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The framework of supply curve of working hours is a main tool to explain how working hours aredetermined or how working hours respond to tax rate changes. However, the estimation task of thesupply curve is not complete yet.We constructed a new model of working hours incorporating both supply and demand curves of working hours. The constructed model demonstrates that (1) working hours (h) and wage earnings (E) are determined jointly at an equilibrium point on a contract curve where demand and supply of workers are equal, and (2) the contract curve passes through the intersection of the supply and demand curves of working hours. These results imply that (1) it is impossible to estimate the supply curve of working hours because equilibrium points are not usually located on a supply curve of working hours, and (2) a contract curve of working hours should be estimated to measure the wage elasticity of working hours. For the estimation of contract curves, geometric mean regression (GMR) is selected for three reasons. First, the constructed model is a type of measurement error model. That is, both variables (h and E) in the contract curve equations are measured with errors. Second, the GMR estimator is the maximum likelihood estimator. Third, the coefficient of determination (R2) of both variables is equal in the GMR. The estimated wage elasticity of working hours for four industry groups is between -0.13 and -0.23. The transportation group has a slightly higher elasticity than the others, and high school graduates have a slightly higher elasticity than college graduates. In GMR, there is a simple relation between the coefficient of determination (R^2) and correlation coefficient (r_xy) as follows: R^2= (1+|r_xy|)/2., 論文, Articles, JEL Code: J22, J23, C13

Previous research suggests that point-of-care (POCT) determination of glycated hemoglobin (HbA1c) is a diagnostic test that can be an adequate alternative to measuring HbA1c in the laboratory. The main goal of this study was to examine the analytical characteristics of the novel INCLIX POCT method for HbA1c determination in order to test its performance before introducing this method into routine use. HbA1c is measured in a duplicate in 44 EDTA blood samples parallel on INCLIX POCT device (Sugitech, Inc.) and using automated turbidimetric immunoinhibition test on Olympus AU400 (Beckman Coulter). The within run imprecision was 7.58%, between runs imprecision was 6.63% and 6.22%, and day-to-day imprecision was 8.80% and 7.51%. Total laboratory imprecision was in agreement with those stated by the manufacturer. A statistically significant Pearson correlation coefficient was calculated (r = 0.871, P < 0.01; linear R2 = 0.757). Using Deming regression analysis, the following equation was obtained: y = - 1.80 + 1.304x. Our results indicate statistically significant correlation, linear relationship, and a significant degree of compatibility between the two analyzed methods. However, the negative bias of the HbA1c values determined on the POCT analyzer compared to the Olympus AU400 was confirmed, highlighting the need to standardize the INCLIX method. Prethodna istraživanja sugerišu da je „point-of-care“ metoda (POCT) za određivanje glikoziliranog hemoglobina (HbA1c) dijagnostički test koji može biti adekvatna alternativa merenju HbA1c u laboratoriji. Glavni cilj ovog istraživanja bio je da se ispitaju analitičke karakteristike nove INCLIX POCT metode za određivanje HbA1c kako bi se testirale karakteristike izvođenja pre uvođenja ove metode u rutinsku upotrebu. HbA1c je meren u duplikatu u 44 uzorka EDTA krvi paralelno na INCLIX POCT uređaju (Sugitech, Inc.) i automatskim turbidimetrijskim imunoinhibicijskim testom na Olympus AU400 biohemijskom analizatoru (Beckman Coulter). Nepreciznost u seriji bila je 7,58%, nepreciznost između serija bila je 6,63% i 6,22%, a nepreciznost iz dana u dan bila je 8,80% i 7,51%. Ukupna laboratorijska nepreciznost bila je u slaganju sa podacima koje je naveo proizvođač. Izračunat je statistički značajan Pearsonov koeficijent korelacije (r = 0,871, P < 0,01; linearni R2 = 0,757). Demingovom regresionom analizom dobijena je sledeća jednačina: y = -1,80 + 1,304x. Naši rezultati ukazuju na statistički značajnu korelaciju, linearnu povezanost i značajan stepen kompatibilnosti između dve analizirane metode. Međutim, potvrđen je negativan bias vrednosti HbA1c određenih na POCT analizatoru u odnosu na Olympus AU400, što ističe potrebu za standardizacijom INCLIX metode.

