Machine Learning to Predict the Likelihood of Acute Myocardial Infarction.
Journal article

Machine Learning to Predict the Likelihood of Acute Myocardial Infarction.

  • Than MP Emergency Department, Christchurch Hospital, Christchurch, New Zealand.
  • Pickering JW Emergency Department, Christchurch Hospital, Christchurch, New Zealand; Christchurch Heart Institute, Department of Medicine, University of Otago, Christchurch, New Zealand.
  • Sandoval Y Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
  • Shah ASV BHF Centre for Cardiovascular Sciences, University of Edinburgh, Edinburgh, United Kingdom; Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.
  • Tsanas A Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.
  • Apple FS Department of Laboratory Medicine and Pathology, Hennepin County Medical Center, and University of Minnesota, Department of Laboratory Medicine & Pathology Minneapolis, MN.
  • Blankenberg S Department of General and Interventional Cardiology, University Heart Center, Hamburg, Germany.
  • Cullen L Emergency Department, Royal Brisbane and Women's Hospital, Brisbane, Australia.
  • Mueller C Universitätsspital Basel, Basel, Switzerland.
  • Neumann JT Abbott Diagnostics, Abbott Laboratories, Lake Forest, IL.
  • Twerenbold R Universitätsspital Basel, Basel, Switzerland.
  • Westermann D Department of General and Interventional Cardiology, University Heart Center, Hamburg, Germany.
  • Beshiri A Abbott Diagnostics, Abbott Laboratories, Lake Forest, IL.
  • Mills NL BHF Centre for Cardiovascular Sciences, University of Edinburgh, Edinburgh, United Kingdom; Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.
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  • 2019-08-17
Published in:
  • Circulation. - 2019
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 [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI3 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 (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) 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. MI3 was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI3 values were <1.6 in 69.5% with a NPV 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 PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the ESC 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, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions.


CLINICAL TRIAL REGISTRATION
Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.
Language
  • English
Open access status
hybrid
Identifiers
Persistent URL
https://sonar.ch/global/documents/273354
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