scholarly journals Influence of Heart Rate on Left and Right Ventricular Longitudinal Strain in Patients with Chronic Heart Failure

2022 ◽  
Vol 12 (2) ◽  
pp. 556
Author(s):  
Vito Di Terlizzi ◽  
Roberta Barone ◽  
Vincenzo Manuppelli ◽  
Michele Correale ◽  
Grazia Casavecchia ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Chronic Heart Failure ◽  
Right Ventricular ◽  
Longitudinal Strain ◽  
Lv Function ◽  
Continuous Variables ◽  
Lower Baseline ◽  
Left And Right ◽  
Strain Measures

Over the past years, a number of studies have demonstrated the relevance of strain assessed by two-dimensional speckle tracking echocardiography (STE) in evaluating ventricular function. The aim of this study was to analyze changes in left (LV) and right ventricular (RV) longitudinal strain associated with variations of heart rate (HR) in participants with and without chronic heart failure (CHF). We enrolled 45 patients, 38 of these diagnosed with CHF and carrying an implantable cardioverter defibrillator, and seven patients with pacemakers and without CHF. The frequency of atrial stimulation was increased to 90 beats/min and an echocardiogram was performed at each increase of 10 beats/min. Global LV and RV longitudinal strain (LVGLS and RVGLS, respectively) and RV free wall longitudinal strain (RVfwLS) were calculated at each HR. When analyzed as continuous variables, significant reductions in LVGLS were detected at higher HRs, whereas improvements in both RVGLS and RVfwLS were observed. Patients with a worsening of LVGLS (76% overall) were more likely to present lower baseline LV function. Only a few patients (18% for RVGLS and 16% for RVfwLS) exhibited HR-related deteriorations of RV strain measures, which was associated with lower levels of baseline RV function and higher pulmonary systolic pressures. Finally, 21 (47%) and 25 (56%) participants responded with improvements in RVGLS and RVfwLS, respectively. Our findings revealed heterogeneous RV and LV responses to increases in HR. These findings might ultimately be used to optimize cardiac functionality in patients diagnosed with CHF.

Right Ventricular Longitudinal Strain Measures Independently Predict Chronic Heart Failure Mortality

2016 ◽  
Vol 33 (7) ◽  
pp. 992-1000 ◽  
Author(s):  
Massimo Iacoviello ◽  
Gaetano Citarelli ◽  
Valeria Antoncecchi ◽  
Roberta Romito ◽  
Francesco Monitillo ◽  
...  
Keyword(s):  
Heart Failure ◽  
Chronic Heart Failure ◽  
Right Ventricular ◽  
Longitudinal Strain ◽  
Strain Measures

scholarly journals Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram.

2021 ◽  
Author(s):  
Akhil Vaid ◽  
Kipp W Johnson ◽  
Marcus A Badgeley ◽  
Sulaiman Somani ◽  
Mesude Bicak ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Right Ventricular ◽  
Systolic Dysfunction ◽  
External Validation ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Lv Function ◽  
Ventricular Ejection Fraction ◽  
Left And Right

Background Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECG) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, while ones to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. Objectives This study sought to develop deep learning models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. Methods A multi-center study was conducted with data from five New York City hospitals; four for internal testing and one serving as external validation. We created novel DL models to classify Left Ventricular Ejection Fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. Results We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used Natural Language Processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, Area Under Curve (AUC) at detection of LVEF<=40%, 40%<LVEF<=50%, and LVEF>50% was 0.94 (95% CI:0.94-0.94), 0.82 (0.81-0.83), and 0.89 (0.89-0.89) respectively. For external validation, these results were 0.94 (0.94-0.95), 0.73 (0.72-0.74) and 0.87 (0.87-0.88). For regression, the mean absolute error was 5.84% (5.82-5.85) for internal testing, and 6.14% (6.13-6.16) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (0.84-0.84) in both internal testing and external validation. Conclusions DL on ECG data can be utilized to create inexpensive screening, diagnostic, and predictive tools for both LV/RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography, and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease. Keywords Artificial Intelligence, Deep Learning, Machine Learning, HFrEF, Right Ventricular Dilation, Right Ventricular Systolic Dysfunction, echocardiography, electrocardiogram, ECG, EKG, LVEF, Left Ventricular Ejection Fraction, Left Heart Failure, Right Heart Failure

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