Sex differences in the association between epicardial adipose tissue volume and left atrial volume index.

BACKGROUND: Sex disparities in the association between epicardial adipose tissue volume (EATV) and cardiovascular disease have been reported. The sex-dependent effects of EATV on left atrial (LA) size have not been elucidated. METHODS: Consecutive 247 subjects (median 65 [interquartile range 57, 75] years; 67% of men) who underwent multi-detector computed tomography without significant coronary artery disease or moderate to severe valvular disease were divided into two groups: patients with sinus rhythm (SR) or atrial fibrillation (AF). Sex differences in the association between the EATV index (EATVI) (mL/m2) and LA volume index (LAVI) in 63 SR (28 men and 35 women) and 184 AF (137 men and 47 women) patients were evaluated using univariate and multivariate regression analyses. RESULTS: In overall that includes both men and women, the relationship between EATVI and LAVI was not significantly correlated for patients with SR and AF. The relationship between EATVI and LAVI differed between men and women in both SR and AF groups. In SR patients, there was a positive relationship between EATVI and LAVI in men, but not in women. In contrast, in patients with AF, a negative relationship was found between EATVI and LAVI in women, whereas no association was found in men. CONCLUSIONS: We evaluated sex differences in the association between EATVI and LAVI in patients with either SR or AF, and found a positive relationship in men with SR and a negative relationship in women with AF. This is the first report to evaluate sex differences in the relationship between EATVI and LAVI, suggesting that EAT may play a role, at least in part, in sex differences in the etiology of AF.

Prognostic value of brachial-ankle pulse wave velocity changes post-transcatheter aortic valve replacement.

Transcatheter aortic valve replacement (TAVR) offers a solution, especially for high-risk aortic stenosis (AS) patients. However, patient outcomes post-TAVR show variability, highlighting the need for reliable prognostic indicators. Brachial-ankle pulse wave velocity (baPWV), a measure of arterial stiffness, may predict outcomes post-TAVR. This study aims to explore baPWV's prognostic value in relation to all-cause mortality post-TAVR. This study prospectively enrolled 212 severe AS patients undergoing TAVR between September 2015 and December 2021, focusing on pre- and post-TAVR baPWV measurements to explore associations with all-cause mortality. Of the 212 patients (119 females, 93 males, mean age 85 years), post-TAVR baPWV increased significantly from 1589 ± 376 to 2010 ± 521 cm/s (p < 0.001). Aortic valve (AV) peak velocity and mean pressure gradient decreased, while AV area increased, indicating procedural success. Despite this, 88% of patients experienced an increase in baPWV, with higher pre-procedure AV peak velocity and mean pressure gradient identified as predictors of increased baPWV post-TAVR. Over 23 months, 29 patients (14%) reached the primary endpoint of all-cause mortality. Notably, changes in baPWV, rather than baseline values, were significantly associated with event-free survival (HR: 0.64 per 1SD increase, p = 0.009). The study highlights the prognostic value of baPWV changes post-TAVR in predicting patient outcomes. Elevated baPWV post-TAVR may reflect a beneficial adaptation to altered hemodynamics, suggesting the need for individualized patient evaluation and the integration of baPWV measurements into clinical practice for improved post-TAVR management.

Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility.

Artificial intelligence (AI) has shown transformative potential in various medical fields, including diagnostic imaging. Recent advances in AI-driven technologies have opened new avenues for improving echocardiographic practices. AI algorithms enhance the image quality, automate measurements, and assist in the diagnosis of cardiovascular diseases. These technologies reduce manual errors, increase consistency, and match the diagnostic performances of experienced echocardiographers. AI in tele-echocardiography offers significant benefits, particularly in rural and remote regions in Japan, where healthcare provider shortages and geographic isolation hinder access to advanced medical care. AI enhances accessibility, provides real-time remote analyses, supports continuous monitoring, and improves the quality and efficiency of remotely delivered cardiac care. However, addressing challenges related to data security, transparency, integration into clinical workflows, and ethical considerations is essential for the successful implementation of AI in echocardiography. On overcoming these challenges, AI will be able to revolutionize echocardiography and ensure timely and effective cardiac care for all patients in the future.

Echocardiographic artificial intelligence for pulmonary hypertension classification.

