Optimization and validation of patient-based real-time quality control procedure using moving average and average of normals with multi-rules for TT3, TT4, FT3, FT3, and TSH on three analyzers.

BACKGROUND: We have designed a patient-based real-time quality control (PBRTQC) procedure to detect analytical shifts and review analytical trends of measurement procedures. METHODS: All the nine months' patient results of total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), free triiodothyronine (FT3), and thyrotropin (TSH) measured by three identical analyzers were divided into three groups according to the source of inpatient patients, outpatient patients, and healthy people. The data in each group were truncated by optimized Box-Plot method and normalized by Box-Cox method if necessary. The z-score charts of internal quality control (IQC) samples' results and PBRTQC data were drawn by IQC levels and groups, respectively. The analytical shifts and analytical trends were detected by multi-rules of 2-2S rules and moving average rules. The performances of PBRTQC were compared with the BIQC in which IQC samples were measurand only once per day at the beginning of the analytical batch. Twelve quality control cases were listed to validate the performances. RESULTS: All the five analytes presented normal distributions when the parameter n of Box-Plot method was 1.2. The percentages of excluded data ranged from 2.9% to 11.6%. 31 and 14 rejections triggered in PBRTQC and BIQC, respectively. 96.8% of the shift rejections in PBRTQC were trend-related shifts and calibration-related shifts, while the proportion was 85.7% in BIQC but 78.6% of the shift rejections in TSH. 25.7% and 8.6% of 105 calibration events which caused analytical shifts were detected by PBRTQC and BIQC, respectively. However, the performance of PBRTQC was not well in TSH because of its large coefficient of variation. CONCLUSIONS: The optimized PBRTQC is high efficiency than BIQC in detecting analytical shifts, trends, and calibration events. The PBRTQC can be used as a low-cost supplementary procedure to IQC every day, especially at the end of the analytical batch on that day when the within-individual biological variation of analyte is not larger than its coefficient of variation in IQC. Further optimization and validation of PBRTQC are still needed.

Serum Bile Acid Profiles in Latent Autoimmune Diabetes in Adults and Type 2 Diabetes Patients.

BACKGROUND: Impaired bile acid (BA) metabolism has been associated with the progression of type 2 diabetes (T2D). However, the contribution of BAs to the pathogenesis of latent autoimmune diabetes in adults (LADA) remains unclear. This study was aimed at investigating the association of serum BAs with different diabetes types and analyzing its correlation with main clinical and laboratory parameters. METHODS: Patients with LADA, patients with T2D, and healthy controls (HCs) were enrolled. Serum BA profiles and inflammatory cytokines were measured. The correlation of BA species with different indicators was assessed by Spearman's correlation method. RESULTS: Patients with diabetes (LADA and T2D) had significantly higher serum BAs, especially conjugated BAs, compared with those in HCs. Nevertheless, serum BA profiles had no special role in the progression of LADA, because no significant differences in BAs were observed between LADA and T2D patients. Interestingly, HbA1c levels and HOMA-ß were found to be correlated with a series of BA species. Proinflammatory cytokines (IL-1ß, IL-6, and TNF-α) and anti-inflammatory cytokine (IL-10) were all positively associated with several BA species, especially the conjugated secondary BAs. CONCLUSION: Serum BAs regulate glucose homeostasis, but have no special value in the pathogenesis of LADA patients. Our study adds further information about the potential value of serum BAs in different types of diabetes.

Agreement of Potassium, Sodium, Glucose, and Hemoglobin Measured by Blood Gas Analyzer With Dry Chemistry Analyzer and Complete Blood Count Analyzer: A Two-Center Retrospective Analysis.

Background: Blood gas analyzers (BGAs) and dry biochemistry analyzers for potassium and sodium are based on direct electrode methods, and both involve glucose oxidase for glucose detection. However, data are lacking regarding whether the results of the two assay systems can be used interchangeably. In addition, there remains controversy over the consistency between BGA-measured hemoglobin and complete blood count analyzer data. Here, we compared the consistency of sodium, potassium, glucose, and hemoglobin levels measured by BGA and dry chemistry and complete blood count analyzers. Methods: Data from two teaching hospitals, the Zhejiang Provincial People's Hospital (ZRY) and the Qianfoshan Hospital (QY), were retrospectively analyzed based on dry biochemistry and complete blood count analyzer results as the reference system (X) and BGA as the experimental system (Y). Plasma was used for biochemical analysis at the ZRY Hospital, and serum at the QY Hospital. Paired data from the respective hospitals were evaluated for consistency, and biases between methods were assessed by simple correlation, Passing-Bablok regression, and Bland-Altman analyses. Results: The correlations of potassium, sodium, glucose, and hemoglobin measured by BGA and dry biochemistry and complete blood count analyzers were high, at 0.9573, 0.8898, 0.9849, and 0.9883 for the ZRY Hospital and 0.9198, 0.8591, 0.9764, and 0.8666, respectively, for the QY Hospital. The results of Passing to Bablok regression analysis showed that the predicted biases at each medical decision level were within clinically acceptable levels for potassium, sodium, glucose, and hemoglobin at the ZRY Hospital. Only the predicted bias of glucose was below the clinically acceptable medical decision levels at the QY Hospital, while potassium, sodium, and hemoglobin were not. Compared with the reference system, the mean bias for BGA measurements at the ZRY Hospital was -0.08 mmol/L (95% confidence interval [CI] -0.091 to -0.069) for potassium, 1.2 mmol/L (95% CI 1.06 to 1.42) for sodium, 0.20 mmol/L (95% CI 0.167 to 0.228) for glucose, and -2.8 g/L for hemoglobin (95% CI -3.14 to -2.49). The mean bias for potassium, sodium, glucose, and hemoglobin at the QY Hospital were -0.46 mmol/L (95% CI -0.475 to -0.452), 3.7 mmol/L (95% CI 3.57 to 3.85), -0.36 mmol/L (95% CI -0.433 to -0.291), and -8.7 g/L (95% CI -9.40 to -8.05), respectively. Conclusion: BGA can be used interchangeably with plasma electrolyte results from dry biochemistry analyzers but does not show sufficient consistency with serum electrolyte results from dry biochemistry analyzers to allow data interchangeability. Good consistency was observed between BGA and plasma or serum glucose results from dry biochemistry analyzers. However, BGA-measured hemoglobin and hematocrit assay results should be treated with caution.