Claudia Baratto

Background: The prognostic significance of right heart remodeling (RHR) in patients with secondary tricuspid regurgitation (STR) who do not meet guideline‐based indications for tricuspid valve surgery remains uncertain. We evaluated whether advanced markers of RHR are associated with outcomes in medically managed patients with significant STR. Methods: We retrospectively included 755 patients with significant STR (age 75±12 years). Patients were stratified into 3 groups: Group 1, no current surgical indication (n=519); Group 2, meeting Class I/IIa guideline criteria (n=144); and Group 3, deemed inoperable (n=92). RHR markers included right atrial volume index, right ventricular end‐diastolic volume index, forward stroke volume/right ventricular end‐systolic volume ratio, and effective right ventricular ejection fraction. The primary end point was a composite of all‐cause mortality or heart failure hospitalization. Results: Group 1 patients had less severe STR and less advanced right ventricular remodeling than Groups 2 and 3. Over a median follow‐up of 24 months (interquartile range, 11–34), 330 primary events occurred; 64 patients underwent tricuspid valve intervention. In Group 1, 30‐month event‐free survival was 71±4% with no RHR markers, 65±3% with 1 or 2 markers, and 33±9% with ≥3 markers (adjusted hazard ratio [aHR], 2.76 [95% CI, 1.74–4.37]; P P P <0.001). Conclusions: Among patients with significant STR without surgical indications, ≥3 markers of RHR identify a high‐risk phenotype with outcomes similar to those meeting surgical criteria or considered inoperable.

Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease, that may lead to exertional dyspnea through dynamic left ventricular outflow tract obstruction (LVOTO), left ventricular (LV) diastolic dysfunction and/or left atrial myopathy. Deciphering the relative contribution of these alterations to exercise pathophysiology may be clinically relevant. We sought to characterize the hemodynamic adaptation to exercise of HCM patients, using supine left and right heart catheterization. Twenty-five HCM patients underwent rest and exercise cardiac catheterization. Patients were subdivided into non-obstructive HCM (no-HOCM) and obstructive HCM (HOCM), these latter defined by LVOTO > 30 mmHg at rest (overt HOCM, irrespective of exercise LVOTO), or ≥ 50 mmHg during or after exercise despite LVOTO <30 mmHg at rest (latent HOCM). Ten patients (40%) were classified as no-HOCM while 15 (60%) were classified as HOCM (8 with overt HOCM and 7 with latent or post-exercise HOCM). LV end-diastolic pressure, mean pulmonary artery wedge pressure (PAWP) and PAWP V waves (the latter a marker of left atrial stiffness), did not differ between HOCM and no-HOCM at rest. However, during exercise both PAWP and PAWP V waves increased to a larger extent in no-HOCM (23±3 vs 14±3 mmHg and +28±4 vs +14±3 mmHg, respectively, p<0.05). In overt HOCM, LVOTO reduced by 22±8 mmHg (p<0.01) during exercise. Our findings suggest that during exercise no-HOCM patients may display steeper increase in left atrial pressure than HOCM, suggesting more advanced left atrial myopathy / low left atrial compliance. LVOTO may paradoxically reduce during supine exercise in overt HOCM.

Invasive exercise hemodynamics are used to diagnose heart failure with preserved ejection fraction (HFpEF), based on pulmonary artery wedge pressure (PAWP) or left atrial (LA) pressure elevations. We hypothesized that applying unsupervised cluster analysis to comprehensive hemodynamic characterization might provide data‐driven phenotypes, with pathophysiological and prognostic implications. Eighty consecutive HFpEF patients underwent right heart catheterization at rest, during passive leg raise, and at peak exercise. We performed unsupervised k ‐means clustering analysis, using eight hemodynamic variables that were not strongly correlated (Pearson correlation coefficient < 0.80). Hemodynamics and clinical characteristics, as well as event‐free survival, were assessed. k = 5 clusters were identified. Hemodynamic severity increased from Cluster 1 to Clusters 4–5 ( p < 0.01 for most of the hemodynamic variables), mirrored by different event‐free survival (log‐rank test p < 0.001). Clusters 1 and 2 presented with either steep PAWP rise or LA hypertension and pulmonary hypertension (PH) only during exercise. Cluster 3 presented with LA hypertension and PH already at rest, as well as with tall PAWP V waves during exercise. Cluster 4 presented with post‐ and precapillary PH, tall PAWP V waves, right atrial (RA) hypertension, dynamic tricuspid regurgitation (TR), and low cardiac output (CO) reserve. Cluster 5 presented with TR and RA hypertension, low CO, and a lack of decrease in PVR. Data‐driven unsupervised cluster analysis of advanced invasive hemodynamics allowed for the identification of distinct HFpEF phenotypes across the spectrum of disease severity. We found a progressive involvement of the pulmonary circulation and of the right heart, coupled with a worse prognosis.

