Significant Disagreement Between Conventional Parameters and 3D Echocardiography-Derived Ejection Fraction in the Detection of Right Ventricular Systolic Dysfunction and Its Association With Outcomes.

AIMS: Conventional echocardiographic parameters such as tricuspid annular plane systolic excursion (TAPSE), fractional area change (FAC), and free-wall longitudinal strain (FWLS) offer limited insights into the complexity of right ventricular (RV) systolic function, while 3D echocardiography-derived RV ejection fraction (RVEF) enables a comprehensive assessment. We investigated the discordance between TAPSE, FAC, FWLS, and RVEF in RV systolic function grading and associated outcomes. METHODS: We analyzed two- and three-dimensional echocardiography data from 2 centers including 750 patients followed up for all-cause mortality. Right ventricular dysfunction was defined as RVEF <45%, with guideline-recommended thresholds (TAPSE <17 mm, FAC <35%, FWLS >-20%) considered. RESULTS: Among patients with normal RVEF, significant proportions exhibited impaired TAPSE (21%), FAC (33%), or FWLS (8%). Conversely, numerous patients with reduced RVEF had normal TAPSE (46%), FAC (26%), or FWLS (41%). Using receiver-operating characteristic analysis, FWLS exhibited the highest area under the curve of discrimination for RV dysfunction (RVEF <45%) with 59% sensitivity and 92% specificity. Over a median 3.7-year follow-up, 15% of patients died. Univariable Cox regression identified TAPSE, FAC, FWLS, and RVEF as significant mortality predictors. Combining impaired conventional parameters showed that outcomes are the worst if at least 2 parameters are impaired and gradually better if only one or none of them are impaired (log-rank P < .005). CONCLUSION: Guideline-recommended cutoff values of conventional echocardiographic parameters of RV systolic function are only modestly associated with RVEF-based assessment. Impaired values of FWLS showed the closest association with the RVEF cutoff. Our results emphasize a multiparametric approach in the assessment of RV function, especially if 3D echocardiography is not available.

Deep Learning-Derived Myocardial Strain.

BACKGROUND: Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses. OBJECTIVES: This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations. METHODS: Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy. RESULTS: In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS. CONCLUSIONS: The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.

A Deep Patient-Similarity Learning Framework for the Assessment of Diastolic Dysfunction in Elderly Patients.

BACKGROUND AND AIMS: Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal LV diastolic function (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model[70] of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF). METHODS: A previously developed DeepNN was tested on 5,596 older participants (66-90 years; 57% female; 20% black) from the Atherosclerosis Risk in Communities study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4,054) and Stage C/D (n = 1,542) subgroups was assessed. RESULTS: The DeepNN-predicted High-Risk compared to the Low-Risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank p < 0.0001 for all). In multivariable analyses, the High-Risk phenogroup remained an independent predictor of HF and death in both Stages A/B (adjusted hazard ratio (HR) [95% confidence interval], 6.52[4.20-10.13] and 2.21(1.68-2.91), both p < 0.0001) and Stage C/D (6.51[4.06-10.44] and 1.03(1.00-1.06), both p < 0.0001) respectively. In addition, DeepNN showed incremental value over the 2016 ASE/EACVI guidelines (Net reclassification index, 0.5[CI:0.4-0.6], p < 0.001; C-statistic improvement, DeepNN [0.76] vs. ASE/EACVI [0.70], p < 0.001) overall and maintained across stage-groups. CONCLUSIONS: Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.

Right Ventricular Structure and Function in Adolescent Athletes: A 3D Echocardiographic Study.

