Citations

Abstract A recent discussion that highlighted the differences between the reported effective orifice area and corresponding incidence of prosthesis-patient mismatch within the 1- and 7-year results of the PERIcardial SurGical AOrtic Valve ReplacemeNt (PERIGON) Trial of the Avalus valve has once again emphasized the instability of such measurements. The effective orifice area, calculated using the continuity equation through measurements of the left ventricular outflow tract diameter, is highly susceptible to measurement variability, as previously demonstrated in literature through both intra- and interobserver variability. To illustrate this problem, a simulation was performed in which the left ventricular outflow tract diameter was systematically altered by −2 mm, −1 mm, 0 mm, +1 mm, and +2 mm. Subsequently, the proportion of severe prosthesis-patient mismatch was recalculated and varied between 13.3%, 6.2%, 3.1% (original), 1.2%, and 0.3%, respectively. These findings demonstrate that small but clinically realistic variability in left ventricular outflow tract measurements can significantly affect the presence of prosthesis-patient mismatch. A measurement variability of 1 mm can nearly double or halve the prevalence of severe prosthesis-patient mismatch. This illustrates that prosthesis-patient mismatch, in its current form, is highly unstable and that indexed effective orifice area-based prosthesis-patient mismatch should not be used in isolation.

Abstract Background Transthoracic echocardiography (TTE) is the most commonly used modality for the diagnosis and follow-up of cardiac diseases. During routine TTE, surrounding extracardiac structures are often visualized, including the lungs, pleural spaces, oesophagus, diaphragm, and sub-diaphragmatic structures such the liver, stomach, bowel, and abdominal aorta. Pathologic conditions affecting these organs may exert extrinsic compression on the cardiac chambers, leading to diagnostic uncertainty or mimicking cardiac disease by producing symptoms, such as dyspnoea, fatigue, chest pain, or palpitations. Case summary We describe five patients who underwent routine TTE and were incidentally found to have unsuspected gastrointestinal abnormalities. The findings subsequently confirmed by other imaging modalities included hiatal hernia, distended bowel loops, oesophageal carcinoma, and oesophageal varices. Reporting of these findings impacted the diagnosis and management of the cases. Conclusion Awareness of the sonographic appearance of gastrointestinal during TTE is essential, as these findings may represent clinically significant pathology that mimics or contributes to cardiac symptoms. Recognition of such findings may be enhanced by modified echocardiographic views, with confirmatory evaluation using chest radiography, computed tomography, or gastrointestinal endoscopy as appropriate

Background Chronic kidney disease (CKD) confers a substantially elevated risk of cardiovascular (CV) mortality, primarily driven by heart failure (HF) and other CV complications. This study aimed to develop a nomogram for predicting incident HF and all-cause death in patients with CKD, and further identify individuals at high risk of these outcomes. Methods We prospectively recruited 440 patients with CKD stages 3–5 and preserved ejection fraction (LVEF) from a nephropathy center between November 2020 to September 2024. Baseline clinical and demographic characteristics, biochemical and echocardiographic parameters, and data from quality-of-life scales were collected. Key predictors were identified using univariate Cox regression, Lasso, Random Forest, and XGBoost algorithms; these identified predictors were then integrated into a nomogram via multivariate Cox regression. Model performance was assessed using the C-index, ROC analysis, and calibration plots. Results Over 29 months (IQR 24–35), 104(23.6%) patients met the primary composite endpoints, which included 62(14.1%) incident HF and 42(9.5%) all-cause death. After selecting variables through forward stepwise regression, age[HR 1.030, P = 0.016], growth differentiation factor 15(GDF-15) [HR 1.076, P < 0.001], lipoprotein(a)[Lp(a)] [HR = 1.011, P = 0.002], ferritin(FER) [HR = 1.009, P = 0.030], and EuroQol-5D (EQ-5D) score [HR 0.099, P = 0.015] were utilized to construct the nomogram. In the training cohort, the model achieved a C-index of 0.772. For temporal validation, time-dependent area under the ROC curve(AUC) values further confirmed its accuracy: 0.779 (95%CI, 0.696–0.863) at 24 months, 0.816(95%CI, 0.743–0.888) at 27 months, 0.836(95%CI, 0.768–0.903) at 32 months, and 0.850(95%CI, 0.771–0.929) at 36 months. Decision curve analysis (DCA) also demonstrated sustained clinical utility across a broad range of threshold probabilities. Conclusion This study identified specific predictors of HF and all-cause mortality in CKD patients. This model has enhanced predictive ability for early identification of these risks, thereby facilitating timely clinical interventions to improve the prognosis of CKD patients.

Abstract Pathogenic variants in cardiomyopathy genes confer substantial disease risk, yet clinical penetrance varies widely. The population-level prevalence, age-specific penetrance, and the extent to which polygenic susceptibility modifies disease risk across diverse ancestries remain poorly defined. This population-based cohort study leveraged genome sequencing and linked electronic health record data from the All of Us Research Program (n=414,830). This study found that P/LP variants in cardiomyopathy-associated genes were present in 0.91% of participants (95% CI, 0.88%–0.94%), with similar prevalence across ancestry groups. Compared with non-carriers, variant carriers had significantly higher odds of all-cause heart failure (ACHF, OR, 2.58; 95% CI, 2.31–2.89), non-ischemic heart failure (NIHF, OR, 2.74; 95% CI, 2.42–3.10), and non-ischemic cardiomyopathy (NICM, OR, 4.99; 95% CI, 4.44–5.63). Among carriers, by age 50 years, 24.5% (95% CI, 14.5%–33.4%) developed ACHF, 17.2% (95% CI, 8.8%–24.8%) developed NIHF, and 30.1% (95% CI, 19.5%–40.2%) developed NICM. Higher polygenic risk for heart failure was independently associated with increased risk of ACHF (OR, 1.81; 95% CI, 1.74–1.89), NIHF (OR, 1.66; 95% CI, 1.58–1.75), and NICM (OR, 1.69; 95% CI, 1.59–1.80). Among carriers with elevated polygenic risk, odds of disease were substantially higher than among non-carriers at average polygenic risk (ACHF: OR, 4.71; 95% CI, 4.17–5.32; NIHF: OR, 4.56; 95% CI, 3.99–5.22; NICM: OR, 8.51; 95% CI, 7.44–9.73). Results showed that P/LP variants in cardiomyopathy genes were relatively common in this large, ancestrally diverse cohort, and conferred increased risk of cardiomyopathy and HF, with incomplete but clinically meaningful penetrance. Polygenic risk for HF independently and additively increased disease risk across ancestries. Integrating both may improve risk stratification and enable more precise, equitable care.

