A Predictive Model for Distinguishing Ischemic from Non-Ischemic Cardiomyopathy
Wanwarang Wongchareon MD*, Arintaya Phrommintikul MD*, Rungsrit Kanjanavanit MD*, Srun Kuanprasert MD*, Apichard Sukonthasarn MD*
Affiliation : * Department of Internal Medicine, Faculty of Medicine, Chiang Mai University
Objectives : To develop a predictive model to distinguish ischemic from non-ischemic cardiomyopathy
Materials and Methods : The authors randomly assigned 137 patients with LV systolic dysfunction into two
subsets - one to derive a predictive model and the other to validate it. Clinical, electrocardiographic and
echocardiographic data were interpreted by blinded investigators to the subsequent coronary angiogram
results. Ischemic cardiomyopathy was diagnosed by the presence of significant coronary artery disease from
the coronary angiogram. The final model had been derived from the clinical data and was validated using the
validating set. The receiver-operating characteristics (ROC) curves and the diagnostic performances of the
model were estimated.
Results : The authors developed the following model: Predictive score = (3 x presence of diabetes mellitus) +
number of ECG leads with abnormal Q waves – (5 x presence of echocardiographic characteristic of non-
ischemic cardiomyopathy). The model was well discriminated (area under ROC curve = 0.94). Performance in
the validating sample was equally good (area under ROC curve = 0.89). When a cut-off point > 0 was used to
predict the presence of significant coronary artery disease, the model had a sensitivity, specificity and positive
and negative predictive values of 100%, 57%, 74% and 100%, respectively.
Conclusion : With the high negative value of this model, it would be useful for use as a screening tool to exclude
non-ischemic cardiomyopathy in heart failure patients and may avoid unnecessary coronary angiograms.
Keywords : Predictive model, Non-invasive, Distinguish, Ischemic cardiomyopathy
