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Response to the editorial by Karl Lackner

  • Marith van Schrojenstein Lantman ORCID logo EMAIL logo und Marc Thelen ORCID logo
Veröffentlicht/Copyright: 30. Juli 2025
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Clinical Chemistry and Laboratory Medicine (CCLM)
Aus der Zeitschrift Clinical Chemistry and Laboratory Medicine (CCLM) Band 63 Heft 12

Keywords: analytical performance specifications; cardiac troponin; reference change value; clinical decision limit; data simulation

To the Editor,

A recent edition of the journal of Clinical Chemistry and Laboratory Medicine (CCLM) features an editorial by Karl J. Lackner about the assay performance in high sensitivity cardiac troponin (hs-cTn) assays [1]. The editorial highlights recent publications on the safety of single sample cTn-based rule-out algorithms for myocardial infarction (MI). The editorial comments on the negligible effect of random analytical variation on the negative predictive value (NPV), which remains above 99 % even in the case of a coefficient of variation (CV) of 10 %. Although the data used justify this conclusion, this is only valid within the scope of direct rule-out of MI. Since hs-cTn is not solely used to rule-out patients at admission, but can also be used evaluate the change in hs-cTn concentration over one or 2 h to rule-out patients, this application comes with its own unique performance specifications (APS). We would like to illustrate why we think that such use of hs-cTn requires separate attention and possibly different APS.

Our concern rises as the few studies that specifically address the safety of the 0/1-2 h protocols have evaluated the impact of analytical variation expressed as a relative CV [2], [3], [4], [5]. However, the delta values mentioned in the ESC guideline are absolute values and can be used at different initial concentrations of hs-cTn, indicating that the performance specifications should be established and evaluated as absolute units [6]. It is known that cardiac troponin results do not have a linear imprecision, but at the lower concentration there is an absolute imprecision expressed as standard deviation (SD) [3]. Therefore, even if compliance at the relative level is acceptable, this does not indicate compliance and safety for patients that are evaluated using absolute delta thresholds.

To our knowledge, the only paper that evaluated the impact of passing or failing absolute analytical performance (APS) on the rate of rule-in and rule-out misclassification is the study by van Schrojenstein Lantman et al. [3]. The rate of misclassification was established by simulating 10.000 estimates of a real-world patient dataset (n=3,289) using precision profiles of laboratories that passed or failed 0/1 h APS. For patients with a t=0 hs-cTnT below 12 or 14 ng/L (for resp. 0/1 h and 0/2 h), a delta of 0 ng/L resulted in false observation in 1.7 % of cases for laboratories that passed 0/1 h APS, vs. 24.2 % when laboratories failed. Aggravatingly, the risk of misclassification of these patients to rule-in increased from 0 to 3.4 % when laboratories failed to comply.

Although we agree that the 0/1 h algorithms can be robust and an excellent example of value-based laboratory medicine, our work shows that the needed analytical performance cannot be taken for granted and needs local verification, corrective actions when needed, and mitigating policies when corrective actions fail. In the case of delta troponin changes, APS should be inferred by making sure the clinical decision delta is larger than the Reference Change Value (RCV). Specifically in the case of cardiac troponin, when following the European Society of Cardiology (ESC) guideline, a two-sided 99 % sensitivity formula should be used. This results in in equation (1), where RCV is the reference change value, SDi is the within-person biological variation in absolute units and SDa is the analytical variation in absolute units.

(1) R C V = 2 × 2.33 × S D i 2 + S D a 2

To determine the maximum allowable imprecision (SDa,allowable), Eq. (1) can be rewritten where the delta (Δ) change is filled in for the RCV, and the SDi is set to 0.7 ng/L (Eq. (2)) or 0 ng/L (Eq. (3)), depending on the interpretation of the laboratory whether natural biological variation is present in healthy persons.

