ArticleScientific reports2020
Kinetic modelling of myocardial necrosis biomarkers offers an easier, reliable and more acceptable assessment of infarct size.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Empagliflozin Effects in Patients with ST-Elevation Myocardial Infarction Undergoing Primary PCI: The EMI-STEMI Randomized Clinical Trial.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2024Trial
- Integrative Proteomic and Transcriptomic Profiling Identifies Candidate Biomarkers for Discriminating Anaphylactic from Cardiac Sudden Death.International journal of molecular sciences · 2026Article
- Cardiac MRI of differing ischemia and reperfusion times in a myocardial infarction pig model.Scientific reports · 2025Article
- Prediction of the Clinical Course of Immune Thrombocytopenia in Children by Platelet Kinetics.HemaSphere · 2023Article
- Rising and Falling High-Sensitivity Cardiac Troponin in Diagnostic Algorithms for Patients With Suspected Myocardial Infarction.Journal of the American Heart Association · 2023Article
- Levosimendan improves postoperative heart function recovery and prognosis in patients with heart disease.American journal of translational research · 2022Article
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11 authors.
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Abstract
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.
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