Citations

Background/Objectives: The study aimed to investigate whether malnutrition influences the obesity paradox and to explore the relationship between obesity with/without malnutrition and in-hospital outcomes among hospitalized ESKD patients. Methods: The study used the National Inpatient Sample database from 2016 to 2021. Hospitalized ESKD patients were included and categorized into three groups: non-obese, obese without malnutrition, and obese with malnutrition. The association between obesity with/without malnutrition and in-hospital outcomes, compared to non-obese patients, were analyzed. Results: Of 674,367 hospitalized ESKD patients included, 125,978 (18.7%) had obesity. Obese ESKD patients without malnutrition were associated with a decreased risk of mortality (odd ratio [OR] 0.87, 95% CI 0.84–0.91), whereas obese patients with malnutrition were associated with an increased risk of mortality (OR 2.08, 95% CI 1.90–2.27), compared to non-obese patients. Furthermore, obesity, with or without malnutrition, was linked to higher infection-related complications and resource utilization, especially when malnutrition was present. Conclusions: Our findings show that obesity is significantly associated with lower in-hospital mortality among ESKD patients without malnutrition. However, when malnutrition coexists, this survival advantage is reversed, underscoring the importance of detecting malnutrition in obese ESKD patients.

Asymptomatic aortic regurgitation (AR) has traditionally been managed conservatively until symptom onset or overt left ventricular systolic dysfunction. However, adverse myocardial remodeling—detected by myocardial strain, volumetric cardiac magnetic resonance, and fibrosis imaging—often precedes current guideline thresholds for interventions and may be irreversible. Advances in multimodal imaging now enable earlier risk stratification beyond conventional metrics. In parallel, intervention strategies are evolving, including valve repair, valve-sparing root replacement, Ross procedure, and transcatheter aortic valve replacement in selected high-risk patients. This narrative review summarizes contemporary advances in imaging and intervention for asymptomatic AR, while critically appraising current evidentiary and technical limitations that constrain earlier intervention. The review is based on a narrative synthesis of the contemporary literature, drawing from recent clinical studies, imaging advances, and guideline documents rather than a systematic evidence search.

Background/Objectives: To evaluate risk factors for postoperative neurological deficits following cervical endoscopic unilateral laminotomy for bilateral decompression (CE-ULBD) and to determine whether intraoperative neurophysiological monitoring (IONM) can predict neurological compromise. Methods: A multicenter retrospective review was performed on 42 CE-ULBD procedures conducted between 2016 and 2024; 33 cases met the inclusion criteria with available imaging and electromyography data. Demographic, operative, and neurophysiological variables were analyzed. Preoperative stenosis severity was graded using the Kang MRI system. Intraoperative IONM data, including electromyography firing and motor evoked potential (MEP) changes, were correlated with new postoperative weakness. Results: The cohort (69.1% male, mean age 70.2 ± 1.7 years, mean BMI 29.6 ± 1.1) included 56 decompressed levels. The most common operative levels were C3-4 (37%) and C4-5 (24%). Postoperative weakness occurred in four patients (12.1%), all of whom had severe (Grade 3) preoperative stenosis. Among these, 50% exhibited preoperative weakness. Neuromonitoring changes correlated significantly with postoperative weakness (Fisher’s Exact, p < 0.001); 100% of patients with new post-operative weakness had sustained MEP decrease at the time of closure. Conclusions: Patients with severe cervical stenosis and preoperative weakness are at heightened risk of postoperative neurological deficits following CE-ULBD. Elevated epidural pressure from continuous irrigation in a constricted canal may exacerbate cord compression, particularly in those with preexisting myelopathy. IONM changes strongly correlate with new deficits and may exacerbate cord compression, particularly in those with preexisting myelopathy, and may serve as an early warning system for impending neurological injury. Surgeons should exercise caution and maintain low irrigation pressures in patients with severe stenosis undergoing endoscopic cervical decompression.

