scholarly journals Deleterious effect of right ventricular pacing in patients with cardiac transthyretin amyloidosis: potential clinical benefit of cardiac resynchronization therapy

2020 ◽  
Vol 4 (3) ◽  
pp. 1-5
Author(s):  
David Aouate ◽  
Aymeric Menet ◽  
Dimitri Bellevre ◽  
Thibaud Damy ◽  
Sylvestre Marechaux
Keyword(s):  
Heart Failure ◽  
Cardiac Resynchronization Therapy ◽  
Right Ventricular ◽  
Cardiac Amyloidosis ◽  
Left Ventricular ◽  
Cardiac Resynchronization ◽  
Ventricular Pacing ◽  
Resynchronization Therapy ◽  
Transthyretin Amyloidosis ◽  
Rv Pacing

Abstract Background Cardiac amyloidosis involvement is associated with a detrimental outcome including frequent arrhythmias, heart failure, and conduction disturbances which may need permanent pacing. Cases summary We report two cases of patients with transthyretin amyloidosis (ATTR) who developed heart failure and depressed left ventricular ejection fraction (LVEF) following permanent right ventricular (RV) pacing but highly responded to cardiac resynchronization therapy (CRT). Discussion The impact of RV pacing and CRT in cardiac amyloidosis is not known. In our cases, the detrimental effect of permanent RV pacing on left ventricular (LV) systolic function and heart failure symptoms was suggested by both permanent RV pacing mediated functional and LV function decline and LV systolic dysfunction reversal following CRT along with QRS width reduction. Whether cardiac resynchronization should be readily recommended in ATTR patients who need ventricular pacing whatever the LVEF deserves further investigation.

scholarly journals Importance of systematic right ventricular assessment in cardiac resynchronization therapy candidates: a machine-learning approach

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
E Galli ◽  
V Le Rolle ◽  
OA Smiseth ◽  
J Duchenne ◽  
JM Aalen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Cardiac Resynchronization Therapy ◽  
Right Ventricular ◽  
Systolic Heart Failure ◽  
Left Ventricular ◽  
Supervised Machine Learning ◽  
Cardiac Resynchronization ◽  
Resynchronization Therapy ◽  
Crt Response

Abstract Funding Acknowledgements Type of funding sources: None. Background Despite having all a systolic heart failure and broad QRS, patients proposed for cardiac resynchronization therapy (CRT) are highly heterogeneous and it remains extremely complicated to predict the impact of the device on left ventricular (LV) function and outcomes. Objectives We sought to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular (LV) remodeling and prognosis of CRT-candidates by the application of machine learning (ML) approaches. Methods 193 patients with systolic heart failure undergoing CRT according to current recommendations were prospectively included in this multicentre study. We used a combination of the Boruta algorithm and random forest methods to identify features predicting both CRT volumetric response and prognosis (Figure 1). The model performance was tested by the area under the receiver operating curve (AUC). We also applied the K-medoid method to identify clusters of phenotypically-similar patients. Results From 28 clinical, electrocardiographic, and echocardiographic-derived variables, 16 features were predictive of CRT-response; 11 features were predictive of prognosis. Among the predictors of CRT-response, 7 variables (44%) pertained to right ventricular (RV) size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with a very good prediction of both CRT response (AUC 0.81, 95% CI: 0.74-0.87) and outcomes (AUC 0.84, 95% CI: 0.75-0.93) (Figure 1, Supervised Machine Learning Panel). An unsupervised ML approach allowed the identifications of two phenogroups of patients who differed significantly in clinical and parameters, biventricular size and RV function. The two phenogroups had significant different prognosis (HR 4.70, 95% CI: 2.1-10.0, p < 0.0001; log –rank p < 0.0001; Figure 1, Unsupervised Machine Learning Panel). Conclusions Machine learning can reliably identify clinical and echocardiographic features associated with CRT-response and prognosis. The evaluation of both RV-size and function parameters has pivotal importance for the risk stratification of CRT-candidates and should be systematically assessed in patients undergoing CRT. Abstract Figure 1

P5680One-year outcomes of heart failure patients undergoing cardiac resynchronization therapy using adaptivCRT algorithm

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Briongos Figuero ◽  
A Estevez ◽  
M L Perez ◽  
J B Martinez-Ferrer ◽  
L Alvarez-Costa ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cardiac Resynchronization Therapy ◽  
Real Life ◽  
Left Ventricular ◽  
Cardiac Resynchronization ◽  
Ventricular Pacing ◽  
Class Iii ◽  
Resynchronization Therapy ◽  
Risk Of Death

Abstract Background Adaptive cardiac resynchronization therapy (aCRT) algorithm provides synchronized left ventricular (LV) only pacing and ambulatory optimization of the intrinsic atrioventricular and interventricular conduction intervals. Studies reporting morbidity and mortality outcomes of aCRT carriers in daily clinical practice are lacking. Purpose To determine in a real-life setting, whether 1-year outcomes were different among CRT carriers undergoing aCRT pacing and those under conventional biventricular (biV) pacing. Methods Symptomatic heart failure (HF) patients with sinus rhythm undergoing first CRT-defibrillator implant were selected from the UMBRELLA nationwide registry (2012–2017). The primary endpoint was the composite of all-cause mortality or HF hospitalization at 12-month follow-up. HF admission was defined as hospitalization due to symptoms requiring intravenous diuretic treatment. Primary healthcare records were used to prospectively collect all data. Results Two hundred and six patients were collected (66.1±8.7 years; 73.3% male). Eighty-seven out of 206 patients were implanted with an aCRT capable device, but this algorithm was activated at implant and remained enabled at 1-year in 59 patients (aCRT group). The other 147 patients composed the non-aCRT group. At implant left bundle branch block was present in 93% of patients, 69.6% of population was in functional class III or IV and mean left ventricle ejection fraction was of 26.5±5.6%. Non-ischemic cardiomyopathy was present in 63.1% of patients and optimal medical treatment was achieved in majority of population (92% of patients with beta-blockers; angiotensin-converting enzyme inhibitorsor angiotensin II receptor blockersin 89%). The percentage of ventricular pacing through 12 months was 96.1±9.4% in non-aCRT patients and 97.5±2.7% in aCRT patients (p=0.261). In aCRT patients, LV-only pacing accounted for a mean of 53.3±37.6% of all ventricular pacing. After 12-month follow-up period, 25 patients (12.1%) met the primary composite endpoint of death or HF hospitalization. Nine patients died and nineteen patients were admitted due to worsening HF. There was no difference in the risk of all-cause death or HF hospitalization between aCRT and non-aCRT patients (10.2% vs. 12.9% respectively; OR=0.76, CI: 0.29–2.01, p=0.585) Conclusions In this contemporary cohort of HF patients undergoing CRT with high percentages of ventricular pacing, clinical performance of aCRT algorithm was adequate. The risk of death or HF hospitalization was low and no differences were observed at one-year follow-up. Future randomized studies will clarify the role of this algorithm in CRT carriers. Acknowledgement/Funding None

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