OBJECTIVE: The classification of pulmonary hypertension (PH) is crucial for determining the appropriate therapeutic strategy. We investigated whether machine learning (ML) algorithms may assist in echocardiographic PH prediction, where current guidelines recommend integrating several different parameters. METHODS: We obtained physical and echocardiographic data from 885 patients who underwent right heart catheterisation (RHC). Patients were classified into three groups: non-PH, precapillary PH and postcapillary PH, based on values obtained from RHC. Using 24 parameters, we created predictive models employing four different classifiers and selected the one with the highest area under the curve. We then calculated the macro-average classification accuracy for PH on the derivation cohort (n=720) and prospective validation data set (n=165), comparing the results with guideline-based echocardiographic assessment obtained from each cohort. RESULTS: Logistic regression with elastic net regularisation had the highest classification accuracy, with areas under the curves of 0.789, 0.766 and 0.742 for normal, precapillary PH and postcapillary PH, respectively. The ML model demonstrated significantly better predictive accuracy than the guideline-based echocardiographic assessment in the derivation cohort (59.4% vs 51.6%, p<0.01). In the independent validation data set, the ML model's accuracy was comparable to the guideline-based PH classification (59.4% vs 57.8%, p=0.638). CONCLUSIONS: This preliminary study suggests promising potential for our ML model in predicting echocardiographic PH. Further research and validation are needed to fully assess its clinical utility in PH diagnosis and treatment decision-making.

Reducing echocardiographic examination time through routine use of fully automated software: a comparative study of measurement and report creation time.

BACKGROUND: Manual interpretation of echocardiographic data is time-consuming and operator-dependent. With the advent of artificial intelligence (AI), there is a growing interest in its potential to streamline echocardiographic interpretation and reduce variability. This study aimed to compare the time taken for measurements by AI to that by human experts after converting the acquired dynamic images into DICOM data. METHODS: Twenty-three consecutive patients were examined by a single operator, with varying image quality and different medical conditions. Echocardiographic parameters were independently evaluated by human expert using the manual method and the fully automated US2.ai software. The automated processes facilitated by the US2.ai software encompass real-time processing of 2D and Doppler data, measurement of clinically important variables (such as LV function and geometry), automated parameter assessment, and report generation with findings and comments aligned with guidelines. We assessed the duration required for echocardiographic measurements and report creation. RESULTS: The AI significantly reduced the measurement time compared to the manual method (159 ± 66 vs. 325 ± 94 s, p < 0.01). In the report creation step, AI was also significantly faster compared to the manual method (71 ± 39 vs. 429 ± 128 s, p < 0.01). The incorporation of AI into echocardiographic analysis led to a 70% reduction in measurement and report creation time compared to manual methods. In cases with fair or poor image quality, AI required more corrections and extended measurement time than in cases of good image quality. Report creation time was longer in cases with increased report complexity due to human confirmation of AI-generated findings. CONCLUSIONS: This fully automated software has the potential to serve as an efficient tool for echocardiographic analysis, offering results that enhance clinical workflow by providing rapid, zero-click reports, thereby adding significant value.

Green Nails, Red Alert: An Unusual Exit site infection Presentation.

Green nail syndrome is an infectious nail disorder caused most commonly by Pseudomonas aeruginosa. We report a rare case of peritoneal dialysis (PD) exit site infection (ESI) accompanied by P. aeruginosa-associated green nail syndrome. The patient was treated with oral and topical antibiotics without the need for PD catheter removal. We aim to emphasise the importance of nail assessment for ESI in patients undergoing PD.

Impact of Transcatheter Aortic Valve Replacement on Cardiac Reverse Remodeling and Prognosis in Mixed Aortic Valve Disease.