Background and aims: Analysis of body composition is a very important issue, and it is often used as one of the crucial markers of overall health. The aim of the study was to analyse the relationship between the percentage of body fat ratio estimated using selected equations and adiposity obtained by bioimpedance in 7-16-year-olds from Krakow (Poland). Methods and results: The study included 1887 children and adolescents. Body weight and height were measured according to standard methods and Body Mass Index was calculated on the basis of those measurements. Biceps, triceps, subscapular, suprailiac, abdominal and calf skinfolds were measured. Adiposity was obtained using the BIA method as well as estimated by Siri, Goran, Slaughter, Deurenberg, Huang, Dezenberg, Ripka and Brozek equations. Relationship between results obtained from the equations and bioimpedance was assessed using Deming regression. All calculations were performed using Microsoft Excel and MedCalc. Statistically significant (p ≤ 0.001) relationship between adiposity obtained by bioimpedance and estimated was observed for all of the analysed equations. For girls, the strongest one was noted for Siri equation, with R = 0.83 (95%CI: 0.81-0.85). Among boys, the strongest relationship was observed for 3-skinfolds based equation developed by Ripka, with the Pearson's correlation coefficient value of 0.84 (95%CI: 0.81-0.85). Conclusions: The most useful equation to estimate the body fat percentage in the population of 7-16-year olds from Krakow (Poland) differed between the sexes. For boys, it was Ripka 3-skinfold equation, while for girls it was the equation developed by Siri.

To investigate the performance of short-time dynamic imaging in quantifying kinetic metrics of 2-[18F]-fluoro-2-deoxy-d-glucose (18F-FDG). Dynamic total-body positron emission tomography (PET) imaging was performed in 11 healthy volunteers for 75 min. The data were divided into 30-, 45- and 75-min groups. Nonlinear regression (NLR) generated constant rates (k1 to k3) and NLR-based Ki in various organs. The Patlak method calculated parametric Ki images to generate Patlak-based Ki values. Paired samples t-test or the Wilcoxon signed-rank test compared the kinetic metrics between the groups, depending on data normality. Deming regression and Bland–Altman analysis assessed the correlation and agreement between NLR- and Patlak-based Ki. A two-sided P

Our objective was to validate and compare the automated and chromatography methods for determining imatinib levels in plasma, in terms of linearity, precision, accuracy and specificity, for therapeutic drug monitoring. Imatinib patterns were prepared in human plasma and were analysed by both methods to determine the calibration curve of each method. The methods were validated for linearity, extraction recovery, intra- and inter-day precision (relative standard deviation, RSD%), accuracy, and specificity of imatinib analysis. Method comparison study was carried out using the imatinib concentrations determined in the 25 patient samples by Deming regression and Bland–Altman analysis. The calibration curve of the automated and chromatography methods were linear over the range 500–2750 ng/ml (r = 0.996) and 500–3000 ng/ml (r = 0.999), respectively. Intra- and inter-day precision showed that not all the CV values of the drug were below 15% for the automated method, recommended by the EMA and the FDA. In contrast, all CV values for precision of the chromatography method are below 6.0%. Average recovery percentage is 94.4% and 100.8 for the automated and chromatography methods, respectively. Agreement between the two methods was illustrated using Bland–Altman plot with a mean difference (Immunoassay—UPLC-UV) of 191.28 ng/ml and a 95% confidence interval of − 201.41 to 583.97 ng/ml. Deming regression analysis showed that the correlation coefficient for the automated method versus the chromatography method was 0.927 The chromatographic technique was the better option for therapeutic drug monitoring of imatinib in clinical practice in patients with Chronic Myeloid Leukemia (CML).

