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Funding Acknowledgements Type of funding sources: None. Background. In patients with transposition of great arteries (TGA) post atrial switch operation or with congenitally corrected TGA (ccTGA), the morphologically right ventricle (RV) has to adapt to the chronically increased systemic pressure. Purpose. To investigate the functional adaptation of the systemic RV in patients with TGA post Mustard repair or ccTGA.  Methods. RV volumes and EF were measured by 3D echocardiography in 33 patients with the systemic RV (21 TGA and 12 ccTGA; 45 ± 13y, 61% male), and in 33 healthy volunteers (44 ± 13y, 61% male).  The 3D RV model was postprocessed by the ReVISION software and its contraction was decomposed along the longitudinal, radial and anteroposterior directions (Fig.A, Systemic RV in TGA) providing longitudinal, radial and anteroposterior EF (LEF, REF and AEF). Relative contribution of each component was measured as the ratio between LEF, REF and AEF to the global RVEF (LEFi, REFi and AEFi). Results. Systemic RV was significantly larger with reduced function compared to controls (Tab). 3D RVEF demonstrated stronger correlation with BNP (Rho -0.76, p < 0.0001) compared to other parameters of RV function (free wall strain 0.55, p = 0.0083; FAC -0.47, p = 0.024; S’ -0.39 and TAPSE 0.06, p > 0.05). While in healthy volunteers, all 3 components of RV systolic function contributed equally to the global RV EF, in patients with TGA the relative contribution of the anteroposterior component was dominant and differed significantly from longitudinal and radial components (AEFi 0.48 ± 0.06 vs LEFi 0.31 ± 0.07 vs REFi 0.36 ± 0.09, p < 0.0001)(Fig. B,C). In patients with ccTGA the longitudinal component was dominant and provided a relative compensation for the reduced anteroposterior and radial components (LEFi 0.47 ± 0.07 vs AEFi 0.34 ± 0.07, p = 0.0002 and vs REFi 0.36 ± 0.09, p = 0.0023)(Fig. B,C). Relative contribution of the radial contraction was significantly reduced in all systemic RV patients. Conclusions. Systemic RV contraction patterns change significantly with anteroposterior contraction being dominant in patients with TGA post Mustard repair and longitudinal component being dominant in ccTGA. 3DE should be a part of routine assessment of the systemic RV, especially in TGA since no conventional echo parameters take into account anteroposterior RV contraction. Parameters of RV systolic function Parameter Control group (N = 33) All SRV patients (N = 33) TGA (N = 21) ccTGA (N = 12) 3D EF, % 60 ± 3.8 36 ± 8.6* 34 ± 7.3* 38 ± 10* FAC, % 41.4 ± 3.7 25.9 ± 9.3* 25.1 ± 9.2* 27.1 ± 9.9* TAPSE, mm 24.6 ± 4.2 11.9 ± 3.9* 11.1 ± 2.9* 13.2 ± 5.1* RV free wall strain, % -32.5 ± 4.2 -14.5 ± 3.5* -14.5 ± 2.9* -15.5 ± 3.5* * p < 0.0001 Abstract Figure.

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.

