Machine Learning to Predict the Likelihood of Acute Myocardial Infarction.

acute coronary syndrome machine learning myocardial infarction troponin

Journal

Circulation
ISSN: 1524-4539
Titre abrégé: Circulation
Pays: United States
ID NLM: 0147763

Informations de publication

Date de publication:
10 Sep 2019
Historique:
medline: 17 8 2019
pubmed: 17 8 2019
entrez: 17 8 2019
Statut: ppublish

Résumé

Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. A machine learning algorithm (myocardial-ischemic-injury-index [MI Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI Using machine learning, MI URL: https://www.anzctr.org.au. Unique identifier: ACTRN12616001441404.

Sections du résumé

BACKGROUND BACKGROUND
Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients.
METHODS METHODS
A machine learning algorithm (myocardial-ischemic-injury-index [MI
RESULTS RESULTS
Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI
CONCLUSIONS CONCLUSIONS
Using machine learning, MI
CLINICAL TRIAL REGISTRATION BACKGROUND
URL: https://www.anzctr.org.au. Unique identifier: ACTRN12616001441404.

Identifiants

DOI: 10.1161/CIRCULATIONAHA.119.041980 PMID: 31416346 PMC: PMC6749969
pubmed: 31416346
doi: 10.1161/CIRCULATIONAHA.119.041980
pmc: PMC6749969
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

899-909

Subventions

Organisme : British Heart Foundation
ID : FS/16/14/32023
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/12/10/29922
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/18/2/33800
Pays : United Kingdom

Auteurs

Martin P Than (MP)

Emergency Department, Christchurch Hospital, New Zealand (M.P.T., J.W.P.).

John W Pickering (JW)

Emergency Department, Christchurch Hospital, New Zealand (M.P.T., J.W.P.).
Christchurch Heart Institute, Department of Medicine, University of Otago, New Zealand (J.W.P.).

Yader Sandoval (Y)

Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Y.S.).

Anoop S V Shah (ASV)

British Heart Foundation Centre for Cardiovascular Sciences (A.S.V.S., N.L.M.), University of Edinburgh, United Kingdom.
Usher Institute (A.S.V.S., A.T., N.L.M.), University of Edinburgh, United Kingdom.

Athanasios Tsanas (A)

Usher Institute (A.S.V.S., A.T., N.L.M.), University of Edinburgh, United Kingdom.

Fred S Apple (FS)

Department of Laboratory Medicine & Pathology, Hennepin Healthcare/Hennepin County Medical Center and University of Minnesota, Minneapolis (F.S.A.).

Stefan Blankenberg (S)

German Center for Cardiovascular Research (DZHK) Partner Site Hamburg. Kiel.Lu.beck (S.B., J.T.N., D.W.).

Louise Cullen (L)

Emergency Department, Royal Brisbane and Women's Hospital, Australia (L.C.).

Christian Mueller (C)

Universitatsspital Basel, Switzerland (C.M.).

Johannes T Neumann (JT)

German Center for Cardiovascular Research (DZHK) Partner Site Hamburg. Kiel.Lu.beck (S.B., J.T.N., D.W.).

Raphael Twerenbold (R)

Dirk Westermann (D)

German Center for Cardiovascular Research (DZHK) Partner Site Hamburg. Kiel.Lu.beck (S.B., J.T.N., D.W.).

Agim Beshiri (A)

Abbott Diagnostics, Abbott Laboratories, Lake Forest, IL (A.B.).

Nicholas L Mills (NL)

British Heart Foundation Centre for Cardiovascular Sciences (A.S.V.S., N.L.M.), University of Edinburgh, United Kingdom.
Usher Institute (A.S.V.S., A.T., N.L.M.), University of Edinburgh, United Kingdom.

Classifications MeSH