A pair-multiple linear regression model which is a synthesis of Deming regression and multiple linear regression model is considered. It is shown that with a change in the type of minimized distance, the pair-multiple regression model transforms smoothly from the pair model into the multiple linear regression model. In this case, pair-multiple regression models retain the ability to interpret the coefficients and predict the values of the explained variable. An aggregated quality criterion of regression models based on four well-known indicators: the coefficient of determination, Darbin – Watson, the consistency of behaviour and the average relative error of approximation is proposed. Using this criterion, the problem of multi-criteria construction of a pair-multiple linear regression model is formalized as a nonlinear programming problem. An algorithm for its approximate solution is developed. The results of this work can be used to improve the overall qualitative characteristics of multiple linear regression models.

The finger-cuff system CNAP (CNSystems Medizintechnik, Graz, Austria) allows non-invasive automated measurement of pulse pressure variation (PPVCNAP). We sought to validate the PPVCNAP-algorithm and investigate the agreement between PPVCNAP and arterial catheter-derived manually calculated pulse pressure variation (PPVINV). This was a prospective method comparison study in patients having neurosurgery. PPVINV was the reference method. We applied the PPVCNAP-algorithm to arterial catheter-derived blood pressure waveforms (PPVINV−CNAP) and to CNAP finger-cuff-derived blood pressure waveforms (PPVCNAP). To validate the PPVCNAP-algorithm, we compared PPVINV−CNAP to PPVINV. To investigate the clinical performance of PPVCNAP, we compared PPVCNAP to PPVINV. We used Bland–Altman analysis (absolute agreement), Deming regression, concordance, and Cohen's kappa (predictive agreement for three pulse pressure variation categories). We analyzed 360 measurements from 36 patients. The mean of the differences between PPVINV−CNAP and PPVINV was −0.1% (95% limits of agreement (95%-LoA) −2.5 to 2.3%). Deming regression showed a slope of 0.99 (95% confidence interval (95%-CI) 0.91 to 1.06) and intercept of −0.02 (95%-CI −0.52 to 0.47). The predictive agreement between PPVINV−CNAP and PPVINV was 92% and Cohen’s kappa was 0.79. The mean of the differences between PPVCNAP and PPVINV was −1.0% (95%-LoA−6.3 to 4.3%). Deming regression showed a slope of 0.85 (95%-CI 0.78 to 0.91) and intercept of 0.10 (95%-CI −0.34 to 0.55). The predictive agreement between PPVCNAP and PPVINV was 82% and Cohen’s kappa was 0.48. The PPVCNAP-algorithm reliably calculates pulse pressure variation compared to manual offline pulse pressure variation calculation when applied on the same arterial blood pressure waveform. The absolute and predictive agreement between PPVCNAP and PPVINV are moderate.

Introduction: Sonographic technologies can estimate extravascular lung water (EVLW) in hemodialysis (HD) patients. This study investigated the suitability of a handheld scanner in contrast to a portable scanner for quantifying EVLW in hospitalized patients requiring HD. Methods: In this prospective study, 54 hospitalized HD patients were enrolled. Bedside lung ultrasound was performed within 30 min before and after dialysis using handheld (phased array transducer, 1.7–3.8 MHz) and portable (curved probe, 5–2 MHz) ultrasound devices. Eight lung zones were scanned for total B-lines number (TBLN). The maximum diameter of inferior vena cava (IVC) was measured. We performed Passing-Bablok regression, Deming regression, Bland-Altman, and logistic regression analysis. Results: The 2 devices did not differ in measuring TBLN and IVC (p > 0.05), showing a high correlation (r = 0.92 and r = 0.51, respectively). Passing-Bablok regression had a slope of 1.11 and an intercept of 0 for TBLN, and the slope of Deming regression was 1.02 within the CI bands of 0.94 and 1.11 in the full cohort. TBLN was logarithmically transformed for Bland-Altman analysis, showing a bias of 0.06 (TBLN = 1.2) between devices. The slope and intercept of the Deming regression in IVC measurements were 0.77 and 0.46, respectively; Bland-Altman plot showed a bias of −0.07. Compared with predialysis, TBLN significantly (p < 0.001) decreased after dialysis, while IVC was unchanged (p = 0.16). Univariate analysis showed that cardiovascular disease (odds ratio [OR] 8.94 [2.13–61.96], p = 0.002), smoking history (OR 5.75 [1.8–20.46], p = 0.003), and right pleural effusion (OR 5.0 [1.2–25.99], p = 0.03) were strong predictors of EVLW indicated by TBLN ≥ 4. Conclusion: The lung and IVC findings obtained from handheld and portable ultrasound scanners are comparable and concordant. Cardiovascular disease and smoking history were strong predictors of EVLW. The use of TBLN to assess EVLW in hospitalized HD patients is feasible. Further studies are needed to determine if TBLN can help guide volume removal in HD patients.

