Large variability in clinical judgement and definitions of left bundle branch block to identify candidates for cardiac resynchronisation therapy

Background: Left bundle branch block (LBBB) morphology is associated with improved outcome of cardiac resynchronisation therapy (CRT) and is an important criterion for patient selection. There are, however, multiple definitions for LBBB. Moreover, applying these definitions seems subjective. We investigated the inter- and intraobserver agreement in the determination of LBBB using available definitions, and clinicians' judgement of LBBB.Methods: Observers were provided with 12-lead ECGs of 100 randomly selected CRT patients. Four observers judged the ECGs based on different LBBB-definitions (ESC, AHA/ACC/HRS, MADIT, and Strauss). Additionally, four implanting cardiologists scored the same 100 ECGs based on their clinical judgement. Observer agreement was summarized through the proportion of agreement (P) and kappa coefficient (k).Results: Relative intra-observer agreement using different LBBB definitions, and within clinical judgement was moderate (range k 0.47-0.74 and k = 0.76 (0.14), respectively). The inter-observer agreement between observers using LBBB definitions as well as between clinical observers was minimal to weak (range k 0.19-0.44 and k = 0.35 (0.20), respectively). The probability of classifying an ECG as LBBB by available definitions varied considerably (range 0.20-0.76). The agreement between different definitions of LBBB ranged from good (P = 0.95 (0.07)) to weak (P = 0.40 (0.22)). Furthermore, correlation between the different LBBB definitions and clinical judgement was poor (range phi 0.30-0.55).Conclusion: Significant variation in the probability of classifying LBBB is present in using different definitions and clinical judgement. Considerable intra-and inter-observer variability adds to this variation. Interdefinition agreement varies significantly and correlation of clinical judgement with LBBB classification by definitions is modest at best. (C) 2019 Elsevier B.V. All rights reserved.