Background and AimRight ventricular (RV) ejection fraction (EF) assessed by 3D echocardiography is a powerful measure to detect RV dysfunction. However, its prognostic value in routine clinical practice has been scarcely explored. Accordingly, we aimed at investigating whether RVEF is associated with 2-year all-cause mortality in patients who underwent diverse cardiovascular procedures and to test whether RVEF can overcome conventional echocardiographic parameters in terms of outcome prediction.Patients and MethodsOne hundred and seventy-four patients were retrospectively identified who underwent clinically indicated transthoracic echocardiography comprising 3D acquisitions. The patient population consisted of heart failure with reduced ejection fraction patients (44%), heart transplanted patients (16%), and severe valvular heart disease patients (39%). Beyond conventional echocardiographic measurements, RVEF was quantified by 3D echocardiography. The primary endpoint of our study was all-cause mortality at two years.ResultsTwenty-four patients (14%) met the primary endpoint. Patients with adverse outcomes had significantly lower RVEF (alive vs. dead; 48±9 vs. 42±9%, p<0.01). However, tricuspid annular plane systolic excursion (21±7 vs. 18±4mm), and RV systolic pressure (36±15 vs. 39±15mmHg) were similar. By Cox analysis, RVEF was found to be associated with adverse outcomes (HR [95% CI]: 0.945 [0.908 – 0.984], p<0.01). By receiver-operator characteristic analysis, RVEF exhibited the highest AUC value compared with the other RV functional measures (0.679; 95% CI: 0.566 – 0.791).ConclusionsConventional echocardiographic measurements may be inadequate to support a granular risk stratification in patients who underwent different cardiac procedures. RVEF may be a robust clinical parameter, which is significantly associated with adverse outcomes
Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Project no. RRF-2.3.1-21-2022-00004 (MILAB) has been implemented with the support provided by the European Union. Introduction Cardiopulmonary exercise testing (CPET)-derived peak oxygen uptake (VO2/kg) is a well-established parameter of exercise capacity allowing the quantification of athletic performance. Although VO2/kg is mainly influenced by anthropometric and demographic factors, several studies demonstrated strong associations between resting echocardiography-based measures and VO2/kg. Artificial intelligence could incorporate information from both features, thus enabling a more accurate prediction of exercise capacity in athletes. Aim Accordingly, we aimed to implement a deep-learning (DL) model that uses 2D echocardiography (2DE)-based apical 4-chamber view videos on top of the anthropometric features (age, sex, body surface area [BSA]) to predict VO2/kg and then assess the model’s performance in a large cohort of athletes. Methods We retrospectively identified 422 competitive athletes (15.4±7.3 training hours/week) who underwent resting 2DE evaluation and then CPET to determine VO2/kg (52.7 ± 7.7 mL/kg/min). To predict VO2/kg values, we trained a deep neural network that can process both modalities of the inputs (i.e. 2DE videos and anthropometric data such as age, sex and BSA) simultaneously (Figure 1). We applied 5-fold cross-validation and used mean squared error (MSE), mean absolute error (MAE), and R squared (R2) metrics to measure our model’s performance. Then, we compared the results with linear regression that was trained using only the 3 anthropometric factors (age, gender, BSA). Additionally, after finalization of the DL-based model, we prospectively recruited further 25 competitive athletes with both 2DE and CPET performed to validate our model. Results Using 2DE videos, our DL-based model was able to achieve an accurate prediction of VO2/kg with an MSE of 35.27, MAE of 4.62, and an R2 of 0.393. In comparison, the linear regression model using only anthropometric factors had worse predictive performance in all metrics with an MSE of 40.51, MAE of 4.88, and R2 of 0.303. In addition, we compared the predictive performance of the DL-based and the linear regression models by their respective squared error values using the Wilcoxon test. Our DL-based model had a significantly better performance compared to the linear regression model (Wilcoxon p = 0.006). In the prospective dataset, our DL-based model achieved an MSE of 16.69, MAE of 3.42, and an R2 of 0.169, whereas the linear regression model was inferior with an MSE of 25.43, MAE of 4.51, and an R2 of −0.268. The DL-based model showed a significantly better performance (Wilcoxon p<0.001). Conclusions Using our DL-based approach on our large athlete database, we were able to implement and prospectively validate a model that incorporated 2DE videos to predict VO2/kg more accurately compared to using anthropometric factors alone. DL techniques may advance sports medicine by personalized monitorization of training phases and accurate prediction of athletic performance.