Background Using commutable external quality assessment (EQA) materials is important for monitoring successful harmonization efforts. We assessed the commutability of four human serum pool (HSP) preparations to identify candidate EQA materials for alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activity measurement. Methods One set each of 85 clinical samples (CSs) was collected for ALT and AST activity measurement. The 15 candidate EQA materials included four types of HSP preparations (A to D): materials A, C, and D contained human original recombinant (HOR) aminotransferases; materials B was mixed leftover samples. The CSs and 15 candidate EQA materials were analyzed using seven routine assays, and the ln-transformed results were analyzed in 21 assay pairs. Commutability was assessed using Deming regression, with a 95% prediction interval (CLSI approach) and the difference in bias with an error component model (International Federation of Clinical Chemistry and Laboratory Medicine [IFCC] approach). Results For ALT, all materials were commutable for 14-21 assay pairs according to the CLSI and IFCC approaches. For AST, B01-03 showed commutability for 14-21 assay pairs, and C01-03 and D01-03 showed commutability for no less than 10 assay pairs according to the two approaches. A01-06 were commutable for 9-16 assay pairs according to the CLSI approach, but for 6-9 assay pairs according to the IFCC approach. Conclusions Mixed leftover samples showed desirable commutability characteristics as candidate EQA materials for routine aminotransferase activity measurements. Human serum bases supplemented with HOR were commutable for most routine ALT activity measurements.

BackgroundAccurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods—ARIMA regression and Holt-Winters exponential smoothing models.MethodsThe historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the official website of World Bank. The optimal models for each forecasting methods were used for forecasting mortality rates to 2030 (ending of Sustainable Development Goal era). The predictive performances of the three methods were evaluated, based on root mean squared errors (RMSE), root mean absolute error (RMAE) and modified Nash-Sutcliffe efficiency (NSE) coefficient. Statistically significant differences in loss function between forecasts of GMDH-type ANN model compared to each of the ARIMA and Holt-Winters models were assessed with Diebold-Mariano (DM) test and Deming regression.ResultsThe modified NSE coefficient was slightly lower for Holt-Winters methods (96.7%), compared to GMDH-type ANN (99.8%) and ARIMA (99.6%). The RMSE of GMDH-type ANN (0.09) was lower than ARIMA (0.23) and Holt-Winters (2.87). Similarly, RMAE was lowest for GMDH-type ANN (0.25), compared with ARIMA (0.41) and Holt-Winters (1.20). From the DM test, the mean absolute error (MAE) was significantly lower for GMDH-type ANN, compared with ARIMA (difference = 0.11,p-value = 0.0003), and Holt-Winters model (difference = 0.62, p-value0 = 0.004 ± standard error: 0.06; 95% confidence interval: − 0.113 to 0.122).ConclusionsGMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.

Introduction: There is a growing amount of evidence showing the significant analytical bias of steroid hormone immunoassays, but large number of available immunoassays makes conduction of a single comprehensive study of this issue hardly feasible. Aim of this study was to assess the analytical bias of six heterogeneous immunoassays for serum aldosterone, cortisol, dehydroepiandrosterone sulphate (DHEAS), testosterone, 17-hydroxyprogesterone (OHP) and progesterone using the liquid chromatography coupled to the tandem mass spectrometry (LC-MS/MS). Materials and methods: This method comparison study included 49 serum samples. Testosterone, DHEAS, progesterone and cortisol immunoassays were performed on the Abbott Architect i2000SR or Alinity i analysers (Abbott Diagnostics, Chicago, USA). DiaSorin’s Liaison (DiaSorin, Saluggia, Italy) and DIAsource’s ETI-Max 3000 analysers (DIAsource ImmunoAssays, Louvain-La-Neuve, Belgium) were chosen for aldosterone and OHP immunoassay testing, respectively. All immunoassays were evaluated against the LC-MS/MS assay relying on the commercial kit (Chromsystems, Gräfelfing, Germany) and LCMS-8050 analyser (Shimadzu, Kyoto, Japan). Analytical biases were calculated and method comparison was conducted using weighted Deming regression analysis. Results: Depending on the analyte and specific immunoassay, mean relative biases ranged from -31 to + 137%. Except for the cortisol, immunoassays were positively biased. For none of the selected steroids slope and intercept 95% confidence intervals simultaneously contained 0 and 1, respectively. Conclusions: Evaluated immunoassays failed to satisfy requirements for methods’ comparability and produced significant analytical biases in respect to the LC-MS/MS assay, especially at low concentrations.

