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Abstract Mitral stenosis (MS) is a complex valvular pathology with significant clinical burden even today. Its effect on the right heart is often overlooked, despite it playing a considerable part in the symptomatic status. We enrolled 39 mitral valve stenosis patients and 39 age- and gender-matched healthy controls. They underwent conventional, speckle-tracking and 3D echocardiographic examinations. The 3D data was analyzed using the ReVISION software to calculate RV functional parameters. In the MS group, 3D RV ejection fraction (EF) (49 ± 7% vs. 61 ± 4%; p

Abstract Choosing the optimal device during cardiac resynchronization therapy (CRT) upgrade can be challenging. Therefore, we sought to provide a solution for identifying patients in whom upgrading to a CRT-defibrillator (CRT-D) is associated with better long-term survival than upgrading to a CRT-pacemaker (CRT-P). To this end, we first applied topological data analysis to create a patient similarity network using 16 clinical features of 326 patients without prior ventricular arrhythmias who underwent CRT upgrade. Then, in the generated circular network, we delineated three phenogroups exhibiting significant differences in clinical characteristics and risk of all-cause mortality. Importantly, only in the high-risk phenogroup was upgrading to a CRT-D associated with better survival than upgrading to a CRT-P (hazard ratio: 0.454 (0.228–0.907), p = 0.025). Finally, we assigned each patient to one of the three phenogroups based on their location in the network and used this labeled data to train multi-class classifiers to enable the risk stratification of new patients. During internal validation, an ensemble of 5 multi-layer perceptrons exhibited the best performance with a balanced accuracy of 0.898 (0.854–0.942) and a micro-averaged area under the receiver operating characteristic curve of 0.983 (0.980–0.986). To allow further validation, we made the proposed model publicly available ( https://github.com/tokmarton/crt-upgrade-risk-stratification ).

Background: Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation. Methods: For 337 patients undergoing VT ablation, we collected 31 parameters including medical history, echocardiography, and procedural data. 17 relevant features were included in the ML-based feature selection, which yielded six and five optimal features for 1-month and 1-year recurrence, respectively. We trained several supervised machine learning models using 10-fold cross-validation for each endpoint. Results: We observed 1-month VT recurrence was observed in 60 (18%) cases and accurately predicted using our model with an area under the receiver operating curve (AUC) of 0.73. Input features used were hemodynamic instability, incessant VT, ICD shock, left ventricular ejection fraction, TAPSE, and non-inducibility of the clinical VT at the end of the procedure. A separate model was trained for 1-year VT recurrence (observed in 117 (35%) cases) with a mean AUC of 0.71. Selected features were hemodynamic instability, the number of inducible VT morphologies, left ventricular systolic diameter, mitral regurgitation, and ICD shock. For both endpoints, a random forest model displayed the highest performance. Conclusions: Our ML models effectively predict VT recurrence post-ablation, aiding in identifying high-risk patients and tailoring follow-up strategies.

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.

Purpose: Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)-derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE). Methods: In this substudy of a prospective, multicenter study, patients from 3 institutions (n = 727) formed an internal cohort, and the fourth institution was reserved as an external test set (n = 518). A previously validated patient similarity analysis model was used for labeling the patients as low-/high-risk phenogroups. These labels were utilized for training an ECG-derived deep neural network model to predict MACE risk per phenogroup. After 5-fold cross-validation training, the model was tested on the reserved external dataset. Results: Our ECG-derived model showed robust classification of patients, with area under the receiver operating characteristic curve of 0.86 (95% CI: 0.79–0.91) and 0.84 (95% CI: 0.80–0.87), sensitivity of 80% and 76%, and specificity of 88% and 75% for the internal and external test sets, respectively. The ECG-derived model demonstrated an increased probability for MACE in high-risk vs low-risk patients (21% vs 3%; P < 0.001), which was similar to the echo-trained model (21% vs 5%; P < 0.001), suggesting comparable utility. Conclusions: This novel ECG-derived machine learning model provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high risk of MACE.

