Startseite Moving to practice with the application of Milan model 1b-based analytical performance specifications
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Moving to practice with the application of Milan model 1b-based analytical performance specifications

  • Mauro Panteghini ORCID logo EMAIL logo
Veröffentlicht/Copyright: 25. September 2025
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Abstract

The definition of analytical performance specifications (APS) by the Milan model 1b is based on indirect approaches investigating the impact of analytical performance of the laboratory test on clinical classification and thereby on the probability of patient outcomes. As direct diagnostic outcome studies (Milan model 1a) for defining APS are now considered very difficult and costly to be performed in practice, expert groups have gathered to reach consensus on how to use available information and apply Milan model 1b to the definition of APS. They have highlighted three major aspects: a) the definition of the clinically acceptable misclassification rate(s); b) the influence of the clinical pathway and patient population and setting (disease prevalence) when diagnostic thresholds are defined, e.g., in guidelines; and c) the intended use of the test. The basic question calling for an answer is how to move forward and provide specific APS for certain measurands that are key in clinical decision making. Here, cardiac troponin testing is used as a practical example for the application of model 1b-derived APS. Proposals are made for moving to practice with the application of this model to APS definition.


Corresponding author: Mauro Panteghini, Department of Laboratory Medicine, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 9 Sklodowskiej-Curie Street, 85-094, Bydgoszcz, Torun, Poland, E-mail:

Acknowledgments

The author would like to thank Prof Marc Thelen for the stimulating discussion about the practical application of innovative tools in laboratory medicine.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has 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 author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

References

1. Çubukçu, HC. Computer simulation approaches to evaluate the interaction between analytical performance characteristics and clinical (mis)classification: a complementary tool for setting indirect outcome-based analytical performance specifications. Clin Chem Lab Med 2025;63:1292–300. https://doi.org/10.1515/cclm-2024-1195.Suche in Google Scholar PubMed

2. Low, HQ, Horvath, AR, Loh, TP, Plebani, M, Lim, CY. Setting analytical performance specification by simulation (Milan model 1b). Clin Chem Lab Med 2025;63:1455–7. https://doi.org/10.1515/cclm-2025-0121.Suche in Google Scholar PubMed

3. Sandberg, S, Zima, T, Panteghini, M. Analytical performance specifications - moving from models to practical recommendations. Clin Chem Lab Med 2024;62:1451–4. https://doi.org/10.1515/cclm-2024-0661.Suche in Google Scholar PubMed

4. Panteghini, M. What the Milan conference has taught us about analytical performance specification model definition and measurand allocation. Clin Chem Lab Med 2024;62:1455–61. https://doi.org/10.1515/cclm-2023-1257.Suche in Google Scholar PubMed

5. Horvath, AR, Bell, KJL, Ceriotti, F, Jones, GRD, Loh, TP, Lord, S, et al.. Task group analytical performance specifications based on outcomes of the European Federation of clinical chemistry and laboratory medicine. Outcome-based analytical performance specifications: current status and future challenges. Clin Chem Lab Med 2024;62:1474–82. https://doi.org/10.1515/cclm-2024-0125.Suche in Google Scholar PubMed

6. Loh, TP, Smith, AF, Bell, KJL, Lord, SJ, Ceriotti, F, Jones, G, et al.. Setting analytical performance specifications using HbA1c as a model measurand. Clin Chim Acta 2021;523:407–14. https://doi.org/10.1016/j.cca.2021.10.016.Suche in Google Scholar PubMed

7. Thygesen, K, Alpert, JS, Jaffe, AS, Chaitman, BR, Bax, JJ, Morrow, DA, et al.. ESC Scientific Document Group. Fourth universal definition of myocardial infarction (2018). Eur Heart J 2019;40:237–69. https://doi.org/10.1093/eurheartj/ehy462.Suche in Google Scholar PubMed

8. Lyon, AW, Kavsak, PA, Lyon, OA, Worster, A, Lyon, ME. Simulation models of misclassification error for single thresholds of high-sensitivity cardiac troponin I due to assay bias and imprecision. Clin Chem 2017;63:585–92. https://doi.org/10.1373/clinchem.2016.265058.Suche in Google Scholar PubMed

9. Pickering, JW, Kavsak, P, Christenson, RH, Troughton, RW, Pemberton, CJ, Richards, AM, et al.. Determination of clinically acceptable analytical variation of cardiac troponin at decision thresholds. Clin Chem 2024;70:967–77. https://doi.org/10.1093/clinchem/hvae059.Suche in Google Scholar PubMed

10. Than, M, Herbert, M, Flaws, D, Cullen, L, Hess, E, Hollander, JE, et al.. What is an acceptable risk of major adverse cardiac event in chest pain patients soon after discharge from the Emergency Department? A clinical survey. Int J Cardiol 2013;166:752–4. https://doi.org/10.1016/j.ijcard.2012.09.171.Suche in Google Scholar PubMed

