Artificial neural network-based model enhances risk stratification and reduces non-invasive cardiac stress imaging compared to Diamond-Forrester and Morise risk assessment models: A prospective study
| Autor: | Paul Cremer, Ayman Hakim, Imad H. Elhajj, Wael A. Jaber, Torkom Garabedian, George E. Sakr, Mustapha Serhan, Hussain A. Isma’eel, Nader Lamaa, Antoine Abchee |
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| Rok vydání: | 2016 |
| Předmět: |
Adult
Male medicine.medical_specialty Stress testing Disease Coronary Artery Disease 030204 cardiovascular system & hematology Risk Assessment Coronary artery disease 03 medical and health sciences 0302 clinical medicine Stress test Predictive Value of Tests Internal medicine Stress Echocardiography Medicine Humans Radiology Nuclear Medicine and imaging 030212 general & internal medicine Prospective Studies Prospective cohort study Aged business.industry Middle Aged medicine.disease Cardiology Exercise Test Female Neural Networks Computer Cardiology and Cardiovascular Medicine Risk assessment business Predictive modelling |
| Zdroj: | Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology. 25(5) |
| ISSN: | 1532-6551 |
| Popis: | 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). 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. 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. 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. |
| Databáze: | OpenAIRE |
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