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How well can signs and symptoms predict AMI in the Malaysian population?

The aim of the study was to use data from an electronic medical record system (EMR) to look for factors that would help us diagnose acute myocardial infarction (AMT) with the ultimate aim of using these factors in a decision support system for chest pain. We extracted 887 records from the electronic...

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Main Authors: Bulgiba, A.M., Razaz, M.
Format: Article
Language:English
Published: 2005
Subjects:
R Medicine
Online Access:http://eprints.um.edu.my/3087/1/How_well_can_signs_and_symptoms_predict_AMI_in_the_Malaysian_population.pdf
http://eprints.um.edu.my/3087/
http://www.sciencedirect.com/science/article/pii/S0167527304004619
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spelling my.um.eprints.30872014-10-20T05:40:42Z http://eprints.um.edu.my/3087/ How well can signs and symptoms predict AMI in the Malaysian population? Bulgiba, A.M. Razaz, M. R Medicine The aim of the study was to use data from an electronic medical record system (EMR) to look for factors that would help us diagnose acute myocardial infarction (AMT) with the ultimate aim of using these factors in a decision support system for chest pain. We extracted 887 records from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. We cleaned the data, extracted 69 possible variables and performed univariate and multivariate analysis. From the univariate analysis we find that 22 variables are significantly associated with a diagnosis of AMT. However, multiple logistic regression reveals that only 9 of these 22 variables are significantly related to a diagnosis of AMT. Race (Indian), male sex, sudden onset of persistent crushing pain, associated sweating and a history of diabetes mellitus are significant predictors of AMT. Pain that is relieved by other means and history of heart disease on treatment are important predictors of a diagnosis other than AMT. The degree of accuracy is high at 80.5. There are 13 factors that are significant in the univariate analysis but are not among the nine significant factors in the multivariate analysis. These are location of pain, associated palpitations, nausea and vomiting; pain relieved by rest, pain aggravated by posture, cough, inspiration and exertion; age more than 40, being a smoker and abnormal chest wall and face examination. We believe that these findings can have important applications in the design of an intelligent decision support system for use in medical care as the predictive capability can be further refined with the use of intelligent computational techniques. (c) 2004 Elsevier Ireland Ltd. All rights reserved. 2005 Article PeerReviewed application/pdf en http://eprints.um.edu.my/3087/1/How_well_can_signs_and_symptoms_predict_AMI_in_the_Malaysian_population.pdf Bulgiba, A.M. and Razaz, M. (2005) How well can signs and symptoms predict AMI in the Malaysian population? International Journal of Cardiology, 102 (1). pp. 87-93. ISSN 0167-5273 http://www.sciencedirect.com/science/article/pii/S0167527304004619 10.1016/j.ijcard.2004.04.002
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic R Medicine
spellingShingle R Medicine
Bulgiba, A.M.
Razaz, M.
How well can signs and symptoms predict AMI in the Malaysian population?
description The aim of the study was to use data from an electronic medical record system (EMR) to look for factors that would help us diagnose acute myocardial infarction (AMT) with the ultimate aim of using these factors in a decision support system for chest pain. We extracted 887 records from the electronic medical record system (EMR) in Selayang Hospital, Malaysia. We cleaned the data, extracted 69 possible variables and performed univariate and multivariate analysis. From the univariate analysis we find that 22 variables are significantly associated with a diagnosis of AMT. However, multiple logistic regression reveals that only 9 of these 22 variables are significantly related to a diagnosis of AMT. Race (Indian), male sex, sudden onset of persistent crushing pain, associated sweating and a history of diabetes mellitus are significant predictors of AMT. Pain that is relieved by other means and history of heart disease on treatment are important predictors of a diagnosis other than AMT. The degree of accuracy is high at 80.5. There are 13 factors that are significant in the univariate analysis but are not among the nine significant factors in the multivariate analysis. These are location of pain, associated palpitations, nausea and vomiting; pain relieved by rest, pain aggravated by posture, cough, inspiration and exertion; age more than 40, being a smoker and abnormal chest wall and face examination. We believe that these findings can have important applications in the design of an intelligent decision support system for use in medical care as the predictive capability can be further refined with the use of intelligent computational techniques. (c) 2004 Elsevier Ireland Ltd. All rights reserved.
format Article
author Bulgiba, A.M.
Razaz, M.
author_facet Bulgiba, A.M.
Razaz, M.
author_sort Bulgiba, A.M.
title How well can signs and symptoms predict AMI in the Malaysian population?
title_short How well can signs and symptoms predict AMI in the Malaysian population?
title_full How well can signs and symptoms predict AMI in the Malaysian population?
title_fullStr How well can signs and symptoms predict AMI in the Malaysian population?
title_full_unstemmed How well can signs and symptoms predict AMI in the Malaysian population?
title_sort how well can signs and symptoms predict ami in the malaysian population?
publishDate 2005
url http://eprints.um.edu.my/3087/1/How_well_can_signs_and_symptoms_predict_AMI_in_the_Malaysian_population.pdf
http://eprints.um.edu.my/3087/
http://www.sciencedirect.com/science/article/pii/S0167527304004619
_version_ 1643687043421700096
score 13.211869

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