Hussain A Isma'eel1,2, George E Sakr3, Mustapha Serhan4, Nader Lamaa4, Ayman Hakim4, Paul C Cremer5, Wael A Jaber5, Torkom Garabedian6, Imad Elhajj7,8, Antoine B Abchee4,7. 1. Division of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon. hi09@aub.edu.lb. 2. Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon. hi09@aub.edu.lb. 3. École Superieurd'Ing. de Beirut (ESIB), St Joseph University, Beirut, Lebanon. 4. Division of Cardiology, Department of Internal Medicine, American University of Beirut, PO-BOX 11-0236, Riad el Solh, Beirut, 11072020, Lebanon. 5. Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA. 6. Department of Internal Medicine, Saint Elizabeth's Medical Center, Boston, MA, USA. 7. Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon. 8. Department of Electrical & Computer Engineering, American University of Beirut, PO-BOX 11-023, Riad el Solh, Beirut, 11072020, Lebanon.
Abstract
BACKGROUND: Coronary artery disease (CAD) accounts for more than half of all cardiovascular events. Stress testing remains the cornerstone for non-invasive assessment of patients with possible or known CAD. Clinical utilization reviews show that most patients presenting for evaluation of stable CAD by stress testing are categorized as low risk prior to the test. Attempts to enhance risk stratification of individuals who are sent for stress testing seem to be more in need today. The present study compares artificial neural networks (ANN)-based prediction models to the other risk models being used in practice (the Diamond-Forrester and the Morise models). METHODS: In our study, we prospectively recruited patients who were 19 years of age or older, and were being evaluated for coronary artery disease with imaging-based stress tests. 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. RESULTS: We prospectively enrolled 486 patients. The mean age of patients undergoing stress test was 55.2 ± 11.2 years, 35% were women, and 12% had a positive stress test for ischemic heart disease. When compared to Diamond-Forrester and Morise risk models, the ANN model for predicting ischemia provided higher discriminatory power (DP)(1.61), had a negative predictive value of 98%, Sensitivity 91% [81%-97%], Specificity 65% [60%-79%], positive predictive value 26%, and a potential 59% reduction of non-invasive imaging. CONCLUSION: The ANN models improved risk stratification when compared to the other risk scores (Diamond-Forrester and Morise) with a 98% negative predictive value and a significant potential reduction in non-invasive imaging tests.
BACKGROUND:Coronary artery disease (CAD) accounts for more than half of all cardiovascular events. Stress testing remains the cornerstone for non-invasive assessment of patients with possible or known CAD. Clinical utilization reviews show that most patients presenting for evaluation of stable CAD by stress testing are categorized as low risk prior to the test. Attempts to enhance risk stratification of individuals who are sent for stress testing seem to be more in need today. The present study compares artificial neural networks (ANN)-based prediction models to the other risk models being used in practice (the Diamond-Forrester and the Morise models). METHODS: In our study, we prospectively recruited patients who were 19 years of age or older, and were being evaluated for coronary artery disease with imaging-based stress tests. 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. RESULTS: We prospectively enrolled 486 patients. The mean age of patients undergoing stress test was 55.2 ± 11.2 years, 35% were women, and 12% had a positive stress test for ischemic heart disease. When compared to Diamond-Forrester and Morise risk models, the ANN model for predicting ischemia provided higher discriminatory power (DP)(1.61), had a negative predictive value of 98%, Sensitivity 91% [81%-97%], Specificity 65% [60%-79%], positive predictive value 26%, and a potential 59% reduction of non-invasive imaging. CONCLUSION: The ANN models improved risk stratification when compared to the other risk scores (Diamond-Forrester and Morise) with a 98% negative predictive value and a significant potential reduction in non-invasive imaging tests.
Authors: Paul C Cremer; Amgad Mentias; David Newton; Venu Menon; Oussama Wazni; Patrick J Tchou; Wael A Jaber Journal: JAMA Intern Med Date: 2015-11 Impact factor: 21.873
Authors: Alan S Go; Dariush Mozaffarian; Véronique L Roger; Emelia J Benjamin; Jarett D Berry; William B Borden; Dawn M Bravata; Shifan Dai; Earl S Ford; Caroline S Fox; Sheila Franco; Heather J Fullerton; Cathleen Gillespie; Susan M Hailpern; John A Heit; Virginia J Howard; Mark D Huffman; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; David Magid; Gregory M Marcus; Ariane Marelli; David B Matchar; Darren K McGuire; Emile R Mohler; Claudia S Moy; Michael E Mussolino; Graham Nichol; Nina P Paynter; Pamela J Schreiner; Paul D Sorlie; Joel Stein; Tanya N Turan; Salim S Virani; Nathan D Wong; Daniel Woo; Melanie B Turner Journal: Circulation Date: 2012-12-12 Impact factor: 29.690
Authors: J H Pope; T P Aufderheide; R Ruthazer; R H Woolard; J A Feldman; J R Beshansky; J L Griffith; H P Selker Journal: N Engl J Med Date: 2000-04-20 Impact factor: 91.245