Literature DB >> 30661850

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

A M W van Stipdonk1, S Vanbelle2, I A H Ter Horst3, J G Luermans4, M Meine3, A H Maass5, A Auricchio6, F W Prinzen7, K Vernooy8.   

Abstract

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.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cardiac resynchronisation therapy; Left bundle branch block; Observer variation; QRS morphology

Mesh:

Year:  2019        PMID: 30661850     DOI: 10.1016/j.ijcard.2019.01.051

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  6 in total

Review 1.  Non-invasive cardiac mapping for non-response in cardiac resynchronization therapy.

Authors:  Marc Strik; Sylvain Ploux; Lior Jankelson; Pierre Bordachar
Journal:  Ann Med       Date:  2019-05-23       Impact factor: 4.709

2.  A Predictive Model for Super-Response to Cardiac Resynchronization Therapy: The QQ-LAE Score.

Authors:  Xi Liu; Yiran Hu; Wei Hua; Shengwen Yang; Min Gu; Hong-Xia Niu; Li-Gang Ding; Jing Wang; Shu Zhang
Journal:  Cardiol Res Pract       Date:  2020-08-28       Impact factor: 1.866

3.  Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

Authors:  Albert K Feeny; John Rickard; Kevin M Trulock; Divyang Patel; Saleem Toro; Laurie Ann Moennich; Niraj Varma; Mark J Niebauer; Eiran Z Gorodeski; Richard A Grimm; John Barnard; Anant Madabhushi; Mina K Chung
Journal:  Circ Arrhythm Electrophysiol       Date:  2020-06-14

4.  The relationship between ECG predictors of cardiac resynchronization therapy benefit.

Authors:  Josef Halamek; Pavel Leinveber; Ivo Viscor; Radovan Smisek; Filip Plesinger; Vlastimil Vondra; Jolana Lipoldova; Magdalena Matejkova; Pavel Jurak
Journal:  PLoS One       Date:  2019-05-31       Impact factor: 3.240

5.  Vectorcardiographic QRS area as a predictor of response to cardiac resynchronization therapy.

Authors:  Mohammed A Ghossein; Antonius Mw van Stipdonk; Frits W Prinzen; Kevin Vernooy
Journal:  J Geriatr Cardiol       Date:  2022-01-28       Impact factor: 3.327

Review 6.  Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View.

Authors:  Arman Naseri Jahfari; David Tax; Marcel Reinders; Ivo van der Bilt
Journal:  JMIR Med Inform       Date:  2022-01-19
  6 in total

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