Kinetic modelling of myocardial necrosis biomarkers offers an easier, reliable and more acceptable assessment of infarct size.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 08 2020
12 08 2020
Historique:
received:
27
03
2020
accepted:
28
07
2020
entrez:
14
8
2020
pubmed:
14
8
2020
medline:
12
1
2021
Statut:
epublish
Résumé
Infarct size is a major prognostic factor in ST-segment elevation myocardial infarction (STEMI). It is often assessed using repeated blood sampling and the estimation of biomarker area under the concentration versus time curve (AUC) in translational research. We aimed at developing limited sampling strategies (LSS) to accurately estimate biomarker AUC using only a limited number of blood samples in STEMI patients. This retrospective study was carried out on pooled data from five clinical trials of STEMI patients (TIMI blood flow 0/1) studies where repeated blood samples were collected within 72 h after admission to assess creatine kinase (CK), cardiac troponin I (cTnI) and muscle-brain CK (CK-MB). Biomarker kinetics was assessed using previously described biomarker kinetic models. A number of LSS models including combinations of 1 to 3 samples were developed to identify sampling times leading to the best estimation of AUC. Patients were randomly assigned to either learning (2/3) or validation (1/3) subsets. Descriptive and predictive performances of LSS models were compared using learning and validation subsets, respectively. An external validation cohort was used to validate the model and its applicability to different cTnI assays, including high-sensitive (hs) cTnI. 132 patients had full CK and cTnI dataset, 49 patients had CK-MB. For each biomarker, 180 LSS models were tested. Best LSS models were obtained for the following sampling times: T4-16 for CK, T8-T20 for cTnI and T8-T16 for CK-MB for 2-sample LSS; and T4-T16-T24 for CK, T4-T12-T20 for cTnI and T8-T16-T20 for CK-MB for 3-sample LSS. External validation was achieved on 103 anterior STEMI patients (TIMI flow 0/1), and the cTnI model applicability to recommended hs cTnI confirmed. Biomarker kinetics can be assessed with a limited number of samples using kinetic modelling. This opens the way for substantial simplification of future cardioprotection studies, more acceptable for the patients.
Identifiants
pubmed: 32788683
doi: 10.1038/s41598-020-70501-4
pii: 10.1038/s41598-020-70501-4
pmc: PMC7423884
doi:
Substances chimiques
Biomarkers
0
Troponin I
0
Creatine Kinase
EC 2.7.3.2
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
13597Références
Thompson, P. L., Fletcher, E. E. & Katavatis, V. Enzymatic indices of myocardial necrosis: Influence on short- and long-term prognosis after myocardial infarction. Circulation59, 113–119. https://doi.org/10.1161/01.cir.59.1.113 (1979).
doi: 10.1161/01.cir.59.1.113
pubmed: 758103
Braunwald, E. Myocardial reperfusion, limitation of infarct size, reduction of left ventricular dysfunction, and improved survival. Should the paradigm be expanded?. Circulation79, 441–444. https://doi.org/10.1161/01.cir.79.2.441 (1989).
doi: 10.1161/01.cir.79.2.441
pubmed: 2914356
Hausenloy, D. J. et al. Targeting reperfusion injury in patients with ST-segment elevation myocardial infarction: Trials and tribulations. Eur. Heart J.38, 935–941. https://doi.org/10.1093/eurheartj/ehw145 (2017).
doi: 10.1093/eurheartj/ehw145
pubmed: 27118196
Heusch, G. & Gersh, B. J. The pathophysiology of acute myocardial infarction and strategies of protection beyond reperfusion: A continual challenge. Eur. Heart J.38, 774–784. https://doi.org/10.1093/eurheartj/ehw224 (2017).
doi: 10.1093/eurheartj/ehw224
pubmed: 27354052
Heusch, G. & Rassaf, T. Time to give up on cardioprotection? A critical appraisal of clinical studies on ischemic pre-, post-, and remote conditioning. Circ. Res.119, 676–695. https://doi.org/10.1161/CIRCRESAHA.116.308736 (2016).
doi: 10.1161/CIRCRESAHA.116.308736
pubmed: 27539973
Bøtker, H. E. et al. Practical guidelines for rigor and reproducibility in preclinical and clinical studies on cardioprotection. Basic Res. Cardiol.113, 39. https://doi.org/10.1007/s00395-018-0696-8 (2018).
doi: 10.1007/s00395-018-0696-8
pubmed: 30120595
pmcid: 6105267
O’Gara, P. T. et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: Executive summary: A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation127, 529–555. https://doi.org/10.1161/CIR.0b013e3182742c84 (2013).
