Suboptimal medical therapy in patients with systolic heart failure is associated with less improvement by cardiac resynchronization therapy

2007 ◽  
Vol 115 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Jeffrey W.H. Fung ◽  
Joseph Y.S. Chan ◽  
Leo C.C. Kum ◽  
Hamish C.K. Chan ◽  
Gabriel W.K. Yip ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cardiac Resynchronization Therapy ◽  
Medical Therapy ◽  
Systolic Heart Failure ◽  
Cardiac Resynchronization ◽  
Resynchronization Therapy

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

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