Measuring serum prothrombin in patients treated with warfarin or direct oral anticoagulants.

BACKGROUND: Warfarin therapy influences generation of γ-carboxyglutamyl (Gla) residues in prothrombin, causing reduced coagulation activity. It will leave such inactive prothrombin in serum after clot formation, resulting in serum prothrombin constituting total inactive prothrombin in these patients. METHODS: An ELISA was developed to measure biologically inactive prothrombin in serum, and applied to serum from warfarin therapy causing a decrease in Gla residues or direct oral anticoagulant (DOAC) therapy as its contrast. RESULTS: The concentrations of serum prothrombin in both the warfarin and DOAC groups were higher than those in the healthy group (p < 0.01 and p < 0.001, respectively). When serum in the previous three groups was treated with barium carbonate to exclude prothrombin, which lost several Gla residues, the prothrombin concentration in the DOAC group decreased to the same level as that in the healthy group, indicating that prothrombin was obtained at a high level only in the warfarin group (p < 0.01). CONCLUSIONS: Warfarin and DOAC led to increase in serum prothrombin concentration. The reason is that DOAC decreases prothrombin recruitment during fibrin clot formation, while warfarin leads to the accumulation of inactive prothrombin, which have a decreased number of Gla residues.

Novel strategy of rapid typing of Shiga toxin-producing Escherichia coli using MALDI Biotyper and ClinProTools analysis.

INTRODUCTION: Rapid detection of Shiga toxin-producing Escherichia coli (STEC) is essential. Matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) allows rapid, accurate, and low-cost microbial identification. We aimed to examine the discrimination ability of MALDI Biotyper (Bruker Daltonics) between STEC and non-STEC. METHODS: In total, 234 strains (79 STEC strains and 155 non-STEC strains) isolated from stool between 2009 and 2014 were measured by MALDI Biotyper and mass spectra of 2000-20,000 m/z were analyzed with ClinProTools (Bruker Daltonics). Eighty-three strains were randomly extracted to produce STEC detection models using 3 algorithms, and 151 strains were used as validation samples to verify STEC detection performance and discrimination performance of Shiga toxins with the STEC detection models. RESULTS: The STEC detection model with Quick Classifier (QC) algorithm was the most sensitive: sensitivity, 84.1% (37/44); specificity, 94.4% (101/107). Discrimination between STEC and non-STEC was excellent, but individual discrimination of Shiga toxins was not possible. CONCLUSION: MALDI Biotyper may be a useful rapid diagnostic method for STEC infection.

Evaluation of the detection ability of uropathogen morphology and vaginal contamination by the Atellica UAS800 automated urine microscopy analyzer and its effectiveness.

BACKGROUND: To help combat the worldwide spread of multidrug-resistant Enterobacterales, which are responsible for many causes of urinary tract infection (UTI), we evaluated the ability of the Atellica UAS800 automated microscopy system, the only one offering the capability of bacterial morphological differentiation, to determine its effectiveness. METHODS: We examined 118 outpatient spot urine samples in which pyuria and bacteriuria were observed using flow cytometry (training set: 81; cross-validation set: 37). The ability of the Atellica UAS800 to differentiate between bacilli and cocci was verified. To improve its ability, multiple logistic regression analysis was used to construct a prediction formula. RESULTS: This instrument's detection sensitivity was 106 CFU/ml, and reproducibility in that range was good, but data reliability for the number of cocci was low. Multiple logistic regression analysis with each explanatory variable (14 items from the Atellica UAS800, age and sex) showed the best prediction formula for discrimination of uropathogen morphology was a model with 5 explanatory variables: number of bacilli (p < 0.001), squamous epithelial cells (p = 0.004), age (p = 0.039), number of cocci (p = 0.107), and erythrocytes (p = 0.111). For a predicted cutoff value of 0.449, sensitivity was 0.879 and specificity was 0.854. In the cross-validation set, sensitivity was 0.813 and specificity was 0.857. CONCLUSIONS: The Atellica UAS800 could detect squamous epithelial cells, an indicator of vaginal contamination, with high sensitivity, which further improved performance. Simultaneous use of this probability prediction formula with urinalysis results may facilitate real-time prediction of uropathogens and vaginal contamination, thus providing helpful information for empiric therapy.

