Clin Res Cardiol (2023). https://doi.org/10.1007/s00392-023-02180-w

Prognostic utility of a multibiomarker panel in a contemporary cohort of patients with suspected myocardial infarction

B. Toprak1, J. Weimann1, J. Lehmacher1, T. Hartikainen2, P. Haller1, A. Schock1, M. Karakas3, T. Renné4, T. Zeller1, D. Westermann2, S. Blankenberg1, R. Twerenbold1, N. A. Sörensen1, J. T. Neumann1, für die Studiengruppe: BACC

1Klinik für Kardiologie, Universitäres Herz- und Gefäßzentrum Hamburg, Hamburg; 2Klinik für Kardiologie und Angiologie, Universitäts-Herzzentrum Freiburg / Bad Krozingen, Bad Krozingen; 3Klinik für Intensivmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg; 4Institut für Klinische Chemie und Laboratoriumsmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg;

Introduction: Rapid and accurate evaluation of patients presenting with suspected myocardial infarction (MI) to the emergency department (ED) is crucial. Despite the routine use of circulating biomarkers, among which high-sensitivity cardiac troponin currently dictates diagnostic protocols in chest pain patients, it still remains unclear which biomarkers are of highest utility for prognostic purposes in this patient collective. Purpose: Therefore, we sought to investigate the prognostic utility of a multibiomarker panel with 29 different biomarkers in a contemporary cohort of patients with suspected MI by using a machine learning based approach. Methods: The multibiomarker panel was measured in stored blood samples that were collected directly at admission from 748 prospectively enrolled patients who presented to the ED of a German tertiary care center with symptoms suggestive of MI between 2013 and 2017. Biomarker values were log transformed to achieve a near-normal distribution. Incident major cardiovascular events (MACE) were subsumed as the composite of cardiovascular death, non-fatal MI (excluding index events), revascularization and cardiac rehospitalization within 1 year after admission. The selection of the best multimarker model, adjusted for age and sex, was performed using Least Absolute Shrinkage and Selection Operator (LASSO) with 5-fold cross-validation.The independent and additive utility of LASSO-selected biomarkers was compared to a clinical reference model (including age, sex, systolic blood pressure, hyperlipoproteinemia, current smoker and diabetes status) and the Global Registry of Acute Coronary Events (GRACE) Score, respectively. Results: Out of 748 patients with available multibiomarker panel, 138 (18.5%) were diagnosed with MI. Median age at admission was 64 (interquartile range [IQR] 50-75) years in the overall cohort, 63.1% were male. 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, amongst which NT-proBNP was the strongest predictor (hazard ratio [HR] per standard deviation [SD] 1.74, 95% confidence interval [CI] 1.44-2.09, p<0.0001; Figure 1). Using LASSO, three biomarkers including NT-proBNP (HR per SD 1.24), Apo A-I (HR per SD 0.98) and with KIM-1 (HR per SD 1.06) were identified as independent predictors of 1-year MACE. The discriminative ability of the LASSO-selected multibiomarker model (bootstrap-corrected C-index 0.63) was rather moderate. Adding the selected biomarkers to the clinical reference model (C-index 0.617) and the GRACE Score (C-index 0.629), however, led to a marked improvement of model performances (C-indices 0.664 and 0.669, respectively) and reclassification yields (category-free Net reclassification improvement 0.41, 95% CI 0.24-0.60 and 0.35, 95% CI 0.17-0.52, respectively). Conclusion: Among 29 biomarkers depicting different pathophysiological axes, NT-proBNP, Apo A-I and KIM-1 emerged as the strongest independent predictors of 1-year MACE. Their routine assessment and integration into clinical risk prediction models may improve personalized risk stratification in patients with symptoms suggestive of MI.

https://dgk.org/kongress_programme/jt2023/aP1703.html