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Predicting coronary heart disease in remote settings: a prospective, cross-sectional observational study
J Dwivedi1, S Sutcliffe2, L Easterbrook2
1Cairns Clinical School, School of Medicine and Dentistry, James Cook University, Cairns, Queensland, Australia.
Insights
Typical chest pain and abnormal ECG are key indicators for predicting coronary heart disease (CHD) risk in remote settings. This simple assessment aids early diagnosis when specialized tests are delayed.
Area of Science:
- Cardiology
- Primary Health Care
- Diagnostic Medicine
Background:
- Coronary heart disease (CHD) presents a significant healthcare challenge, particularly in remote Australian settings.
- Access to specialized investigations for CHD diagnosis is often limited in remote primary care.
- Developing accessible predictive models for CHD is crucial for timely patient management.
Purpose of the Study:
- To develop a predictive model for CHD risk in patients with chest pain.
- To evaluate the utility of high-sensitivity C-reactive protein (hsCRP) in remote CHD risk assessment.
- To focus on resources readily available in primary healthcare settings.
Main Methods:
- Prospective, cross-sectional observational study.
- Inclusion of patients without prior CHD diagnosis presenting to a chest pain clinic.
- Data collected from November 2012 to May 2013.
Main Results:
- 38 out of 163 participants (23.3%) were identified as likely having CHD.
- Typical chest pain was strongly associated with likely CHD (OR 83.7).
- An abnormal baseline electrocardiogram (ECG) was also independently associated with likely CHD (OR 12.8).
Conclusions:
- Typical chest pain and abnormal resting ECG are primary predictors of CHD.
- These accessible clinical findings can expedite CHD assessment in remote primary care.
- The study emphasizes leveraging readily available information for efficient patient evaluation.
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.
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