Urinary creatinine is measured to assess kidney function and also as part of sample validity testing in drugs of abuse. Creatinine methods based on alkaline picrate Jaffe’s reaction require extra cleaning on chemistry analysers to minimise any interference from picric acid and sodium hydroxide on other reagents on board. Enzymatic methods reagents are not as invasive and more specific. Siemens enzymatic method is more sensitive and specific when compared to Thermo Fisher alkaline picrate method but there were no available analytical parameters to setup the Siemens enzymatic method on Beckman-Coulter AU5800 analyser as an open system analyser. Emulating setup parameters from Siemens chemistry analysers did not work. The analytical parameters were developed through systematic testing using different settings to adopt and optimise the method. A full correlation was performed using the developed parameters with alkaline picrate method. The Deming regression weighted analysis for 438 samples showed a good correlation at a 95% confidence interval. Slope is 1.029 to 1.041, Y-intercept when X=0.0 is -0.9156 to -0.7481 and correlation coefficient (r) is 0.998. Alkaline picrate method Mean and SD is 12.059 and 7.248 respectively and for the Enzymatic method is 11.653 and 7.504 respectively. Many interferences from drugs and other substances are eliminated when using the enzymatic method and the stability of other reagents on-board improved because the reagents used in the enzymatic method are less invasive.

Aims/Introduction A flash glucose monitoring (FGM) system has become available. To clarify the relationship between FGM and self‐monitoring blood glucose (SMBG) values, we compared the two values after simultaneous measurement in Japanese patients with type 1 diabetes, under daily life settings. Materials and Methods A total of 20 outpatients with type 1 diabetes were analyzed. When FGM and SMBG were carried out simultaneously (within ±3 min), the values were adopted and each FGM value was matched and compared with the corresponding SMBG value. In addition, we analyzed other cases of simultaneity defined as “within ±2 min,” “within ±1 min” and “at the exact same time.” Results The percentage of SMBG and FGM values in the clinically acceptable zone A + B in Clarke and consensus error grid analyses were 97.9 and 99.2%, respectively. Deming regression (x‐axis: FGM values, y‐axis: SMBG values) determined a slope of 0.9128 (95% confidence interval 0.9008–0.9247) and an intercept of +15.94 mg/dL (95% confidence interval 14.05–17.84). FGM values were lower than SMBG values in the lower glucose range, and higher in the higher glucose range. The shorter the time lag between measurements, the higher the rate of concordance between FGM and SMBG values. Conclusions The results of this study provided evidence on the reliability of FGM in Japanese patients with type 1 diabetes in home conditions. Based on the results, if an abnormal glucose value is detected by FGM, SBMG should then be used to confirm the result., Flash glucose monitoring values were lower than self‐monitoring blood glucose values in the lower glucose range, and higher in the higher glucose range. The shorter the time lag between measurements, the higher the rate of concordance between flash glucose monitoring and self‐monitoring blood glucose values.

