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Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients
KI, Stockholm.
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2007 (English)In: International Journal of Cardiology, ISSN 0167-5273, Vol. 114, no 3, 366-374 p.Article in journal (Refereed) Published
Abstract [en]

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. © 2006 Elsevier Ireland Ltd. All rights reserved.

Place, publisher, year, edition, pages
2007. Vol. 114, no 3, 366-374 p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-36922DOI: 10.1016/j.ijcard.2005.12.019Local ID: 33067OAI: diva2:257771
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-09-11

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Swahn, Eva
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