Abstract Aims While global longitudinal strain (GLS) is considered to be a sensitive marker of left ventricular (LV) function, it is significantly influenced by loading conditions. We hypothesized that global myocardial work index (GMWI), a novel marker of LV function, may show better correlation with load‐independent markers of LV contractility in rat models of pressure‐induced or volume overload‐induced heart failure. Methods and results Male Wistar rats underwent either transverse aortic constriction (TAC; n = 12) or aortocaval fistula creation (ACF; n = 12), inducing LV pressure or volume overload, respectively. Sham procedures were performed to establish control groups (n = 12/12). Echocardiographic loops were obtained to determine GLS and GMWI. Pressure‐volume analysis with transient occlusion of the inferior caval vein was carried out to calculate preload recruitable stroke work (PRSW), a load‐independent ‘gold‐standard’ parameter of LV contractility. Myocardial samples were collected to assess interstitial and perivascular fibrosis area and also myocardial atrial‐type natriuretic peptide (ANP) and brain‐type natriuretic peptide (BNP) relative mRNA expression. Compared with controls, GLS was substantially lower in the TAC group (−7.0 ± 2.8 vs. −14.5 ± 2.5%; P

BackgroundRight ventricular (RV) three-dimensional (3D) strains can be measured using novel 3D RV analytical software (ReVISION). Our objective was to investigate the prognostic value of RV 3D strains.MethodsWe retrospectively selected patients who underwent both 3D echocardiography (3DE) and cardiac magnetic resonance from January 2014 to October 2020. 3DE datasets were analyzed with 3D speckle tracking software and the ReVISION software. The primary end point was a composite of cardiac events, including cardiac death, heart failure hospitalization, or ventricular tachyarrhythmia.Results341 patients were included in this analysis. During a median of 20 months of follow-up, 49 patients reached a composite of cardiac events. In univariate analysis, 3D RV ejection fraction (RVEF) and three 3D strain values [RV global circumferential strain (3D RVGCS), RV global longitudinal strain (3D RVGLS), and RV global area strain (3D RVGAS)] were significantly associated with cardiac death, ventricular tachyarrhythmia, or heart failure hospitalization (Hazard ratio: 0.88 to 0.93, p < 0.05). Multivariate analysis revealed that 3D RVEF, three 3D strain values were significantly associated with cardiac events after adjusting for age, chronic kidney disease, and left ventricular systolic/diastolic parameters. Kaplan-Meier survival curves showed that 3D RVEF of 45% and median values of 3D RVGCS, 3D RVGLS, and 3D RVGAS stratified a higher risk for survival rates. Classification and regression tree analysis, including 22 clinical and echocardiographic parameters, selected 3D RVEF (cut-off value: 34.5%) first, followed by diastolic blood pressure (cut-off value: 53 mmHg) and 3D RVGAS (cut-off value: 32.4%) for stratifying two high-risk group, one intermediate-risk group, and one low-risk group.ConclusionsRV 3D strain had an equivalent prognostic value compared with 3D RVEF. Combining these parameters with 3D RVEF may allow more detailed stratification of patient's prognosis in a wide array of cardiac diseases.

Abstract Aims The PREPARE‐MVR study (PRediction of Early PostoperAtive Right vEntricular failure in Mitral Valve Replacement/Repair patients) sought to investigate the alterations of right ventricular (RV) contraction pattern in patients undergoing mitral valve replacement/repair (MVR) and to explore the associations between pre‐operative RV mechanics and early post‐operative RV dysfunction (RVD). Methods and results We prospectively enrolled 42 patients (63 ± 11 years, 69% men) undergoing open‐heart MVR. Transthoracic three‐dimensional (3D) echocardiography was performed pre‐operatively, at intensive care unit discharge, and 6 months after surgery. The 3D model of the RV was reconstructed, and RV ejection fraction (RVEF) was calculated. We decomposed the motion of the ventricle to compute longitudinal ejection fraction (LEF) and radial ejection fraction (REF). Pulmonary artery catheterization was performed to monitor RV stroke work index (RVSWi). RVEF was slightly decreased after MVR [52 (50–55) vs. 51 (46–54)%; P = 0.001], whereas RV contraction pattern changed notably. Before MVR, the longitudinal shortening was the main contributor to global systolic RV function [LEF/RVEF vs. REF/RVEF; 0.53 (0.47–0.58) vs. 0.33 (0.22–0.42); P