Objectives Ultrasound‐guided percutaneous needle fenestration (UPNF) and percutaneous needle tenotomy (UPNT) are minimally invasive procedures commonly used to treat chronic tendinopathies. However, these techniques can be painful, potentially limiting patient tolerance, satisfaction, and procedural feasibility. Although local anesthetics (LAs) provide analgesia, their potential inhibitory effects on tenocyte activity and the efficacy of orthobiologics raise concerns regarding their impact on tendon healing. The objectives of this study is to propose a practical, anatomy‐based approach for the use of ultrasound‐guided peripheral nerve blocks (USG‐PNBs) as a strategy to reduce pain and improve patient comfort during UPNF and UPNT, with or without orthobiologic adjuncts. Methods We reviewed commonly affected anatomical sites in chronic tendinopathies and identified corresponding peripheral nerve targets that can be reliably and safely blocked under ultrasound guidance. For each condition, we outline appropriate nerve block techniques, anatomical landmarks, and recommended patient positioning to optimize procedural analgesia. Results A set of targeted USG‐PNBs was defined for commonly treated tendinopathies, including supraspinatus tendinopathy, lateral and medial epicondylitis, gluteus medius and/or minimus tendinopathy, patellar tendinopathy, Achilles tendinopathy, and plantar fasciopathy. Conclusion Ultrasound‐guided peripheral nerve blocks represent a practical and effective approach to manage pain during percutaneous procedures for chronic tendinopathies. By reducing procedural discomfort and avoiding the potential drawbacks of peritendinous LAs, these techniques may enhance patient tolerance, procedural success, and overall clinical outcomes.

Abstract Background Aortic valve calcification (AVC), as measured by gold-standard computed tomography (CT) Agatston score, provides an anatomic assessment of aortic stenosis (AS) severity and is a key predictor of AS progression and need for valve replacement. AVC detection and quantification from transthoracic echocardiography (TTE) could expand AS early diagnosis and risk stratification, currently limited by CT availability and radiation exposure. Methods A multi-view video-based deep learning framework was developed using 1166 TTE aortic valve videos from 187 TTE studies acquired in 110 patients with available AVC score by CT. EchoAVC architecture includes a feature extraction model followed by a quality-aware model that aggregates video-level information to obtain patient-level predictions for AVC detection and quantification (score). The framework was validated internally with 173 TTE studies from 86 patients across seven centres, and externally using 430 TTE studies from 280 patients across four different centres. The associations between EchoAVC estimations and AS severity, progression, and need for aortic valve replacement were examined. Results EchoAVC demonstrated excellent performance in AVC detection (AUROC 0.98, accuracy 94.4%), and quantification (R = 0.64) in the external multi-centre testing set. EchoAVC score was correlated with echocardiographic AS severity descriptors, including aortic valve mean pressure gradient (ρ = 0.749) and peak velocity (ρ = 0.757), and predicted future increase in mean pressure gradient (ρ = 0.382), peak velocity (ρ = 0.433) and calcium score by CT (ρ = 0.650). In 361 patients followed for a median of 3.8 years, 139 underwent aortic valve replacement. Baseline presence and extent of AVC as predicted by EchoAVC showed strong risk-stratification power for aortic valve replacement, remarkably in line with those obtained by CT, and incremental over TTE AS descriptors. EchoAVC was further tested in routine clinical practice images, confirming strong associations with AS severity and progression, including stratification for incident AS in previously unaffected individuals (p<0.001). Conclusions EchoAVC enables accurate and non-invasive detection and quantification of AVC, offering substantial diagnostic and prognostic value for aortic stenosis progression and need for valve replacement. This technique holds promise as a scalable tool for early detection and clinical management of aortic valve stenosis. Clinical Perspective What Is New? * Aortic valve calcification can be detected and quantified on standard, two-dimensional transthoracic echocardiography * EchoAVC score is associated with echocardiographic metrics of aortic stenosis severity * EchoAVC predicts progressive increase in aortic stenosis severity and need for aortic valve replacement What Are the Clinical Implications? * This openly available deep learning framework can be used to estimate aortic valve calcium in patients at risk of or presenting aortic stenosis to predict aortic stenosis progression * EchoAVC may help identify patients likely to have high aortic valve calcium and prioritize them for confirmatory CT assessment, thereby supporting earlier identification of calcific aortic valve disease in clinical pathways.