Automation systems in laboratory have been developed to make testing processes more standardized and efficient for workers to complete their tasks without human errors [1-3]. Currently, a laboratory automation system provides new options for a clinical laboratory to automate front-end processing with its applications widened [4-6]. Although calibration and quality control (QC) processes are the first step of routine laboratory testing, laboratory automation system has been mainly focusing on processes other than calibration and QC. In general, total laboratory automation system does not include automation of calibration or QC unless instruments installed in the automation system provide automated calibration and QC functions.

A laboratory automation system can also be applied to help routine calibration and QC processes regardless of auto-calibration and auto-QC functions of installed instruments depending on manufacturers [7]. Calibration and QC processes are routinely performed almost every day as the front step of laboratory testing process which is very important for the accuracy and traceability of tests [8,9]. Although it should be different depending on the size of hospital, the total time taken for these processes is not short, lasting as long as one hour or more. Therefore, if this front step is automatically completed before technicians arrive at their workplace, we can save time and report test results sooner than usual by skipping manual processes needed for the calibration and QC. This not only reduces total testing time, but also improves efficiency of laboratory routines. Once calibration and QC processes are performed automatically by the machine, we can expect improvement in reproducibility and precision of repeated tests by reducing inevitable bias caused by individual differences or even differences within the same individual [10].

The Aptio total laboratory automation system (Siemens Healthcare Diagnostics Inc., Tarrytown, NY, USA) can perform serial testing processes such as decapping, testing, recapping, and storing of reagents and sample tubes as it is designed with a middleware. We had an Aptio total laboratory automation system and ADVIA centaur XPT system (Siemens Healthcare Diagnostics Inc.) installed in our laboratory. Because the ADVIA centaur XPT chemistry analyzer does not provide auto-calibration or auto-QC functions, we devised an algorithm for the Aptio automation system to automatically conduct the calibration and QC processes using various functions needed for automated calibration and QC. We compared performances of calibration and QC processes conducted by conventional manual method with those conducted by the Aptio automation system in this study. The objective of this study was to determine whether manually performed calibration and QC processes could be replaced with laboratory automated system when installed analyzers failed to provide automated calibration and QC functions [11].

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Alanine aminotransferase (ALT), total cholesterol (TC), creatinine (Cr), direct bilirubin (DB), and lipase (Lip) items were used as analytes. These were selected because ALT, TC, Cr, DB, and Lip are commonly requested and, empirically, we have confronted more problems with these test items regarding accuracy and traceability in our laboratory in relation to other common test items. We prepared pooled serum samples at 10 levels for each test item and divided them into two groups; five for the analytical measurement range (AMR) group and five for the medical decision point (MDP) group. AMR is the range of analyte values that a method can directly measure on the specimen without any dilution, concentration, or other pretreatments [12]. We set the AMR according to the claimed range by the manufacturer of each analyte. MDP range included medically important values or cutoffs for abnormal test results in each test item. We performed calibration and QC processes for five consecutive days and measured each test item in the two groups.

Each testing was done three times for each level. We also used analyte-specific QC materials with high and low levels to calculate precisionfs for the AMR group and the MDP group, respectively. All statistical analyses were performed with Analyze-it version 5.11 (Analyze-it Software Ltd., Leeds, UK) and SPSS version 25 (IBM Corp., Armonk, NY, USA).

Automated calibration and QC activity were performed using an Aptio total laboratory automation system (Siemens Healthcare Diagnostics Inc.). An ADVIA XPT chemistry analyzer (Siemens Healthcare Diagnostics Inc.) was used to measure tested items using pooled serum and QC materials. All processes including storing, decapping, and recapping of reagents for calibration and QC activities during this study were programmed in advance and performed by the Aptio automated system to evaluate the performance of the automated system.

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All experiments proceeded when QC results were within allowable ranges after the calibration process was accepted through automated and manual methods.

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Currently, many laboratories are experiencing difficulties resolving confronting problems with regard to the increasing workload caused by requests for tests and burdens due to increasing expenditure and consequent labor intensity issues related to technicians [15,16]. A laboratory automation system can improve workflow, efficiently perform qualified testing process, and reduce human errors [4]. As automated calibration and QC functions are recently provided for instruments such as chemistry analyzers, the gaining from a laboratory automation system has become more visualized. However, there are still laboratories that use instruments without automated calibration and QC functions, although they are equipped with a total laboratory automation system. In this study, the Aptio total laboratory automation system could compensate for the absence of automated calibration and QC functions in analyzers by manipulating its middleware.

