Introduction

Since the introduction of procalcitonin (PCT) as a potential diagnostic marker of sepsis in 1993, its role in clinical practice has expanded significantly. In 2005, Food and Drug Administration (FDA) approval of the first automated PCT assay, the BRAHMS PCT KRYPTOR produced by Thermo Fisher Scientific, marked a significant advancement in this field [1]. This approval supported extensive research on the clinical utility and efficacy of PCT, thereby expanding its application beyond initial diagnosis of systemic infections. Currently, PCT assays are integral to managing antibiotic therapy, diagnosing pneumonia and postoperative infections, and as prognostic markers in intensive care unit patients at high risk of infection [2].

The initial automated method was developed using reagents and antibodies from BRAHMS GmbH assay, setting a standard that subsequent FDA-cleared methods continued to follow, predominantly through using BRAHMS antibodies [3,4]. However, in recent years, several non-BRAHMS methods have emerged in clinical laboratories, including the latex-based immunoturbidimetric assay by Diazyme, fluorescence immunoassay by Boditech, particle-enhanced turbidimetric immunoassay by Diasys, and paramagnetic particle chemiluminescent immunoassay by Beckman Coulter [4,5]. These assays utilize unique monoclonal antibodies targeting PCT, which differ from those used in BRAHMS methodologies [6].

Although BRAHMS PCT KRYPTOR was the first FDA-accredited automated method and is regarded as a substitute for the reference method, a lack of established reference materials, methodologies, and harmonization across platforms remains [7,8]. This discrepancy is evident in the variability observed both within the BRAHMS assays and when comparing BRAHMS and non-BRAHMS assays, and is often attributed to differences in calibrators or the underlying principles of the methods [9]. These variations have implications for clinical decision-making, particularly regarding the applicability of consistent cutoff values across various clinical conditions and assay methods.

The bias inherent in these assays is well documented, but not consistent; it varies with the concentration of PCT, thus complicating the interpretation of results across a range of clinical scenarios [10]. Most studies have attempted to describe this bias comprehensively across all concentrations [11], data comparing methods specifically based on cutoff points remain sparse.

In the present study, we evaluated the Access PCT immunoassay using DxI 800 (Beckman Coulter), a paramagnetic particle chemiluminescence immunoassay. The analytical performance (precision, detection capability, linearity, carry-over, and reference range) was compared with that of the Elecsys BRAHMS PCT assay conducted on a Cobas e 601 (Roche Diagnostics International). In addition, we assessed the compatibility of the Access PCT assay with laboratories where BRAHMS methods have previously been utilized, considering varying PCT concentrations.

Methods

Ethical statements: The protocol was approved by the Institutional Review Board of Kosin University Gospel Hospital (No. KUGH 2019-09-025). This study was performed in accordance with the Declaration of Helsinki. Informed consent was waived.

1. Evaluation of precision

To assess precision, we evaluated the coefficient of variation (CV; %) using commercially available quality control (QC) materials, specifically MAS Omni-IMMUNE Immunoassay Controls (Thermo Fisher Scientific Inc.). The concentrations for the QC materials were set at 0.53, 3.68, and 29.20 μg/L, representing low, intermediate, and high concentrations, respectively. Each QC material was tested over a period of 20 days, with two runs daily and two replicates per run, based on the protocols of the Clinical and Laboratory Standards Institute (CLSI) EP15-A3 [12,13]. We calculated the CV% values for repeatability and within-laboratory precision by comparing these values with the manufacturer’s suggested CV% values listed in the product insert. Statistical analyses were conducted using the Analyse-it software (version 5.90; Analyse-it Software Ltd.).

2. Limits of blank, detection, and quantitation verification

The limits of blank (LoB), limits of detection (LoD), and limits of quantitation (LoQ) were verified based on the CLSI guideline EP12-A2 [14]. To verify the LoB and LoD, we repeated the tests for both blank and low-concentration patient samples 20 times. In addition, to confirm the LoQ, we tested five patient samples with concentrations close to the manufacturer’s claimed LoQ. Each sample was tested ten times to assess whether the test results were within the permissible total error.

3. Validation of linearity

Linearity validation was conducted based on CLSI guidelines EP06-ED2 [15]. A high-concentration patient sample was serially diluted with the low-level material at ratios of 4:0, 3:1, 2:2, 1:3, and 0:4. Each of the five dilutions was tested in duplicate. The data were analyzed as outlined in CLSI EP06-ED2. The allowable deviation from linearity (ADL) for each of the five levels was calculated and deviations from linearity were assessed. If the deviation was within the calculated ADL, the linearity of the assay was validated.

