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)-algorithms 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 Stratus CS measurements of troponin I (TnI) and myoglobin in 310 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 μg/L within 24 h (‘TnI 0.1 AMI’) and TnI ≥ 0.4 μg/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 AMI’ 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.