To examine the extents to which low tidal volume (VT) and endotracheal tube (ETT) leakeage influence the accuracy of ETCO2 for estimating arterial PCO2 (PaCO2) in very low birth weight (VLBW) infants with mechanical ventilation. An observational study. We evaluated a total of 287 paired ETCO2 and PaCO2 values as well as VTs obtained from 22 VLBW infants with ventilation. Deming regression, quadratic discriminant analysis, and Bland–Altman analysis were performed. ETCO2 and PaCO2 were correlated (r2 = 0.5897, p 0 group (r2 = 0.7234, p

Background Validation of hospital pneumatic tube systems (PTS) is recommended to predict and prevent errors caused by sample hemolysis. 3-Axis accelerometer dataloggers have been successfully implemented as tools for PTS validation, but the most suitable device for such validation has not been investigated. The aim of this study was to evaluate the performance of four commercially available 3-axis accelerometers for PTS validation. Methods PCE-VD3 (PCE), CEM DT-178A (CEM), Extech VB300 (EXT), and MSR 145 (MSR) dataloggers were placed into a single PTS carrier and repeatedly transported through one of three PTS routes. The number and magnitude of accelerations within each PTS route was collected by each device. Deming regression analysis was used to compare device performance. Results The MSR datalogger captured the greatest number of g-forces >3 g, 5 g, 10 g, and 15 g, and the greatest magnitude of g-force (26.7 g) relative to the other devices (CEM: 23.0 g, EXT: 23.3 g, PCE: 23.7 g). As a result of increased sampling frequency, the MSR recorded the lowest AUC and the greatest number of g-forces exceeding 3 g relative to the other devices. Subjectively, the data were difficult to extract from 4 tested devices. Conclusions Commercially available dataloggers differ in their ability to detect the number and magnitude of g-forces within PTSs. We recommend that one device be used to perform all PTS evaluations, with baseline evaluations for tolerable AUC, number, and magnitude of g-forces established internally. Lack of harmonization, cumbersome data processing, and time-consuming data analysis are substantial barriers to universal implementation of dataloggers for PTS validation and monitoring.

Background: Drug nonadherence is one of the major challenges faced by resistant hypertension patients, and identification of this problem is needed for optimizing pharmacotherapy. Dried blood spot (DBS) sampling is a minimally invasive method designed to detect and determine the degree of nonadherence. In this study, a DBS method for qualifying 8 antihypertensive drugs (AHDs) and 4 active metabolites was developed and validated using ultra highperformance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Method: The DBS assay was validated analytically and clinically, in accordance with FDA requirements. Analytical validation was accomplished using UHPLC-MS/MS. For clinical validation, paired peak and trough levels of DBS and plasma samples were simultaneously collected and comparatively analyzed using Deming regression and Bland–Altman analyses. All concentrations below the set lower limit were excluded. Deming regression analysis was used to predict comparison bias between the collected plasma and DBS samples, with DBS concentrations corrected accordingly. Results: The UHPLC-MS/MS method for simultaneously measuring 8 AHDs and their metabolites in DBS, was successfully validated. With Deming regression no bias was observed in N = 1; constant bias was seen in N = 6 and proportional bias in N = 11 of the AHDs and metabolites. After correction for bias, only one metabolite (canrenone) met the 20% acceptance limit for quantification, after Bland–Altman analyses. In addition, amlodipine, valsartan, and [enalaprilate] met the 25% acceptance limit. Conclusions: A novel DBS assay for simultaneously qualifying and quantifying 8 AHDs and their metabolites, has been successfully developed and validated. The DBS assay is therefore a suitable method to detect drug nonadherence. However, with the exception of canrenone, the interchangeable use of plasma and DBS sampling to interpret drug quantities should be avoided.

