Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients

Background: To prospectively validate artificial neural network (ANN)-algorithnns for early diagnosis of myocardial infarction (AMI) and prediction of 'major infarct' size in patients with chest pain and without ECG changes diagnostic for AMI. Methods: Results of early and frequent Strums CS measurements of troponin I (TnI) and myoglobin in 3 10 patients were used to validate four prespecified ANN-algorithms with use of cross-validation techniques. Two separate biochemical criteria for diagnosis of AMI were applied: TnI >= 0.1 mu g/L within 24 h ('TnI 0.1 AMI') and TnI >= 0.4 mu g/L within 24 h ('TnI 0.4 AMI'). To be considered clinically useful, the ANN-indications of AMI had to achieve a predefined positive predictive value (PPV) >= 78% and a negative predictive value (NPV) >= 94% at 2 h after admission. 'Major infarct' size was defined by peak levels of CK-MB within 24 h. Results: For the best performing ANN-algorithms, the PPV and NPV for the indication of 'TnI 0.1 AMI' were 87% (p = 0.009) and 99% (p = 0.0001) at 2 h, respectively. For the indication of 'TnI 0.4 AMI', the PPV and NPV were 90% (p = 0.006) and 99% (p = 0.0004), respectively. Another ANN-algorithm predicted 'major AMF at 2 h with a sensitivity of 96% and a specificity of 78%. Corresponding PPV and NPV were 73% and 97%, respectively. Conclusions: Specially designed ANN-algorithms allow diagnosis of AMI within 2 h of monitoring. These algorithms also allow early prediction of 'major AMI' size and could thus, be used as a valuable instrument for rapid assessment of chest pain patients. (c) 2006 Elsevier Ireland Ltd. All rights reserved.