11. Collet, JP, Thiele, H, Barbato, E, Barthélémy, O, Bauersachs, J, Bhatt, DL, et al.. ESC Scientific Document Group. 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.Suche in Google Scholar PubMed

12. Krintus, M, Panteghini, M. Judging the clinical suitability of analytical performance of cardiac troponin assays. Clin Chem Lab Med 2023;61:801–10. https://doi.org/10.1515/cclm-2023-0027.Suche in Google Scholar PubMed

13. 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

14. Kavsak, PA, Clark, L, Arnoldo, S, Lou, A, Shea, JL, Eintracht, S, et al.. Analytic result variation for high-sensitivity cardiac troponin: interpretation and consequences. Can J Cardiol 2023;39:947–51. https://doi.org/10.1016/j.cjca.2023.04.013.Suche in Google Scholar PubMed

15. Li, Z, Tew, YY, Kavsak, PA, Aakre, KM, Jaffe, AS, Apple, FS, et al.. Impact of high-sensitivity cardiac troponin I assay imprecision on the safety of a single-sample rule-out approach for myocardial infarction. Clin Chem Lab Med 2025;63:e59–62. https://doi.org/10.1515/cclm-2024-1011.Suche in Google Scholar PubMed PubMed Central

16. Knoll, M, Daniels, LB, Mueller, C, Rösser, AFA, Kurtoic, D, Wahl, A, et al.. Analytical performance of a troponin T high-sensitivity gen 6 assay. Clin Chem Lab Med 2025;63:S473.Suche in Google Scholar

17. Ceriotti, F, Buoro, S, Pasotti, F. How clinical laboratories select and use analytical performance specifications (APS) in Italy. Clin Chem Lab Med 2024;62:1470–3. https://doi.org/10.1515/cclm-2023-1314.Suche in Google Scholar PubMed

18. Oosterhuis, WP. Analytical performance specifications in clinical chemistry: the holy grail? J Lab Precis Med 2017;2:78. https://doi.org/10.21037/jlpm.2017.09.02.Suche in Google Scholar

19. Ceriotti, F, Fernandez-Calle, P, Klee, GG, Nordin, G, Sandberg, S, Streichert, T, et al.. EFLM Task and Finish Group on Allocation of laboratory tests to different models for performance specifications (TFG-DM). Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference. Clin Chem Lab Med 2017;55:189–94. https://doi.org/10.1515/cclm-2016-0091.Suche in Google Scholar PubMed

20. Panteghini, M. Redesigning the surveillance of in vitro diagnostic medical devices and of medical laboratory performance by quality control in the traceability era. Clin Chem Lab Med 2023;61:759–68. https://doi.org/10.1515/cclm-2022-1257.Suche in Google Scholar PubMed

21. Panteghini, M, Krintus, M. Establishing, evaluating and monitoring analytical quality in the traceability era. Crit Rev Clin Lab Sci 2025;62:148–81. https://doi.org/10.1080/10408363.2024.2434562.Suche in Google Scholar PubMed

22. Braga, F, Panteghini, M. Performance specifications for measurement uncertainty of common biochemical measurands according to Milan models. Clin Chem Lab Med 2021;59:1362–8. https://doi.org/10.1515/cclm-2021-0170.Suche in Google Scholar PubMed

23. Braga, F, Pasqualetti, S, Borrillo, F, Capoferri, A, Chibireva, M, Rovegno, L, et al.. Definition and application of performance specifications for measurement uncertainty of 23 common laboratory tests: linking theory to daily practice. Clin Chem Lab Med 2023;61:213–23. https://doi.org/10.1515/cclm-2022-0806.Suche in Google Scholar PubMed

24. Çubukçu, HC, Vanstapel, F, Thelen, M, van Schrojenstein Lantman, M, Bernabeu-Andreu, FA, Meško Brguljan, P, et al.. APS calculator: a data-driven tool for setting outcome-based analytical performance specifications for measurement uncertainty using specific clinical requirements and population data. Clin Chem Lab Med 2024;62:597–607. https://doi.org/10.1515/cclm-2023-0740.Suche in Google Scholar PubMed

25. Jones, GRD, Bell, KJL, Ceriotti, F, Loh, TP, Lord, S, Sandberg, S, et al.. Applying the Milan models to setting analytical performance specifications – considering all the information. Clin Chem Lab Med 2024;62:1531–7. https://doi.org/10.1515/cclm-2024-0104.Suche in Google Scholar PubMed

26. Kaplan, LA. Determination and application of desirable analytical performance goals: the ISO/TC 212 approach. Scand J Clin Lab Invest 1999;59:479–82. https://doi.org/10.1080/00365519950185193.Suche in Google Scholar PubMed

27. Fraser, CG. The 1999 Stockholm consensus conference on quality specifications in laboratory medicine. Clin Chem Lab Med 2015;53:837–40. https://doi.org/10.1515/cclm-2014-0914.Suche in Google Scholar PubMed

Received: 2025-08-06
Accepted: 2025-09-17
Published Online: 2025-09-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 30.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cclm-2025-1014/html?lang=de
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