Abstract
Objectives
Confounding factors, including sex, age, and renal dysfunction, affect high-sensitivity cardiac troponin T (hs-cTnT) concentrations and the acute myocardial infarction (AMI) diagnosis. This study assessed the effects of these confounders through logistic regression models and evaluated the diagnostic performance of an optimized, integrated prediction model.
Methods
This retrospective study included a primary derivation cohort of 18,022 emergency department (ED) patients at a US medical center and a validation cohort of 890 ED patients at a Canadian medical center. Hs-cTnT was measured with 0/3 h sampling. The primary outcome was index AMI diagnosis. Logistic regression models were optimized to predict AMI using delta hs-cTnT and its confounders as covariates. The diagnostic performance of model cutoffs was compared to that of the hs-cTnT delta thresholds. Serial logistic regressions were carried out to evaluate the relationship between covariates.
Results
The area under the curve of the best-fitted model was 0.95. The model achieved a 90.0% diagnostic accuracy in the validation cohort. The optimal model cutoff yielded comparable performance (90.5% accuracy) to the optimal sex-specific delta thresholds (90.3% accuracy), with 95.8% agreement between the two diagnostic methods. Serial logistic regressions revealed that delta hs-cTnT played a more predominant role in AMI prediction than its confounders, among which sex is more predictive of AMI (total effect coefficient 1.04) than age (total effect coefficient 0.05) and eGFR (total effect coefficient −0.008).
Conclusions
The integrated prediction model incorporating confounding factors does not outperform hs-cTnT delta thresholds. Sex-specific hs-cTnT delta thresholds remain to provide the highest diagnostic accuracy.
-
Research funding: This work was partially supported by the United States National Institute of Environmental Health Sciences at the National Institutes of Health (Grant T32ES007271). The study sponsors had no role in the study design; in the collection, analysis, or interpretation of the data; in the writing of the report; or in the decision to submit the article for publication.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Peter Kavsak has received grants and provision of study materials from Roche Diagnostics, Abbott Laboratories, Beckman Coulter, Ortho Clinical Diagnostics, Randox Laboratories, and Siemens Healthcare Diagnostics. He has received consulting fees or honoraria from Abbott Laboratories, Beckman Coulter, Quidel, Roche Diangostics, Siemens Healthcare Diagnostics, and Thermo Fisher Scientific. He has received support for attending meetings from Randox Laboratories. Peter Kavsak and Andrew Worster have a pending patent application filed by McMaster University as inventors in the acute cardiovascular biomarker field. Andrew Worster has received research grant from the Canadian Institute of Health Research. Xueya Cai has received research grant from the National Institutes of Health. No other financial relationships were declared.
-
Informed consent: Not applicable.
-
Ethical approval: This retrospective study consisted of a primary cohort approved by the University of Rochester Institutional Review Board and an external validation cohort with previous approval by the Hamilton Integrated Research Ethics Board.
-
Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
1. Thygesen, K, Alpert, JS, Jaffe, AS, Chaitman, BR, Bax, JJ, Morrow, DA, et al.. Executive Group on behalf of the Joint European Society of Cardiology (ESC)/American College of Cardiology (ACC)/American Heart Association (AHA)/World Heart Federation (WHF) Task Force for the Universal Definition of Myocardial Infarction. Fourth universal definition of myocardial infarction (2018). Circulation 2018;138:e618–51. Erratum in: Circulation 2018;138:e652. https://doi.org/10.1016/j.jacc.2018.08.1038.Search in Google Scholar PubMed
2. 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.Search in Google Scholar PubMed
3. Collinson, PO, Heung, YM, Gaze, D, Boa, F, Senior, R, Christenson, R, et al.. Influence of population selection on the 99th percentile reference value for cardiac troponin assays. Clin Chem 2012;58:219–25. https://doi.org/10.1373/clinchem.2011.171082.Search in Google Scholar PubMed
