title: 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.
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.
authors.affiliation: Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon.|Vascular Medicine Program, American University of Beirut Medical Center, Riad el Solh, PO Box 11-023, Beirut, 11072020, Lebanon.|Visiting Clinical Scholar, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, 9500 Euclid Ave, Desk J1-5, Cleveland, OH, 44195, USA.
authors.forename: Hussain A
authors.initials: HA
authors.lastname: Isma'eel
authors.affiliation: Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA.
authors.forename: Paul C
authors.initials: PC
authors.lastname: Cremer
authors.affiliation: Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA.
authors.forename: Shaden
authors.initials: S
authors.lastname: Khalaf
authors.affiliation: Vascular Medicine Program, American University of Beirut Medical Center, Riad el Solh, PO Box 11-023, Beirut, 11072020, Lebanon.|Division of Vascular Endothelium and Microcirculation, Department of Medicine III, TU Dresden, Dresden, Germany.
authors.forename: Mohamad M
authors.initials: MM
authors.lastname: Almedawar
authors.affiliation: Vascular Medicine Program, American University of Beirut Medical Center, Riad el Solh, PO Box 11-023, Beirut, 11072020, Lebanon.|Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon.
authors.forename: Imad H
authors.initials: IH
authors.lastname: Elhajj
authors.affiliation: Vascular Medicine Program, American University of Beirut Medical Center, Riad el Solh, PO Box 11-023, Beirut, 11072020, Lebanon. ges07.aub@gmail.com.|Ecole Supérieure d'Ingénieurs de Beyrouth (ESIB), Faculty of Engineering, Saint Joseph University of Beirut, Beirut, Lebanon. ges07.aub@gmail.com.
authors.forename: George E
authors.initials: GE
authors.lastname: Sakr
authors.affiliation: Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA. jaberw@ccf.org.
authors.forename: Wael A
authors.initials: WA
authors.lastname: Jaber
mesh_terms: D054058:Acute Coronary Syndrome / Q000097:blood / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D000368:Aged; D015415:Biomarkers / Q000097:blood; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000097:blood / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D023921:Coronary Stenosis / Q000097:blood / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000503:physiopathology; D003661:Decision Support Techniques; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D055414:Myocardial Perfusion Imaging; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D015899:Tomography, Emission-Computed, Single-Photon; D014336:Troponin / Q000097:blood*; D019564:Unnecessary Procedures
chemical_list: D015415:Biomarkers; D014336:Troponin
keywords: Artificial neural networks; Myocardial perfusion imaging; Single-photon emission computed tomography; Thrombolysis in myocardial infarction score