Key result
Three-sample Bayesian kinetic modeling estimates myocardial necrosis biomarker AUC in STEMI with ~96% accuracy.
Why the study?
Infarct size assessment in STEMI often requires repeated blood sampling to estimate biomarker AUC, prompting the development of limited sampling strategies to estimate AUC accurately using fewer samples.
Can limited sampling strategies accurately estimate myocardial necrosis biomarker AUC compared to full sampling profiles in STEMI patients?
Observational (n=284)
Yes
Can limited sampling strategies accurately estimate myocardial necrosis biomarker AUC compared to full sampling profiles in STEMI patients?
Kinetic modeling allows accurate estimation of infarct size using only 2-3 blood samples within 24 hours, significantly simplifying future cardioprotection studies in STEMI patients.
Answers are grounded in this paper and cite their sources.
May simplify infarct size estimation in STEMI with fewer samples; hypothesis-generating for cardioprotection trials.
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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Chadet et al. (2020) conducted an observational in ST-segment elevation myocardial infarction (STEMI) (n=284). Limited sampling strategies (LSS) using Bayesian kinetic modelling vs. Full kinetic sampling profiles (trapezoidal rule AUC) was evaluated on Biomarker area under the concentration versus time curve (AUC) estimation accuracy. Bayesian kinetic modelling using a 3-sample limited sampling strategy accurately estimated the area under the curve for myocardial necrosis biomarkers in STEMI patients, achieving an R2 greater than 96%.
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