4. Gore, MO, Seliger, SL, Defilippi, CR, Nambi, V, Christenson, RH, Hashim, IA, et al.. Age- and sex-dependent upper reference limits for the high-sensitivity cardiac troponin T assay. J Am Coll Cardiol 2014;63:1441–8. https://doi.org/10.1016/j.jacc.2013.12.032.Search in Google Scholar PubMed PubMed Central
5. de Lemos, JA, Drazner, MH, Omland, T, Ayers, CR, Khera, A, Rohatgi, A, et al.. Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population. JAMA 2010;304:2503–12. https://doi.org/10.1001/jama.2010.1768.Search in Google Scholar PubMed PubMed Central
6. Normann, J, Mueller, M, Biener, M, Vafaie, M, Katus, HA, Giannitsis, E. Effect of older age on diagnostic and prognostic performance of high-sensitivity troponin T in patients presenting to an emergency department. Am Heart J 2012;164:698–705.e4. https://doi.org/10.1016/j.ahj.2012.08.003.Search in Google Scholar PubMed
7. deFilippi, CR, Herzog, CA. Interpreting cardiac biomarkers in the setting of chronic kidney disease. Clin Chem 2017;63:59–65. https://doi.org/10.1373/clinchem.2016.254748.Search in Google Scholar PubMed
8. deFilippi, C, Seliger, SL, Kelley, W, Duh, SH, Hise, M, Christenson, RH, et al.. Interpreting cardiac troponin results from high-sensitivity assays in chronic kidney disease without acute coronary syndrome. Clin Chem 2012;58:1342–51. https://doi.org/10.1373/clinchem.2012.185322.Search in Google Scholar PubMed
9. Apple, FS, Ler, R, Murakami, MM. Determination of 19 cardiac troponin I and T assay 99th percentile values from a common presumably healthy population. Clin Chem 2012;58:1574–81. https://doi.org/10.1373/clinchem.2012.192716.Search in Google Scholar PubMed
10. Krintus, M, Kozinski, M, Boudry, P, Lackner, K, Lefèvre, G, Lennartz, L, et al.. Defining normality in a European multinational cohort: critical factors influencing the 99th percentile upper reference limit for high sensitivity cardiac troponin I. Int J Cardiol 2015;187:256–63. https://doi.org/10.1016/j.ijcard.2015.03.282.Search in Google Scholar PubMed
11. Zeller, T, Ojeda, F, Brunner, FJ, Peitsmeyer, P, Münzel, T, Binder, H, et al.. High-sensitivity cardiac troponin I in the general population-defining reference populations for the determination of the 99th percentile in the Gutenberg health study. Clin Chem Lab Med 2015;53:699–706. https://doi.org/10.1515/cclm-2014-0619.Search in Google Scholar PubMed
12. Kimenai, DM, Henry, RM, van der Kallen, CJ, Dagnelie, PC, Schram, MT, Stehouwer, CD, et al.. Direct comparison of clinical decision limits for cardiac troponin T and I. Heart 2016;102:610–6. https://doi.org/10.1136/heartjnl-2015-308917.Search in Google Scholar PubMed
13. Rubini Giménez, M, Twerenbold, R, Boeddinghaus, J, Nestelberger, T, Puelacher, C, Hillinger, P, et al.. Clinical effect of sex-specific cutoff values of high-sensitivity cardiac troponin T in suspected myocardial infarction. JAMA Cardiol 2016;1:912–20. https://doi.org/10.1001/jamacardio.2016.2882.Search in Google Scholar PubMed
14. Eggers, KM, Jernberg, T, Lindahl, B. Prognostic importance of sex-specific cardiac troponin T 99th percentiles in suspected acute coronary syndrome. Am J Med 2016;129:880.e1–12. https://doi.org/10.1016/j.amjmed.2016.02.047.Search in Google Scholar PubMed
15. Yang, S, Huai, W, Qiao, R, Cui, L, Liu, G, Wu, J, et al.. Age and gender tailored cutoff value of hs-cTnT contributes to rapidly diagnose acute myocardial infarction in chest pain patients. Clin Lab 2016;62:1451–9. https://doi.org/10.7754/Clin.Lab.2016.151201.Search in Google Scholar PubMed
16. Cullen, L, Greenslade, JH, Carlton, EW, Than, M, Pickering, JW, Ho, A, et al.. Sex-specific versus overall cut points for a high sensitivity troponin I assay in predicting 1-year outcomes in emergency patients presenting with chest pain. Heart 2016;102:120–6. https://doi.org/10.1136/heartjnl-2015-308506.Search in Google Scholar PubMed
17. Kaur, S. High sensitivity cardiac troponin and the under-diagnosis of myocardial infarction in women: prospective cohort study. Ann Clin Biochem 2015;52:622–9. https://doi.org/10.1177/0004563215597105.Search in Google Scholar PubMed
