Abstract
Background
Pulmonary wave reflection, an important loading component on RV, would help better describe right ventricular (RV) – arterial uncouplingin type II pulmonary hypertension (PH).
...Methods
This study included 105patients with type II PH (age = 72±13 years old, ejection fraction = 47±21%). Pulmonary wave reflection was characterised by estimating pressure and velocity profiles from Doppler measurement and separating pressure waveform into forward (Pf) and backward pressure (Pb) waves based on the concept of wave intensity. The relationship of RV strain against Pb or pulmonary artery systolic pressure (PASP) was compared between patients with and without subsequent event of worsening heart failure, using discriminant analysis.
Results
Sample profiles of pressure waveforms illustrated Pb accounted for a significant proportion of total pressure (figure A). When RV strain was plotted against Pb, the distribution of both group were clearly separated, indicating significant uncoupling in patients with events, whereas when plotted against PASP, the both groups were overlapped (figure B and C).
Conclusions
Pulmonary wave reflection can better describe RV-arterial uncoupling in type II PH.
Funding Acknowledgement
Type of funding source: None
Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
Right heart failure has been shown to portend poor prognosis. The pathophysiology of right heart function is complex, as ...right ventricular (RV) function is easily affected by preload and afterload.
Purpose
To test the hypothesis that machine learning would detect heterogeneity in right heart function and improve risk stratifications in a heart failure population
Methods
This study included 403 heart failure patients who had a history of hospitalization for decompensation. Patients were excluded if they had primary heart valve disease or pericardial disease or a previous cardiac surgery. Hierarchical clustering was undertaken on right heart variables (RV strain, right ventricular systolic pressure (RVSP), vena contracta of tricuspid regurgitation (TR) and diameter of inferior vena cava) to identify homogenous groups of patients with similar profiles of the variables. Cox hazard analysis was used to elucidate the benefit of clustering over each variable for prognosticating heart failure. Endpoint was hospitalization for worsening heart failure.
Results
Cluster analysis identified three groups with distinct right heart function. Cluster 1 (n = 191) represented patients with preserved RV function and low RVSP (figure A). On the other hand, cluster 2 (n = 144) had reduced RV function and low RVSP, while cluster 3 (n = 68) had preserved RV function and high RVSP, associated with severe TR and high central venous pressure. The latter 2 clusters carried worse outcome than cluster 1 (p < 0.001, figure B). Cox hazard analysis demonstrated that, although the addition of each right heart variable to baseline model constructed from left heart variables did not improve predictive power, clusters predicted events with a hazard ratio of 1.566, independent from and incremental to the left heart variables (Figure C).
Conclusion
Cluster analysis identified two distinct phenotypes of right heart failure that were associated with adverse outcomes. This data-driven phenotyping can help in categorizing right heart failure and better prognosticating heart failure.
Abstract Figure.
Abstract
Funding Acknowledgements
Type of funding sources: Public hospital(s). Main funding source(s): International Medical Research and Development Fund
Background
Measuring pulmonary vascular ...resistance (PVR) is essential for diagnosing pulmonary hypertension. PVR can be estimated from a simple ratio of tricuspid regurgitation velocity (TRV) to time-velocity integral of flow through right ventricular outflow tract (RVOT) (Abbas equation), but this relationship has been shown to be unreliable in patients with high PVR. Once PVR is elevated, there is an increased pressure wave reflected from the peripheral of pulmonary artery (PA). We tested the hypothesis that assessing PA wave reflection could be an alternative method for estimating PVR.
Methods
This study recruited 83 patients (69 ± 14 years old, 44 men), including 41 patients with left-sided heart disease, 23 patients with pulmonary arterial hypertension and 19 patients with chronic thromboembolic pulmonary hypertension. PA wave reflection was assessed by separating PA pressure waveform derived from a Doppler tracing of TRV into forward and backward pressure (Pf and Pb), using a velocity profile at RVOT. This separation was based on the concept of wave intensity. PVR was estimated using Abbas equation. Pb and PVR by Abbas equation were compared for the correlations with direct measurement of PVR by right heart catheterisation within 48 hours after echocardiography.
