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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: BIOMARK MED, Vol. 14, No. 9, 06.2020, p. 775-784.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Neumann, JT, Sörensen, NA, Zeller, T, Magaret, CA, Barnes, G, Rhyne, RF, Peters, C, Goßling, A, Hartikainen, TS, Haller, PM, Lehmacher, J, Schäfer, S, Januzzi, JL & Westermann, D 2020, 'Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction', BIOMARK MED, vol. 14, no. 9, pp. 775-784. https://doi.org/10.2217/bmm-2019-0584

APA

Neumann, J. T., Sörensen, N. A., Zeller, T., Magaret, C. A., Barnes, G., Rhyne, R. F., Peters, C., Goßling, A., Hartikainen, T. S., Haller, P. M., Lehmacher, J., Schäfer, S., Januzzi, J. L., & Westermann, D. (2020). Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction. BIOMARK MED, 14(9), 775-784. https://doi.org/10.2217/bmm-2019-0584

Vancouver

Neumann JT, Sörensen NA, Zeller T, Magaret CA, Barnes G, Rhyne RF et al. Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction. BIOMARK MED. 2020 Jun;14(9):775-784. https://doi.org/10.2217/bmm-2019-0584

Bibtex

@article{3c7e2aecb5c94fe98b175ff6136ec2bc,
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. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov).",
keywords = "Aged, Biomarkers/metabolism, Female, Humans, Machine Learning, Male, Middle Aged, Myocardial Infarction/diagnosis, Predictive Value of Tests, Prognosis, Risk Assessment",
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",
year = "2020",
month = jun,
doi = "10.2217/bmm-2019-0584",
language = "English",
volume = "14",
pages = "775--784",
journal = "BIOMARK MED",
issn = "1752-0363",
publisher = "Future Medicine Ltd",
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

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. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov).

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. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov).

KW - Aged

KW - Biomarkers/metabolism

KW - Female

KW - Humans

KW - Machine Learning

KW - Male

KW - Middle Aged

KW - Myocardial Infarction/diagnosis

KW - Predictive Value of Tests

KW - Prognosis

KW - Risk Assessment

U2 - 10.2217/bmm-2019-0584

DO - 10.2217/bmm-2019-0584

M3 - SCORING: Journal article

C2 - 32462911

VL - 14

SP - 775

EP - 784

JO - BIOMARK MED

JF - BIOMARK MED

SN - 1752-0363

IS - 9

ER -