Journal article
Machine Learning to Predict the Likelihood of Acute Myocardial Infarction
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Than, Martin P.
Emergency Department, Christchurch Hospital, New Zealand (M.P.T., J.W.P.).
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Pickering, John W.
Christchurch Heart Institute, Department of Medicine, University of Otago, New Zealand (J.W.P.).
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Sandoval, Yader
Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Y.S.).
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Shah, Anoop S.V.
Usher Institute (A.S.V.S., A.T., N.L.M.), University of Edinburgh, United Kingdom.
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Tsanas, Athanasios
Usher Institute (A.S.V.S., A.T., N.L.M.), University of Edinburgh, United Kingdom.
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Apple, Fred S.
Department of Laboratory Medicine & Pathology, Hennepin Healthcare/Hennepin County Medical Center and University of Minnesota, Minneapolis (F.S.A.).
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Blankenberg, Stefan
German Center for Cardiovascular Research (DZHK) Partner Site Hamburg. Kiel.Lu.beck (S.B., J.T.N., D.W.).
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Cullen, Louise
Emergency Department, Royal Brisbane and Women’s Hospital, Australia (L.C.).
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Mueller, Christian
Universitatsspital Basel, Switzerland (C.M.).
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Neumann, Johannes T.
German Center for Cardiovascular Research (DZHK) Partner Site Hamburg. Kiel.Lu.beck (S.B., J.T.N., D.W.).
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Twerenbold, Raphael
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Westermann, Dirk
German Center for Cardiovascular Research (DZHK) Partner Site Hamburg. Kiel.Lu.beck (S.B., J.T.N., D.W.).
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Beshiri, Agim
Abbott Diagnostics, Abbott Laboratories, Lake Forest, IL (A.B.).
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Mills, Nicholas L.
Usher Institute (A.S.V.S., A.T., N.L.M.), University of Edinburgh, United Kingdom.
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George, Peter M
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Richards, A Mark
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Troughton, Richard W
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Aldous, Sally J
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Chapman, Andrew R
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Anand, Atul
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Greenslade, Jaimi
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Parsonage, William
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Boeddinghaus, Jasper
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Wildi, Karin
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Nestelberger, Thomas
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Badertscher, Patrick
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Du, Shaoqing
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Huang, Janel
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Smith, Stephen W
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Sörensen, Nils A
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Ojeda, Francisco
Show more…
Published in:
- Circulation. - Ovid Technologies (Wolters Kluwer Health). - 2019, vol. 140, no. 11, p. 899-909
English
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:
A machine learning algorithm (myocardial-ischemic-injury-index [MI
3
]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI
3
uses gradient boosting to compute a value (0–100) reflecting an individual’s likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI
3
thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways.
Results:
Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI
3
was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956–0.971] in the test set and similar performance in early and late presenters. Example MI
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thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI
3
values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5–99.8%) and sensitivity of 97.8% (96.7–98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9–75.0%) and specificity of 96.7% (96.3–97.1%). Using these thresholds, MI
3
performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5–88.8%]; specificity, 92.2% [90.7–93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4–91.6%]); specificity, 89.3% [88.6–90.0%]).
Conclusions:
Using machine learning, MI
3
provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions.
Clinical Trial Registration:
URL:
https://www.anzctr.org.au
. Unique identifier: ACTRN12616001441404.
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Language
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Open access status
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hybrid
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Identifiers
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Persistent URL
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https://sonar.ch/global/documents/238139
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