings occurred at a u-albumin of 117, 420, and 1200 mg/L, respectively. CONCLUSIONS: Probit regression is a useful tool to study the analytical performance of qualitative and semi-quantitative tests.

Dry ice exposure of plasma samples influences pH and lupus anticoagulant analysis.

BACKGROUND: Tests for lupus anticoagulant (LA), including silica clotting time (SCT) and diluted Russel's viper venom time (dRVVT) are used to diagnose antiphospholipid syndrome. Due to sample instability, it is recommended that samples are frozen if analysis is postponed >4 h. Shipping on dry ice is common practice to keep samples frozen during transport. Recent data suggest that exposure to dry ice may affect sample pH and results in subsequent analyses. We aimed to determine the effect of dry ice on pH and LA analysis. METHODS: Citrated plasma from eight healthy volunteers was allocated to three preanalytical regimes: 1) storage at -20 °C; 2) dry ice exposure followed by storage at -20 °C; or 3) dry ice exposure followed by storage at -80 °C. RESULTS: Samples stored at -20 °C after dry ice exposure had significantly lower median pH (1.2 units, p=0.01) and prolonged clotting time ratios (up to 55% for dRVVT tests, p<0.02) in LA analysis, compared to samples not exposed to dry ice. This resulted in poor test specificity (25%). Similar changes were not observed in samples placed at -80 °C after dry ice exposure. CONCLUSIONS: Dry ice may affect sample pH and increase the fraction of false positive LA results. This preanalytical factor should be taken into account by laboratories receiving frozen samples for these tests.

Using the likelihood ratio to evaluate allowable total error--an example with glycated hemoglobin (HbA1c).

BACKGROUND: Allowable total error is derived in many ways, often from data on biological variation in normal individuals. We present a new principle for evaluating allowable total error: What are the diagnostic consequences of allowable total errors in terms of errors in likelihood ratio (LR)? Glycated hemoglobin A1c in blood (HbA1c) in diagnosing diabetes mellitus is used as an example. Allowable total error for HbA1c is 3.0% derived from data on biological variation compared to 6.0% as defined by National Glycohemoglobin Standardization Program (NGSP). METHODS: We estimated a function for LR of HbA1c in diagnosing diabetes mellitus using logistic regression with a clinical database (n=572) where diabetes status was defined by WHO criteria. Then we estimated errors in LR that correspond to errors in the measurement of HbA1c. RESULTS: Measuring HbA1c 3% too low at HbA1c of 6.5 percentage points (the suggested diagnostic limit) gives a LR of 0.36 times the correct LR, while measuring HbA1c 3% too high gives a LR of 2.77 times the correct LR. The corresponding errors in LR for allowable total error of 6% are 0.13 and 7.69 times the correct LR, respectively. CONCLUSIONS: These principles of evaluating allowable total error can be applied to any diagnostically used analyte where the distribution of the analyte's concentration is known in patients with and without the disease in a clinically relevant population. In the example used, the allowable total error of 6% leads to very erroneous LRs, suggesting that the NGSP limits of ±6% are too liberal.

Validating precision--how many measurements do we need?

BACKGROUND: A quantitative analytical method should be sufficiently precise, i.e. the imprecision measured as a standard deviation should be less than the numerical definition of the acceptable standard deviation. We propose that the entire 90% confidence interval for the true standard deviation shall lie below the numerical definition of the acceptable standard deviation in order to assure that the analytical method is sufficiently precise. We also present power function curves to ease the decision on the number of measurements to make. METHODS: Computer simulation was used to calculate the probability that the upper limit of the 90% confidence interval for the true standard deviation was equal to or exceeded the acceptable standard deviation. Power function curves were constructed for different scenarios. RESULTS: The probability of failure to assure that the method is sufficiently precise increases with decreasing number of measurements and with increasing standard deviation when the true standard deviation is well below the acceptable standard deviation. For instance, the probability of failure is 42% for a precision experiment of 40 repeated measurements in one analytical run and 7% for 100 repeated measurements, when the true standard deviation is 80% of the acceptable standard deviation. Compared to the CLSI guidelines, validating precision according to the proposed principle is more reliable, but demands considerably more measurements. CONCLUSIONS: Using power function curves may help when planning studies to validate precision.

Lower hemoglobin with lower ferritin - results from the HUNT 2 Study.

AIM: We wanted to study the association between blood hemoglobin concentration (b-hemoglobin) and serum ferritin concentration (s-ferritin) in a healthy female population, and compare the findings to those in a previous study of ambulant female patients. METHODS: We compared median b-hemoglobin and the fraction with anemia in groups of women with s-ferritin from less than 10 µg/L to 100 µg/L. These women, aged 20-55 years, were part of a health screening survey (HUNT 2) where they reported to have 'good' or 'very good' general health and were found to have normal s-creatinine. The s-ferritin values were adjusted to the level of the previous study. The 10, 50 and 90 percentiles of b-hemoglobin were modelled as functions of s-ferritin using quantile regression. RESULTS: Among 2122 healthy females the entire b-hemoglobin distribution was shifted downwards in women with s-ferritin less than 20 µg/L. Accordingly, the median b-hemoglobin was statistically significantly lower. In women with s-ferritin less than 20 µg/L the fraction with anemia was 0.15. CONCLUSIONS: Lower s-ferritin is associated with lower b-hemoglobin in many more subjects than those labelled anemic.

