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

Abstract

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.

First 70 Days Critical Data Trend for COVID-19 in Four Regions of Northern Italy: A Pilot Study

“…However also the data relating to deaths and the use of intensive care places are not free from bias, due to delays in formalization, which affect the observed trend. We used then the moving average method [ 7 ] to obtain a data set where each point was an expression not of a single day, but of a longer period of time. For example, we considered the interval from 25 to 27 February (the average of the values of these 3 days represents the first point); then we considered the interval 26–28 February (the average of the values of these 3 days is the second point) and so on.…”

Assessment of patient based real‐time quality control on comparative assays for common clinical analytes

“…For analytes with large between‐subject biological variation (CV G ), the ability of PBRTQC to detect potential deviations was dramatically weakened on account of a smaller sample size and larger population variations. 10 , 22 Likewise, for analytes ALT, ALP, and GGT, of which CV G s were ranked highest, we observed false‐positive and negative biased events for the OP group when using analyzer C. Nevertheless, for the analytes with relatively narrow biological variation, the desired performance was still achieved regardless of changes in sample size and patient categories. These findings emphasize the significance of considering the potential influences of low‐ and medium‐sized populations, especially for highly varied analytes.…”

“…The study by Song et al implied that classifying patient data according to sources of specimens and setting quality control rules in consideration of different groups could efficiently reduce the false‐positive rate of PBRTQCs. 22 As influences within a group were relatively limited and did not interfere with other groups, we divided the truncated data into the inpatient (IP), outpatient (OP) and total patient (TP) groups. The negative bias detected for analyte UA when using analyzer C for the TP group was probably false, as comparative results from both serum samples and categorizing groups were excellent (Table 2 ).…”

“…Caution is particularly needed in laboratories that employ multiple instruments to measure the same analyte. The study by Song et al implied that classifying patient data according to sources of specimens and setting quality control rules in consideration of different groups could efficiently reduce the false‐positive rate of PBRTQCs 22 . As influences within a group were relatively limited and did not interfere with other groups, we divided the truncated data into the inpatient (IP), outpatient (OP) and total patient (TP) groups.…”

A unique Levey–Jennings control chart used for internal quality control in human papillomavirus detection

“…According to the Westgard‐Sigma rules [ 28 , 33 , 34 ], only if the negative quality controls are correct are the results considered to be out of control in the following situations: if the positive rate is more than 3 SDs above the mean (1–3S); if the positive rate is 2 SDs above the mean, which is a "warning " but not regarded as “out of control” (1–2S); or if two consecutive “warnings”have appeared (2 – 2S).…”