Prediction of Uropathogens by Flow Cytometry and Dip-stick Test Results of Urine Through Multivariable Logistic Regression Analysis.

PURPOSE: Multidrug-resistant Enterobacteriaceae in urinary tract infection (UTI) has spread worldwide; one cause is overuse of broad-spectrum antimicrobial agents such as fluoroquinolone antibacterials. To improve antimicrobial agent administration, this study aimed to calculate a probability prediction formula to predict the organism strain causing UTI in real time from dip-stick testing and flow cytometry. METHODOLOGY: We examined 372 outpatient spot urine samples with observed pyuria and bacteriuria using dip-stick testing and flow cytometry. We performed multiple logistic-regression analysis on the basis of 11 measurement items and BACT scattergram analysis with age and sex as explanatory variables and each strain as the response variable and calculated a probability prediction formula. RESULTS: The best prediction formula for discrimination of the bacilli group and cocci or polymicrobial group was a model with 5 explanatory variables that included percentage of scattergram dots in an angular area of 0-25° (P<0.001), sex (P<0.001), nitrite (P = 0.002), and ketones (P = 0.133). For a predicted cut-off value of Y = 0.395, sensitivity was 0.867 and specificity was 0.775 (cross-validation group: sensitivity = 0.840, specificity = 0.760). The best prediction formula for P. mirabilis and other bacilli was a model with percentage of scattergram dots in an angular area of 0-20° (P<0.001) and nitrite (P = 0.090). For a predicted cut-off value of Y = 0.064, sensitivity was 0.889 and specificity was 0.788 (cross-validation group: sensitivity = 1.000, specificity = 0.766). CONCLUSION: Simultaneous use of the calculated probability prediction formula with urinalysis results facilitates real-time prediction of organisms causing UTI, thus providing helpful information for empiric therapy.

Evaluation of menstrual cycle-related changes in 85 clinical laboratory analytes.

OBJECTIVE: The menstrual cycle-related changes in clinical laboratory values were analysed by use of data obtained in the Asian multicentre study aimed at derivation of common reference intervals for 85 major clinical laboratory tests. METHODS: Among 1876 healthy female volunteers, 893 had regular menstruation. They were classified into five groups according to dates between sample collection and the start of the last menstrual cycle: early follicular phase (1-6 days), late follicular phase (7-12 days), ovulatory phase (13-16 days), early luteal phase (17-22 days), and late luteal phase (23-31 days). Multiple linear regression analysis was performed to evaluate the menstrual cycle-related changes in test results. The magnitude was expressed as a standard deviation ratio of between-phase standard deviation to between-individual standard deviation based on nested ANOVA. RESULTS: Aside from obvious changes for four sex hormones (oestradiol, progesterone, follicle-stimulating hormone, and luteinizing hormone), we observed statistically significant menstrual cycle-related changes in the following tests (standard deviation ratio >0.15): Na, Cl, creatine kinase, C-reactive protein, serum amyloid A, carbohydrate antigen 125, and parathyroid hormone were higher during the early follicular phase, while insulin, total cholesterol, and white blood cell were higher during the luteal phase. Significant associations of those test items with the four sex hormones were revealed. CONCLUSIONS: The menstrual cycle-related changes in laboratory test results were revealed in some commonly tested items other than sex hormones. The findings are of interest in understanding female physiology in relation to hormonal changes, but the magnitude of changes is rather small and not very relevant in interpreting test results.

[Relationship between spot urine pH and number of metabolic syndrome features].