Determining sample size when assessing mean equivalence.

BACKGROUND: When we want to assess whether two analytical methods are equivalent, we could test if the difference between the mean results is within the specification limits of 0 ± an acceptance criterion. Testing the null hypothesis of zero difference is less interesting, and so is the sample size estimation based on testing that hypothesis. Power function curves for equivalence testing experiments are not widely available. In this paper we present power function curves to help decide on the number of measurements when testing equivalence between the means of two analytical methods. METHODS: Computer simulation was used to calculate the probability that the 90% confidence interval for the difference between the means of two analytical methods would exceed the specification limits of 0 ± 1, 0 ± 2 or 0 ± 3 analytical standard deviations (SDa), respectively. RESULTS: The probability of getting a nonequivalence alarm increases with increasing difference between the means when the difference is well within the specification limits. The probability increases with decreasing sample size and with smaller acceptance criteria. We may need at least 40-50 measurements with each analytical method when the specification limits are 0 ± 1 SDa, and 10-15 and 5-10 when the specification limits are 0 ± 2 and 0 ± 3 SDa, respectively. CONCLUSIONS: The power function curves provide information of the probability of false alarm, so that we can decide on the sample size under less uncertainty.

Allowable systematic difference between two instruments measuring the same analyte.

BACKGROUND: If a laboratory has two analytical instruments for measuring the concentration of the same analyte and samples from the patients are randomly allocated to either of the two, then an allowable systematic difference between the two instruments should be defined. We present a solution to this problem, based on the traditional criterion that the total analytical standard deviation (SD) shall be less than half the within-subject biological SD. METHODS: We derived a formula for estimating the SD of the distribution of analytical results that may stem from two instruments with different means and SDs and different probabilities of being used. The formula was used to estimate the allowable systematic difference between the two instruments. RESULTS: The allowable systematic difference depends on the within-subject biological SD, the SDs of the two instruments, and the probability that a sample is analyzed with a certain instrument. When this probability is 0.5, the allowable systematic difference approaches the magnitude of the within-subject biological SD as the analytical SDs approach zero, while no systematic difference is allowed when the two analytical SDs are equal to their maximum allowable value of half the within-subject biological SD. CONCLUSIONS: In a monitoring situation, the allowable systematic difference between two analytical instruments depends on the probability that a sample is allocated to each of the instruments as well as the analytical SDs and the within-subject biological SD.

Three family members with elevated plasma cobalamin, transcobalamin and soluble transcobalamin receptor (sCD320).

BACKGROUND: Plasma cobalamin is requested in order to diagnose cobalamin deficiency and low levels confirm a deficient state. Here, we present three family members with unexpected high levels of cobalamin. METHODS: We included a patient referred for cobalamin measurement due to neurological symptoms, her son and her daughter. Mother and son both suffered from myotonic dystrophy type II, while the daughter tested negative for this disease. Blood samples were analyzed for cobalamin, haptocorrin, transcobalamin, holoTC, and sCD320. We employed gel filtration and antibody precipitation for further characterization. The protein coding region of the TCN2 gene, encoding transcobalamin, was sequenced. RESULTS: The patient, her {son} and [daughter] all had cobalamin levels above the measurement range of the routine method employed (>1476 pmol/L). Total transcobalamin and (holoTC) were 5980 (1500), {5260 (2410)} and [5630 (1340)] pmol/L, which is well above the upper reference limits of 1500 (160) pmol/L. The sCD320 concentration was also well above the upper reference limit of 97 arb.u.: 1340, {1510} and [1090] arb.u. Haptocorrin levels were within the reference range and no signs of cobalamin deficiency were found. DNA sequencing of the TCN2 gene revealed several known polymorphisms not associated with highly elevated transcobalamin levels. Upon gel filtration, sCD320 eluted as a larger molecule than previously reported. By incubation with anti-transcobalamin antibodies, we precipitated both transcobalamin and part of sCD320. CONCLUSIONS: The high cobalamin levels were mainly explained by high levels of holoTC, possibly caused by complex formation with its soluble receptor, sCD320. The family occurrence points to a genetic explanation.

The diagnostic accuracy of unbound iron binding capacity (UIBC) as a test for empty iron stores.

BACKGROUND: Unbound iron binding capacity (UIBC) in serum, which is s-total iron binding capacity (2 times s- transferrin) minus s-iron, may be a more accurate marker of empty iron stores than serum transferrin saturation. Previously we have shown this for healthy females of childbearing age. METHODS: Now we used receiver operating characteristic (ROC) curve analysis to compare the diagnostic accuracy of s-iron, s-transferrin, s-transferrin saturation and s-UIBC in diagnosing empty iron stores in 29,251 female and 19,652 male outpatients. Empty iron stores were defined as s-ferritin less than 10, 15 or 20 µg/L. RESULTS: At all definitions of empty iron stores s-UIBC had a better diagnostic accuracy than the other tests in both male and female outpatients, with an area under the ROC curve of 0.85-0.97. Also in subpopulations with elevated s-CRP or low b-hemoglobin s-UIBC was more accurate than the other tests. All tests performed better in males than in females, and generally they were more accurate in adults than in children. CONCLUSION: When diagnosing empty iron stores calculation of s-UIBC is a better way to utilize the information in s-iron and s-transferrin than the calculation of s-transferrin saturation.