Purpose Many patients attending their primary care physician with symptoms suggestive of new onset heart failure, have a 12 lead electrocardiogram (ECG) as part of an initial triage work up. However, the role of ECG in predicting heart failure in the community is not yet defined. We thus examined the ability of ECG to predict heart failure in this patient population.
Method All 733 patients attending the rapid access clinic for possible heart failure in St Vincent’s University Hospital, Dublin, from the period of 2000 till 2012 were included in this study. 12-lead ECG was performed using the Agilent Page Writer 100 ECG machine and interpreted by independent cardiologists. The ECGs were analysed along side the diagnosis of heart failure. ROC curves were performed to assess the robustness of the ECG in predicting heart failure.
Result Heart failure patients had significant prolonged QRS duration, prolonged QT duration, prolonged QTc and more rightward T wave axis compared to the non heart failure group. They also had significant ECG evidence of prior myocardial ischaemia, intraventricular conduction disorder, abnormal axis, ventricular hypertrophy and atrial fibrillation. Using the ECG evidence of myocardial ischaemia, intraventricular conduction disorder, atrioventricular disorder, abnormal axis, atrial enlargement, ventricular hypertrophy, ventricular arrhythmia and atrial fibrillation as a predictive model, the ROC analysis showed that the ECG model is a reasonable test (AUC = 0.81) to help predict heart failure in the community. Adding BNP to the model increased the robustness of the model in predicting heart failure (AUC = 0.88).
Conclusion The utility of the 12-lead ECG in predicting heart failure in the community is under appreciated. This study showed that this simple test is useful and can offer primary care physicians the ability to expedite the diagnosis of heart failure in order to start relevant further investigation and treatment in the community. In conclusion, ECG is a useful test in predicting heart failure in the community, however addition of BNP into the model helped to increase the robustness of the test.
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