Practical application of the patient data-based quality control method: the potassium example.

Introduction: Internal quality control (IQC) is a core pillar of laboratory quality control strategies. Internal quality control commercial materials lack the same characteristics as patient samples and IQC contributes to the costs of laboratory testing. Patient data-based quality control (PDB-QC) may be a valuable supplement to IQC; the smaller the biological variation, the stronger the ability to detect errors. Using the potassium concentration in serum as an example study compared error detection effectiveness between PDB-QC and IQC. Materials and methods: Serum potassium concentrations were measured by using an indirect ion-selective electrode method. For the training database, 23,772 patient-generated data and 366 IQC data from April 2022 to September 2022 were used; 15,351 patient-generated data and 246 IQC data from October 2022 to January 2023 were used as the testing database. For both PDB-QC and IQC, average values and standard deviations were calculated, and z-score charts were plotted for comparison purposes. Results: Five systematic and three random errors were detected using IQC. Nine systematic errors but no random errors were detected in PDB-QC. The PDB-QC showed systematic error warnings earlier than the IQC. Conclusions: The daily average value of patient-generated data was superior to IQC in terms of the efficiency and timeliness of detecting systematic errors but inferior to IQC in detecting random errors.

Intraday Changes and Clinical Applications of Thyroid Function Biomarkers in Healthy Subjects.

OBJECTIVE: We evaluated the intraday changes of thyroid function biomarkers in healthy subjects to help clinicians diagnose thyroid diseases in appropriate timing. METHODS: Blood samples were collected from 31 subjects at 0:00, 4:00, 8:00, 12:00, 16:00 and 20:00 on the sampling day and analyzed for thyroid-stimulating hormone (TSH), triiodothyronine (T3), thyroxine (T4), free T3 (FT3), and free T4 (FT4). The intraday concentration changes were analyzed using Friedman's 2-way analysis of variance by ranks. RESULTS: The concentrations of TSH, T3, T4, FT3, and FT4 in males were significantly higher than those in females (P < .01). The obvious peak circadian rhythm of TSH was observed at 0:00 AM with gradual decline thereafter, whereas other biomarkers showed no rhythmic changes. CONCLUSION: Sex differences should be considered in interpreting thyroid function tests. It is important to select the sampling time according to the clinician's diagnostic needs, especially at night when TSH secretion peaks.

Biological variation in the serum and urine kidney injury markers of a healthy population measured within 24 hours.

BACKGROUND AND AIMS: To explore the biological variation (BV) of kidney injury markers in serum and urine of healthy subjects within 24 hours to assist with interpretation of future studies using these biomarkers in the context of known BV. MATERIALS AND METHODS: Serum and urine samples were collected every 4 hours (0, 4, 8, 12, 16 and 20 hours) from 31 healthy subjects within 24 hours and serum creatinine (s-Crea), serum ß2-microglobin (s-ß2MG), serum cystatin C (s-CYSC), serum neutrophil gelatinase-associated lipoprotein (s-NGAL), urine creatinine (u-Crea), urine ß2-microglobin (u-ß2MG), urine cystatin C (u-CYSC), urine neutrophil gelatinase-associated lipoprotein (u-NGAL) were measured. Outlier and variance homogeneity analyses were performed, followed by CV-ANOVA analysis on trend-corrected data (if relevant), and analytical (CVA), within-subject (CVI), and between-subject (CVG) biological variation were calculated. RESULTS: The concentration of kidney injury markers in male was higher than that in female, except for u-CYSC and u-NGAL. There were no significant difference in serum and urine kidney injury markers concentration at different time points. Serum CVI was lower than urine CVI, serum CVG was higher than CVI, and urine CVG was lower than CVI. The individual index (II) of serum kidney injury markers was less than 0.6, while the II of urinary kidney injury markers was more than 1.0. CONCLUSIONS: This study provides new short-term BV data for kidney injury markers in healthy subjects within 24 hours, which are of great significance in explaining other AKI / CKD studies.

Correlation between glycosylated serum albumin and glycosylated haemoglobin in the southwest Chinese population: Establishment of a regression model.

AIMS: To correlate glycated albumin (GA) and glycosylated haemoglobin (HbA1c) and establish a novel formula for estimating HbA1c from GA. METHODS: We retrospectively enrolled 20,381 cases and excluded HbA1c and GA outliers by residual analysis. HbA1c ranged from 4.0-12.0% and GA from 7.5-45%. The HbA1c range of 4.0-8.0% in both sexes was stratified into eight groups with an increase of 0.5%, and the means of GA and HbA1c were compared. HbA1c was divided into 38 groups with increments of 0.1% (range, 4.3-8.0%), and the correlation between HbA1c and GA was investigated. RESULTS: There was no significant sex-based difference between HbA1c and GA. The analysis showed that when HbA1c was 6.2% or GA was 12.28%, the linear relationship between the two parameters was not continuous. When HbA1c was <6.2% or GA < 12.28%, we devised the formula: HbA1c = 1.136 × GA - 7.289 (R2 = 0.824). For HbA1c ≥ 6.2% or GA ≥ 12.28%, the equation was: HbA1c = 0.252 × GA + 3.163 (R2 = 0.948). CONCLUSION: A discontinuous linear relationship exists between HbA1c and GA when HbA1c is 6.2% or GA is 12.28%, although with a significant turning point. The GA value can be used to estimate the HbA1c value with the two regression equations to accurately estimate the long-term average blood glucose level of patients.