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

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Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction. / Neumann, Johannes T; Sörensen, Nils A; Zeller, Tanja; Magaret, Craig A; Barnes, Grady; Rhyne, Rhonda F; Peters, Celine; Goßling, Alina; Hartikainen, Tau S; Haller, Paul M; Lehmacher, Jonas; Schäfer, Sarina; Januzzi, James L; Westermann, Dirk.

in: Biomarker Med, Jahrgang 14, Nr. 9, 06.2020, S. 775-784.

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Bibtex

@article{69260adaf74548f785418a10da541922,
title = "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.",
keywords = "ACS, artificial intelligence, biomarkers, machine learning, major adverse cardiac events, myocardial infarction, noninvasive risk assessment, outcome, prediction",
author = "Neumann, {Johannes T} and S{\"o}rensen, {Nils A} and Tanja Zeller and Magaret, {Craig A} and Grady Barnes and Rhyne, {Rhonda F} and Celine Peters and Alina Go{\ss}ling and Hartikainen, {Tau S} and Haller, {Paul M} and Jonas Lehmacher and Sarina Sch{\"a}fer and Januzzi, {James L} and Dirk Westermann",
note = "Publisher Copyright: {\textcopyright} 2020 Future Medicine Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = jun,
doi = "10.2217/bmm-2019-0584",
language = "English",
volume = "14",
pages = "775--784",
number = "9",

}

RIS

TY - JOUR

T1 - Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction

AU - Neumann, Johannes T

AU - Sörensen, Nils A

AU - Zeller, Tanja

AU - Magaret, Craig A

AU - Barnes, Grady

AU - Rhyne, Rhonda F

AU - Peters, Celine

AU - Goßling, Alina

AU - Hartikainen, Tau S

AU - Haller, Paul M

AU - Lehmacher, Jonas

AU - Schäfer, Sarina

AU - Januzzi, James L

AU - Westermann, Dirk

N1 - Publisher Copyright: © 2020 Future Medicine Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/6

Y1 - 2020/6

N2 - 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.

AB - 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.

KW - ACS

KW - artificial intelligence

KW - biomarkers

KW - machine learning

KW - major adverse cardiac events

KW - myocardial infarction

KW - noninvasive risk assessment

KW - outcome

KW - prediction

UR - http://www.scopus.com/inward/record.url?scp=85089301882&partnerID=8YFLogxK

U2 - 10.2217/bmm-2019-0584

DO - 10.2217/bmm-2019-0584

M3 - SCORING: Journal articles

C2 - 32462911

VL - 14

SP - 775

EP - 784

IS - 9

ER -