Recent findings emphasize the potential role of invasive hemodynamic assessment in guiding transcatheter mitral and tricuspid valve percutaneous interventions. Right heart catheterization-derived parameters offer insights into hemodynamic changes associated with valvular heart diseases, pulmonary hypertension phenotyping, and right ventricular to pulmonary artery coupling. This might improve prognostic stratification for candidates to transcatheter therapies. This review provides a clinical overview of available data regarding the utility of preoperative right heart catheterization-derived parameters in patients undergoing mitral and tricuspid percutaneous repair or replacement.

BACKGROUND: The determinants of tricuspid regurgitation (TR) hemodynamic severity remain to be established. We explored the hemodynamic correlates of right atrial (RA) pressure and stroke volume (SV) in patients with and without TR, and assessed the reliability of the indirect Fick method in relation to TR severity. METHODS: In this observational study, right ventricular (RV) 3-dimensional echocardiography (3DE) was obtained simultaneously with direct Fick right heart catheterization. RVSV _3DE and SV _RHC were combined to determine the TR regurgitant fraction (RegFr=RVSV _3DE -RVSV _RHC /RVSV _3DE ). RA pressures and strain (or 3DE volumes) were combined to derive RA compliance. RESULTS: Out of 74 patients, 61% had moderate or severe TR. TR severity was associated with larger right heart chambers, lower RA compliance, higher values and lower inspiratory decrease of RA pressure, and lower cardiac index ( P r =−0.57) and with ln-transformed RA compliance _strain ( r =−0.74); SV index was associated with RegFr ( r =−0.65). The effect of RegFr on V wave was mediated by ln-transformed RA compliance _strain (β, 13.9 [95% CI, 7.6–20.2]). In multivariable analysis, RA V wave remained associated with 1/RA compliance _strain (β, 2.1 [95% CI, 1.4–2.7]), while SV index was associated with RegFr (β, −97.6 [95% CI, −120.1 to −75.0]). The indirect Fick method overestimated cardiac index proportionally to RegFr ( P <0.01). CONCLUSIONS: SV index is related to TR severity, while the effect of TR on RA V wave is mediated by RA compliance. Respiratory-related changes in RA hemodynamics are associated with TR severity. The indirect Fick method overestimates cardiac index proportionally to TR severity.

Conventional echocardiographic parameters of right ventricular (RV) function are afterload-dependent. Therefore, incorporating RV pressures may enable the formulation of new parameters that reflect intrinsic RV function accurately. Accordingly, we sought to develop an artificial intelligence–based method to reconstruct the RV pressure curve based on the peak RV pressure. We invasively acquired RV pressure in 29 heart failure patients before and after implanting a left ventricular (LV) assist device. Using these tracings, we trained various machine learning models to reconstruct the RV pressure curve of the entire cardiac cycle based on the peak value of the curve. The best-performing model was compared with two other methods that estimated RV pressures based on a reference LV and RV pressure curve, respectively. Seventeen consecutive patients from another centre who underwent right heart catheterization and simultaneous echocardiography served as an external validation cohort. Among the evaluated algorithms, multilayer perceptron (MLP) achieved the best performance with an R² of 0.887 (0.834–0.941). The RV and LV reference curve–based methods achieved R² values of 0.879 (0.815–0.943) and 0.636 (0.500–0.771), respectively. During external validation, MLP exhibited similarly good performance [R² 0.911 (0.873–0.948)], which decreased only modestly if the echocardiography-derived peak RV pressure was used instead of the invasively measured peak RV pressure [R² 0.802 (0.694–0.909)]. The proposed method enables the reconstruction of the RV pressure curve using only the peak value as input. Thus, it may serve as a fundamental component for developing new echocardiographic tools targeting the afterload-adjusted assessment of RV function. Can artificial intelligence be useful in reconstructing the right ventricular pressure curve of the entire cardiac cycle using only the peak pressure value as input? A multilayer perceptron model predicted pressure values with a balanced, low bias throughout the cardiac cycle, even slightly outperforming the reference curve–based methods at both internal and external validations. The accurate prediction of the right ventricular pressure curve may enable the formulation of new echocardiographic parameters targeting the afterload-adjusted assessment of right ventricular function. LV, left ventricular; PVC, pulmonary valve closure; PVO, pulmonary valve opening; RV, right ventricular; TVC, tricuspid valve closure; TVO, tricuspid valve opening.