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Diagnosing left ventricular dysfunction after myocardial infarction: the Dundee algorithm
D Darbar1, N Gillespie, A M Choy
1Division of Clinical Pharmacology, Vanderbilt University School of Medicine, Nashville, USA.
Insights
A new bedside algorithm can identify patients with left ventricular dysfunction after acute myocardial infarction (AMI). This helps determine who benefits most from angiotensin converting enzyme (ACE) inhibitors.
Area of Science:
- Cardiology
- Clinical Medicine
- Medical Diagnostics
Background:
- Angiotensin converting enzyme (ACE) inhibitors show greatest benefit in patients with left ventricular (LV) dysfunction post-acute myocardial infarction (AMI).
- Early LV function assessment after AMI is challenging due to resource and personnel limitations.
- A bedside clinical algorithm is needed to identify patients with LV dysfunction (LVEF ≤ 40%) as an alternative to echocardiography.
Purpose of the Study:
- To devise and validate a clinical algorithm for bedside use to identify patients with left ventricular ejection fraction (LVEF) ≤ 40% after AMI.
- To provide a tool for identifying patients who would benefit from ACE inhibitor therapy.
Main Methods:
- A clinical algorithm was developed based on specific criteria: clinical signs of heart failure, anterior myocardial infarction with Q-wave, or lack of thrombolytic therapy in specific high-risk patients.
- The algorithm was prospectively tested in two hospital coronary care units (UK and USA).
Main Results:
- In the UK center, the algorithm demonstrated 82% sensitivity and 72% specificity for identifying patients with LVEF ≤ 40%.
- In the US center, sensitivity was 91% and specificity was 78% for identifying LV dysfunction.
- The algorithm proved effective in both centers for identifying patients with reduced LVEF.
Conclusions:
- A simple clinical algorithm has been validated for bedside use.
- This algorithm reliably identifies patients who would benefit from ACE inhibitors following AMI.
- It serves as a valuable alternative to echocardiography for early risk stratification.
Abstract:
Large-scale trials of angiotensin converting enzyme (ACE) inhibitors after acute myocardial infarction (AMI) suggest that the benefits are greatest in patients with left ventricular (LV) dysfunction. However, early evaluation of LV function in all patients after AMI by current methods can be difficult due to a lack of resources and skilled personnel. Thus a clinical algorithm that could be used at the bedside to reliably identify patients with a left ventricular ejection fraction (LVEF) < or = 40% would be helpful as an occasional alternative to echocardiography. We have devised such an algorithm based on the presence of one of: (i) clinical signs of heart failure; (ii) an index Q-wave anterior myocardial infarction; (iii) lack of thrombolytic therapy when there is a history of two or more previous myocardial infarctions and a CK rise > 1000 U/l. We tested this new algorithm prospectively in the coronary care units of two hospitals (one UK and one USA). In the UK centre, the sensitivity and specificity of the algorithm at identifying patients with a LVEF < or = 40% were 82% and 72%, respectively. In the US centre, the sensitivity of the algorithm was 91% and the specificity 78% at identifying patients with LV dysfunction. We have validated a simple clinical algorithm which can be used at the bedside for identifying patients who would benefit from an ACE inhibitor after AMI.