Results
Figure A illustrates Pb and Pf waveforms obtained from a patient with chronic thromboembolic pulmonary hypertension. Pb increased from mid systole and formed a late peak of PA pressure waveform. Pb correlated strongly with PVR by catheter (figure B), whereas PVR by Abbas equation underestimated PVR especially when PVR was high, resulting in a moderate correlation (figure C). Receiver-operator characteristic curves showed a higher accuracy of Pb for identifying PVR > 3WU as well as 6WU, compared with PVR by Abbas equation (figure D and E).
Conclusions
This novel echocardiographic method for assessing PA wave reflection helps diagnose the severity of pulmonary hypertension.
Abstract Figure.
Abstract
Background
Grading the severity of aortic stenosis (AS) is challenging, since there is a discrepancy between aortic valve area (AVA) and mean pressure gradient (mPG). Arotic valve resistance ...(RES) has been proposed as a usuful descriptor of AS severity, but it is not commonly used for clinical decision-making, because its robust validation of clinical-outcome efficacy is lacking. This study aimed to investigate whether RES holds an incremental value for risk-stratifying AS.
Methods
This study recuited 565 AS patients (AVA < 1.5cm²) referred to echocardiography for valve assessment. The patients were divided into three different groups, according to the guidelines: high-gradient AS (HG-AS, mPG≥40mmHg, n = 157), low-gradient AS (LG-AS, mPG < 40mmHg + AVA ≤ 1.0cm², n = 155) and moderate AS (Mod-AS, mPG < 40mmHg + AVA > 1.0cm², n = 253). RES was calculated from Doppler measurement of mPG and stoke volume. The diagnositic cutoff point for RES was determined at 190 dynes × s×cm-5 by substituting AVA = 1.0cm² and mPG = 40mmHg into the definition formula of RES and Gorlin formula. The patients were followed up for 2 years. The endpoint was a composite of cardiac death, hospitalization for heart failure and aortic valve replacement necessitated by the development of AS-related symptoms.
Result
Kaplan-Meier analyses showed that LG-AS exhibited an intermediate outcome between HG-AS and Mod-AS (event-free survival at 2 years = 20.9% for HG-AS, 59.7% for LG-AS, 89.9% for Mod-AS, p < 0.001, figure A). When LG-AS was stratified by RES, the survival curves showed a significant separation (event-free survival at 2 years = 35.3% for high RES, 70.7% for low RES, p < 0.001, figure B). This trend persisted even when analysed separately for norml (stroke volume index > 35ml/m²) and low (stroke volume index ≤ 35ml/m²) flow state ((normal flow) event-free survival at 2 years = 38.7% for high RES, 70.4% for low RES, p = 0.023, figure C; (low flow) event-free survival at 2 years = 26.7% for high RES, 74.6% for low RES, p < 0.001, figure D).
Conclusion
This study confirmed the clinical efficacy of RES for risk-stratifying LG-AS patients.
Abstract P289 Figure.
Abstract
Background
A delayed heart rate recovery (HRR) after exercise is related to mortality in sinus rhythm. This study aimed to investigate this concept can be applied to patients with atrial ...fibrillation (AF).
Methods
We analyzed 483 patients with AF (mean 65 years, male 74%). HRR integral was calculated by integrating the difference in HR in every 3 second between the end of exercise and the specified time after the exercise (30, 60, 120 and 180 seconds). After evaluating the prognostic power of each HRR integral, we selected HRR integral of 180 seconds (180HRR-integral).
Results
We divided the patients into two groups using median value of 180HRR-integral. All-cause mortality, the incidence of cardiovascular events and heart failure events were higher in the poor 180HRR-integral. After adjustment for covariates, the impact of the high 180HRR-integral for all-cause mortality was 3.15 (p=0.057), 1.77 for cardiovascular events (p=0.067) and 1.28 for heart failure events (p=0.519).