4. Verification of reference intervals

We verified the manufacturer’s reference interval for PCT (0–0.065 μg/L) using samples obtained from a healthy population. Residual blood specimens originally collected during routine health screenings, unrelated to the current research, were utilized. Participants met criteria for a "healthy" status, including being aged 18–50 years. Twenty samples were obtained from Kosin University Gospel Hospital and analyzed based on CLSI guideline EP28-A3 [16]. This process was designed to confirm whether the reference interval was appropriate for our patient population.

5. Assessment of carry-over

Carry-over was evaluated by sequentially testing a high- and low-level sample, each tested four times. The results from the high-level samples were labeled H1–H4, and those from the low-level samples were labeled L1–L4. The carry-over rate (%) was calculated using the equation below. The acceptable criterion for carry-over was set to <1% [17].

Carry−over rate %=L1−L3+L4/2H3+H2/2−L3+L4/2 ×100

6. Comparative evaluation of PCT assays

We collected 190 anonymized patient samples from Kosin University Gospel Hospital for the comparative evaluation of the PCT assays. Samples were transferred to serum separator tubes, divided into two aliquots, and analyzed separately. One aliquot was tested in a routine laboratory using the Elecsys BRAHMS PCT assay, whereas the other was analyzed using the Access PCT assay. Samples exceeding the analytical measurement range of any analyzer (>100 μg/L) were excluded from the study.

The PCT values from the two analyzers were classified into four risk categories based on medical decision points: low risk, group 1 (<0.5 μg/L); intermediate risk, group 2 (≥0.5 to <2.0 μg/L); high risk, group 3 (≥2.0 to <10 μg/L); and very high risk of sepsis, group 4 (≥10 μg/L) [18]. Data comparison was performed using Passing-Bablok regression, correlation coefficients, and Bland-Altman plots. In addition, an agreement test was conducted to calculate the weighted kappa (κ) to assess concordance.

Results

1. Precision assessment of Access PCT assay

The repeatability CV%s and the within-laboratory CVs for low (0.53 μg/L), intermediate (3.68 μg/L), and high (29.20 μg/L) concentration QC materials in the Access PCT assay were 4.41% and 5.45%, 2.34% and 2.68%, and 2.24% and 3.13%, respectively (Table 1). All measured CV% values were below the manufacturer’s precision threshold of 6%.

2. Detection capability

The LoB, LoD, and LoQ were successfully verified, meeting the manufacturer’s limits of <0.005, <0.01, and <0.02 μg/L, respectively.

3. Linearity validation

In the linear regression analysis, the assay demonstrated robust linearity ranging 0.00–93.92 μg/L. In the linearity analysis, deviations from linearity for all the prepared materials were within the allowable limits of deviation (Fig. 1).

4. Reference range verification

The PCT levels of all 20 participants were below the proposed reference interval, allowing the adoption of the manufacturer’s reference range.

5. Carry-over evaluation

The carry-over was evaluated by testing four high-concentration samples measuring at 67.05, 66.06, 66.88, and 64.60 μg/L, respectively. The low-concentration samples were measured at 0.97, 1.09, 1.05, and 0.97 μg/L, respectively. The calculated carry-over rate was –0.06%, thus confirming that the carry-over falls within the acceptable range.

6. Comparative analysis of Access PCT and Elecsys BRAHMS PCT assays

Eight samples that exceeded the analytical measurement range were excluded, resulting in 182 samples used for the analysis. According to the Bland-Altman plot (Fig. 2), Access PCT displayed values that were 14.36% higher on mean than Elecsys BRAHMS PCT, with a 95% confidence interval (CI) ranging, 29.56%–58.24%. Despite this notable bias, the correlation was deemed excellent, as evidenced by the Passing-Bablok regression analysis, which showed a correlation coefficient (r) of 0.996.

To further explore bias within subgroups, samples were categorized based on the Elecsys BRAHMS PCT results, which served as a reference. The subgroup sample sizes were 75, 43, 37, and 27 for groups 1–4, respectively. The derivative results from the Bland-Altman plot and Passing-Bablok analysis are shown in Fig. 3. The mean bias was lowest in group 1 (bias, –3.14%; 95% CI, –40.11% to 33.83%) and increased in groups 2–4, with biases of 24.02% (95% CI, –2.35% to 50.40%), 34.40% (95% CI, 0.02% to 68.79%), and 20.10% (95% CI, –1.33% to 41.54%), respectively. Excellent correlations were maintained across all subgroups, with correlation coefficients ranging 0.958–0.993.

