Evidence map›Paper›PMID 32788683›Full record

ArticleScientific reports2020

Kinetic modelling of myocardial necrosis biomarkers offers an easier, reliable and more acceptable assessment of infarct size.

Stéphanie Chadet, David Ternant, François Roubille, Theodora Bejan-Angoulvant, Fabrice Prunier, Nathan Mewton, Gilles Paintaud, Michel Ovize, Anne Marie Dupuy, Denis Angoulvant and 1 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Stéphanie ChadetLoire Valley Cardiovascular Collaboration, Université de Tours, EA 4245 T2I & FHU SUPORT, Tours, France.
David TernantLoire Valley Cardiovascular Collaboration, Université de Tours, EA 4245 T2I & FHU SUPORT, Tours, France.
François RoubilleDepartment of Cardiology, PhyMedExp, Université de Montpellier, INSERM U1046, CNRS UMR 9214, CHU de Montpellier, Montpellier, France.
Theodora Bejan-AngoulvantLoire Valley Cardiovascular Collaboration, Université de Tours, EA 4245 T2I & FHU SUPORT, Tours, France.
Fabrice PrunierUniversité d'Angers, EA 3860 CRT, Angers, France.
Nathan MewtonUniversité Claude Bernard Lyon 1, INSERM U1060 CarMeN, Lyon, France.
Gilles PaintaudLoire Valley Cardiovascular Collaboration, Université de Tours, EA 4245 T2I & FHU SUPORT, Tours, France.
Michel OvizeUniversité Claude Bernard Lyon 1, INSERM U1060 CarMeN, Lyon, France.
Anne Marie DupuyDepartment of Cardiology, PhyMedExp, Université de Montpellier, INSERM U1046, CNRS UMR 9214, CHU de Montpellier, Montpellier, France.
Denis AngoulvantLoire Valley Cardiovascular Collaboration, Université de Tours, EA 4245 T2I & FHU SUPORT, Tours, France.
Fabrice IvanesLoire Valley Cardiovascular Collaboration, Université de Tours, EA 4245 T2I & FHU SUPORT, Tours, France. fabrice.ivanes@univ-tours.fr.ORCID http://orcid.org/0000-0003-2407-6387

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AgedArea Under CurveBiomarkersClinical Trials as TopicCreatine KinaseFemaleHumansKineticsMaleMiddle AgedMyocardiumNecrosisRetrospective StudiesST Elevation Myocardial InfarctionTroponin IBiomarkersCreatine KinaseTroponin I

Identifiers

PMID32788683
PMCPMC7423884

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.