Conclusion
Poor HRR was associated with worse prognosis in patients with AF.
Funding Acknowledgement
Type of funding source: None
Abstract
Background
Recently, the analysis of electrocardiogram (ECG) waveform by artificial intelligence has been reported to pick out those who have atrial fibrillation (AF) or have a high ...potential of developing AF, which, however, cannot explain the mechanisms or algorisms for the prediction from its nature.
Purpose
The purpose of this study is to conduct a comprehensive analysis to investigate the difference of weighting in predicting capability for AF among hundreds of automatically-measured ECG parameters using a single ECG at sinus rhythm.
Methods and results
Out of Shinken Database 2010–2017 (n=19170), 12825 patients were extracted, where those with ECG showing AF rhythm at the initial visit (including all persistent/permanent AF and a part of paroxysmal AF) and those with structural heart diseases were excluded. Out of 639 automatically-measured ECG parameters in MUSE data management system (GE Healthcare, USA), 438 were used. Analysis 1 A predicting model for paroxysmal AF were determined by logistic regression analysis (Total, n=12825; paroxysmal AF, n=1138), showing a high predictive capability (AUC = 0.780, p<0.001). In this model, the relative contribution of ECG parameters (by coefficient of determination) according to the time phase were P:72.4%, QRS:32.7%, and ST-T:13.7%, respectively (Figure A). Analysis 2 Excluding AF at baseline, a predicting model for new-developed AF were determined by Cox regression analysis (Total, n=11687; new-developed AF, n=87), showing a high predictive capability (AUC = 0.887, p<0.001). In this model, the relative contribution of parameters (by log likelihood) according to the time phase were P:40.8%, QRS:42.5%, and ST-T:24.9%, respectively (Figure B).
Conclusions
We determined ECG parameters that potentially contribute to picking up existing AF or predicting future development of AF, where the measurement of P wave strongly contributed in the former whereas all time phases were similarly important in the latter.
Weighting of parameters to predict AF
Funding Acknowledgement
Type of funding source: Private hospital(s). Main funding source(s): Self funding of the institute
Abstract
Background
The assessment of diastolic function is still challenging in the setting of heart failure (HF). We tested the hypothesis that applying a machine learning algorithm would detect ...heterogeneity in diastolic function and improve risk stratification in HF population.
Methods
This study included consecutive 279 patients with clinically stable HF referred for echocardiographic assessment, for whom diastolic function variables were measured according to the current guidelines. Cluster analysis, an unsupervised machine learning algorithm, was undertaken on these variables to form homogeneous groups of patients with similar profiles of the variables. Sequential Cox models paralleling the clinical sequence of HF assessment were used to elucidate the benefit of cluster-based classification over guidelines-based classification. The primary endpoint was a hospitalization for worsening HF.
Results
Cluster analysis identified 3 clusters with distinct properties of diastolic function that shared similarities with guidelines-based classification. The clusters were associated with brain natriuretic peptide level (p < 0.001, figure A). During follow-up period of 2.6 ± 2.0 years, 62 patients (22%) experienced the primary endpoint. Cluster-based classification exhibited a significant prognostic value (c2 = 20.3, p < 0.001, figure B), independent from and incremental to an established clinical risk score for HF (MAGGIC score) and left ventricular end-diastolic volume (hazard ratio = 1.677, p = 0.017, model c2: from 47.5 to 54.1, p = 0.015, figure D). Although guideline-based classification showed a significant prognostic value (c2 = 13.1, p = 0.001, figure C), it did not significantly improve overall prognostication from the baseline (model c2: from 47.5 to 49.9, p = 0.199, figure D).
Conclusion
Machine learning techniques help grading diastolic function and stratifying the risk for decompensation in HF.
Abstract 153 Figure.
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