Background Dried blood spots (DBS) are used in human medicine to measure total 25-hydroxyvitamin D (25-OHD) in the blood. However, this easy and affordable sampling technique has not been evaluated in primates to measure vitamin D concentrations. Objectives We aimed to compare 25-OHD measurements in chimpanzee serum at two different laboratories and determine the precision and accuracy of the DBS method by comparing DBS and serum results. Methods Blood samples from 17 captive chimpanzees were collected, and 25-OHD3 and 25-OHD2 were measured in serum at two accredited laboratories using liquid chromatography-tandem mass spectrometry. The same analytes were measured on DBS cards, and results were compared with that of serum. Data were assessed using the Spearman correlation, Deming regression, and Bland-Altman analyses. Results The correlation coefficient between the two measurements in serum was rs = .51 (P = .04), and the mean bias was -1.25 ± 14.83. When comparing 25-OHD concentrations measured in DBS and serum at the same laboratory, the rs was 0.7 (P = .002), and the mean bias was 1.42 ± 14.58. Estimated intra-assay and inter-assay coefficients of variation for DBS results were 6% and 12.6%, respectively. Conclusions Although substantial analytical variability was found in 25-OHD measurements regardless of the sample type, the identification of both constant and proportional error and wider limits of agreement with the DBS technique makes the interpretation of DBS results challenging, especially for values close to clinical cut-off points. The DBS and serum methods were not interchangeable, and further studies are needed to validate DBS samples for vitamin D measurements in chimpanzees.

Context Venous or arterial blood is used for the estimation of electrolytes in neonates in neonatal intensive care unit (NICU). In addition to the measurement of blood gases and bicarbonate in the arterial blood, arterial blood gas analysis also estimates electrolytes thus circumventing the need to collect a venous sample for electrolytes. The literature survey revealed studies comparing the electrolyte levels in arterial and venous blood in adults and older children, but to our knowledge none were found in neonates, hence the study. Aims The aim of the study is to compare the electrolytes in arterial and venous samples in neonates in a critical care set up and derive in-house prediction equation to correlate the arterial and venous electrolytes. Settings and Design Hospital-based, retrospective cross-sectional study done in critically ill neonates. Materials and Methods All the newborns (age ≤28 days) admitted in NICU from July 2016 to June 2018 were selected for the study and data collected with the help of Hospital Information System. Statistical Analysis MedCalc and NCSS 12 (trial version) software was used. Deming Regression and Bland Altman analysis were performed. Results A strong positive correlation between the arterial and venous blood electrolytes observed. An in-house prediction equation was derived for the venous electrolytes. Deming regression analysis showed that only potassium levels are statistically equivalent between the instruments and the sample type. Bland Altman Analysis between the arterial and venous electrolytes showed a mean difference which was well within the accepted Clinical Laboratory Improvement Amendment guidelines. Conclusion The electrolyte levels estimated by arterial blood in neonates can be used in an interchangeable manner only for potassium levels, whereas sodium and chloride estimation necessitates one to be cautious.

Background: White blood cell (WBC) counts are used to monitor bone marrow function and to screen for infections. The HemoCue WBC DIFF Point-Of-Care (POC) instrument classifies WBCs through cell image recognition. To evaluate its suitability for monitoring cancer patients, we examined its performance in samples from patient with leukopenia and in samples containing nRBC. Methods: Sysmex samples with WBCs 0.05-3.40 × 109/L were examined on the HemoCue WBC DIFF, and the correlations between the instruments were assessed by Deming regression for total WBC, neutrophils, and lymphocytes. The theoretical CV% (CVt), calculated from number of cells counted by the HemoCue WBC DIFF, was used to determine the statistical error of the WBC counts. The interference of nRBC was also evaluated. Results: The counting variation was primarily the source of statistical error in the lower counts with an imprecision between 3.8–9.2% for total WBC (0.56–2.29 ×109/L), 8.7–14.3% for neutrophils (0.36–1.33 ×109/L) and 9.8–15.1% for lymphocytes (0.35–0.89 ×109/L). The correlation coefficient was between 0.658 and 0.986—poorest for lymphocytes. The total WBC count on the HemoCue WBC DIFF was significantly increased in nRBC samples due to lymphocyte count overestimation, and not by other WBCs. Conclusions: The HemoCue WBC DIFF provided reliable and accurate counts of total WBC, neutrophil, and lymphocyte in leukopenic samples. Until POC instruments that can perform an accurate complete blood count are available, the HemoCue WBC DIFF can be used to assist physicians in making decisions in situations of postchemotherapy leukopenia and neutropenia.

