Predicting Coronary Heart Disease in Remote Settings: A Prospective, Cross-sectional Observational Study Academic Article uri icon

abstract

  • BACKGROUND: Coronary heart disease (CHD) places a major burden on the Australian health care system. Determining the likelihood of CHD in a patient presenting with chest pain can be particularly difficult in a remote setting where access to transportation and specialised investigations including myocardial stress studies and coronary angiography can be difficult and delayed. The objective is to develop a predictive model for determining the risk of CHD, including the value of high sensitivity C-reactive protein (hsCRP), in patients presenting with chest pain with a particular emphasis on resources and information likely to be available in a remote primary health care setting. METHODS: A prospective, cross-sectional observational study of patients with no prior diagnosis of CHD presenting to a specialist chest pain assessment clinic at Cairns Hospital from November 2012 to May 2013. RESULTS: Out of the 163 participants included in the study analyses, a total of 38 were classified as CHD likely (23.3% (95% CI 17.1-30.6)). Logistic regression modelling identified two factors that were independently associated with likely CHD, namely the presence of typical chest pain (OR 83.7 (95% CI 21.7-322.1)) and an abnormal baseline ECG (OR 12.8 (95% CI 1.9-86.0)). CONCLUSION: In this study, it was demonstrated that the presence of typical chest pain and an abnormal resting ECG, remain the cornerstone of predicting a subsequent diagnosis of CHD. This information is easily accessible in remote primary health care and should be utilised to expedite assessment in patients presenting with symptoms suggestive of CHD.

authors

publication date

  • 2014