Introduction: The objective of the study was to evaluate the prevalence of cardiovascular, renal, and hepatic biomarkers that fall outside guideline-based thresholds in adults with type 1 diabetes (T1D), and to identify demographic and clinical factors associated with these biomarkers. Methods: This observational study linked data on 2,923 adults with T1D from the T1D Exchange Registry and NorstellaLinQ’s laboratory data (2022–2023). Prevalence of biomarkers outside guideline-recommended thresholds was calculated. Generalized estimating equations (GEE) were used to model associations with age, sex, race/ethnicity, insurance, T1D duration, body mass index (BMI), and hemoglobin A1c (HbA1c), while accounting for within-subject correlation due to repeated measures. Results: Elevated low-density lipoprotein (LDL) cholesterol (≥70 mg/dL, above the recommended goal for individuals with diabetes at high risk for atherosclerotic cardiovascular disease) was common (80.2%), as were overweight (33.8%) and obesity (30.3%). Additional findings included high total cholesterol (≥200 mg/dL, 22.6%), low high-density lipoprotein (HDL) cholesterol (<40 mg/dL males, <50 mg/dL females, 13.4%), elevated urinary albumin-to-creatinine ratio (≥30 mg/g, 13.0%), reduced estimated glomerular filtration rate (<60 mL/min/1.73 m 2 , 6.2%), and elevated fibrosis-4 index (FIB-4) (≥1.3, 14.4%). In adjusted GEE models, higher HbA1c was significantly associated with increased odds of abnormal cardiovascular and renal biomarkers across most measures. Male sex and BMI were linked to lower HDL cholesterol, while older age was strongly associated with elevated FIB-4. Conclusion: Obesity and elevated LDL cholesterol are highly prevalent in adults with T1D, indicating substantial cardiometabolic risk. The lack of outcome trials in this population underscores the need for studies to evaluate strategies to improve weight and lipid management and thereby reduce cardiovascular and renal risk in adults with T1D.

Abstract This work presents a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), allows for deployment of deep learning models and pulse sequence updates. In this study, PEREGRINE operated a time-domain (TD) and frequency-domain (FD) convolutional autoencoder that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the medial prefrontal cortex of healthy volunteers using a MEGA-edited MRS experiment. The degree of OOV contamination (OOV Score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV Scores above a user-defined threshold triggered an update of the crusher gradient scheme, iterating through 48 permutations (6 axis transpositions × 8 polarity flips). Within each 2-second TR, PEREGRINE successfully provided single-transient OOV Scores and updated gradients accordingly. No difference was observed between the OOV Scores from the full (“Full” condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV Scores than the AI-off scan when selecting the transients where PEREGRINE persisted (“Dwell” condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN) from linear combination modeling improved significantly for the AI-on compared to the AI-off scan. PEREGRINE enabled an adaptive and AI-integrated sequence allowing for real-time evaluation and response to OOV artifacts, identifying gradient modifications that produced less OOV contamination. Graphical Abstract Graphical Abstract Summary PEREGRINE, Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation, allows for pulse sequence updates using on-scanner deployed neural networks. To demonstrate the utility, PEREGRINE was used to operate two neural networks (time/frequency domain), trained to detect out-of-voxel artifacts, during a MEGA-edited MRS experiment in the prefrontal cortex. When artifacts were detected, PEREGRINE updated the crusher gradients within the same TR. Real-time, adaptive, and AI-driven MR will provide a long-awaited solution for combatting artifacts and poor data quality. Graphical Abstract Figure

ABSTRACT Macrophages play a central role in determining the outcomes of healing, coordinating regeneration in some injuries and scar formation in others. In both cases, this coordination involves the cross-talk between macrophages and surrounding cells. But what drives the different cross-communication pathways to determine healing outcomes is not well known. In this study, we make use of the mouse digit tip amputation model, in which an amputation through the third phalangeal element (P3) is able to completely regenerate whereas an amputation through the second phalangeal element (P2) forms a scar. We identify a population of macrophages that is specific to the P3 regenerating digit. By integrating single-cell RNAseq, spatial transcriptomics, and metabolomic analyses, we show that this population localizes specifically to the growing bone front, express BMP ligands that drive downstream BMP activation in neighboring osteoblasts and is governed by a two-part metabolic switch involving increased fatty acid oxidation coupled with reduced glycolytic activity. This spatially restricted, BMP-expressing macrophage population is entirely absent in the scar-forming P2 injury, and our data indicate that environmental conditions unique to the regenerating digit are responsible for its emergence. Together these findings identify a regeneration-specific macrophage signaling center for patterned bone formation and suggest that targeting the metabolic conditions that drive this population could improve the efficacy of regenerative therapies.