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Artificial neural network modeling enhances risk stratification and can reduce downstream testing for patients with suspected acute coronary syndromes, negative cardiac biomarkers, and normal ECGs

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Abstract

Despite uncertain yield, guidelines endorse routine stress myocardial perfusion imaging (MPI) for patients with suspected acute coronary syndromes, unremarkable serial electrocardiograms, and negative troponin measurements. In these patients, outcome prediction and risk stratification models could spare unnecessary testing. This study therefore investigated the use of artificial neural networks (ANN) to improve risk stratification and prediction of MPI and angiographic results. We retrospectively identified 5354 consecutive patients referred from the emergency department for rest-stress MPI after serial negative troponins and normal ECGs. Patients were risk stratified according to thrombolysis in myocardial infarction (TIMI) scores, ischemia was defined as >5 % reversible perfusion defect, and obstructive coronary artery disease was defined as >50 % angiographic obstruction. For ANN, the network architecture employed a systematic method where the number of neurons is changed incrementally, and bootstrapping was performed to evaluate the accuracy of the models. Compared to TIMI scores, ANN models provided improved discriminatory power. With regards to MPI, an ANN model could reduce testing by 59 % and maintain a 96 % negative predictive value (NPV) for ruling out ischemia. Application of an ANN model could also avoid 73 % of invasive coronary angiograms while maintaining a 98 % NPV for detecting obstructive CAD. An online calculator for clinical use was created using these models. The ANN models improved risk stratification when compared to the TIMI score. Our calculator could also reduce downstream testing while maintaining an excellent NPV, though further study is needed before the calculator can be used clinically.

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Author notes

    Authors and Affiliations

    1. Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon

      Hussain A. Isma’eel

    2. Vascular Medicine Program, American University of Beirut Medical Center, Riad el Solh, PO Box 11-023, Beirut, 11072020, Lebanon

      Hussain A. Isma’eel, Mohamad M. Almedawar, Imad H. Elhajj & George E. Sakr

    3. Visiting Clinical Scholar, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, 9500 Euclid Ave, Desk J1-5, Cleveland, OH, 44195, USA

      Hussain A. Isma’eel

    4. Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA

      Paul C. Cremer, Shaden Khalaf & Wael A. Jaber

    5. Division of Vascular Endothelium and Microcirculation, Department of Medicine III, TU Dresden, Dresden, Germany

      Mohamad M. Almedawar

    6. Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon

      Imad H. Elhajj

    7. Ecole Supérieure d’Ingénieurs de Beyrouth (ESIB), Faculty of Engineering, Saint Joseph University of Beirut, Beirut, Lebanon

      George E. Sakr

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    1. Hussain A. Isma’eel
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    2. Paul C. Cremer
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    Correspondence to George E. Sakr or Wael A. Jaber.

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    Conflict of interest

    Author Isma’eel HA declares that he has no conflict of interest. Author Cremer PC declares that he has no conflict of interest. Author Khalaf S declares that she has no conflict of interest. Author Almedawar MM declares that he has no conflict of interest. Author Elhajj IH declares that he has no conflict of interest. Author Sakr GE declares that he has no conflict of interest. Author Jaber WA declares that he has no conflict of interest.

    Ethical approval

    All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

    Informed consent

    Informed consent was obtained from all individual participants included in the study.

    Additional information

    Hussain A. Isma’eel and Paul C. Cremer are co-authors with equal contribution.

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    Isma’eel, H.A., Cremer, P.C., Khalaf, S. et al. Artificial neural network modeling enhances risk stratification and can reduce downstream testing for patients with suspected acute coronary syndromes, negative cardiac biomarkers, and normal ECGs. Int J Cardiovasc Imaging 32, 687–696 (2016). https://doi.org/10.1007/s10554-015-0821-9

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    • DOI: https://doi.org/10.1007/s10554-015-0821-9

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