Sökning: WFRF:(Jonas Kai J.)
> (2007-2009) >
Artificial neural n...
Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients
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- Eggers, Kai (författare)
- Uppsala universitet,Institutionen för medicinska vetenskaper,Uppsala
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- Ellenius, J. (författare)
- KI, Stockholm
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- Dellborg, Mikael, 1954 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för akut och kardiovaskulär medicin,Institute of Medicine, Department of Emergeny and Cardiovascular Medicine,Göteborg
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- Groth, Torgny (författare)
- Uppsala universitet,Institutionen för medicinska vetenskaper,Uppsala
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- Oldgren, Jonas (författare)
- Uppsala universitet,Institutionen för medicinska vetenskaper,Uppsala
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- Swahn, Eva, 1949- (författare)
- Östergötlands Läns Landsting,Linköpings universitet,Hälsouniversitetet,Kardiologi,Kardiologiska kliniken
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- Lindahl, Bertil (författare)
- Uppsala universitet,Institutionen för medicinska vetenskaper,Uppsala
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(creator_code:org_t)
- Elsevier BV, 2007
- 2007
- Engelska.
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Ingår i: Int J Cardiol. - : Elsevier BV. - 1874-1754 .- 0167-5273. ; 114:3, s. 366-74
- Relaterad länk:
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https://gup.ub.gu.se...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Nyckelord
- *Algorithms
- Biological Markers/blood
- Chest Pain/blood/*diagnosis/pathology
- Chi-Square Distribution
- Diagnosis
- Differential
- Electrocardiography
- Female
- Humans
- Male
- Myocardial Infarction/blood/*diagnosis/pathology
- Myoglobin/blood
- *Neural Networks (Computer)
- Predictive Value of Tests
- Prospective Studies
- ROC Curve
- Sensitivity and Specificity
- Troponin I/blood
- Chest pain
- MEDICINE
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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