Comparison of lead aVR “net QRS area” and “peak-to-peak amplitude” as indices of all limb electrocardiogram leads: Implications for the diagnosis, management, and follow-up in patients with heart failure

Nonreproducibility of precordial ECG leads in serial ECGs has led to emphasis on the limb leads. The sum of the “peak-to-peak amplitude” (Amp) of the QRS complexes of all 6 limb leads ∑ 6 LbLds , has been employed as a metric in the follow-up of patients with edematous states (ES), including heart failure (HF). Since electrocardiographs measure leads I and II and calculate on line the remaining 4 limb leads, the sum of leads I and II (I + II), in any mathematical form, conceivably may reflect the corresponding variable of ∑6LbLds. In turn, due to the aVR = −(I + II)/2 relationship, aVR could be regarded as an index of ∑6LbLds. It is not known whether aVR “net QRS area”, i.e., positive minus negative QRS areas, has advantages over aVR Amp, as an index of ∑6LbLds. Automation-based measurements of Amps and “net QRS areas” of all 6 limb leads in 100 consecutive ECGs were analyzed, both employing and ignoring the algebraic signs (i.e., using the absolute or modulus, or |a| values), in the characterization of lead aVR or summations of metrics. There was an excellent correlation between “net QRS area” of aVR and I + II, between aVR and ∑6LbLds, and between I + II and ∑6LbLds (P = 0.0005) when the algebraic signs were considered. There was an excellent correlation between the “net QRS area” of aVR and I + II, and between I + II and ∑6LbLds, but the correlation between aVR and ∑6LbLds (P = 0.0005) was good, when the algebraic signs were ignored, and lower than when the algebraic signs were considered. Correlations between aVR or I + II, with ∑6LbLds were better when the Amp(s) than the “net QRS area(s) were considered. The QRS Amp aVR is better than the “net QRS area” aVR as an index of ∑6LbLds; however correlation of all the above ECG metrics with body weights and fluid balances in patients with ES and HF are needed to identify the optimal ECG metric(s) for clinical adoption.