OBJECTIVE: A neural network system was designed to predict whether coronary arteriography on a given patient would reveal any occurrence of significant coronary stenosis (>50%), a degree of stenosis which often leads to coronary intervention. METHODOLOGY: A dataset of 2004 records from male cardiology patients was derived from a national cardiac catheterization database. The catheterizations selected for analysis from the database were first-time and elective, and they were precipitated by chest pain. Eleven patient variables were used as inputs in an artificial neural network system. The network was trained on the earliest 902 records in the dataset. The next 902 records formed a cross-validation file, which was used to optimize the training. A third file composed of the next 100 records facilitated the choice of a cutoff number between 0 and 1. The cutoff number was applied to the last 100 records, which comprised a test file. RESULTS: When a cutoff of 0.25 was compared to the network outputs of all 100 records in the test file, 12 of 46 (specificity=26%) patients without significant stenosis had outputs<or=0.25, but all patients with significant stenosis had outputs>0.25 (sensitivity=100%). Therefore, the network identified a fraction of the patients in the test file who did not have significant coronary artery stenosis, while at the same time the network identified all of the patients in the test file who had significant stenosis capable of causing chest pain. CONCLUSION: Artificial neural networks may be helpful in reducing unnecessary cardiac catheterizations.
OBJECTIVE: A neural network system was designed to predict whether coronary arteriography on a given patient would reveal any occurrence of significant coronary stenosis (>50%), a degree of stenosis which often leads to coronary intervention. METHODOLOGY: A dataset of 2004 records from male cardiology patients was derived from a national cardiac catheterization database. The catheterizations selected for analysis from the database were first-time and elective, and they were precipitated by chest pain. Eleven patient variables were used as inputs in an artificial neural network system. The network was trained on the earliest 902 records in the dataset. The next 902 records formed a cross-validation file, which was used to optimize the training. A third file composed of the next 100 records facilitated the choice of a cutoff number between 0 and 1. The cutoff number was applied to the last 100 records, which comprised a test file. RESULTS: When a cutoff of 0.25 was compared to the network outputs of all 100 records in the test file, 12 of 46 (specificity=26%) patients without significant stenosis had outputs<or=0.25, but all patients with significant stenosis had outputs>0.25 (sensitivity=100%). Therefore, the network identified a fraction of the patients in the test file who did not have significant coronary artery stenosis, while at the same time the network identified all of the patients in the test file who had significant stenosis capable of causing chest pain. CONCLUSION: Artificial neural networks may be helpful in reducing unnecessary cardiac catheterizations.
Authors: Michael J Zellweger; Andrew Tsirkin; Vasily Vasilchenko; Michael Failer; Alexander Dressel; Marcus E Kleber; Peter Ruff; Winfried März Journal: EPMA J Date: 2018-08-16 Impact factor: 6.543