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- Eggers, Kai, et al.
(författare)
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Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients
- 2007
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Ingår i: Int J Cardiol. - : Elsevier BV. - 1874-1754 .- 0167-5273. ; 114:3, s. 366-74
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Tidskriftsartikel (refereegranskat)abstract
- 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 > or = 0.1 microg/L within 24 h ('TnI 0.1 AMI') and TnI > or = 0.4 microg/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) > or = 78% and a negative predictive value (NPV) > or = 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.
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