BACKGROUND Busulfan (Bu) is one of the conditioning regimen components for pediatric hematopoietic stem cell transplantation. Bu therapeutic drug monitoring (TDM) is essential for a successful treatment outcome and toxicity evasion. Dried blood spot (DBS) sampling is a rapid and simple method for Bu TDM, compared with conventional plasma sampling. This study evaluated the feasibility of using the DBS method for Bu TDM. The hematocrit (Hct) and conditioning day were also examined for their impact on the DBS method's performance. METHODS Venous blood collected from 6 healthy volunteers was diluted, using their plasma into 4 samples of varying Hct values. Each sample was spiked with Bu calibrators (300, 600, and 1400 ng/mL), prepared using DBS and dried plasma spot (DPS) sampling and analyzed using a validated liquid-chromatography tandem-mass spectrometry method. Clinical blood samples (n = 153) from pediatric patients (n = 15) treated with Bu (mainly from doses 1, 2, 5, and 9) were used to prepare paired volumetric DBS and DPS samples. A Bland-Altman plot and Deming regression were used to define the agreement between the paired DBS and DPS measurements. Passing-Bablok regression analyses investigated the effects of Hct and conditioning day on the linearity between both methods. RESULTS In vitro analyses showed good agreement between DBS and DPS measurements, with a mean difference of -5.4% and a 95% confidence interval on the limits of agreement of -15.3% to 4.6%. Clinical samples showed good correlation (Pearson correlation coefficient = 0.96; slope = 1.00) between the DBS and DPS methods. The DBS method met the clinical acceptance limits for clinical samples, with a bias

The macular ellipsoid zone intensity (mEZi) is a known marker of disease severity in a number of diverse ocular diseases. The purpose of this study was to establish an automated method (AM) for mEZi quantification and to compare the method’s performance with that of a manual method (MM) for glaucoma patients and healthy controls. Seventy-one (71) mild-to-moderate glaucoma patients, 71 severe-glaucoma patients, and 51 controls were enrolled. Both calibration (n = 160) and validation (n = 33) image sets were compiled. The correlation of AM to MM quantification was assessed by Deming regression for the calibration set, and a compensation formula was generated. Then, for each image in the validation set, the compensated AM quantification was compared with the mean of five repetitive MM quantifications. The AM quantification of the calibration set was found to be linearly correlated with MM in the normal-to-severe-stage glaucoma patients (R2 = 0.914). The validation set’s compensated AM quantification produced R2 = 0.991, and the relationship between the 2 quantifications was AM = 1.004(MM) + 0.139. In the validation set, the compensated AM quantification fell within MM quantification’s 95% confidence interval in 96.9% of the images. An AM for mEZi quantification was calibrated and validated relative to MM quantification for both glaucoma patients and healthy controls.

Objective The objective of this study was to compare performance of a maternal surface electrode patch with ultrasound- and tocodynamometer-based monitoring to detect fetal heart rate and uterine contractility in late preterm labors. Study Design Thirty women between 340/7 and 366/7 weeks' gestation were monitored simultaneously with a Doppler/tocodynamometer system and a wireless fetal–maternal abdominal surface electrode system. Fetal and maternal heart rate and uterine contraction data from both systems were compared. Reliability was measured by the success rate and percent agreement. Deming regression and Bland–Altman analysis estimated the concordance between the systems. Uterine contractions were assessed by visual interpretation of monitor tracings. Results The success rate for the surface electrode system was 89.5% (95% confidence interval [CI], 85.7–93.3), and for ultrasound it was 88.4% (95% CI, 84.9–91.9; p = 0.73), with a percent agreement of 88.1% (95% CI, 84.2–92.8). Results were uninfluenced by the patients' body mass. The mean Deming slope was 1 and the y-intercept was −3.0 beats per minute (bpm). Bland–Altman plots also showed a close relationship between the methods, with limits of agreement less than 10 bpm. The percent agreement for maternal heart rate was 98.2% (95% CI, 97.4–98.8), and for uterine contraction detection it was 89.5% (95% CI, 85.5–93.4). Conclusion Fetal heart rate and uterine contraction monitoring at 340/7 to 366/7 weeks using abdominal surface electrodes was not inferior to Doppler ultrasound/tocodynamometry for fetal–maternal assessment. Registration clinicaltrials.gov/February 20, 2017/identifier NCT03057275. Key Points