The aim of this study was to characterize the right ventricular (RV) contraction pattern and its associations with exercise capacity in a large cohort of adolescent athletes using resting three-dimensional echocardiography (3DE). We enrolled 215 adolescent athletes (16±1 years, 169 males, 12±6 hours of training/week) and compared them to 38 age and sex-matched healthy, sedentary adolescents. We measured the 3DE-derived biventricular ejection fractions (EF). We also determined the relative contributions of longitudinal EF (LEF/RVEF) and radial EF (REF/RVEF) to the RVEF. Same-day cardiopulmonary exercise testing was performed to calculate VO2/kg. Both LV and RVEFs were significantly lower (athletes vs. controls; LVEF: 57±4 vs 61±3, RVEF: 55±5 vs 60±5%, p<0.001). Interestingly, while the relative contribution of radial shortening to the global RV EF was also reduced (REF/RVEF: 0.40±0.10 vs 0.49±0.06, p<0.001), the contribution of the longitudinal contraction was significantly higher in athletes (LEF/RVEF: 0.45±0.08 vs 0.40±0.07, p<0.01). The supernormal longitudinal shortening correlated weakly with a higher VO2/kg (r=0.138, P=0.044). Similarly to the adult athlete's heart, the cardiac adaptation of adolescent athletes comprises higher biventricular volumes and lower resting functional measures with supernormal RV longitudinal shortening. Characteristic exercise-induced structural and functional cardiac changes are already present in adolescence.

PCSK9, A Promising Novel Target for Age-Related Cardiovascular Dysfunction.

Cardiovascular diseases (CVDs) are the leading cause of death among elderly people. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is an important regulator of cholesterol metabolism. Herein, we investigated the role of PCSK9 in age-related CVD. Both in humans and rats, blood PCSK9 level correlated positively with increasing age and the development of cardiovascular dysfunction. Age-related fatty degeneration of liver tissue positively correlated with serum PCSK9 levels in the rat model, while development of age-related nonalcoholic fatty liver disease correlated with cardiovascular functional impairment. Network analysis identified PCSK9 as an important factor in age-associated lipid alterations and it correlated positively with intima-media thickness, a clinical parameter of CVD risk. PCSK9 inhibition with alirocumab effectively reduced the CVD progression in aging rats, suggesting that PCSK9 plays an important role in cardiovascular aging.

Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease.

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.

Phenogrouping and risk stratification of patients undergoing cardiac resynchronization therapy upgrade using topological data analysis.

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 ).

Topological data analysis to identify cardiac resynchronization therapy patients exhibiting benefit from an implantable cardioverter-defibrillator.

BACKGROUND: Current guidelines recommend considering multiple factors while deciding between cardiac resynchronization therapy with a defibrillator (CRT-D) or a pacemaker (CRT-P). Nevertheless, it is still challenging to pinpoint those candidates who will benefit from choosing a CRT-D device in terms of survival. OBJECTIVE: We aimed to use topological data analysis (TDA) to identify phenogroups of CRT patients in whom CRT-D is associated with better survival than CRT-P. METHODS: We included 2603 patients who underwent CRT-D (54%) or CRT-P (46%) implantation at Semmelweis University between 2000 and 2018. The primary endpoint was all-cause mortality. We applied TDA to create a patient similarity network using 25 clinical features. Then, we identified multiple phenogroups in the generated network and compared the groups' clinical characteristics and survival. RESULTS: Five- and 10-year mortality were 43 (40-46)% and 71 (67-74)% in patients with CRT-D and 48 (45-50)% and 71 (68-74)% in those with CRT-P, respectively. TDA created a circular network in which we could delineate five phenogroups showing distinct patterns of clinical characteristics and outcomes. Three phenogroups (1, 2, and 3) included almost exclusively patients with non-ischemic etiology, whereas the other two phenogroups (4 and 5) predominantly comprised ischemic patients. Interestingly, only in phenogroups 2 and 5 were CRT-D associated with better survival than CRT-P (adjusted hazard ratio 0.61 [0.47-0.80], p < 0.001 and adjusted hazard ratio 0.84 [0.71-0.99], p = 0.033, respectively). CONCLUSIONS: By simultaneously evaluating various clinical features, TDA may identify patients with either ischemic or non-ischemic etiology who will most likely benefit from the implantation of a CRT-D instead of a CRT-P. Topological data analysis to identify phenogroups of CRT patients in whom CRT-D is associated with better survival than CRT-P. AF atrial fibrillation, CRT cardiac resynchronization therapy, CRT-D cardiac resynchronization therapy defibrillator, CRT-P cardiac resynchronization therapy pacemaker, DM diabetes mellitus, HTN hypertension, LBBB left bundle branch block, LVEF left ventricular ejection fraction, MDS multidimensional scaling, MRA mineralocorticoid receptor antagonist, NYHA New York Heart Association.

Deep Learning-Based Prediction of Right Ventricular Ejection Fraction Using 2D Echocardiograms.