doi: 10.1161/CIR.0b013e3182742c84
pubmed: 23247303
Thygesen, K. et al. Fourth universal definition of myocardial infarction (2018). Eur. Heart J.40, 237–269. https://doi.org/10.1093/eurheartj/ehy462 (2019).
doi: 10.1093/eurheartj/ehy462
Vatner, S. F., Baig, H., Manders, W. T. & Maroko, P. R. Effects of coronary artery reperfusion on myocardial infarct size calculated from creatine kinase. J. Clin. Investig.61, 1048–1056. https://doi.org/10.1172/JCI109004 (1978).
doi: 10.1172/JCI109004
pubmed: 659577
Nordlander, R., Nyquist, O. & Sylvén, C. Estimation of infarct size by creatine kinase. A comparison between maximal value, planimetry and computer calculation. Cardiology68, 201–205. https://doi.org/10.1159/000173283 (1981).
doi: 10.1159/000173283
pubmed: 7326667
Gibbons, R. J., Valeti, U. S., Araoz, P. A. & Jaffe, A. S. The quantification of infarct size. J. Am. Coll. Cardiol.44, 1533–1542. https://doi.org/10.1016/j.jacc.2004.06.071 (2004).
doi: 10.1016/j.jacc.2004.06.071
pubmed: 15489082
Roberts, R., Henry, P. D. & Sobel, B. E. An improved basis for enzymatic estimation of infarct size. Circulation52, 743–754. https://doi.org/10.1161/01.cir.52.5.743 (1975).
doi: 10.1161/01.cir.52.5.743
pubmed: 1236776
Staat, P. et al. Postconditioning the human heart. Circulation112, 2143–2148. https://doi.org/10.1161/CIRCULATIONAHA.105.558122 (2005).
doi: 10.1161/CIRCULATIONAHA.105.558122
pubmed: 16186417
Prunier, F. et al. The RIPOST-MI study, assessing remote ischemic perconditioning alone or in combination with local ischemic postconditioning in ST-segment elevation myocardial infarction. Basic Res. Cardiol.109, 400. https://doi.org/10.1007/s00395-013-0400-y (2014).
doi: 10.1007/s00395-013-0400-y
pubmed: 24407359
Ternant, D. et al. Revisiting myocardial necrosis biomarkers: Assessment of the effect of conditioning therapies on infarct size by kinetic modelling. Sci. Rep.7, 10709. https://doi.org/10.1038/s41598-017-11352-4 (2017).
doi: 10.1038/s41598-017-11352-4
pubmed: 28878319
pmcid: 5587689
Piot, C. et al. Effect of cyclosporine on reperfusion injury in acute myocardial infarction. N. Engl. J. Med.359, 473–481. https://doi.org/10.1056/NEJMoa071142 (2008).
doi: 10.1056/NEJMoa071142
pubmed: 18669426
Thibault, H. et al. Long-term benefit of postconditioning. Circulation117, 1037–1044. https://doi.org/10.1161/CIRCULATIONAHA.107.729780 (2008).
doi: 10.1161/CIRCULATIONAHA.107.729780
pubmed: 18268150
Thuny, F. et al. Post-conditioning reduces infarct size and edema in patients with ST-segment elevation myocardial infarction. J. Am. Coll. Cardiol.59, 2175–2181. https://doi.org/10.1016/j.jacc.2012.03.026 (2012).
doi: 10.1016/j.jacc.2012.03.026
pubmed: 22676937
Mewton, N. et al. Postconditioning attenuates no-reflow in STEMI patients. Basic Res. Cardiol.108, 383. https://doi.org/10.1007/s00395-013-0383-8 (2013).
doi: 10.1007/s00395-013-0383-8
pubmed: 24022373
Sheiner, L. B. & Beal, S. L. Bayesian individualization of pharmacokinetics: Simple implementation and comparison with non-Bayesian methods. J. Pharm. Sci.71, 1344–1348. https://doi.org/10.1002/jps.2600711209 (1982).
doi: 10.1002/jps.2600711209
pubmed: 7153881
Prémaud, A. et al. Maximum a posteriori bayesian estimation of mycophenolic acid pharmacokinetics in renal transplant recipients at different postgrafting periods. Ther Drug Monit27, 354–361. https://doi.org/10.1097/01.ftd.0000162231.90811.38 (2005).
doi: 10.1097/01.ftd.0000162231.90811.38
pubmed: 15905807
Woillard, J.-B. et al. Mycophenolic mofetil optimized pharmacokinetic modelling, and exposure-effect associations in adult heart transplant recipients. Pharmacol. Res.99, 308–315. https://doi.org/10.1016/j.phrs.2015.07.012 (2015).
