|
1000
|
Abstract/Summary
|
-
<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>The accurate identification of patients with high cardiovascular risk in suspected myocardial infarction (MI) is an unmet clinical need. Therefore, we sought to investigate the prognostic utility of a multi-biomarker panel with 29 different biomarkers in in 748 consecutive patients with symptoms indicative of MI using a machine learning-based approach.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>Incident major cardiovascular events (MACE) were documented within 1 year after the index admission. The selection of the best multi-biomarker model was performed using the least absolute shrinkage and selection operator (LASSO). The independent and additive utility of selected biomarkers was compared to a clinical reference model and the Global Registry of Acute Coronary Events (GRACE) Score, respectively. Findings were validated using internal cross-validation.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>Median age of the study population was 64 years. At 1 year of follow-up, 160 cases of incident MACE were documented. 16 of the investigated 29 biomarkers were significantly associated with 1-year MACE. Three biomarkers including NT-proBNP (HR per SD 1.24), Apolipoprotein A-I (Apo A-I; HR per SD 0.98) and kidney injury molecule-1 (KIM-1; HR per SD 1.06) were identified as independent predictors of 1-year MACE. Although the discriminative ability of the selected multi-biomarker model was rather moderate, the addition of these biomarkers to the clinical reference model and the GRACE score improved model performances markedly (∆C-index 0.047 and 0.04, respectively).</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusion</jats:title>
<jats:p>NT-proBNP, Apo A-I and KIM-1 emerged as strongest independent predictors of 1-year MACE in patients with suspected MI. Their integration into clinical risk prediction models may improve personalized risk stratification.</jats:p>
</jats:sec><jats:sec>
<jats:title>Graphical abstract</jats:title>
<jats:p>Prognostic utility of a multi-biomarker approach in suspected myocardial infarction. In a cohort of 748 patients with symptoms indicative of myocardial infarction (MI) to the emergency department, we measured a 29-biomarker panel and performed regressions, machine learning (ML)-based variable selection and discriminative/reclassification analyses. We identified three biomarkers as top predictors for 1-year major adverse cardiovascular events (MACE). Their integration into a clinical risk prediction model and the Global Registry of Acute Coronary Events (GRACE) Score allowed for marked improvement in discrimination and reclassification for 1-year MACE. <jats:italic>Apo</jats:italic> apolipoprotein; <jats:italic>CRP</jats:italic> C-reactive protein; <jats:italic>CRS</jats:italic> clinical risk score; <jats:italic>ECG</jats:italic> electrocardiogram; <jats:italic>EN-RAGE</jats:italic> extracellular newly identified receptor for advanced glycation end-products binding protein; <jats:italic>FABP</jats:italic> fatty acid–binding protein; <jats:italic>GS</jats:italic> Grace Score; <jats:italic>hs-cTnI</jats:italic> high-sensitivity cardiac troponin I; <jats:italic>KIM-1</jats:italic> kidney injury molecule–1; <jats:italic>LASSO</jats:italic> least absolute shrinkage and selection operator; <jats:italic>MACE</jats:italic> major adverse cardiovascular events; <jats:italic>MI</jats:italic> myocardial infarction; <jats:italic>NRI</jats:italic> net reclassification improvement; <jats:italic>NT-proBNP</jats:italic> N-terminal prohormone of brain natriuretic peptide.</jats:p>
</jats:sec>
|
|
1000
|
Hinweis
|
-
DeepGreen-ID: 3d0e5ac7501c4e26a526985e919a33d4 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
|