Background: The cardiovascular effects of SARS-CoV-2 in elite athletes are still a matter of debate. Accordingly, we sought to perform a comprehensive echocardiographic characterization of post-COVID athletes by comparing them to a non-COVID athlete cohort.Methods: 107 elite athletes with COVID-19 were prospectively enrolled (P-CA; 23 ± 6 years, 23% female) 107 healthy athletes were selected as a control group using propensity score matching (N-CA). All athletes underwent 2D and 3D echocardiography. Left (LV) and right ventricular (RV) end-diastolic volumes (EDVi) and ejection fractions (EF) were quantified. To characterize LV longitudinal deformation, 2D global longitudinal strain (GLS) and the ratio of free wall vs. septal longitudinal strain (FWLS/SLS) were also measured. To describe septal flattening (SF—frequently seen in P-CA), LV eccentricity index (EI) was calculated.Results: P-CA and N-CA athletes had comparable LV and RVEDVi (P-CA vs. N-CA; 77 ± 12 vs. 78 ± 13mL/m2; 79 ± 16 vs. 80 ± 14mL/m2). P-CA had significantly higher LVEF (58 ± 4 vs. 56 ± 4%, p < 0.001), while LVGLS values did not differ between P-CA and N-CA (−19.0 ± 1.9 vs. −18.8 ± 2.2%). EI was significantly higher in P-CA (1.13 ± 0.16 vs. 1.01 ± 0.05, p < 0.001), which was attributable to a distinct subgroup of P-CA with a prominent SF (n = 35, 33%), further provoked by inspiration. In this subgroup, the EI was markedly higher compared to the rest of the P-CA (1.29 ± 0.15 vs. 1.04 ± 0.08, p < 0.001), LVEDVi was also significantly higher (80 ± 14 vs. 75 ± 11 mL/m2, p < 0.001), while RVEDVi did not differ (82 ± 16 vs. 78 ± 15mL/m2). Moreover, the FWLS/SLS ratio was significantly lower in the SF subgroup (91.7 ± 8.6 vs. 97.3 ± 8.2, p < 0.01). P-CA with SF experienced symptoms less frequently (1.4 ± 1.3 vs. 2.1 ± 1.5 symptom during the infection, p = 0.01).Conclusions: Elite athletes following COVID-19 showed distinct morphological and functional cardiac changes compared to a propensity score-matched control athlete group. These results are mainly driven by a subgroup, which presented with some echocardiographic features characteristic of constrictive pericarditis.

In various team sports, such as handball, referees work on the court by continuously moving with the players. Therefore, their physical fitness also has an impact on their reaction time, which could affect their professional decisions. The cardiorespiratory fitness status of healthy Hungarian elite handball referees was examined via body composition analysis and vita maxima cardiopulmonary exercise testing with lactate measurements. One hundred referees were examined (age: 29.0 ± 7.9 years; male: 64.0%; training: 4.3 ± 2.0 hours/week; ratio of former elite handball players: 39.0%; 51.0% first and 49.0% second division referees of the Hungarian National Handball Leagues). A resting heart rate (HR) of 79.0 ± 12.6 BPM was measured. On the basis of the body composition analysis the fat-free mass index proved to be 19.9 ± 2.6 kg/m2. The referees achieved a maximal oxygen uptake (V̇O2max) of 44.6 ± 6.1 ml/kg/min, with a maximal HR of 187.2 ± 11.1 BPM (which was 98.1 ± 4.6% of their calculated maximal HR) and a peak lactate of 9.2 ± 3.2 mmol/l at 557.1 ± 168.3 sec on our continuous speed, increasing slope treadmill protocol. Second division referees were younger, on a weekly average they trained more, achieved higher treadmill exercise time (respectively, 463.8 ± 131.9 vs 658.4 ± 143.9 sec, p < 0.001) and anaerobic threshold time (respectively, 265.8 ± 100.9 vs 348.2 ± 117.1 sec, p < 0.001), while the two different divisional referees had similar V̇O2max values. Regarding our physical fitness measurements, huge individual differences were observed between the referees (exercise time range: 259.0-939.0 sec, V̇O2max range: 25.3-62.4 ml/kg/min). Since it can affect their performance as referees, individual training planning, regular physical fitness measurements, and strict selection methods are suggested.

