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

  • Hussain A. Isma’eel
  • Paul C. Cremer
  • Shaden Khalaf
  • Mohamad M. Almedawar
  • Imad H. Elhajj
  • George E. SakrEmail author
  • Wael A. JaberEmail author
Original Paper


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.


Artificial neural networks Thrombolysis in myocardial infarction score Single-photon emission computed tomography Myocardial perfusion imaging 


Compliance with ethical standards

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.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Hussain A. Isma’eel
    • 1
    • 2
    • 3
  • Paul C. Cremer
    • 4
  • Shaden Khalaf
    • 4
  • Mohamad M. Almedawar
    • 2
    • 5
  • Imad H. Elhajj
    • 2
    • 6
  • George E. Sakr
    • 2
    • 7
    Email author
  • Wael A. Jaber
    • 4
    Email author
  1. 1.Division of Cardiology, Department of Internal MedicineAmerican University of BeirutBeirutLebanon
  2. 2.Vascular Medicine ProgramAmerican University of Beirut Medical CenterBeirutLebanon
  3. 3.Visiting Clinical Scholar, Department of Cardiovascular MedicineCleveland Clinic FoundationClevelandUSA
  4. 4.Heart and Vascular InstituteCleveland ClinicClevelandUSA
  5. 5.Division of Vascular Endothelium and Microcirculation, Department of Medicine IIITU DresdenDresdenGermany
  6. 6.Department of Electrical and Computer EngineeringAmerican University of BeirutBeirutLebanon
  7. 7.Ecole Supérieure d’Ingénieurs de Beyrouth (ESIB), Faculty of EngineeringSaint Joseph University of BeirutBeirutLebanon

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