The agreement analysis results are detailed in Table 2, with the sample values assigned to the cells based on their measurements using Access PCT (rows) and Elecsys BRAHMS PCT (columns). Approximately 85.2% of the samples were classified into the same subgroup by both analyzers, and the weighted kappa value was 0.941, indicating high agreement. Discrepancies tended to show Access PCT, thus classifying samples into one subgroup higher than the Elecsys BRAHMS PCT. This tendency is consistent with the observed mean bias of 14.36% between the two assays. Fig. 3A shows that the Access PCT consistently reported higher values than the Elecsys BRAHMS PCT, starting at approximately 0.35 μg/L.

Discussion

The Elecsys BRAHMS PCT assay is widely used in South Korea and is well documented in the literature [19,20]. In contrast, Beckman Coulter’s Access PCT assay was introduced more recently to the South Korean market, resulting in a comparative dearth of analyses, especially between these two assays [21]. In the present study, the analytical performance of the Beckman Coulter Access PCT assay met the manufacturer’s specifications for precision, detection capacity, linearity, reference interval, and carry-over, indicating reliable performance within the specified parameters. These findings are broadly consistent with those reported in previous studies [3,21].

Both Elecsys BRAHMS PCT and Access PCT assays have previously shown satisfactory concordance with the BRAHMS KRYPTOR assay developed by BRAHMS GmbH (now part of Thermo Fisher Scientific), which is considered the reference method. However, despite these strong correlations, variations in bias have been observed across different concentration intervals among various testing kits [22]. The mean bias was influenced by the distribution of elements across groups. In our study, the results from group 1 showed a 3.14% lower bias in Access PCT than in Elecsys BRAHMS PCT, whereas the differences in other groups ranged from +20.10% to +34.40%. The sizes of group 1 and the other groups were 75 and 107, respectively. Increasing the proportion of patients in group 1 could potentially reduce the overall observed bias. Previous studies have reported varying biases when different analyzers assessed the same methods. For instance, the bias between Access PCT and Elecsys BRAHMS PCT was reported to be 32.10% [21], which was significantly higher than that in our findings. This discrepancy can be attributed to differences in sample size distribution; in the cited study, there were only 26 samples with levels of <0.5 μg/L, compared to 134 samples with levels above this threshold.

As variability in biases across studies has been observed even when the same assays were evaluated, the authors noted that comparing all methods directly is challenging because of the differing biases across concentration ranges. The clinical cutoff values have decreased from the initial values of 0.5 and 2.0 when PCT was first introduced [23]. However, the commonly used PCT decision points were adjusted to 0.1, 0.25, 0.5, and 1.0 [24]. Biases below 2.0, particularly at 0.5, were considered critical for decision-making. In our study, for group 1 (<0.5), the absolute bias was 3.14. At these pragmatic clinical decision points, the performance of the Access PCT was observed to be reasonably comparable to that of the Elecsys BRAHMS PCT.

Owing to changing analytical methods is a common issue in clinical laboratories, it is advisable to segment bias analysis by decision-point groups to better understand the variations affecting clinical outcomes. Moreover, when reviewing prior studies, it is crucial to consider the bias at each level or group and its distribution, along with the total assay bias. Notably, the mean bias can vary substantially, which may obscure significant analytical performance issues depending on the proportion of each component used in the calculation.

Recent discussions have raised concerns about whether clinical thresholds established from one assay can be accurately applied to subsequent assays [24,25]. Similar to the bias, the reliability of the agreement tests can be significantly influenced by the sample distribution. In a previous study, analysis of clinical decision points revealed that despite reasonable analytical agreement among all methods evaluated, several samples were categorized differently [7]. In agreement analysis, the concordance rate was 85.2%. These discrepancies were primarily attributed to instances in which Access PCT reported higher PCT levels than Elecsys BRAHMS PCT, consistent with the observed biases. Similar discrepancies were observed when the same assays were compared with a previous study. Such variations are commonly noted in comparisons between assays. While these discrepancies or biases were previously accepted, the increasing emphasis on patient safety and evidence-based medicine necessitates reassessment of the biases between assays and cutoff points for medical decisions [26,27]. Notably, there is a growing demand for a common reference calibrator, and the International Federation of Clinical Chemistry and Laboratory Medicine working group has initiated efforts to produce commutable reference materials [28].