Three main mechanisms contribute to global right ventricular (RV) function: longitudinal shortening, radial displacement of the RV free wall (bellows effect), and anteroposterior shortening (as a consequence of left ventricular contraction). Since the importance of these mechanisms may vary in different cardiac conditions, a technology being able to assess their relative influence on the global RV pump function could help to clarify the pathophysiology and the mechanical adaptation of the chamber. Previously, we have introduced our 3D echocardiography (3DE)-based solution—the Right VentrIcular Separate wall motIon quantificatiON (ReVISION) method—for the quantification of the relative contribution of the three aforementioned mechanisms to global RV ejection fraction (EF). Since then, our approach has been applied in several clinical scenarios, and its strengths have been demonstrated in the in-depth characterization of RV mechanical pattern and the prognostication of patients even in the face of maintained RV EF. Recently, various new features have been implemented in our software solution to enable the convenient, standardized, and more comprehensive analysis of RV function. Accordingly, in our current technical paper, we aim to provide a detailed description of the latest version of the ReVISION method with special regards to the volumetric partitioning of the RV and the calculation of longitudinal, circumferential, and area strains using 3DE datasets. We also report the results of the comparison between 3DE- and cardiac magnetic resonance imaging-derived RV parameters, where we found a robust agreement in our advanced 3D metrics between the two modalities. In conclusion, the ReVISION method may provide novel insights into global and also segmental RV function by defining parameters that are potentially more sensitive and predictive compared to conventional echocardiographic measurements in the context of different cardiac diseases.

Background: The relative importance of variables explaining sex-related differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in CRT patients. We also aimed to assess the sex-specific differences in predictors of mortality utilizing ML.Methods: Using a retrospective registry of 2,191 CRT patients, ML models were implemented in 6 partially overlapping patient subsets (all patients, females, or males with 1- or 3-year follow-up). Each cohort was randomly split into training (80%) and test sets (20%). After hyperparameter tuning in the training sets, the best performing algorithm was evaluated in the test sets. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC). The most important predictors were identified using the permutation feature importances method.Results: Conditional inference random forest exhibited the best performance with AUCs of 0.728 (0.645–0.802) and 0.732 (0.681–0.784) for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction, and QRS morphology had higher predictive power, whereas hemoglobin was less important in females compared to males. The importance of atrial fibrillation and age increased, while the importance of serum creatinine decreased from 1- to 3-year follow-up in both sexes.Conclusions: Using ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in CRT patients. Sex-specific patterns of predictors were identified, showing a dynamic variation over time.

Abstract Background Measurement of systolic and diastolic function in animal models is challenging by conventional non-invasive methods. Therefore, we aimed at comparing speckle-tracking echocardiography (STE)-derived parameters to the indices of left ventricular (LV) pressure–volume (PV) analysis to detect cardiac dysfunction in rat models of type-1 (T1DM) and type-2 (T2DM) diabetes mellitus. Methods Rat models of T1DM (induced by 60 mg/kg streptozotocin, n = 8) and T2DM (32-week-old Zucker Diabetic Fatty rats, n = 7) and corresponding control animals (n = 5 and n = 8, respectively) were compared. Echocardiography and LV PV analysis were performed. LV short-axis recordings were used for STE analysis. Global circumferential strain, peak strain rate values in systole (SrS), isovolumic relaxation (SrIVR) and early diastole (SrE) were measured. LV contractility, active relaxation and stiffness were measured by PV analysis. Results In T1DM, contractility and active relaxation were deteriorated to a greater extent compared to T2DM. In contrast, diastolic stiffness was impaired in T2DM. Correspondingly, STE described more severe systolic dysfunction in T1DM. Among diastolic STE parameters, SrIVR was more decreased in T1DM, however, SrE was more reduced in T2DM. In T1DM, SrS correlated with contractility, SrIVR with active relaxation, while in T2DM SrE was related to cardiac stiffness, cardiomyocyte diameter and fibrosis. Conclusions Strain and strain rate parameters can be valuable and feasible measures to describe the dynamic changes in contractility, active relaxation and LV stiffness in animal models of T1DM and T2DM. STE corresponds to PV analysis and also correlates with markers of histological myocardial remodeling.

