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Reliability of bedside evaluation in determining left ventricular function: correlation with left ventricular
Insights
Clinical data can predict abnormal left ventricular ejection fraction (LVEF) in coronary artery disease patients. However, predicting the severity of ventricular dysfunction using these methods is less reliable, underscoring the value of radionuclide ventriculography.
Area of Science:
- Cardiology
- Medical Imaging
- Clinical Prediction Models
Background:
- Chronic coronary artery disease (CAD) significantly impacts left ventricular function.
- Accurate assessment of left ventricular ejection fraction (LVEF) is crucial for managing CAD.
- Predicting LVEF using traditional clinical data is an ongoing area of research.
Purpose of the Study:
- To evaluate the reliability of historical, physical, electrocardiographic, and radiologic data in predicting LVEF in patients with chronic CAD.
- To determine the accuracy of clinical assessments in differentiating normal, mildly reduced, and severely reduced LVEF.
- To identify the most significant clinical predictors of LVEF in this population.
Main Methods:
- Prospective evaluation of 99 patients with chronic CAD.
- Classification of patients into groups based on LVEF measured by radionuclide angiography (normal ≥50%, abnormal <50%, severe <30%).
- Stepwise linear regression analysis to identify predictive clinical variables.
Main Results:
- Clinical data accurately predicted the presence of abnormal LVEF (85%) but struggled to predict the degree of dysfunction (53% for moderate, 47% for severe).
- Cardiomegaly on chest roentgenography was the single most predictive variable (R²=0.52).
- A combination of four variables (cardiomegaly, myocardial infarction on ECG, dyspnea, rales) explained only 61% of LVEF variability.
Conclusions:
- While clinical, radiographic, and electrocardiographic data are useful for identifying abnormal LVEF in chronic CAD, they have limitations in quantifying the severity of ventricular dysfunction.
- Radionuclide ventriculography provides significant additional discriminatory power for assessing ventricular function compared to clinical parameters alone.
- Further research may explore integrating advanced imaging or biomarkers to improve prediction of LVEF severity in CAD.
Abstract:
Ninety-nine patients with chronic coronary artery disease were prospectively evaluated to determine the reliability of historical, physical, electrocardiographic and radiologic data in predicting left ventricular ejection fraction. The left ventricular ejection fraction measured by radionuclide angiography was normal (greater than or equal to 50%) in 44 patients (group 1) and abnormal (less than 50%) in 55 patients; 36 of those 55 patients had an ejection fraction between 30 and 49% (group 2) and the remaining 19 patients had an ejection fraction of less than 30% (group 3). The ejection fraction was correctly predicted in 33 of the 44 patients (75%) in group 1 and in 47 of the 55 patients (85%) with abnormal ejection fraction (groups 2 and 3), but the degree of ventricular dysfunction was correctly predicted in only 19 patients (53%) in group 2 and in only 9 patients (47%) in group 3. Stepwise linear regression analysis was performed. The single most predictive variable was cardiomegaly as seen on chest roentgenography (R2 = 0.52). Four optimal predictive variables--cardiomegaly, myocardial infarction as seen on electrocardiography, dyspnea and rales--could explain only 61% of the observed variables in left ventricular ejection fraction. Thus, radionuclide ventriculography adds significantly to the discriminant power of the clinical, radiographic and electrocardiographic characterization of ventricular function in patients with chronic coronary heart disease.