BACKGROUND: The management of mixed aortic valve disease (MAVD), defined as the concomitant presence of aortic stenosis (AS) and aortic regurgitation, remains a clinical challenging. The present study assessed the impact of transcatheter aortic valve replacement (TAVR) on cardiac geometry and prognosis in patients with MAVD. METHODS AND RESULTS: A retrospective multicenter TAVR registry was conducted, including patients who underwent TAVR for severe symptomatic AS between January 2015 and March 2019. Patients were subdivided into 2 groups according to concomitant presence of moderate or more severe aortic regurgitation as the MAVD group, and with mild or less severe aortic regurgitation as the isolated AS group. The primary outcome was a composite of cardiovascular death and rehospitalization due to cardiovascular causes. A total of 1742 patients (isolated AS, 1522 patients; MAVD, 220 patients) were included (84.0±5.2 years). Although MAVD exhibited significantly larger left ventricular volumes and higher left ventricular mass index at the TAVR procedure than isolated AS (respectively, P<0.001), MAVD showed a greater improvement of left ventricular volumes and left ventricular mass index after TAVR (respectively, P≤0.001). During a median follow-up of 747 days, 301 patients achieved the primary event. The prognosis post-TAVR was comparable between the 2 groups (log-rank P=0.65). Even after adjustment using propensity score matching to reduce the potential bias between the 2 groups, similar results were obtained for the entire cohort. CONCLUSIONS: Despite more advanced cardiac remodeling in MAVD at the time of TAVR compared with isolated AS, a greater improvement of cardiac reverse remodeling was found in MAVD, and the prognosis following TAVR was comparable between the 2 groups.

Multicenter validation study for automated left ventricular ejection fraction assessment using a handheld ultrasound with artificial intelligence.

We sought to validate the ability of a novel handheld ultrasound device with an artificial intelligence program (AI-POCUS) that automatically assesses left ventricular ejection fraction (LVEF). AI-POCUS was used to prospectively scan 200 patients in two Japanese hospitals. Automatic LVEF by AI-POCUS was compared to the standard biplane disk method using high-end ultrasound machines. After excluding 18 patients due to infeasible images for AI-POCUS, 182 patients (63 ± 15 years old, 21% female) were analyzed. The intraclass correlation coefficient (ICC) between the LVEF by AI-POCUS and the standard methods was good (0.81, p < 0.001) without clinically meaningful systematic bias (mean bias -1.5%, p = 0.008, limits of agreement ± 15.0%). Reduced LVEF < 50% was detected with a sensitivity of 85% (95% confidence interval 76%-91%) and specificity of 81% (71%-89%). Although the correlations between LV volumes by standard-echo and those by AI-POCUS were good (ICC > 0.80), AI-POCUS tended to underestimate LV volumes for larger LV (overall bias 42.1 mL for end-diastolic volume). These trends were mitigated with a newer version of the software tuned using increased data involving larger LVs, showing similar correlations (ICC > 0.85). In this real-world multicenter study, AI-POCUS showed accurate LVEF assessment, but careful attention might be necessary for volume assessment. The newer version, trained with larger and more heterogeneous data, demonstrated improved performance, underscoring the importance of big data accumulation in the field.

Unsupervised cluster analysis reveals different phenotypes in patients after transcatheter aortic valve replacement.

Aims: The aim of this study was to identify phenotypes with potential prognostic significance in aortic stenosis (AS) patients after transcatheter aortic valve replacement (TAVR) through a clustering approach. Methods and results: This multi-centre retrospective study included 1365 patients with severe AS who underwent TAVR between January 2015 and March 2019. Among demographics, laboratory, and echocardiography parameters, 20 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and outcomes were compared between clusters. Patients were randomly divided into a derivation cohort (n = 1092: 80%) and a validation cohort (n = 273: 20%). Three clusters with markedly different features were identified. Cluster 1 was associated predominantly with elderly age, a high aortic valve gradient, and left ventricular (LV) hypertrophy; Cluster 2 consisted of preserved LV ejection fraction, larger aortic valve area, and high blood pressure; and Cluster 3 demonstrated tachycardia and low flow/low gradient AS. Adverse outcomes differed significantly among clusters during a median of 2.2 years of follow-up (P < 0.001). After adjustment for clinical and echocardiographic data in a Cox proportional hazards model, Cluster 3 (hazard ratio, 4.18; 95% confidence interval, 1.76-9.94; P = 0.001) was associated with increased risk of adverse outcomes. In sequential Cox models, a model based on clinical data and echocardiographic variables (χ2 = 18.4) was improved by Cluster 3 (χ2 = 31.5; P = 0.001) in the validation cohort. Conclusion: Unsupervised cluster analysis of patients after TAVR revealed three different groups for assessment of prognosis. This provides a new perspective in the categorization of patients after TAVR that considers comorbidities and extravalvular cardiac dysfunction.

The APOA1 p.Leu202Arg variant potentially causes autosomal recessive cardiac amyloidosis.