Background and Objectives: No data are available on whether the heritability of left ventricle (LV) systolic and diastolic parameters are independent of each other. Therefore, our aim was to assess the magnitude of common and independent genetic and environmental factors defining LV systolic and diastolic function. Materials and Methods: We analyzed 184 asymptomatic twins (65% female, mean age: 56 ± 9 years). Transthoracic echocardiography was performed to measure LV systolic (global longitudinal and circumferential strain; basal and apical rotation) and diastolic (early diastolic velocity of mitral inflow and lateral mitral annulus tissue; deceleration time and early diastolic strain rate) parameters using conventional and speckle-tracking echocardiography. Genetic structural equation models were evaluated to quantify the proportion of common and specific genetic (Ac, As) and environmental factors (Ec, Es) contributing to the phenotypes. Results: LV systolic parameters had no common genetic or environmental heritability (Ac range: 0–0%; Ec range: 0–0%; As range: 57–77%; Es range: 24–43%). Diastolic LV parameters were mainly determined by common genetic and environmental effects (Ac range: 9–40%; Ec range: 11–49%; As range: 0–29%; Es range: 0–51%). Systolic parameters had no common genetic or environmental factors (Ac = 0%; Ec = 0%) with diastolic metrics. Conclusions: Systolic LV parameters have a strong genetic predisposition to any impact. They share no common genetic or environmental factors with each other or with diastolic parameters, indicating that they may deteriorate specifically to given effects. However, diastolic functional parameters are mainly affected by common environmental influences, suggesting that pathological conditions may deteriorate them equally. Estimation of the genetic and environmental influence and interdependence on systolic and diastolic LV function may help the understanding of the pathomechanism of different heart failure classification types.

Abstract Three major mechanisms contribute to right ventricular (RV) pump function: (i) shortening of the longitudinal axis with traction of the tricuspid annulus towards the apex; (ii) inward movement of the RV free wall; (iii) bulging of the interventricular septum into the RV and stretching the free wall over the septum. The relative contribution of the aforementioned mechanisms to RV pump function may change in different pathological conditions. Our aim was to develop a custom method to separately assess the extent of longitudinal, radial and anteroposterior displacement of the RV walls and to quantify their relative contribution to global RV ejection fraction using 3D data sets obtained by echocardiography. Accordingly, we decomposed the movement of the exported RV beutel wall in a vertex based manner. The volumes of the beutels accounting for the RV wall motion in only one direction (either longitudinal, radial, or anteroposterior) were calculated at each time frame using the signed tetrahedron method. Then, the relative contribution of the RV wall motion along the three different directions to global RV ejection fraction was calculated either as the ratio of the given direction’s ejection fraction to global ejection fraction and as the frame-by-frame RV volume change (∆V/∆t) along the three motion directions. The ReVISION (Right VentrIcular Separate wall motIon quantificatiON) method may contribute to a better understanding of the pathophysiology of RV mechanical adaptations to different loading conditions and diseases.

Abstract After SARS-CoV-2 infection, strict recommendations for return-to-sport were published. However, data are insufficient about the long-term effects on athletic performance. After suffering SARS-CoV-2 infection, and returning to maximal-intensity trainings, control examinations were performed with vita-maxima cardiopulmonary exercise testing (CPET). From various sports, 165 asymptomatic elite athletes (male: 122, age: 20y (IQR: 17-24y), training:16 h/w (IQR: 12–20 h/w), follow-up:93.5 days (IQR: 66.8–130.0 days) were examined. During CPET examinations, athletes achieved 94.7 ± 4.3% of maximal heart rate, 50.9 ± 6.0 mL/kg/min maximal oxygen uptake (V̇O2max), and 143.7 ± 30.4L/min maximal ventilation. Exercise induced arrhythmias (n = 7), significant horizontal/descending ST-depression (n = 3), ischemic heart disease (n = 1), hypertension (n = 7), slightly elevated pulmonary pressure (n = 2), and training-related hs-Troponin-T increase (n = 1) were revealed. Self-controlled CPET comparisons were performed in 62 athletes: due to intensive re-building training, exercise time, V̇O2max and ventilation increased compared to pre-COVID-19 results. However, exercise capacity decreased in 6 athletes. Further 18 athletes with ongoing minor long post-COVID symptoms, pathological ECG (ischemic ST-T changes, and arrhythmias) or laboratory findings (hsTroponin-T elevation) were controlled. Previous SARS-CoV-2-related myocarditis (n = 1), ischaemic heart disease (n = 1), anomalous coronary artery origin (n = 1), significant ventricular (n = 2) or atrial (n = 1) arrhythmias were diagnosed. Three months after SARS-CoV-2 infection, most of the athletes had satisfactory fitness levels. Some cases with SARS-CoV-2 related or not related pathologies requiring further examinations, treatment, or follow-up were revealed.