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

International Journal of Cardiology

Severity of Illness Index, Risk Factors, Retrospective Studies, Regression Analysis, Predictive Value of Tests, Population Surveillance, epidemiology, diagnosis, complications, Myocardial Infarction, Middle Aged, Male, Malaysia, Incidence, Humans, statistics & numerical data, Hospital Records, Female, Exercise Test, Diagnosis, Differential, etiology, Chest Pain, Age Factors, Adult

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      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 (AMI) 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 AMI. However, multiple logistic regression reveals that only 9 of these 22 variables are significantly related to a diagnosis of AMI. Race (Indian), male sex, sudden onset of persistent crushing pain, associated sweating and a history of diabetes mellitus are significant predictors of AMI. Pain that is relieved by other means and history of heart disease on treatment are important predictors of a diagnosis other than AMI. 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.

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