ApoA-I amyloidosis is an extremely rare form of systemic amyloidosis that commonly involves the heart, kidneys, and liver. ApoA-I amyloidosis is caused by amyloidogenic variants of APOA1 that are inherited in an autosomal dominant manner. Here, we report a 69-year-old man with sporadic cardiac amyloidosis who was born to consanguineous parents and carried a homozygous variant of p.Leu202Arg in APOA1.

Effects of ipragliflozin on left ventricular diastolic function in patients with type 2 diabetes: A sub-analysis of the PROTECT trial.

BACKGROUND: We hypothesized that the beneficial effects of sodium-glucose cotransporter-2 (SGLT2) inhibitors on diastolic function might depend on baseline left ventricular (LV) systolic function. METHODS: To investigate the effects of SGLT2 inhibitors on LV diastolic function in patients with type 2 diabetes mellitus (T2DM), we conducted a post-hoc sub-study of the PROTECT trial, stratifying the data according to LV ejection fraction (LVEF) at baseline. After excluding patients without echocardiographic data at baseline or 24 months into the PROTECT trial, 31 and 38 patients with T2DM from the full analysis dataset of the PROTECT trial who received ipragliflozin or no SGLT2 inhibitor (control), respectively, were included. The primary endpoint was a comparison of the changes in echocardiographic parameters and N-terminal pro-brain natriuretic peptide levels from baseline to 24 months between the two groups stratified according to baseline LVEF. RESULTS: Differences in diastolic functional parameters (e' and E/e') were noted between the two groups. Among the subgroups defined according to median LVEF values, those with higher LVEF (≥60 %) who received ipragliflozin appeared to have a higher e' and lower E/e' than did those who received the standard of care with no SGLT2 inhibitor, indicating longitudinal improvements between baseline and follow up (p = 0.001 and 0.016, respectively). CONCLUSIONS: Ipragliflozin generally improved LV diastolic function in patients with type 2 diabetes, the extent of this improvement might appear to vary with LV systolic function.

Pulmonary pressure-flow responses to exercise in heart failure treated with angiotensin receptor neprilysin inhibitor.

BACKGROUND: The role of the angiotensin receptor neprilysin inhibitor (ARNI) in cardiac function, particularly its impact on pulmonary circulation, remains underexplored. Recent studies have described abnormal mean pulmonary artery pressure (mPAP)-cardiac output (CO) responses as having the potential to assess the disease state. The aim of this study was to assess the effects of ARNI on pulmonary circulation in heart failure. We measured echocardiographic parameters post 6-min walk (6 MW) and compared the changes with baseline and follow-up. Our hypothesis was that pulmonary pressure-flow relationship of the pulmonary circulation obtained by 6 MW stress echocardiography would be improved with treatment. METHODS: We prospectively enrolled 39 heart failure patients and conducted the 6 MW test indoors. Post-6 MW echocardiography measured echocardiographic variables, and CO was derived from electric cardiometry. Individualized ARNI doses were optimized, with follow-up echocardiographic evaluations after 1 year. RESULTS: Left ventricular (LV) volume were significantly reduced (160.7 ± 49.6 mL vs 136.0 ± 54.3 mL, P < 0.001), and LV ejection fraction was significantly improved (37.6 ± 11.3% vs 44.9 ± 11.5%, P < 0.001). Among the 31 patients who underwent 6 MW stress echocardiographic study at baseline and 1 year later, 6 MW distance increased after treatment (380 m vs 430 m, P = 0.003). The ΔmPAP/ΔCO by 6 MW stress decreased with treatment (6.9 mmHg/L/min vs 2.8 mmHg/L/min, P = 0.002). The left atrial volume index was associated with the response group receiving ARNI treatment for pulmonary circulation. CONCLUSIONS: Initiation of ARNI was associated with improvement of left ventricular size and LVEF. Additionally, the 6 MW distance increased and the ΔmPAP/ΔCO was improved to within normal range with treatment.

Pulmonary Arterial Hypertension in Neurofibromatosis Type 1: A Case with a Novel NF1 Gene Mutation.

Neurofibromatosis type 1 (NF1) is an autosomal dominant multi-organ disease. The clinical manifestations include not only skin lesions and malignant tumors but also lung complications, including pulmonary arterial hypertension (PAH). However, the association between gene mutations in NF1 and the occurrence of PAH has not yet been elucidated. We herein report a case of isolated PAH in a 67-year-old woman with NF1, presumably caused by a novel heterozygous mutation, c.4485_4486delinsAT (p.Lys1496Ter), in the NF1 gene.