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Background Both secondary tricuspid regurgitation (STR) and heart failure with preserved ejection fraction (HFpEF) are relevant public health problems in the elderly population, presenting with potential overlaps and sharing similar risk factors. However, the impact of severe STR on hemodynamics and cardiorespiratory adaptation to exercise in HFpEF remains to be clarified. Aim To explore the impact of STR on exercise hemodynamics and cardiorespiratory adaptation in HFpEF. Methods We analyzed invasive hemodynamics and gas-exchange data obtained at rest and during exercise from HFpEF patients with severe STR (HFpEF-STR), compared with 1:1 age-, sex-, and body mass index (BMI)- matched HFpEF patients with mild or no STR (HFpEF-controls). Results Twelve HFpEF with atrial-STR (mean age 72 years, 92% females, BMI 28 Kg/m 2 ) and 12 HFpEF-controls patients were analyzed. HFpEF-STR had higher ( p < 0.01) right atrial pressure than HFpEF-controls both at rest (10 ± 1 vs. 5 ± 1 mmHg) and during exercise (23 ± 2 vs. 14 ± 2 mmHg). Despite higher pulmonary artery wedge pressure (PAWP) at rest in HFpEF-STR than in HFpEF-controls (17 ± 2 vs. 11 ± 2, p = 0.04), PAWP at peak exercise was no more different (28 ± 2 vs. 29 ± 2). Left ventricular transmural pressure and cardiac output (CO) increased less in HFpEF-STR than in HFpEF-controls (interaction p -value < 0.05). This latter was due to lower stroke volume (SV) values both at rest (48 ± 9 vs. 77 ± 9 mL, p < 0.05) and at peak exercise (54 ± 10 vs. 93 ± 10 mL, p < 0.05). Despite these differences, the two groups of patients laid on the same oxygen consumption isophlets because of the increased peripheral oxygen extraction in HFpEF-STR ( p < 0.01). We found an inverse relationship between pulmonary vascular resistance and SV, both at rest and at peak exercise ( R 2 = 0.12 and 0.19, respectively). Conclusions Severe STR complicating HFpEF impairs SV and CO reserve, leading to pulmonary vascular de-recruitment and relati, SCOPUS: ar.j, info:eu-repo/semantics/published

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients’ clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent. Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality. Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.

Background: Although the assessment of left atrial (LA) mechanics has been reported to refine atrial fibrillation (AF) risk prediction, it doesn't completely predict AF recurrences. The potential added role of right atrial (RA) function in this setting is unknown. Accordingly, this study sought to evaluate the added value of RA longitudinal reservoir strain (RASr) for the prediction of AF recurrence after electrical cardioversion (ECV). Methods: We retrospectively studied 132 consecutive patients with persistent AF who underwent elective ECV. Complete two-dimensional and speckle-tracking echocardiography analyses of LA and RA size and function were obtained in all patients before ECV. The endpoint was AF recurrence. Results: During a 12-month follow-up of, 63 patients (48%) showed AF recurrence. Both LA and RASr were significantly lower in patients experiencing AF recurrence than in patients with persistent sinus rhythm (LASr 10±6 vs 13±7%, and RASr 14±10 vs 20±9 %, respectively, p<0.001 for both). RASr (AUC=0.77, 95%IC 0.69-0.84, p<0.0001) was more strongly associated with the recurrence of AF after ECV than LASr (AUC=0.69 (95%IC 0.60-0.77), p<0.0001]. Kaplan-Meier curves showed that patients with both LASr≤10% and RASr ≤15% had a significantly increased risk for AF recurrences (log-rank, p<0.001). However, at multivariable Cox regression, RASr [HR 3.26, 95%CI (1.73-6.13), p< 0.001] was the only parameter independently associated with the AF recurrence. RASr was more strongly associated with the occurrence of AF relapse after ECV than LASr, LA and RA volumes. Conclusions: RASr was independently and more strongly associated than LASr with AF recurrence after elective ECV. This study highlights the importance of assessing the functional remodeling of both RA and LA in patients with persistent AF.