The evolution of cardiac resynchronization therapy and an introduction to conduction system pacing: a conceptual review

EP Europace ◽  
2020 ◽  
Author(s):  
Bengt Herweg ◽  
Allan Welter-Frost ◽  
Pugazhendhi Vijayaraman

Abstract In chronic systolic heart failure and conduction system disease, cardiac resynchronization therapy (CRT) is the only known non-pharmacologic heart failure therapy that improves cardiac function, functional capacity, and survival while decreasing cardiac workload and hospitalization rates. While conventional bi-ventricular pacing has been shown to benefit patients with heart failure and conduction system disease, there are limitations to its therapeutic success, resulting in widely variable clinical response. Limitations of conventional CRT evolve around myocardial scar, fibrosis, and inability to effectively simulate diseased tissue. Studies have shown endocardial stimulation in closer proximity to the specialized conduction system is more effective when compared with epicardial stimulation. Several observational and acute haemodynamic studies have demonstrated improved electrical resynchronization and echocardiographic response with conduction system pacing (CSP). Our objective is to provide a systematic review of the evolution of CRT, and an introduction to CSP as an intriguing, though experimental physiologic alternative to conventional CRT.

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
E Galli ◽  
V Le Rolle ◽  
OA Smiseth ◽  
J Duchenne ◽  
JM Aalen ◽  
...  

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


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