(2) S D a , a l l o w a b l e = ∆ 2 2 × 2.33 2 − S D i 2
(3) S D a , a l l o w a b l e = ∆ 2 × 2.33

Table 1 shows delta values and required APS based on Eqs. (2) and (3). From these calculations, various aspects about required analytical performance become clear which cannot just be assumed to be met. Firstly, inclusion of biological variation of 0.7 ng/L leaves minimal room for analytical variation. Only delta values of 3 or greater have achievable APS when biological variation is considered. Secondly, relative to the threshold ‘low’ under which the 0/1 h or 0/2 h rule-out protocol applies, APS for some suppliers should be more tight tan the suggested ‘rule of thumb of standard of 10 % (e.g. Roche), whereas for other 12–24 % CVa is acceptable. This is attributable to the extremely low threshold under which these rule-out algorithms are valid (e.g. patients can be ruled out using the Vitros when the delta is <1 and patient values are <2). Inherently, the rounding to integer units prevents this rule-out from happening in the first place, and provides a mission impossible by definition. What these numbers do indicate is at which APS, both absolute and relative, one can safely assume that there is less than 1 % chance of falsely exceeding the delta due to analytical variation. Lastly, it becomes clear how much the APS for the 0/1 h and 0/2 h differ and that this discrepancy is vastly different among manufacturers. Considering the data presented before regarding Roche and the marginal relative increase in allowable performance (7.6–8.7 % at the level of 12 and 14 ng/L) and the substantial impact on incorrect rule-in of rule-out patients (which reduces from 3.4 % in the simulation of van Schrojenstein Lantman to 0.001 %), we can gauge that meeting APS is absolutely vital and that a ballpark ‘10 % CVa is okay’ does not cut the cake for delta-based rule-out.

Table 1:

Overview of used delta decision-limits for rule-out of a non-ST-elevated myocardial infarction (NSTEMI) using 0/1 h or 0/2 h time points [6].

Platform 0/1 h algorithm delta decision points 0/2 h algorithm delta decision points
No 1 hΔ Low APS (SD) APS (CVa) APS (SD,+SDi) APS (CVa,+CVi) No 2 hΔ Low APS (SD) APS (CVa) APS (SD,+SDi) APS (CVa,+CVi)
Roche elecsys <3 <12 0.9 8 % 0.6 5 % <4 <14 1.2 9 % 1.0 7 %
Abbott Architect <2 <5 0.6 12 % NP NP <2 <6 0.6 10 % NP NP
Siemens Centaur <3 <6 0.9 15 % 0.6 10 % <7 <8 2.1 27 % 2.0 25 %
Access, Beckman Coulter <4 <5 1.2 24 % 1.0 20 % <5 <5 1.5 30 % 1.3 27 %
Clarity Singulex <1 <2 0.3 15 % NP NP TBD TBD TBD TBD TBD TBD
Vitros Clinical diagnostics <1 <2 0.3 15 % NP NP TBD TBD TBD TBD TBD TBD
Pathfast LSI medicine <3 <4 0.9 23 % 0.6 15 % TBD TBD TBD TBD TBD TBD
Triagetrue, Quidel <3 <5 0.9 18 % 0.6 12 % TBD TBD TBD TBD TBD TBD

  1. A NSTEMI can be ruled out if the delta between t=0 and t=1/2 h is lower than “no hΔ” and both values are below “Low”. The calculated absolute APS (absolute as SD, and relative at the level of “Low” as CVa in %) are noted, also when including the within-person biological variation (SDi) of 0.7 ng/L. When APS cannot be met the result is not possible (NP), whereas TBD indicates the decision limits are to be determined.

Lastly, the editorial of Lackner highlighted that an elevated CV had a negligible effect for direct rule-out NPV, we cannot assume similar conclusions for delta-based decision limits. Namely, patients who are subjected to a delta-based decision limit already did not pass the direct rule-out, and thus a-priori risk is higher in this cohort. Whilst the diagnosis may be identical to direct rule-out, delta rule-out is done in a different population and requires different APS, and thus highlights the need for separate evaluation and monitoring of its analytical and clinical efficacy. This includes the application of hs-cTn results in clinical decision aids and (artificial intelligence (AI)) risk scores that incorporate hs-cTn and the time between sampling [4], [7]. Application of such tools in clinical practice requires tailoring of the APS while incorporating the impact of uncertainty on composite end results [8]. Ideally, this incorporates the inherent misclassification, rather than assuming perfect clinical specificity and sensitivity.