18. Giannitsis, E. Counterpoint: potential concerns regarding the use of sex-specific cutpoints for high-sensitivity troponin assays. Clin Chem 2017;63:264–6. https://doi.org/10.1373/clinchem.2016.254680.Search in Google Scholar PubMed
19. Mueller, C, Kavsak, PA. Sex-specific cutoffs for cardiac troponin using high-sensitivity assays-Is there clinical equipoise? Clin Biochem 2015;48:749–50. https://doi.org/10.1016/j.clinbiochem.2015.07.005.Search in Google Scholar PubMed
20. Zhao, Y, Izadnegahdar, M, Lee, MK, Kavsak, PA, Singer, J, Scheuermeyer, F, et al.. High-sensitivity cardiac troponin-optimizing the diagnosis of acute myocardial infarction/injury in women (CODE-MI): rationale and design for a multicenter, stepped-wedge, cluster-randomized trial. Am Heart J 2020;229:18–28. https://doi.org/10.1016/j.ahj.2020.06.013.Search in Google Scholar PubMed
21. Shah, ASV, Anand, A, Strachan, FE, Ferry, AV, Lee, KK, Chapman, AR, et al.. High-STEACS Investigators. High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial. Lancet 2018;392:919–28. https://doi.org/10.1016/S0140-6736(18)31923-8.Search in Google Scholar PubMed PubMed Central
22. Mueller, T, Egger, M, Peer, E, Dieplinger, B. 5th generation cardiac troponin I and T assays in clinical routine – a head-to-head comparison with data from the Linz troponin (LITROP) study. Clin Chim Acta 2018;485:195–204. https://doi.org/10.1016/j.cca.2018.06.027.Search in Google Scholar PubMed
23. Liu, L, Consagra, W, Cai, X, Mathias, A, Worster, A, Ma, J, et al.. Sex-specific absolute delta thresholds for high-sensitivity cardiac troponin T assay. Clin Chem 2022;68:441–9. https://doi.org/10.1093/clinchem/hvab230.Search in Google Scholar PubMed
24. McRae, A, Graham, M, Abedin, T, Ji, Y, Yang, H, Wang, D, et al.. Sex-specific, high-sensitivity cardiac troponin T cutoff concentrations for ruling out acute myocardial infarction with a single measurement. CJEM 2019;21:26–33. https://doi.org/10.1017/cem.2018.435.Search in Google Scholar PubMed
25. Boeddinghaus, J, Nestelberger, T, Twerenbold, R, Neumann, JT, Lindahl, B, Giannitsis, E, et al.. APACE, BACC, and TRAPID-AMI Investigators. Impact of age on the performance of the ESC 0/1h-algorithms for early diagnosis of myocardial infarction. Eur Heart J 2018;39:3780–94. https://doi.org/10.1093/eurheartj/ehy514.Search in Google Scholar PubMed
26. Mueller-Hennessen, M, Lindahl, B, Giannitsis, E, Biener, M, Vafaie, M, deFilippi, CR, et al.. TRAPID-AMI Investigators. Diagnostic and prognostic implications using age- and gender-specific cutoffs for high-sensitivity cardiac troponin T – sub-analysis from the TRAPID-AMI study. Int J Cardiol 2016;209:26–33. https://doi.org/10.1016/j.ijcard.2016.01.213.Search in Google Scholar PubMed
27. Parikh, RH, Seliger, SL, deFilippi, CR. Use and interpretation of high sensitivity cardiac troponins in patients with chronic kidney disease with and without acute myocardial infarction. Clin Biochem 2015;48:247–53. https://doi.org/10.1016/j.clinbiochem.2015.01.004.Search in Google Scholar PubMed
28. Miller-Hodges, E, Anand, A, Shah, ASV, Chapman, AR, Gallacher, P, Lee, KK, et al.. High-Sensitivity cardiac troponin and the risk stratification of patients with renal impairment presenting with suspected acute coronary syndrome. Circulation 2018;137:425–35. https://doi.org/10.1161/circulationaha.117.030320.Search in Google Scholar
29. Neumann, JT, Twerenbold, R, Ojeda, F, Sörensen, NA, Chapman, AR, Shah, ASV, et al.. Application of high-sensitivity troponin in suspected myocardial infarction. N Engl J Med 2019;380:2529–40. https://doi.org/10.1056/NEJMoa1803377.Search in Google Scholar PubMed
30. Kavsak, PA, Worster, A, Ma, J, Shortt, C, Clayton, N, Sherbino, J, et al.. High-sensitivity cardiac troponin risk cutoffs for acute cardiac outcomes at emergency department presentation. Can J Cardiol 2017;33:898–903. https://doi.org/10.1016/j.cjca.2017.04.011.Search in Google Scholar PubMed
31. Kavsak, PA, Neumann, JT, Cullen, L, Than, M, Shortt, C, Greenslade, JH, et al.. Clinical chemistry score versus high-sensitivity cardiac troponin I and T tests alone to identify patients at low or high risk for myocardial infarction or death at presentation to the emergency department. CMAJ 2018;190:E974–84. https://doi.org/10.1503/cmaj.180144.Search in Google Scholar PubMed PubMed Central