The main reason for our laboratory considering automated calibration and QC was to alleviate workload and reduce turnaround time of testing. It is important to assure that the quality of calibration and QC processes in an automation system is not significantly different from that with the conventional manual method. As for repeatability, although the repeatability CV of manual method was slightly lower than that of the automation system (2.0% vs. 2.1%), the difference seemed to be acceptable between the two systems for the AMR group. However, for the MDP group, the repeatability CV showed relatively greater difference for total samples. Repeatability CVs with the manual method and automation system were 3.8% and 1.8%, respectively. This phenomenon became aggravated further based on laboratory CVs (5.1% with the automated method vs. 2.7% with the manual method). The difference of within laboratory CVs between the two systems was higher for the MDP group than for the AMR group (5.1% vs. 2.6%). Especially in the MDP group, the repeatability and within laboratory CVs for Cr showed the biggest differences between the automated method and the manual method (repeatability and within laboratory CVs were 10.2% and 13.1%, respectively, with the manual method and 1.7% and 2.1% with the automated method, respectively). Here, five Cr levels ranged from 0.792 mg/dL to 1.490 mg/dL on average. On the other hand, TG levels ranged from 199.7 mg/dL to 260.0 mg/dL, with the difference between manual and automated methods being relatively lower than that for other items (1.1% vs. 0.9%). This clearly explains that the automation system can operate repeated testing with better precision since the value of test item is low.

Although differences in repeatability and within laboratory CVs between the automated system and the conventional manual system were lower in the AMR group, significant mean differences in the value of tested items between the two systems were observed in the AMR group, but not in the MDP group except for Lip by Wilcoxon signed-rank test (p=0.029). In Table 4, mean differences of Cr levels in AMR and MDP groups were 0.13 mg/dL (p<0.001) and 0.07 mg/dL (p=0.057 in t-test), respectively. This shows that a small difference in the calibration process may result in a significant difference. We cannot affirm which value from AMR group or MDP group is more accurate or the specific trend of reported values of test items between the two systems (Figs. 1–3). However, it is certain that reported values can be different depending on calibration and QC method.

It is known that it is difficult to measure cholesterol and Cr levels accurately [17,18]. For cholesterol, various efforts have been made to accurately measure TC levels. However, obstacles still exist for accurate measurement of TC level in terms of standardization and traceability to various factors such as calibrators and QC materials [19]. Calibration and QC processes are very important to guarantee the accuracy of test results [20-22]. Especially, calibration and QC processes play more important role in these troublesome test items such as Cr and TC. As Cr has narrow reference range and the amount of quantified unit is very low, imprecision of calibration and QC can relatively give more effects on the accuracy of test results. From this perspective, automation of calibration and QC processes can be included in a routine front step among whole testing process for better precision.

Computer-directed managing technology such as an artificial intelligence is being applied to general medical industries including medical testing [23,24]. This high technology is expected to affect laboratory automation by converting its concern on automation of testing process based on a hardware-centered approach to software-based manipulation [25]. Therefore, quality management has become more important because it is essential that computer-controlled automation needs to meet requirements for quality issues to ensure quality assurance in laboratory practice as conventional QC processes do [26]. Ultimately, test results reported after automated calibration and QC processes should be as accurate as those by conventional manual method. In this study, imprecision results by the automated method seem to be acceptable compared to those by the conventional method. Additionally, all calibration and QC processes were successfully implemented with the Aptio total laboratory automation system. Regarding efficiency, the front process of laboratory testing workflow is expected to be improved by adapting the automated calibration and QC system. This supports that the idea of using automated calibration and QC processes in laboratory practice is feasible.

This research has some limitations. First, we evaluated five test items, which limited expanding drawn conclusions to other untested items. Second, we could identify significant difference of reported values between calibration and QC methods. However, standard materials were not used to evaluate the accuracy of those values in either system. Thus, which system is better in terms of accuracy could not be confirmed.

In conclusion, repeatability and within laboratory CVs between the automated system and the conventional manual system in the AMR group were similar, although means of reported values of test items were significantly different between the two systems. In the MDP group, the repeatability and within laboratory CVs were better with the automation system. Taken together, we think that the Aptio total laboratory automation system could be applied to routine practice to improve precision and efficiency.

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