IntroductionDespite the significant contribution of circumferential shortening to the global ventricular function, data are scarce concerning its prognostic value on long-term mortality. Accordingly, our study aimed to assess both left (LV) and right ventricular (RV) global longitudinal (GLS) and global circumferential strain (GCS) using three-dimensional echocardiography (3DE) to determine their prognostic importance.MethodsThree hundred fifty-seven patients with a wide variety of left-sided cardiac diseases were retrospectively identified (64 ± 15 years, 70% males) who underwent clinically indicated 3DE. LV and RV GLS, and GCS were quantified. To determine the prognostic power of the different patterns of biventricular mechanics, we divided the patient population into four groups. Group 1 consisted of patients with both LV GLS and RV GCS above the respective median values; Group 2 was defined as patients with LV GLS below the median while RV GCS above the median, whereas in Group 3, patients had LV GLS values above the median, while RV GCS was below median. Group 4 was defined as patients with both LV GLS and RV GCS below the median. Patients were followed up for a median of 41 months. The primary endpoint was all-cause mortality.ResultsFifty-five patients (15%) met the primary endpoint. Impaired values of both LV GCS (HR, 1.056 [95% CI, 1.027–1.085], p < 0.001) and RV GCS (1.115 [1.068–1.164], p < 0.001) were associated with increased risk of death by univariable Cox regression. Patients with both LV GLS and RV GCS below the median (Group 4) had a more than 5-fold increased risk of death compared with those in Group 1 (5.089 [2.399–10.793], p < 0.001) and more than 3.5-fold compared with those in Group 2 (3.565 [1.256–10.122], p = 0.017). Interestingly, there was no significant difference in mortality between Group 3 (with LV GLS above the median) and Group 4, but being categorized into Group 3 versus Group 1 still held a more than 3-fold risk (3.099 [1.284–7.484], p = 0.012).DiscussionThe impaired values of both LV and RV GCS are associated with long-term all-cause mortality, emphasizing the importance of assessing biventricular circumferential mechanics. Reduced RV GCS is associated with significantly increased risk of mortality even if LV GLS is preserved.
Introduction: Right ventricular (RV) ejection fraction (EF) assessed by 3D echocardiography is a powerful measure to detect RV dysfunction. However, its prognostic value in routine clinical practice has been scarcely explored. Accordingly, we aimed at investigating whether RVEF is associated with 2-year all-cause mortality in patients who underwent diverse cardiovascular procedures and to test whether RVEF can overcome conventional echocardiographic parameters in terms of outcome prediction. Methods: One hundred and seventy-four patients were retrospectively identified who underwent clinically indicated transthoracic echocardiography comprising 3D acquisitions. The patient population consisted of heart failure with reduced ejection fraction patients (44%), heart transplanted patients (16%), and severe valvular heart disease patients (39%). Beyond conventional echocardiographic measurements, RVEF was quantified by 3D echocardiography. The primary endpoint of our study was all-cause mortality at two years. Results: Twenty-four patients (14%) met the primary endpoint. Patients with adverse outcomes had significantly lower RVEF (alive vs. dead; 48±9 vs. 42±9%, p<0.01). However, tricuspid annular plane systolic excursion (21±7 vs. 18±4mm), and RV systolic pressure (36±15 vs. 39±15mmHg) were similar. By Cox analysis, RVEF was found to be associated with adverse outcomes (HR [95% CI]: 0.945 [0.908 - 0.984], p<0.01). By receiver-operator characteristic analysis, RVEF exhibited the highest AUC value compared with the other RV functional measures (0.679; 95% CI: 0.566 - 0.791). Conclusions: Conventional echocardiographic measurements may be inadequate to support a granular risk stratification in patients who underwent different cardiac procedures. RVEF may be a robust clinical parameter, which is significantly associated with adverse outcomes.
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