Funding Acknowledgements Type of funding sources: None. Background. Right ventricular (RV) systolic dysfunction in patients with left-sided heart disease is known adverse factor. However, the RV adaptation at the different degrees of left ventricular (LV) dysfunction remains to be clarified. Purpose  to assess the change in RV contraction pattern in relation to LV ejection fraction (EF) in patients with left-sided heart disease. Methods. LV and RV volumes and EF were measured by 3D-echocardiography in 295 patients with left-sided heart disease (59 ± 17years, 69% male). The 3D meshmodel of the RV was postprocessed by the ReVISION software and its contraction pattern was decomposed along the longitudinal, radial and anteroposterior directions (Fig. A) providing longitudinal, radial and anteroposterior EF (LEF, REF, AEF). Relative contribution of each component to the RV systolic function was measured as the ratio between LEF, REF and AEF and global RVEF (LEFi, REFi, AEFi). Results. Patients with LV systolic dysfunction also had reduced RVEF. Relative contribution of the longitudinal and anteroposterior components decreased, while radial component increased in patients with reduced LVEF (Table). RV LEF and AEF significantly correlated with the LVEF (Rho 0.50 and 0.51, p < 0.0001), while the correlation between REF and LVEF was weak (Rho 0.22, p = 0.0002). There was a significant drop in LEF and AEF (Fig. B) and their relative contribution to the total RVEF (Fig. C) starting from the earlier stages of LV dysfunction. However, it was effectively compensated by significant increase in the radial RV component resulting in preservation of total RVEF in those with normal, mildly and moderately reduced LVEF (50 [46;54] vs 47 [44;52] vs 46 [42;49]%), whereas total RVEF dropped significantly only in severe LV dysfunction (30 [25;39]%; p < 0.0001) (Fig. D). Conclusions. The longitudinal and anteroposterior RV contraction was related to the LVEF and decreased from early stages of the LV systolic dysfunction. Increase in the radial component compensated for the loss of longitudinal and anteroposterior RV components in mild and moderate LV dysfunction to maintain total RVEF. Drop in all three components resulted in significant reduction of total RVEF in severe LV dysfunction. Characteristics of study population Overall (N = 295) LVEF≥50% (N = 166) LVEF < 50% (N = 129) LV EF, % 49.6 ± 14.3 59.9 ± 5.6 36.4 ± 10.9* RV EF, % 46.5 ± 9.2 49.8 ± 6.9 42.3 ± 10.0* RV LEFi 0.42 ± 0.09 0.45 ± 0.09 0.38 ± 0.09* RV REFi 0.47 ± 0.1 0.45 ± 0.1 0.50 ± 0.09* RV AEFi 0.39 ± 0.08 0.41 ± 0.08 0.37 ± 0.07* *p < 0.0001 Abstract Figure.

During the pandemic, several studies were carried out on the short-term effects of acute SARS-CoV-2 infection in athletes. As some cases of young athletes with serious complications like myocarditis or thromboembolism and even sudden death were reported, strict recommendations for return to sport were published. However, we have less data about athletes who have already returned to high-intensity trainings after a SARS-CoV-2 infection. Athletes underwent cardiology screening (personal history, physical examination, 12-lead resting ECG, laboratory tests with necroenzyme levels and echocardiography) 2 to 3 weeks after suffering a SARS-CoV-2 infection. In case of negative results, they were advised to start low intensity trainings and increase training intensity regularly until achieving maximal intensity a minimum of 3 weeks later. A second step of cardiology screening was also carried out after returning to maximal intensity trainings. The above mentioned screening protocol was repeated and was completed with vita maxima cardiopulmonary exercise testing (CPET) on running treadmill. If the previous examinations indicated, 24h Holter ECG recording, 24h ambulatory blood pressure monitoring or cardiac MR imaging were also carried out. Data are presented as mean±SD. Two-step screening after SARS-CoV-2 infection was carried out in 111 athletes (male:74, age:22.4±7.4y, elite athlete:90%, training hours:14.8±5.8 h/w, ice hockey players:31.5%, water polo players:22.5%, wrestlers:18.9%, basketball players:18.0%). Second screenings were carried out 94.5±31.5 days after the first symptoms of the infection. A 5% of the athletes was still complaining of tiredness and decreased exercise capacity. Resting heart rate was 70.3±13.0 b.p.m., During CPET examinations, athletes achieved a maximal heart rate of 187.3±11.6 b.p.m., maximal relative aerobic capacity of 49.2±5.5 ml/kg/min, and maximal ventilation of 138.6±31.2 l/min. The athletes reached their anaerobic threshold at 87.8±6.3% of their maximal aerobic capacity, with a heart rate of 93.3±3.7% of their maximal values. Heart rate recovery was 29.9±9.2/min. During the CPET examinations, short supraventricular runs, repetititve ventricular premature beats + ventricular quadrigeminy and inferior ST depression were found in 1–1 cases. Slightly higher pulmonary pressure was measured on the echocardiography in 4 cases. Hypertension requiring drug treatment was found in 5.4% of the cases. Laboratory examinations revealed decreased vitamin D3 levels in 26 cases, decreased iron storage levels in 18 athletes. No SARS-CoV-2 infection related CMR changes were revealed in our athlete population. Three months after SARS-CoV-2 infection, most of the athletes examined had satisfactory fitness levels. However, some cases of decreased exercise capacity, decreased vitamin D3 or iron storage levels, arrhythmias, hypertension and elevated pulmonary pressure requiring further examinations, treatment or follow-up were revealed. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This project was supported by a grant from the National Research, Development and Innovation Office (NKFIH) of Hungary; The research was financed by the Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary, within the framework of the Therapeutic Development and Bioimaging programmes of the Semmelweis University