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Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
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2007 (English)In: International Journal of Cardiology, ISSN 0167-5273, E-ISSN 1874-1754, 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.

Place, publisher, year, edition, pages
2007. Vol. 114, no 3, 366-374 p.
Keyword [en]
Chest pain, Artificial neural networks, Troponin, Myocardial infarction, Infarct size
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-96035DOI: 10.1016/j.ijcard.2005.12.019ISI: 000243241500012PubMedID: 16797088OAI: oai:DiVA.org:uu-96035DiVA: diva2:170451
Available from: 2007-05-30 Created: 2007-05-30 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Cardiac Troponins in Patients with Suspected or Confirmed Acute Coronary Syndrome: New Applications for Biomarkers in Coronary Artery Disease
Open this publication in new window or tab >>Cardiac Troponins in Patients with Suspected or Confirmed Acute Coronary Syndrome: New Applications for Biomarkers in Coronary Artery Disease
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The cardiac troponins are the biochemical markers of choice for the diagnosis of acute myocardial infarction (AMI) and risk prediction in patients with acute coronary syndrome (ACS). In this thesis, the role of early serial cardiac troponin I (cTnI) testing was assessed in fairly unselected patient populations admitted because of chest pain and participating in the FAST II-study (n=197) and the FASTER I-study (n=380). Additionally, the importance of cTnI testing in stable post-ACS patients from the FRISC II-study (n=1092) was studied.

The analyses in chest pain patients demonstrate that cTnI is very useful for early diagnostic and prognostic assessment. cTnI allowed already 2 hours after admission the reliable exclusion of AMI and the identification of low-risk patients when ECG findings and a renal marker such as cystatin C were added as conjuncts. Other biomarkers such as CK-MB, myoglobin, NT-pro BNP or CRP did not provide superior clinical information. However, myoglobin may be valuable in combination with cTnI results for the early prediction of an impending major AMI when used as input variable for an artificial neural network. Such an approach applying cTnI results only may also furthermore improve the early diagnosis of AMI.

Persistent cTnI elevation > 0.01 μg/L was detectable using a high-sensitive assay in 26% of the stable post-ACS patients from the FRISC II-study. NT-pro BNP levels at 6 months were the most important variable independently associated to persistent cTnI elevation besides male gender, indicating a relationship between adverse left ventricular remodeling processes and cTnI leakage. Patients with persistent cTnI elevation had a considerable risk for both mortality and AMI during 5 year follow-up.

These analyses thus, confirm the value of cTnI for early assessment of chest pain patients and provide new and unique evidence regarding the role of cTnI for risk prediction in post-ACS populations.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2007. 73 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 269
Keyword
Internal medicine, Ischemic heart disease, Acute myocardial infarction, Troponin, Natriuretic peptide, CRP, Cystatin C, Artificial neural network, Point of care measurements, Risk prediction, Invärtesmedicin
Identifiers
urn:nbn:se:uu:diva-7945 (URN)978-91-554-6924-5 (ISBN)
Public defence
2007-09-14, Robergsalen, Ingång 40, Akademiska Sjukhuset, Uppsala, 09:15
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Available from: 2007-05-30 Created: 2007-05-30 Last updated: 2011-01-19Bibliographically approved

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Eggers, KaiGroth, TorgnyOldgren, JonasLindahl, Bertil

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