BACKGROUND: Evidence has shown the independent prognostic value of right ventricular (RV) function, even in patients with left-sided heart disease. The most widely used imaging technique to measure RV function is echocardiography; however, conventional 2-dimensional (2D) echocardiographic assessment is unable to leverage the same clinical information that 3-dimensional (3D) echocardiography-derived right ventricular ejection fraction (RVEF) can provide. OBJECTIVES: The authors aimed to implement a deep learning (DL)-based tool to estimate RVEF from 2D echocardiographic videos. In addition, they benchmarked the tool's performance against human expert reading and evaluated the prognostic power of the predicted RVEF values. METHODS: The authors retrospectively identified 831 patients with RVEF measured by 3D echocardiography. All 2D apical 4-chamber view echocardiographic videos of these patients were retrieved (n = 3,583), and each subject was assigned to either the training or the internal validation set (80:20 ratio). Using the videos, several spatiotemporal convolutional neural networks were trained to predict RVEF. The 3 best-performing networks were combined into an ensemble model, which was further evaluated in an external data set containing 1,493 videos of 365 patients with a median follow-up time of 1.9 years. RESULTS: The ensemble model predicted RVEF with a mean absolute error of 4.57 percentage points in the internal and 5.54 percentage points in the external validation set. In the latter, the model identified RV dysfunction (defined as RVEF <45%) with an accuracy of 78.4%, which was comparable to an expert reader's visual assessment (77.0%; P = 0.678). The DL-predicted RVEF values were associated with major adverse cardiac events independent of age, sex, and left ventricular systolic function (HR: 0.924 [95% CI: 0.862-0.990]; P = 0.025). CONCLUSIONS: Using 2D echocardiographic videos alone, the proposed DL-based tool can accurately assess RV function, with similar diagnostic and prognostic power as 3D imaging.

There is more than just longitudinal strain: Prognostic significance of biventricular circumferential mechanics.

Introduction: Despite 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. Methods: Three 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. Results: Fifty-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). Discussion: The 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.

Association of Right Ventricular Functional Parameters With Adverse Cardiopulmonary Outcomes: A Meta-analysis.

AIMS: We aimed to confirm that three-dimensional echocardiography-derived right ventricular ejection fraction (RVEF) is better associated with adverse cardiopulmonary outcomes than the conventional echocardiographic parameters. METHODS: We performed a meta-analysis of studies reporting the impact of unit change of RVEF, tricuspid annular plane systolic excursion (TAPSE), fractional area change (FAC), and free-wall longitudinal strain (FWLS) on clinical outcomes (all-cause mortality and/or adverse cardiopulmonary outcomes). Hazard ratios (HRs) were rescaled by the within-study SDs to represent standardized changes. Within each study, we calculated the ratio of HRs related to a 1 SD reduction in RVEF versus TAPSE, or FAC, or FWLS, to quantify the association of RVEF with adverse outcomes relative to the other metrics. These ratios of HRs were pooled using random-effects models. RESULTS: Ten independent studies were identified as suitable, including data on 1,928 patients with various cardiopulmonary conditions. Overall, a 1 SD reduction in RVEF was robustly associated with adverse outcomes (HR = 2.64 [95% CI, 2.18-3.20], P < .001; heterogeneity: I2 = 65%, P = .002). In studies reporting HRs for RVEF and TAPSE, or RVEF and FAC, or RVEF and FWLS in the same cohort, head-to-head comparison revealed that RVEF showed significantly stronger association with adverse outcomes per SD reduction versus the other 3 parameters (vs TAPSE, HR = 1.54 [95% CI, 1.04-2.28], P = .031; vs FAC, HR = 1.45 [95% CI, 1.15-1.81], P = .001; vs FWLS, HR = 1.44 [95% CI, 1.07-1.95], P = .018). CONCLUSION: Reduction in three-dimensional echocardiography-derived RVEF shows stronger association with adverse clinical outcomes than conventional right ventricular functional indices; therefore, it might further refine the risk stratification of patients with cardiopulmonary diseases.

Cardiopulmonary examinations of athletes returning to high-intensity sport activity following SARS-CoV-2 infection.

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.

Cardiorespiratory fitness status of elite handball referees in Hungary.

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.