Introduction
The clinical applications of urinary quantitative biochemical analytes such as potassium (K), sodium (Na), calcium (Ca), phosphorus (P) creatinine (Crea), total protein (TP), and microalbumin (mAlb), are becoming increasingly widespread.1, 2, 3, 4 The levels of K, Na, Ca, and P reflect the excretion and reabsorption functions of the kidneys.5, 6 The levels of Crea, TP, and mALB mainly reflect the degree of kidney damage caused by various diseases.7, 8, 9 The biochemical analysis of these urinary analytes can have an important adjuvant role in the diagnosis and evaluation of a number of clinical problems. It may, however, be confounded in ICU settings and ideally should be integrated into the broader clinical context to inform about optimal management.10 With the widespread application of urinary biochemical analytes in clinics, the testing capabilities of laboratories are increasingly becoming a challenge. Thus, there is an increasing need for, laboratories to urgently design a quality evaluation strategy to evaluate the analytical performance of urinary biochemical analytes. The performances of an analyte are expressed in statistical terms such as CV and Bias. The CV of an analyte can be obtained from IQC while bias can be obtained from EQC data such as EQA. Total allowable error for an analyte is obtained from published literature.
Materials and Methods
This study was done in a clinical laboratory setting and the urinary biochemical analytes involved in this study were microalbumin, protein, creatinine, calcium, phosphorous, sodium and potassium. All the analytes were processed in the Roche Cobas 6000 analyzer and with its dedicated reagents. Bio-Rad Laboratories (Bio-Rad Inc., California, USA), including the following two levels: the normal level (level 1, lot no:63471) and high level (level 2, lot no: 88192) were used as internal quality control (IQC) materials. Alternate month Bio- Rad EQAS Urine chemistry program cycle 14, lot no:251300 was selected. The methods for detecting urinary biochemical analyte levels are briefly described as follows: K, Na levels were detected using the indirect ion selective electrode method. mAlb levels were detected by using immunoturbidimetric method; TP levels detected using the benzethonium chloride, Crea levels were detected using the alkaline picrate method, Ca levels were detected by using BAPTA method; P levels were detected using phosphomolybdate method.
Calculation of TEa
Referring to formula: TEa = 2*CV%+Bias %. TEa was calculated for each analyte.
The CV data represent the imprecision of each analyte and were derived from six alternative months of IQC (two levels) analysis from January to December 2022. Two levels of IQC were run at peak time in the morning and one level at every 8 hourly daily protocols was used. Mean and Standard deviation (SD) were calculated. Monthly CV % was calculated by the formula: CV% = SD / Mean * 100. The highest CV% out of two levels was selected for each month and finally the average CV% was calculated.
Bias represents the trueness of each analyte, and it was determined based on EQA samples of urinary biochemical analytes in 2022. Bio- Rad EQAS report was used for the average absolute value of the above single percentage difference was defined as the bias of that analyte and used for the calculation of its TEa.
Bias % = Value(measured) – Value(target) / Value(target) × 100%.
Average Bias % was calculated for each urine biochemical analytes. TEa % calculated for each analytes using the above said formula.
In 1974, the concept of total error was first introduced by Westgard based on analytical imprecision (reproducibility of the result) and bias (systematic error).11 It must be noted that there are three possible TEa targets for analytes: desirable, minimum, and optimal.12 (Table 1)
Results and Discussion
Coefficient of variation (CV%), obtained from IQC data for each analyte describes the variation of the test & signifies the degree of imprecision in general. Lower CV signifies a better performance method whereas higher CV implies poorer performance.13 From Table 2, Table 3, in our study, analytical CV% of all analytes was within acceptable limits of minimum, desirable & optimum quality specifications. This suggested good precision & minimum variability of urine chemistry parameters in our laboratory.
Table 2
Urine biochemical analytes CV% and bias % data for the year 2022
Table 3
Total analytical error calculation
Biological variation and CLIA guidelines are the most commonly used sources. In our study, from Table 2, Table 3, it is evident that urine chemistry parameters were within all the three quality specifications except for urine creatinine wherein the bias% was not within acceptable performances as per minimum quality specifications. It is ideal to calculate the bias by using reference method value as “true value”.14
Table 4
Comparison of TV from BV and our lab study
|
Urine Parameters |
TEa from Ricos |
Our Lab TEa |
|
Albumin |
40.6 |
16.5 |
|
Calcium |
34.1 |
7.59 |
|
Creatinine |
42.1 |
9.15 |
|
Phosphate |
22.1 |
7.83 |
|
Potassium |
28.4 |
9.51 |
|
Protein |
40.0 |
12.19 |
|
Sodium |
32.0 |
12.24 |
Table 4 shows comparison of TEa from Ricos and Our lab TEa. All the urine parameters showed lower TEa compared to the Ricos et al. TEa values.12 There are multiple sources for TEa targets derived from medically important measures or clinical decision thresholds. A laboratory should decide which TEa target is best suited for clinical decision. In our study, from Table 2, Table 3, it is also evident that the Total analytical error adopted as bias (%) + 2CV(%) which is consistent with CLIA recommendations, are within acceptable limits for all the urine parameters except for Urine Crea did not fulfil the minimum quality specification. TEa biological variability values are very stringentand perhaps too challenging for analyzing the analytical performance.15 The applications of TEa are to evaluate the performance qualification of the instrument, to guide comparison of test results across laboratories and clinics using the same or different analytical methods & to help interpret results from external QA (proficiency testing) programs or inter-laboratory comparison as a part of proficiency testing activity. 16
Thus, evaluation of the performance of Urinary biochemical analytes helps to minimize the errors and improve process quality. A better analytical quality of these urinary biochemical tests can be fulfilled by setting and implementing evidence-based analytical quality specifications, improving metrological traceability and correcting biases and systematic errors.
