scholarly journals Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Marcinkiewicz-Siemion ◽  
M. Kaminski ◽  
M. Ciborowski ◽  
K. Ptaszynska-Kopczynska ◽  
A. Szpakowicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Ejection Fraction ◽  
Current Knowledge ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Metabolic Derangement ◽  
Ventricular Ejection Fraction ◽  
Diagnostic Panel ◽  
Selection Of

AbstractThe metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.

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.

Heart Failure with Preserved Ejection Fraction – A Review

2012 ◽  
Vol 9 (1) ◽  
pp. 90-95 ◽  
Author(s):  
Otto A Smiseth ◽  
Anders Opdahl ◽  
Espen Boe ◽  
Helge Skulstad
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Diastolic Heart Failure ◽  
The Elderly ◽  
Left Ventricular ◽  
Filling Pressure ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Reduced Ejection Fraction ◽  
Objective Evidence

Heart failure with preserved left ventricular ejection fraction (HF-PEF), sometimes named diastolic heart failure, is a common condition most frequently seen in the elderly and is associated with arterial hypertension and left ventricular (LV) hypertrophy. Symptoms are attributed to a stiff left ventricle with compensatory elevation of filling pressure and reduced ability to increase stroke volume by the Frank-Starling mechanism. LV interaction with stiff arteries aggravates these problems. Prognosis is almost as severe as for heart failure with reduced ejection fraction (HF-REF), in part reflecting co-morbidities. Before the diagnosis of HF-PEF is made, non-cardiac etiologies must be excluded. Due to the non-specific nature of heart failure symptoms, it is essential to search for objective evidence of diastolic dysfunction which, in the absence of invasive data, is done by echocardiography and demonstration of signs of elevated LV filling pressure, impaired LV relaxation, or increased LV diastolic stiffness. Antihypertensive treatment can effectively prevent HF-PEF. Treatment of HF-PEF is symptomatic, with similar drugs as in HF-REF.

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).

Sign in / Sign up

Export Citation Format

Share Document