Corresponding author: Marith van Schrojenstein Lantman, Stichting Kwaliteitsbewaking Medische Laboratoriumdiagnostiek, SKML, 6525EC, Nijmegen, The Netherlands, Toernooiveld 300, 6525EC, Nijmegen, The Netherlands; and Department of Laboratory Medicine, Radboudumc, 6525GA, Nijmegen, The Netherlands, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

References

1. Lackner, K. High sensitivity cardiac troponin assays, rapid myocardial infarction rule-out algorithms, and assay performance. Clin Chem Lab Med 2025;63:462–4. https://doi.org/10.1515/cclm-2025-0013.Suche in Google Scholar PubMed

2. Clerico, A, Zaninotto, M, Aimo, A, Padoan, A, Passino, C, Fortunato, A, et al.. Advancements and challenges in high-sensitivity cardiac troponin assays: diagnostic, pathophysiological, and clinical perspectives: on behalf of the Italian study Group on Cardiac Biomarkers. Clin Chem Lab Med 2025;63:1260–78. https://doi.org/10.1515/cclm-2024-1090.Suche in Google Scholar PubMed

3. van Schrojenstein Lantman, M, Grobben, R, van Herwaarden, AE, van Berkel, M, Schaap, J, Thelen, M. To rule-in, or not to falsely rule-out, that is the question: evaluation of hs-cTnT EQA performance in light of the ESC-2020 guideline. Clin Chem Lab Med 2024;62:1158–66. https://doi.org/10.1515/cclm-2023-1226.Suche in Google Scholar PubMed

4. Hatherley, J, Miller, G, Collinson, P, Shantsila, E, Fearon, H, Lambert, A, et al.. Why is single sample rule out of non-ST elevation myocardial infarction using high-sensitivity cardiac troponin T safe when analytical imprecision is so high? A joint statistical and clinical demonstration. Clin Chem Lab Med 2025;63:e63–6. https://doi.org/10.1515/cclm-2024-0647.Suche in Google Scholar PubMed

5. Nomura, O, Hashiba, K, Kikuchi, M, Kojima, S, Hanada, H, Mano, T, et al., Japan Resuscitation Council (JRC) Acute Coronary Syndrome (ACS) Task Force and the Guideline Editorial Committee on behalf of the Japanese Circulation Society (JCS) Emergency and Critical Care Committee. Performance of the 0-hour/1-hour algorithm for diagnosing myocardial infarction in patients with chest pain in the emergency department – A systematic review and meta-analysis. Circ Rep 2022;4:241–7. https://doi.org/10.1253/circrep.CR-22-0001.Suche in Google Scholar PubMed PubMed Central

6. Collet, JP, Thiele, H, Barbato, E, Barthélémy, O, Bauersachs, J, Bhatt, DL, ESC Scientific Document Group, et al.. 2020 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J 2021;42:1289–367. https://doi.org/10.1093/eurheartj/ehaa575. Erratum in: Eur Heart J. 2021 May 14;42(19):1908. DOI: 10.1093/eurheartj/ehaa895. Erratum in: Eur Heart J. 2021 May 14;42(19):1925. DOI: 10.1093/eurheartj/ehab088. Erratum in: Eur Heart J. 2021 Jun 14;42(23):2298. doi: 10.1093/eurheartj/ehab285. Erratum in: Eur Heart J. 2024 Feb 1;45(5):404-405. doi: 10.1093/eurheartj/ehad879. PMID: 32860058.Suche in Google Scholar PubMed

7. Apple, FS, Collinson, PO, Kavsak, PA, Body, R, Ordóñez-Llanos, J, Saenger, AK, et al.. The IFCC committee on clinical applications of cardiac bio-markers, getting cardiac troponin right: appraisal of the 2020 European Society of Cardiology Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation by the International Federation of Clinical Chemistry and Laboratory Medicine Committee on clinical applications of cardiac bio-markers. Clin Chem 2021;67:730–5. https://doi.org/10.1093/clinchem/hvaa337.Suche in Google Scholar PubMed

8. van Schrojenstein Lantman, M, Thelen, MHM. The impact of measurement uncertainty on the uncertainty of ordinal medical scores based on continuous quantitative laboratory results. Clin Chem Lab Med 2021;59:e309–12. https://doi.org/10.1515/cclm-2020-1890.Suche in Google Scholar PubMed

Received: 2025-03-18
Accepted: 2025-07-16
Published Online: 2025-07-30
Published in Print: 2025-11-25