doi: 10.1016/j.phrs.2015.07.012
pubmed: 26192348
Zhao, W. et al. Population pharmacokinetics and Bayesian estimator of mycophenolic acid in children with idiopathic nephrotic syndrome. Br. J. Clin. Pharmacol.69, 358–366. https://doi.org/10.1111/j.1365-2125.2010.03615.x (2010).
doi: 10.1111/j.1365-2125.2010.03615.x
pubmed: 20406220
pmcid: 2848409
Laugaudin, G. et al. Kinetics of high-sensitivity cardiac troponin T and I differ in patients with ST-segment elevation myocardial infarction treated by primary coronary intervention. Eur. Heart J. Acute Cardiovasc. Care5, 354–363. https://doi.org/10.1177/2048872615585518 (2016).
doi: 10.1177/2048872615585518
pubmed: 25943557
de Vries Schultink, A. H. M. et al. Pharmacodynamic modeling of cardiac biomarkers in breast cancer patients treated with anthracycline and trastuzumab regimens. J. Pharmacokinet. Pharmacodyn.45, 431–442. https://doi.org/10.1007/s10928-018-9579-8 (2018).
doi: 10.1007/s10928-018-9579-8
pubmed: 29429038
pmcid: 5953989
Duffull, S. B., Wright, D. F. B. & Winter, H. R. Interpreting population pharmacokinetic-pharmacodynamic analyses—A clinical viewpoint. Br. J. Clin. Pharmacol.71, 807–814. https://doi.org/10.1111/j.1365-2125.2010.03891.x (2011).
doi: 10.1111/j.1365-2125.2010.03891.x
pubmed: 21204908
pmcid: 3099367
Mould, D. R. & Upton, R. N. Basic concepts in population modeling, simulation, and model-based drug development. CPT Pharmacometr. Syst. Pharmacol.1, e6. https://doi.org/10.1038/psp.2012.4 (2012).
doi: 10.1038/psp.2012.4
Mould, D. R. & Upton, R. N. Basic concepts in population modeling, simulation, and model-based drug development-part 2: Introduction to pharmacokinetic modeling methods. CPT Pharmacometr. Syst. Pharmacol.2, e38. https://doi.org/10.1038/psp.2013.14 (2013).
doi: 10.1038/psp.2013.14
Sheiner, L. B., Rosenberg, B. & Marathe, V. V. Estimation of population characteristics of pharmacokinetic parameters from routine clinical data. J. Pharmacokinet. Biopharm.5, 445–479. https://doi.org/10.1007/BF01061728 (1977).
doi: 10.1007/BF01061728
pubmed: 925881
Puymirat, E. et al. Acute myocardial infarction: Changes in patient characteristics, management, and 6-month outcomes over a period of 20 years in the FAST-MI program (French Registry of acute ST-elevation or Non-ST-elevation myocardial infarction) 1995 to 2015. Circulation136, 1908–1919. https://doi.org/10.1161/CIRCULATIONAHA.117.030798 (2017).
doi: 10.1161/CIRCULATIONAHA.117.030798
pubmed: 28844989
Rogers, W. J. et al. Correlation of angiographic estimates of myocardial infarct size and accumulated release of creatine kinase MB isoenzyme in man. Circulation56, 199–205. https://doi.org/10.1161/01.cir.56.2.199 (1977).
doi: 10.1161/01.cir.56.2.199
pubmed: 872311
Rigaud, M. et al. Regional left ventricular function assessed by contrast angiography in acute myocardial infarction. Circulation60, 130–139. https://doi.org/10.1161/01.cir.60.1.130 (1979).
doi: 10.1161/01.cir.60.1.130
pubmed: 445715
Lapeyre, A. C., St Gibson, W., Bashore, T. M. & Gibbons, R. J. Quantitative regional wall motion analysis with early contrast ventriculography for the assessment of myocardium at risk in acute myocardial infarction. Am. Heart J.145, 1051–1057. https://doi.org/10.1016/S0002-8703(03)00112-1 (2003).
doi: 10.1016/S0002-8703(03)00112-1
pubmed: 12796762
Feild, B. J., Russell, R. O., Dowling, J. T. & Rackley, C. E. Regional left ventricular performance in the year following myocardial infarction. Circulation46, 679–689. https://doi.org/10.1161/01.cir.46.4.679 (1972).
doi: 10.1161/01.cir.46.4.679
pubmed: 5072769
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2014). https://www.R-project.org .