Predicting coronary heart disease in remote settings: a prospective, cross-sectional observational study

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.