A significant inverse relationship between the 24-hour urine pH and number of metabolic syndrome (MS) features(BMI ≥ 30 kg/m2, circumference > 88cm, HDL-cholesterol < 50mg/dL, etc.) was identified in individuals, which means that 24-hour urine pH values decrease with an increasing number of MS features. In this respect, we examined the association between the number of MS features and spot urine pH instead of 24-hour urine pH. A positive relationship was observed between the spot urine pH and number of MS features by analysis of covariance, with the age, gender, and eGFR as covariates. On the other hand, a high level of serum uric acid (UA) indicated a significantly lower spot urine pH compared with a normal level of UA. Serum UA was a significant independent variable explaining the decrease of the urine pH. The results of path analysis showed that UA had negative effects on the number of MS features and urine pH. Therefore, a positive relationship between the number of MS features and urine pH will be a spurious correlation with UA as a confounding factor. In addition, the negative relationship between the number of MS features and UA was also a spurious correlation with age as a confounding factor. From these results, the serum UA level should be considered to evaluate the relationship between the number of MS features and spot urine pH.

Present status of the standardization of HDL-C, LDL-C, and TG measurement values available in Japan.

BACKGROUND: Control sera used as evaluation samples in external quality control of HDL-C and LDL-C sometimes show disparities in results between direct methods differing in reaction principles. As a result, the present standardization status is unclear. In 2008, we investigated the present status of standardization of HDL-C, LDL-C, and TG measurement values available in Japan. SUBJECTS AND METHODS: To evaluate accuracy, refrigerated fresh human serum pools used as samples were analyzed irrespective of the manufacturer's method. To evaluate precision, a questionnaire survey regarding the internal quality control status at each institution was carried out. As evaluation criteria, the permissible limits of error of BA and CVA based on JSCC proposals, and the accuracy and imprecision criterion NCEP proposals were used. RESULTS: There were 70 participating institutions for HDL-C, 65 for LDL-C, and 71 for TG. TG values from the institutions showed 83.1-91.5% for both the JSCC BA range and the NCEP criterion range. HDL-C values within the JSCC BA range were 81.4-82.6% and within the NCEP criterion range the value was 98.6%. Similarly, LDL-C values within the JSCC BA range were 89.2% and within the NCEP criterion range the value were 81.5-83.1%, respectively. Concerning precision, a questionnaire regarding internal quality control of each institution was completed. More than 90% of the institutions showed values within the CVA range proposed by the JSCC for all of HDL-C, LDL-C, and TG. CONCLUSION: Standardization of lipid assays used for metabolic syndrome-based health checkups has been mostly achieved.

[Analysis of the causal relations between serum ferritin elevation and latent pathologic factors by structural equation modeling].

Serum ferritin increases in various disorders and clinical conditions such as iron overload, tissue disorders, and inflammation. However causal associations between the serum ferritin level and clinical factors that influence serum ferritin level or between these factors are not well characterized. We analyzed the ferritin level and other laboratory data including transferrin saturation (TF%), enzyme activities, and inflammatory protein levels in the sera of 124 patients with hyperferritinemia. We identified latent pathologic factors representing cell damage and inflammatory status and assumed causal relations between serum ferritin elevation and these factors. Structural equation modeling was used to verify causal relations and the adequacy of latent factors. A model that quantitatively explained serum ferritin elevation was identified. The direct effect of the cell damage factor on serum ferritin indicated cell destruction and decreased ferritin clearance. In contrast, the inflammation factor directly increased the ferritin level, but indirectly decreased it via TF%. These causal relations may quantitatively explain the mechanism of serum ferritin levels in patients with hyperferritinemia.

Determination of leukocyte counts in cerebrospinal fluid with a disposable plastic hemocytometer.

