The rational clinical examination. Is this patient having a myocardial infarction?
A A Panju1, B R Hemmelgarn, G H Guyatt
1Department of Medicine, McMaster University, Hamilton, Ontario, Canada. panjuaa@fhs.csu.mcmaster.ca
Insights
Diagnosing myocardial infarction (MI) relies on focused patient history, physical exams, and ECGs. Key indicators like ST-segment elevation increase MI probability, while normal ECGs and positional pain decrease it.
Area of Science:
- Cardiology
- Emergency Medicine
- Diagnostic Imaging
Background:
- Acute chest pain necessitates differentiating myocardial infarction (MI) from other causes.
- Clinical decisions regarding thrombolysis, angioplasty, and coronary care unit admission depend on MI suspicion.
- Electrocardiogram (ECG) changes guide treatment decisions for suspected MI.
Purpose of the Study:
- To identify key clinical and ECG findings that aid in the diagnosis of myocardial infarction.
- To evaluate the diagnostic utility of specific historical and physical examination elements in acute chest pain patients.
Main Methods:
- Review of clinical history, physical examination findings, and ECG results in patients presenting with acute chest pain.
- Analysis of likelihood ratios (LRs) for various clinical features to determine their association with MI.
- Exploration of the potential role of computer-derived algorithms integrating clinical and ECG data.
Main Results:
- New ST-segment elevation (LR 5.7-53.9) and new Q waves (LR 5.3-24.8) are strong indicators of MI.
- Specific symptoms like bilateral arm radiation (LR 7.1), third heart sound (LR 3.2), and hypotension (LR 3.1) increase MI probability.
- Features decreasing MI probability include normal ECG (LR 0.1-0.3), pleuritic pain (LR 0.2), reproducible pain (LR 0.2-0.4), sharp pain (LR 0.3), and positional pain (LR 0.3).
Conclusions:
- A focused history, physical examination, and ECG remain crucial for diagnosing MI in acute chest pain.
- Specific clinical features and ECG findings significantly alter the probability of MI.
- Computer algorithms incorporating clinical and ECG data may enhance MI risk stratification.
Abstract:
When faced with a patient with acute chest pain, clinicians must distinguish myocardial infarction (MI) from all other causes of acute chest pain. If MI is suspected, current therapeutic practice includes deciding whether to administer thrombolysis or primary percutaneous transluminal coronary angioplasty and whether to admit patients to a coronary care unit. The former decision is based on electrocardiographic (ECG) changes, including ST-segment elevation or left bundle-branch block, the latter on the likelihood of the patient's having unstable high-risk ischemia or MI without ECG changes. Despite advances in investigative modalities, a focused history and physical examination followed by an ECG remain the key tools for the diagnosis of MI. The most powerful features that increase the probability of MI, and their associated likelihood ratios (LRs), are new ST-segment elevation (LR range, 5.7-53.9); new Q wave (LR range, 5.3-24.8); chest pain radiating to both the left and right arm simultaneously (LR, 7.1); presence of a third heart sound (LR, 3.2); and hypotension (LR, 3.1). The most powerful features that decrease the probability of MI are a normal ECG result (LR range, 0.1-0.3), pleuritic chest pain (LR, 0.2), chest pain reproduced by palpation (LR range, 0.2-0.4), sharp or stabbing chest pain (LR, 0.3), and positional chest pain (LR, 0.3). Computer-derived algorithms that depend on clinical examination and ECG findings might improve the classification of patients according to the probability that an MI is causing their chest pain.
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