Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction.

Abstract

Background: In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). Methodology & results: A previously described prognostic panel for MACE consisting of four biomarkers was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. Conclusion: Among patients with suspected MI, we validated a multibiomarker panel for predicting 1-year MACE. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov).

• Authors: Johannes T Neumann, Nils A Sörensen, Tanja Zeller, Craig A Magaret, Grady Barnes, Rhonda F Rhyne, Celine Peters, Alina Goßling, Tau S Hartikainen, Paul M Haller, Jonas Lehmacher, Sarina Schäfer, James L Januzzi, Dirk Westermann
• Journal: Biomarkers in medicine (Biomark Med) Vol. 14 Issue 9 Pg. 775-784 (06 2020) ISSN: 1752-0371 [Electronic] England
• PMID: 32462911 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers

Topics

• Aged
• Biomarkers (metabolism)
• Female
• Humans
• Machine Learning
• Male
• Middle Aged
• Myocardial Infarction (diagnosis, metabolism)
• Predictive Value of Tests
• Prognosis
• Risk Assessment