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.

Bibliografische Daten

OriginalspracheEnglisch
ISSN1752-0363
DOIs
StatusVeröffentlicht - 06.2020

Anmerkungen des Dekanats

Funding Information:
The BACC study was supported by an unrestricted grant by Prevencio and Abbott Diagnostics. J Neumann was supported by a grant from the German Heart Foundation/German Foundation of Heart Research, the Else Kröner Fresenius Stiftung and the DZHK and is recipient of a research fellowship by the Deutsche Forschungsgemeinschaft (NE 2165/1-1). J Neumann received honoraria from Siemens and Abbott Diagnostics. CA Magaret, RF Rhyne, G Barnes and C Peters are employed by Prevencio. JL Januzzi is supported in part by the Hutter Family Professorship, is a Trustee of the American College of Cardiology, has received grant support from Novartis Pharmaceuticals, Roche Diagnostics, Abbott, Singulex and Prevencio, consulting income from Abbott, Janssen, Novartis, Pfizer, Merck and Roche Diagnostics; and participates in clinical endpoint committees/data safety monitoring boards for Abbott, AbbVie, Amgen, Boehringer-Ingelheim, Janssen and Takeda. D Westermann reports personal fees from Bayer, Boehringer-Ingelheim, Berlin Chemie, AstraZeneca, Biotronik and Novartis. PM Haller reports lecture fees from Beckman Coulter. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

PubMed 32462911