Our study had several limitations. First, the BRAHMS PCT LIA and BRAHMS KRYPTOR assays are considered traceable surrogate reference methods. Although comparability with the BRAHMS PCT KRYPTOR has been reported in previous studies, including evaluations using these reference assays is desirable. However, our study only compared the results of the Access PCT with the Elecsys BRAHMS PCT, which utilizes original materials from BRAHMS. Second, we aimed to subdivide the PCT values to include a cutoff of 0.1, but this was not feasible due to sample shortage. Lastly, clinical information such as patient diagnoses or laboratory tests was not collected, precluding a comprehensive clinical analysis in cases of discrepancy.

Despite these limitations, this study has several strengths. Unlike previous studies that have often analyzed total biases, we analyzed biases based on subdivided values. This approach should be particularly useful for laboratories interested in understanding detailed bias of the Access PCT assay.

In conclusion, although the Access PCT and Elecsys BRAHMS PCT assays both provide reliable and consistent PCT measurements, differences in bias across concentration ranges highlight the need for careful calibration and potentially uniform reference materials to ensure that clinical decisions are based on precise and comparable PCT values. These findings support the requirement for ongoing evaluation and standardization efforts to measure PCT across different assay platforms.

Article information

Conflicts of interest

This research was supported by Beckman Coulter company in 2019.

Hyunyong Hwang is an editorial board member of the journal but was not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

Funding

This research was supported by Beckman Coulter company in 2019.

Author contributions

Conceptualization: HH. Data curation; Formal analysis: HC, SSL, HH. Funding acquisition: HH. Investigation; Methodology; Resources: HC, SSL, HH. Supervision: HH. Validation; Visualization: HC, SSL, HH. Writing - original draft: HC, SSL. Writing - review & editing: HC, SSL, HH. All authors read and approved the final manuscript.

Fig. 1.

Linearity analysis of PCT. (A) Linearity data versus linearity fit. (B) Comparison of ADL to the deviation from linearity. PCT, procalcitonin; ADL, allowable deviation from linearity; CI, confidence interval.

Fig. 2.

Comparative analysis of the Access PCT and Elecsys PCT assays. (A) Bland-Altman plot assessing agreement. (B) Passing-Bablok regression for correlation. PCT, procalcitonin; LoA, limits of agreement.

Fig. 3.

Comparison of Bland-Altman plots (blue line) and Passing-Bablok (red line) regression of the Elecsys PCT and Access PCT assays. (A, B) Group 1, <0.5 μg/L; (C, D) group 2, ≥0.5 to <2.0 μg/L; (E, F) group 3, 2.0 ≥ to <10 μg/L; and (G, H) group 4, ≥10 μg/L. PCT, procalcitonin; LoA, limits of agreement.

