Browsing by Author "Voigt, Jens-Uwe"

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    Imaging in Patients with Cardiovascular Implantable Electronic Devices: Part 1-Imaging Before and During Device Implantation. A Clinical Consensus Statement of the European Association of Cardiovascular Imaging (EACVI) and the European Heart Rhythm Association (EHRA) of the ESC
    (2023) Stankovic, Ivan; Voigt, Jens-Uwe; Burri, Haran; Muraru, Denisa; Sade, Leyla Elif; Haugaa, Kristina Hermann; Lumens, Joost; Biffi, Mauro; Dacher, Jean Nicolas; Marsan, Nina Ajmone; Bakelants, Elise; Manisty, Charlotte; Dweck, Marc R.; Smiseth, Otto A.; Donal, Erwan; 37861372
    More than 500 000 cardiovascular implantable electronic devices (CIEDs) are implanted in the European Society of Cardiology countries each year. The role of cardiovascular imaging in patients being considered for CIED is distinctly different from imaging in CIED recipients. In the former group, imaging can help identify specific or potentially reversible causes of heart block, the underlying tissue characteristics associated with malignant arrhythmias, and the mechanical consequences of conduction delays and can also aid challenging lead placements. On the other hand, cardiovascular imaging is required in CIED recipients for standard indications and to assess the response to device implantation, to diagnose immediate and delayed complications after implantation, and to guide device optimization. The present clinical consensus statement (Part 1) from the European Association of Cardiovascular Imaging, in collaboration with the European Heart Rhythm Association, provides comprehensive, up-to-date, and evidence-based guidance to cardiologists, cardiac imagers, and pacing specialists regarding the use of imaging in patients undergoing implantation of conventional pacemakers, cardioverter defibrillators, and resynchronization therapy devices. The document summarizes the existing evidence regarding the use of imaging in patient selection and during the implantation procedure and also underlines gaps in evidence in the field. The role of imaging after CIED implantation is discussed in the second document (Part 2).
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    Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach
    (2021) Galli, Elena; Le Rolle, Virginie; Smiseth, Otto A.; Duchenne, Jurgen; Aalen, John M.; Larsen, Camilla K.; Sade, Elif A.; Hubert, Arnaud; Anilkumar, Smitha; Penicka, Martin; Linde, Cecilia; Leclercq, Christophe; Hernandez, Alfredo; Voigt, Jens-Uwe; Donal, Erwan; 33422667
    Background: Despite all having systolic heart failure and broad QRS intervals, patients screened for cardiac resynchronization therapy (CRT) are highly heterogeneous, and it remains extremely challenging to predict the impact of CRT devices on left ventricular function and outcomes. The aim of this study was to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular remodeling and prognosis of CRT candidates by the application of machine learning approaches. Methods: One hundred ninety-three patients with systolic heart failure receiving CRT according to current recommendations were prospectively included in this multicenter study. A combination of the Boruta algorithm and random forest methods was used to identify features predicting both CRT volumetric response and prognosis. Model performance was tested using the area under the receiver operating characteristic curve. The k-medoid method was also applied to identify clusters of phenotypically similar patients. Results: From 28 clinical, electrocardiographic, and echocardiographic variables, 16 features were predictive of CRT response, and 11 features were predictive of prognosis. Among the predictors of CRT response, eight variables (50%) pertained to right ventricular size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with particularly good prediction of both CRT response (area under the curve, 0.81; 95% CI, 0.74-0.87) and outcomes (area under the curve, 0.84; 95% CI, 0.75-0.93). An unsupervised machine learning approach allowed the identification of two phenogroups of patients who differed significantly in clinical variables and parameters of biventricular size and right ventricular function. The two phenogroups had significantly different prognosis (hazard ratio, 4.70; 95% CI, 2.1-10.0; P < .0001; log-rank P < .0001). Conclusions: Machine learning can reliably identify clinical and echocardiographic features associated with CRT response and prognosis. The evaluation of both right ventricular size and functional parameters has pivotal importance for the risk stratification of CRT candidates and should be systematically performed in patients undergoing CRT. (J Am Soc Echocardiogr 2021;34:494-502.)