22 Role of 12-lead electrocardiography in predicting heart failure in the community

Abstract

PurposeMany 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.MethodAll 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.ResultHeart 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).ConclusionThe 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.

A convolutional neural network approach to detect congestive heart failure

“…According to a recent study by Taylor and Hobbs [52], heart failure is associated with abnormal ECG in 89% of the cases. Moreover, James et al [53] showed that heart failure patients have significant prolonged QRS duration, prolonged QT duration, prolonged QTc and more rightward T wave axis. Also, de Luna et al [54] reported long QT interval and visible variations of the shape, amplitude or polarity of the T wave on an alternate-beat basis as ECG changes associated with heart failure.…”

Management of Heart Failure in Patients With Diabetes Mellitus in the UAE: A Call to Action

“…23 However, it should be noted that patients with a normal NT-ProBNP and abnormal ECG may have underlying atherosclerosis due to diabetes and metabolic syndrome, and this should be excluded particularly if there is no evidence of HF. 30,31 Albuminuria (including both micro-and macroalbuminuria) is another factor that is associated with left ventricular systolic and diastolic dysfunction in T2DM patients and is predictive of CV and HF events. Presence of albuminuria should trigger the suspicion of and confirmation of HF.…”

“…23,29,30 In a study on the patient community attending their primary care physicians (PCPs) with symptoms suggestive of new-onset HF, adding BNP to the ECG model was found to increase the robustness in HF prediction (area under the curve [AUC] = 0.88) in the receiver operating characteristic curve analyses as compared to using ECG model alone (AUC = 0.81). 31 The presence of cardiac stress and volume overload leads to the production of NT-proBNP by cardiac ventricles to promote cardioprotection. 32 Evidence from various studies has reflected the role of NT-proBNP in predicting cardiac events, including the risk of HF.…”

“…34 The PONTIAC and the STOP-HF studies have particularly led to the incorporation of NT-proBNP in the standard clinical care pathway, which leads to the detection of HF in diabetic patients at risk of HF, that is, the American College of Cardiology/American Heart Association (ACC/AHA) stage A and B HF. 30,31 The ESC guidelines suggest that if ECG is completely normal, then HF is unlikely. Hence, its routine use is recommended to rule out HF.…”

“…23 However, it should be noted that patients with a normal NT-ProBNP and abnormal ECG may have underlying atherosclerosis due to diabetes and metabolic syndrome, and this should be excluded particularly if there is no evidence of HF. 30,31…”