Prognostic utility of a multi-biomarker panel in patients with suspected myocardial infarction

BackgroundThe 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.MethodsIncident 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.ResultsMedian 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 ( increment C-index 0.047 and 0.04, respectively).ConclusionNT-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.Graphical abstractPrognostic 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. Apo apolipoprotein; CRP C-reactive protein; CRS clinical risk score; ECG electrocardiogram; EN-RAGE extracellular newly identified receptor for advanced glycation end-products binding protein; FABP fatty acid-binding protein; GS Grace Score; hs-cTnI high-sensitivity cardiac troponin I; KIM-1 kidney injury molecule-1; LASSO least absolute shrinkage and selection operator; MACE major adverse cardiovascular events; MI myocardial infarction; NRI net reclassification improvement; NT-proBNP N-terminal prohormone of brain natriuretic peptide.