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/cclm-2025-0334
Schlagwörter für diesen Artikel
analytical performance specifications; cardiac troponin; reference change value; clinical decision limit; data simulation
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Frontmatter
  2. Editorials
  3. Challenging the dogma: why reviewers should be allowed to use AI tools
  4. Multivariate approaches to improve the interpretation of laboratory data
  5. Review
  6. Interference of therapeutic monoclonal antibodies with electrophoresis and immunofixation of serum proteins: state of knowledge and systematic review
  7. Opinion Papers
  8. Urgent call to the European Commission to simplify and contextualize IVDR Article 5.5 for tailored and precision diagnostics
  9. The importance of laboratory medicine in the management of CKD-MBD: insights from the KDIGO 2023 controversies conference
  10. Supplementation of pyridoxal-5′-phosphate in aminotransferase reagents: a matter of patient safety
  11. HCV serology: an unfinished agenda
  12. From metabolic profiles to clinical interpretation: multivariate approaches to population-based and personalized reference intervals and reference change values
  13. Genetics and Molecular Diagnostics
  14. A multiplex allele specific PCR capillary electrophoresis (mASPCR-CE) assay for simultaneously analysis of SMN1/SMN2/NAIP copy number and SMN1 loss-of-function variants
  15. General Clinical Chemistry and Laboratory Medicine
  16. From assessment to action: experience from a quality improvement initiative integrating indicator evaluation and adverse event analysis in a clinical laboratory
  17. Evaluation of measurement uncertainty of 11 serum proteins measured by immunoturbidimetric methods according to ISO/TS 20914: a 1-year laboratory data analysis
  18. Assessing the harmonization of current total vitamin B12 measurement methods: relevance and implications
  19. The current status of serum insulin measurements and the need for standardization
  20. Method comparison of plasma and CSF GFAP immunoassays across multiple platforms
  21. Cerebrospinal fluid leptin in Alzheimer’s disease: relationship to plasma levels and to cerebrospinal amyloid
  22. Verification of the T50 Calciprotein Crystallization test: bias estimation and interferences
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  24. Oral salt loading combined with postural stimulation tests for confirming and subtyping primary aldosteronism
  25. Evaluating the performance of a multiparametric IgA assay for celiac disease diagnosis
  26. Clinical significance of anti-mitochondrial antibodies and PBC-specific anti-nuclear antibodies in evaluating atypical primary biliary cholangitis with normal alkaline phosphatase levels
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  28. Establishment of region-, age- and sex-specific reference intervals for aldosterone and renin with sandwich chemiluminescence immunoassays
  29. Validation of a plasma GFAP immunoassay and establishment of age-related reference values: bridging analytical performance and routine implementation
  30. Comparative analysis of population-based and personalized reference intervals for biochemical markers in peri-menopausal women: population from the PALM cohort study
  31. Hematology and Coagulation
  32. Evaluation of stability and potential interference on the α-thalassaemia early eluting peak and immunochromatographic strip test for α-thalassaemia --SEA carrier screening
  33. Cardiovascular Diseases
  34. Analytical and clinical evaluation of an automated high-sensitivity cardiac troponin I assay for whole blood
  35. Diabetes
  36. Method comparison of diabetes mellitus associated autoantibodies in serum specimens
  37. Letters to the Editor
  38. Permitting disclosed AI assistance in peer review: parity, confidentiality, and recognition
  39. Response to the editorial by Karl Lackner
  40. Hemolysis detection using the GEM 7000 at the point of care in a pediatric hospital setting: does it affect outcomes?
  41. Estimation of measurement uncertainty for free drug concentrations using ultrafiltration
  42. Cryoglobulin pre-analysis over the weekend
  43. Accelerating time from result to clinical action: impact of an automated critical results reporting system
  44. Recent decline in patient serum folate test levels using Roche Diagnostics Folate III assay
  45. Kidney stones consisting of 1-methyluric acid
  46. Congress Abstracts
  47. 7th EFLM Conference on Preanalytical Phase
  48. Association of Clinical Biochemists in Ireland Annual Conference
  49. Association of Clinical Biochemists in Ireland Annual Conference
  50. 17th Congress of the Portuguese Society of Clinical Chemistry, Genetics and Laboratory Medicine
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