32. Kavsak, PA, Andruchow, JE, McRae, AD, Worster, A. Profile of Roche’s Elecsys Troponin T Gen 5 STAT blood test (a high-sensitivity cardiac troponin assay) for diagnosing myocardial infarction in the emergency department. Expert Rev Mol Diagn 2018;18:481–9. https://doi.org/10.1080/14737159.2018.1476141.Search in Google Scholar PubMed
33. Shortt, C, Ma, J, Clayton, N, Sherbino, J, Whitlock, R, Pare, G, et al.. Rule-in and rule-out of myocardial infarction using cardiac troponin and glycemic biomarkers in patients with symptoms suggestive of acute coronary syndrome. Clin Chem 2017;63:403–14. https://doi.org/10.1373/clinchem.2016.261545.Search in Google Scholar PubMed
34. Lloyd, CJ. Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems. J Am Stat Assoc 1998;93:1356–64. https://doi.org/10.1080/01621459.1998.10473797.Search in Google Scholar
35. Youden, WJ. Index for rating diagnostic tests. Cancer 1950;3:32–5. https://doi.org/10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3.10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3Search in Google Scholar
36. Than, MP, Pickering, JW, Sandoval, Y, Shah, ASV, Tsanas, A, Apple, FS, et al.. MI3 Collaborative. Machine learning to predict the likelihood of acute myocardial infarction. Circulation 2019;140:899–909. https://doi.org/10.1161/circulationaha.119.041980.Search in Google Scholar
37. Kavsak, PA, Cerasuolo, JO, Ko, DT, Ma, J, Sherbino, J, Mondoux, SE, et al.. Using the clinical chemistry score in the emergency department to detect adverse cardiac events: a diagnostic accuracy study. CMAJ Open 2020;8:E676–84. https://doi.org/10.9778/cmajo.20200047.Search in Google Scholar
38. Gwet, KL. Computing inter-rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol 2008;61:29–48. https://doi.org/10.1348/000711006X126600.Search in Google Scholar
39. Reichlin, T, Schindler, C, Drexler, B, Twerenbold, R, Reiter, M, Zellweger, C, et al.. One-hour rule-out and rule-in of acute myocardial infarction using high-sensitivity cardiac troponin T. Arch Intern Med 2012;172:1211–8. https://doi.org/10.1001/archinternmed.2012.3698.Search in Google Scholar
40. Pickering, JW, Greenslade, JH, Cullen, L, Flaws, D, Parsonage, W, George, P, et al.. Validation of presentation and 3 h high-sensitivity troponin to rule-in and rule-out acute myocardial infarction. Heart 2016;102:1270–8. https://doi.org/10.1136/heartjnl-2015-308505.Search in Google Scholar
41. Reichlin, T, Irfan, A, Twerenbold, R, Reiter, M, Hochholzer, W, Burkhalter, H, et al.. Utility of absolute and relative changes in cardiac troponin concentrations in the early diagnosis of acute myocardial infarction. Circulation 2011;124:136–45. https://doi.org/10.1161/circulationaha.111.023937.Search in Google Scholar PubMed
42. Mueller, M, Biener, M, Vafaie, M, Doerr, S, Keller, T, Blankenberg, S, et al.. Absolute and relative kinetic changes of high-sensitivity cardiac troponin T in acute coronary syndrome and in patients with increased troponin in the absence of acute coronary syndrome. Clin Chem 2012;58:209–18. https://doi.org/10.1373/clinchem.2011.171827.Search in Google Scholar PubMed
43. Jankowski, J, Floege, J, Fliser, D, Böhm, M, Marx, N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation 2021;143:1157–72. https://doi.org/10.1161/circulationaha.120.050686.Search in Google Scholar PubMed PubMed Central
44. Foley, RN, Parfrey, PS, Sarnak, MJ. Clinical epidemiology of cardiovascular disease in chronic renal disease. Am J Kidney Dis 1998;32:S112–9. https://doi.org/10.1053/ajkd.1998.v32.pm9820470.Search in Google Scholar PubMed
45. QuickStats. Percentage of emergency department (ED) visits made by patients with chronic kidney disease among persons aged ≥18 years, by race/ethnicity and sex — national Hospital Ambulatory Medical Care Survey, 2015–2016. MMWR Morb Mortal Wkly Rep 2019;68:23.10.15585/mmwr.mm6801a7Search in Google Scholar PubMed
46. Masina, J, Moolla, M, Motara, F, Kalla, IS, Laher, AE. Clinical profile of adult patients presenting with renal dysfunction to a tertiary hospital emergency department. Cureus 2022;14:e21873. https://doi.org/10.7759/cureus.21873.Search in Google Scholar PubMed PubMed Central
© 2023 Walter de Gruyter GmbH, Berlin/Boston
