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Aims: To assess whether the combination of transthoracic echocardiography (TTE) and multidetector computed tomography (MDCT) data affects the grading of aortic stenosis (AS) severity under consideration of the energy loss index (ELI) in patients undergoing transcatheter aortic valve replacement (TAVR).
Methods And Results: Multimodality imaging was performed in 197 patients with symptomatic severe AS undergoing TAVR at the University Hospital Zurich, Switzerland. Fusion aortic valve area index (fusion AVAi) assessed by integrating MDCT derived planimetric left ventricular outflow tract area into the continuity equation was significantly larger as compared to conventional AVAi (0.41 ± 0.1 vs. 0.51 ± 0.1 cm2/m2; P < 0.01). A total of 62 patients (31.4%) were reclassified from severe to moderate AS with fusion AVAi being >0.6 cm2/m2. ELI was obtained for conventional AVAi and fusion AVAi based on sinotubular junction area determined by TTE (ELILTL 0.47 ± 0.1 cm2/m2; fusion ELILTL 0.60 ± 0.1 cm2/m2) and MDCT (ELIMDCT 0.48 ± 0.1 cm2/m2; fusion ELIMDCT 0.61 ± 0.05 cm2/m2). When ELI was calculated with fusion AVAi the effective orifice area was >0.6 cm2/m2 in 85 patients (43.1%). Survival rate 3 years after TAVR was higher in patients reclassified to moderate AS according to multimodality imaging derived ELI (78.8% vs. 67%; P = 0.01).
Conclusion: Multimodality imaging derived ELI reclassifies AS severity in 43% undergoing TAVR and predicts mid-term outcome.
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http://dx.doi.org/10.1093/ehjci/jeaa100 | DOI Listing |
Alzheimers Dement
September 2025
Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, China.
Cognitive impairment and dementia, including Alzheimer's disease (AD), pose a global health crisis, necessitating non-invasive biomarkers for early detection. This review highlights the retina, an accessible extension of the central nervous system (CNS), as a window to cerebral pathology through structural, functional, and molecular alterations. By synthesizing interdisciplinary evidence, we identify retinal biomarkers as promising tools for early diagnosis and risk stratification.
View Article and Find Full Text PDFHum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFPediatr Transplant
November 2025
Division of Urology, University of Toronto, Toronto, Canada.
Introduction: Differentiating acute tubular necrosis (ATN) from rejection in pediatric kidney transplant (KT) recipients remains challenging and necessitates invasive biopsy. Doppler ultrasound-derived resistive index (RI) is a noninvasive modality to assess graft status, but its diagnostic utility in children is unclear. This study evaluates RI's ability to distinguish ATN and rejection in KT.
View Article and Find Full Text PDFRetin Cases Brief Rep
September 2025
Retinal Disorders and Ophthalmic Genetics Division, Stein Eye Institute, University of California of Los Angeles, David Geffen School of Medicine at UCLA, Los Angeles, California, United States.
Purpose: To describe a case of recalcitrant bilateral peripapillary pachychoroid syndrome (PPS) treated with high-dose (HD) intravitreal aflibercept injections.
Methods: Medical and imaging records were retrospectively evaluated. Multimodal imaging included ultra-widefield indocyanine green and fluorescein angiography and fundus autofluorescence.
Front Digit Health
August 2025
Department of Ophthalmology, Stanford University, Palo Alto, CA, United States.
Introduction: Vision language models (VLMs) combine image analysis capabilities with large language models (LLMs). Because of their multimodal capabilities, VLMs offer a clinical advantage over image classification models for the diagnosis of optic disc swelling by allowing a consideration of clinical context. In this study, we compare the performance of non-specialty-trained VLMs with different prompts in the classification of optic disc swelling on fundus photographs.
View Article and Find Full Text PDF