Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis

Abstract

Abstract Neural network analysis was applied for early diagnosis/exclusion of acute myocardial infarction (AMI), prediction of infarct size, and estimation of “time from onset of infarction.” Eighty-eight patients admitted within 8 h after onset of chest pain were included. Blood samples for measurement of myoglobin, creatine kinase isoform MB, and troponin T were obtained every 30 min during the first 3 h and then after successively longer intervals. Data from 50 patients were used to train a set of neural network components of a decision support system. The performance of the system was evaluated and compared with experienced clinicians for the remaining 38 patients. The computer system detected myocardial infarction and predicted infarct size earlier than the clinicians, but did not differ significantly in terms of diagnostic sensitivity, specificity, and predictive values when disregarding time for diagnosis. With a cross-validation procedure the cumulated sensitivities of the computer system for the first five measurements were estimated to be (mean ± 2SEM, n = 100): 0.77 ± 0.03, 0.89 ± 0.02, 0.94 ± 0.02, 0.97 ± 0.01, and 0.99 ± 0.01, respectively, with corresponding cumulated specificities between 0.93 ± 0.01 and 0.91 ± 0.01. We concluded that neural network analysis of serial measurements of biochemical markers might provide useful support for the early assessment of patients with suspected AMI.