New Unipolar Electrogram Criteria to Identify Irreversibility of Nonischemic Left Ventricular Cardiomyopathy

ObjectivesThis study sought to assess the value of left ventricular (LV) endocardial unipolar electroanatomical mapping (EAM) in identifying irreversibility of LV systolic dysfunction in patients with left ventricular nonischemic cardiomyopathy (LVCM).BackgroundIdentifying irreversibility of LVCM would be helpful but cannot be reliably accomplished by bipolar EAM or cardiac magnetic resonance identification of macroscopic scar.MethodsDetailed endocardial LV EAM was performed in 3 groups: 1) 24 patients with irreversible LVCM (I-LVCM) but with no or minimal macroscopic scar (<15% LV surface) evidenced on bipolar voltage EAM and/or cardiac magnetic resonance; 2) 14 patients with reversible ventricular premature depolarization–mediated LVCM (R-LVCM); and 3) 17 patients with structurally normal hearts. LV endocardial unipolar electrogram amplitude and area of unipolar amplitude abnormality were defined after excluding macroscopic scar.ResultsUnipolar amplitude differed in the 3 groups: median of 7.6 (interquartile range [IQR]: 5.5 to 9.7) mV in I-LVCM group, 13.2 (IQR: 10.4 to 16.2) mV in R-LVCM group, and 16.3 (IQR: 13.6 to 19.8) mV in structurally normal hearts group (p < 0.001). Areas of unipolar abnormality represented a large proportion of total LV surface in I-LVCM, 64.7% (IQR: 47.5% to 75.9%) compared with R-LVCM, 5.2% (IQR: 0.0% to 19.1%) and structurally normal hearts, 0.1% (IQR: 0.0% to 0.9%), groups (p < 0.001). A unipolar abnormality area cutoff of 32% of total LV surface was 96% sensitive and 100% specific in identifying irreversible cardiomyopathy among patients with LV dysfunction (I-LVCM and R-LVCM), p < 0.001.ConclusionsDetailed unipolar voltage mapping can identify irreversible myocardial dysfunction consistent with fibrosis, even in the absence of bipolar EAM or cardiac magnetic resonance abnormalities, and may serve as valuable prognostic tool in patients presenting with LVCM to facilitate clinical decision making