scholarly journals Multi‐modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45%

2020 ◽  
Vol 7 (6) ◽  
pp. 3716-3725
Author(s):  
Gary Tse ◽  
Jiandong Zhou ◽  
Samuel Won Dong Woo ◽  
Ching Ho Ko ◽  
Rachel Wing Chuen Lai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Risk Stratification ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Learning Approach ◽  
Ventricular Ejection Fraction ◽  
Machine Learning Approach

scholarly journals Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e023724 ◽  
Author(s):  
Fanqi Meng ◽  
Zhihua Zhang ◽  
Xiaofeng Hou ◽  
Zhiyong Qian ◽  
Yao Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Sudden Cardiac Death ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Study Protocol ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

IntroductionLeft ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF.Methods and analysisWe will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study.Ethics and disseminationThe study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences.Trial registration numberChiCTR-POC-17011842; Pre-results.

ACCESSMENT OF RIGHT VENTRICULAR SYSTOLIC FUNCTION USING TAPSE

2011 ◽  
pp. 62-70
Author(s):  
Lien Nhut Nguyen ◽  
Anh Vu Nguyen
Keyword(s):  
Heart Failure ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Right Ventricular ◽  
Systolic Function ◽  
Systolic Heart Failure ◽  
Left Ventricular ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

Background: The prognostic importance of right ventricular (RV) dysfunction has been suggested in patients with systolic heart failure (due to primary or secondary dilated cardiomyopathy - DCM). Tricuspid annular plane systolic excursion (TAPSE) is a simple, feasible, reality, non-invasive measurement by transthoracic echocardiography for evaluating RV systolic function. Objectives: To evaluate TAPSE in patients with primary or secondary DCM who have left ventricular ejection fraction ≤ 40% and to find the relation between TAPSE and LVEF, LVDd, RVDd, RVDd/LVDd, RA size, severity of TR and PAPs. Materials and Methods: 61 patients (36 males, 59%) mean age 58.6 ± 14.4 years old with clinical signs and symtomps of chronic heart failure which caused by primary or secondary DCM and LVEF ≤ 40% and 30 healthy subject (15 males, 50%) mean age 57.1 ± 16.8 were included in this study. All patients and controls were underwent echocardiographic examination by M-mode, two dimentional, convensional Dopler and TAPSE. Results: TAPSE is significant low in patients compare with the controls (13.93±2.78 mm vs 23.57± 1.60mm, p<0.001). TAPSE is linearly positive correlate with echocardiographic left ventricular ejection fraction (r= 0,43; p<0,001) and linearly negative correlate with RVDd (r= -0.39; p<0.01), RVDd/LVDd (r=-0.33; p<0.01), RA size (r=-0.35; p<0.01), TR (r=-0.26; p<0.05); however, no correlation was found with LVDd and PAPs. Conclusions: 1. Decreased RV systolic function as estimated by TAPSE in patients with systolic heart failure primary and secondary DCM) compare with controls. 2. TAPSE is linearly positive correlate with LVEF (r= 0.43; p<0.001) and linearly negative correlate with RVDd (r= -0.39; p<0.01), RVDd/LVDd (r=-0.33; p<0.01), RA size (r=-0.35; p<0.01), TR (r=-0.26; p<0.05); however, no correlation is found with LVDd and PAPs. 3. TAPSE should be used routinely as a simple, feasible, reality method of estimating RV function in the patients systolic heart failure DCM (primary and secondary).

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