Manual microscopic cell counting in a Fuchs-Rosenthal (FR) chamber has been the gold standard for quantification of leukocytes in cerebrospinal fluid (CSF). However, for accurate determination of the number and differentiation of cells by chamber counting, hemocytometers must be prepared carefully and kept clean. Improper fitting of the chamber and coverslip changes the volume of sample introduced into the chamber well. Moreover, because conventional hemocytometers are used repeatedly and are breakable, there is a risk of exposure to potentially infectious material. To address these issues, disposable plastic hemocytometers have been developed. However, the accuracy, precision, and clinical usefulness of disposable chambers for CSF cells counting have not been determined. In the present study, we evaluated use of a disposable plastic counting chamber (C-Chip DHC-F01) by comparing its performance with that of an FR chamber for counting of CSF specimens and cell suspensions. Within-run precision of C-Chip counting was comparable or superior to that of FR chamber counting, and excellent correlation between cell counts obtained with the C-Chip chamber and FR chamber was observed. However, C-Chip chambers that were kept at 4 degrees C yielded significantly low cell counts. The disposable hemocytometer will reduce the risk of exposure to potentially infectious material. However, use of C-Chip chambers should be avoided in cold environments.

Evaluation of a model of latent pathologic factors in relation to serum ferritin elevation.

OBJECTIVES: Serum ferritin increases in various disorders and clinical conditions. However, causal associations between the serum ferritin level and clinical factors that influence serum ferritin level are not well characterized. We report a model that quantitatively analyzes the causal relations between the serum ferritin level and clinical factors. DESIGN AND METHODS: We analyzed the ferritin level and other laboratory data in the sera of 274 patients. Structural equation modeling was used to verify causal relations and the adequacy of latent factors. RESULTS: Three factors representing clinical status were identified: cell damage, hepatic function, and inflammation. Serum iron (SI) had the strongest effect on serum ferritin elevation. The effect of the cell damage factor on serum ferritin indicated cell destruction, and that of the hepatic function factor represented decreased serum ferritin clearance. The cell damage factor also indirectly increased the ferritin level via SI or the hepatic function factor. The total effect of the inflammatory status factor on ferritin level was very weak. CONCLUSIONS: These causal relations may explain the mechanism of serum ferritin level elevation in various clinical conditions.

Urine flow cytometer quantification of leukocytes in samples containing a large proportion of lymphocytes.

OBJECTIVE: We compared leukocyte counts obtained by cytometric analysis and Fuchs-Rosenthal (FR) chamber counting in different proportions of lymphocytes (Lym%) suspensions and cerebrospinal fluid (CSF). DESIGN AND METHODS: UF-100 (UF) was evaluated. For preparation of cell suspensions, gradient density centrifugation method was used. RESULTS: The regression equation for UF and FR chamber counting of the cell suspensions was y=0.88x+18.8 WBC/microL (r=0.832, n=106). For a few high Lym% samples, markedly underestimated WBC counts were obtained by UF. CONCLUSIONS: Underestimated WBC count is due not to systematic error but to random error. Counts of the "other" population by UF may be useful for detection of underestimated samples.

Enzymatic assay for determination of bicarbonate ion in plasma using urea amidolyase.

BACKGROUND: One of the important buffering systems to maintain blood pH is carbonic acid-bicarbonate. Together with other clinical tests, the measurement of bicarbonate ion concentrations is widely used for the diagnosis of the acid-base balance. We developed a kinetic assay for measurement of bicarbonate ion in plasma using urea amidolyase (EC 3.5.1.45) from yeast species. We evaluated the analytical performance of the present enzymatic method and examined the relationship between bicarbonate ion concentrations by present method and with ABL 520 blood gas system. METHODS: Urea amidolyase catalyzes the reaction of bicarbonate ion with urea to rise to allophanate. We eliminated endogenous ammonium ion by the use of glutamate dehydrogenase (EC 1.4.1.4), and then monitored the production of ammonium ion in the presence of urea amidolyase, urea, ATP, potassium, and magnesium ions. Ammonium ion was produced proportional to the bicarbonate ion concentration and was determined by adding glutamate dehydrogenase to produce NADP(+) in the presence of 2-oxoglutarate and NADPH, and the change of absorbance at 340 nm was monitored. RESULTS: The within-assay and day-to-day assay coefficient variations (CVs) of the present method were 1.3-2.8% and 3.1-5.4%, respectively. The analytical recoveries were 90-110%. The presence of ascorbic acid, bilirubin, hemoglobin, lipemic material, hydrogen phosphate, dihydrogen phosphate, ammonium, or calcium ion did not affect this assay. The correlation coefficient between the values obtained by present method (y) and Radiometer ABL 520 blood gas system (x) was 0.983 (y = 1.029x-0.737 mmol/l, Sy/x = 0.764, n = 100), with a mean difference of 0.03 +/- 0.77 mmol/l [(values by reference method-that of present method) +/- S.D.] using the Bland-Altman technique.