References

• 1. Hamade B, Huang DT. Procalcitonin: where are we now? Crit Care Clin 2020;36:23–40.ArticlePubMed
• 2. Samsudin I, Vasikaran SD. Clinical utility and measurement of procalcitonin. Clin Biochem Rev 2017;38:59–68.ArticlePubMedPMC
• 3. Lippi G, Salvagno GL, Gelati M, Pucci M, Demonte D, Faggian D, et al. Analytical evaluation of the new Beckman Coulter access procalcitonin (PCT) chemiluminescent immunoassay. Diagnostics (Basel) 2020;10:128.ArticlePubMedPMC
• 4. Dipalo M, Guido L, Micca G, Pittalis S, Locatelli M, Motta A, et al. Multicenter comparison of automated procalcitonin immunoassays. Pract Lab Med 2015;2:22–8.ArticlePubMedPMC
• 5. Dupuy AM, Bargnoux AS, Larcher R, Merindol A, Masetto T, Badiou S, et al. Bioanalytical performance of a new particle-enhanced method for measuring procalcitonin. Diagnostics (Basel) 2020;10:461.ArticlePubMedPMC
• 6. Dupuy AM, Ruffel L, Bargnoux AS, Badiou S, Cristol JP. Analytical evaluation of the performances of a new procalcitonin immunoassay. Clin Chem Lab Med 2021;60:77–80.ArticlePubMed
• 7. Chambliss AB, Hayden J, Colby JM. Evaluation of procalcitonin immunoassay concordance near clinical decision points. Clin Chem Lab Med 2019;57:1414–21.ArticlePubMed
• 8. Huynh HH, Boeuf A, Pfannkuche J, Schuetz P, Thelen M, Nordin G, et al. Harmonization status of procalcitonin measurements: what do comparison studies and EQA schemes tell us? Clin Chem Lab Med 2021;59:1610–22.ArticlePubMed
• 9. Lippi G, Salvagno GL, Gelati M, Pucci M, Lo Cascio C, Demonte D, et al. Two-center comparison of 10 fully-automated commercial procalcitonin (PCT) immunoassays. Clin Chem Lab Med 2019;58:77–84.ArticlePubMed
• 10. CLSI. User verification of precision and estimation of bias: approved guideline. 3rd ed. Clinical and Laboratory Standards Institute; 2014.
• 11. Johnson R. Assessment of bias with emphasis on method comparison. Clin Biochem Rev 2008;29 Suppl 1(Suppl 1):S37–42.PubMed
• 12. Aguirre JJ, Ness K, Algeciras-Schimnich A. Application of the CLSI EP15-A3 guideline as an alternative troubleshooting tool for verification of assay precision. Am J Clin Pathol 2019;152(Suppl 1):S88.Article
• 13. Jeong S, Hong YR, Hwang H. Performance comparison between Elecsys Anti-SARS-CoV-2 and Anti-SARS-CoV-2 S and Atellica IM SARS-CoV-2 Total and SARS-CoV-2 IgG assays. Kosin Med J 2022;37:154–62.ArticlePDF
• 14. Clinical and Laboratory Standards Institute (CLSI). User protocol for evaluation of qualitative test performance: approved guideline. 2nd ed. CLSI; 2008.
• 15. Cho J. Evaluating and establishing the linearity interval and extended measuring interval. Lab Med Online 2024;14:163–75.Article
• 16. Badakhshan SN, Ghazizadeh H, Mohammadi-Bajgiran M, Esmaily H, Khorasani MY, Bohn MK, et al. Age-specific reference intervals for liver function tests in healthy neonates, infants, and young children in Iran. J Clin Lab Anal 2023;37:e24995.ArticlePubMedPMC
• 17. Broughton PM. Carry-over in automatic analysers. J Automat Chem 1984;6:94–5.ArticlePubMedPMC
• 18. Schuetz P. How to best use procalcitonin to diagnose infections and manage antibiotic treatment. Clin Chem Lab Med 2022;61:822–8.ArticlePubMed
• 19. Heo W, Park HD. Analytical and clinical performance of the Advansure i3 procalcitonin assay. Scand J Clin Lab Invest 2021;81:546–51.ArticlePubMed
• 20. Cho HW, Kim SH, Cho Y, Jeong SH, Lee SG. Concordance of three automated procalcitonin immunoassays at medical decision points. Ann Lab Med 2021;41:419–23.ArticlePubMedPMC
• 21. Choi H, Tashpulatova Z, Moon SY, Choi J, Kim JY, Lee SM. Evaluation of the Beckman Coulter access procalcitonin assay: analytical and clinical performance. Clin Chem Lab Med 2021;60:e50–3.ArticlePubMed
• 22. Eidizadeh A, Wiederhold M, Schnelle M, Binder L. Comparison of a novel automated DiaSys procalcitonin immunoassay with four different BRAHMS-partnered immunoassays. Pract Lab Med 2022;30:e00274.ArticlePubMedPMC
• 23. Meisner M. Procalcitonin: biochemistry and clinical diagnosis. Uni-Med Verlag AG; 2010.
• 24. Chambliss AB, Patel K, Colon-Franco JM, Hayden J, Katz SE, Minejima E, et al. AACC guidance document on the clinical use of procalcitonin. J Appl Lab Med 2023;8:598–634.ArticlePubMedPDF
• 25. Farooq A, Colon-Franco JM. Procalcitonin and its limitations: why a biomarker's best isn't good enough. J Appl Lab Med 2019;3:716–9.ArticlePubMedPDF
• 26. Hall MK, Kea B, Wang R. Recognizing bias in studies of diagnostic tests part 1: patient selection. Emerg Med J 2019;36:431–4.ArticlePubMed
• 27. Lorde N, Elgharably A, Kalaria T. Impact of variation between assays and reference intervals in the diagnosis of endocrine disorders. Diagnostics (Basel) 2023;13:3453.ArticlePubMedPMC
• 28. Huynh HH, Boeuf A, Vinh J, Delatour V; IFCC Working Group on Standardization of Procalcitonin assays (WG-PCT). Evaluation of the necessity and the feasibility of the standardization of procalcitonin measurements: activities of IFCC WG-PCT with involvement of all stakeholders. Clin Chim Acta 2021;515:111–21.ArticlePubMed

Figure & Data

References

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