How well can signs and symptoms predict AMI in the Malaysian population? A.M. Bulgiba a, * , M. Razaz b a Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia b School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom Received 21 October 2003; received in revised form 12 March 2004; accepted 25 April 2004 Available online 1 August 2004 Abstract 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. D 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Acute myocardial infarction; Diagnosis; Prediction; Multiple logistic regression 1. Introduction Acute chest pain in adults is a frequently encountered symptom in all healthcare settings [1]. It warrants immediate attention and assessment because of the high rates of morbidity and mortality associated with the pathology. It is a symptom that can be quite perplexing for the doctor because of the wide range of differential diagnoses possible for the patient. Differential diagnoses can range from the most life threatening of illnesses to simple problems that can be treated in the outpatient clinic. It is vital for the emergency physician not to miss a diagnosis of AMI as ischaemic heart disease is one of the leading causes of morbidity and mortality in the western world [2]. In a review of sudden deaths in England, it was found that for cases involving myocardial tissue, death was ascribed to ischaemic heart disease in 82.4% of cases [3]. In the elderly, hypertension and ischaemic heart disease have been found to be significant predictors of emergency room admissions [4]. There are many risk factors for AMI. In the elderly, for example it has been shown that hyperlipidaemia, smoking, hypertension, diabetes and a family history of heart disease are independently and strongly related to the risk of AMI [5]. In addition to these, males are often at substantially higher risk than females and renal impairment does appear to influence mortality for AMI [6]. Hypertension treatment has been shown to be of benefit in hypertensives with diabetes, ischaemic heart disease and high global cardio- vascular risk although in smokers, these should be accompanied by efforts to induce smoking cessation [7]. Blood pressure was at least as strongly associated with 0167-5273/$ - see front matter D 2004 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ijcard.2004.04.002 * Corresponding author. E-mail address: awang@um.edu.my (A.M. Bulgiba). International Journal of Cardiology 102 (2005) 87 – 93 www.elsevier.com/locate/ijcard