Relation between iron content of serum ferritin and clinical status factors extracted by factor analysis in patients with hyperferritinemia.

OBJECTIVES: The aims of this study were to develop a new technique for determination of iron content of serum ferritin (ICF, micromol Fe/mg protein) and to investigate relations between ICF and clinical status in patients with hyperferritinemia. METHODS: ICF values were determined by a combination of immunoprecipitation of ferritin and direct colorimetric iron assay. One hundred fifty patients with hyperferritinemia were screened. Factor analysis of the results of 11 laboratory tests was applied to extract factors representing the clinical status of patients. Relations between the extracted factors and the ICF values or serum ferritin concentrations were assessed. RESULTS: Within-run coefficients of variation (CVs) of the ICF assay were <==5.7%. The mean ICF value of 150 patients was 0.423 micromol/mg (SD, 0.211 micromol/mg). Three factors representing clinical status were identified: inflammation, tissue cell damage, and body iron status. Serum ferritin level correlated with all three factors. In contrast, ICF correlated significantly only with the factor representing tissue cell damage (r = 0.293, p = 0.001), and this correlation was independent of inflammation and iron status (p = 0.008). CONCLUSIONS: ICF changes in response to tissue cell damage independent of inflammatory and body iron statuses, whereas serum ferritin changes in response to all three pathologic statuses.

Modification of fully automated total iron-binding capacity (TIBC) assay in serum and comparison with dimension TIBC method.

BACKGROUND: We previously reported the development of a fully automated assay for total iron-binding capacity (TIBC) in serum, using a multipurpose automated analyzer. However, this method requires four different reagents and is thus useful only with a limited number of available analyzers. We simplified our original assay and compared the analytical performance of the modified method with that of a commercial, fully automated TIBC assay (Dimension TIBC assay). METHODS: We simplified our original method to require only three reagents. Calibration was also altered and was performed with human transferrin standard solutions. An advantage of this method is that it does not require separation of excess unbound iron after the first step of transferrin saturation. Unbound iron is eliminated by formation of a complex with the chromogenic reagent ferrozine in the second step. Iron dissociated from transferrin by acidic pH reacts with ferrozine to form a colored complex in the final step, and the increase in absorbance at 570/660 nm is directly proportional to the TIBC measured. TIBC values were determined for 49 healthy individuals and 148 patients with this modified TIBC assay and with a commercial, fully automated TIBC method (Dimension clinical chemistry system), and calculation of TIBC based on the sum of the serum iron and unsaturated iron-binding capacity was performed for 97 patients. RESULTS: The within-run CVs for the modified TIBC assay and the Dimension TIBC assay were <4.8% and <2.4%, and the between-run CVs were 1.2% and 1.7%, respectively. The dilution curves were linear for TIBC values up to at least 180 micromol/L with both methods. TIBC values obtained by our method were linearly correlated with serum transferrin concentrations (r = 0.984; S(y/x) = 3.18 micromol/L; P <0.001). The correlation between the values obtained with the present method (y) and those obtained with the Dimension TIBC method (x) was y = 1.04x + 1.19 micromol/L (r = 0.985; S(y/x) = 2.47 micromol/L), and with the calculation method (x) was y = 1.18x + 2.62 micromol/L (r = 0.976; S(y/x) = 3.27 micromol/L). CONCLUSIONS: Our modified, fully automated TIBC assay performed similarly to the Dimension TIBC assay and is adaptable for use with many multipurpose automated analyzers.