Search Results (4)

Background/Objectives: After an initial course of anticoagulation for deep vein thrombosis (DVT), identifying patients at higher risk of recurrence remains a clinical challenge. The role of residual vein thrombosis (RVT) in this setting is still debated, as most available evidence derives from retrospective studies or from the Warfarin era. We conducted a study to evaluate the incidence of RVT in patients treated with direct oral anticoagulants (DOACs) and to identify the clinical factors associated with its persistence. We also compared the outcomes from the two most prescribed drugs in Italy, Apixaban and Rivaroxaban. Methods: A total of 113 patients with newly diagnosed DVT underwent follow-up visits at 6 weeks (T1), 3 months (T2) and 6 months (T3) after diagnosis. RVT was assessed by compression ultrasonography and clinical, family and pathological history data were collected. Ninety-six patients were included in the final statistical analysis. Results: RVT was detected in 68.2%, 52.1% and 37.7% of patients at T1, T2 and T3, respectively. Factors significantly associated with RVT at T2 were male sex, femoral vein involvement and a family history of DVT. No significant differences were observed between Apixaban and Rivaroxaban. Prior episodes of thrombosis, smoking, diabetes and obesity were not associated with RVT at 3 months. Conclusions: Our findings confirm that RVT rates progressively decrease over time, as previously observed in the Coumarins era, but suggest a stronger early response to DOACs, particularly during the first three months of therapy. Moreover, DOACs appear to provide more effective protection in patients with risk factors for venous disease. Full article

Atrial fibrillation (Afib) recurrence after catheter ablation (CA) remains a significant clinical challenge, driven by a complex and dynamic interplay of structural, electrical, and autonomic mechanisms. While pulmonary vein isolation (PVI) is the cornerstone of CA, recurrence rates remain substantial, highlighting the need to understand the evolving pathophysiology beyond PV reconnection. Post-ablation changes, including inflammation, edema, oxidative stress, and ischemia, create a transient proarrhythmic state that may contribute to early recurrence. Over time, atrial remodeling, fibrosis, and residual autonomic activity further sustain arrhythmogenicity. Additionally, epicardial adipose tissue promotes atrial myopathy, accelerating disease progression, particularly in patients with risk factors such as older age, female sex, obesity, hypertension, obstructive sleep apnea, and heart failure. The multifactorial nature of Afib recurrence underscores the limitations of a “one-size-fits-all” ablation strategy. Instead, a patient-specific approach integrating advanced mapping techniques, multimodal imaging, and computational modeling is essential. Artificial intelligence (AI) and digital twin models hold promise for predicting recurrence by simulating individualized disease progression and optimizing ablation strategies. However, challenges remain regarding the standardization and validation of these novel approaches. A deeper understanding of the dynamic interconnections between the mechanisms driving recurrence is crucial for improving long-term CA outcomes. This review explores the evolving nature of Afib recurrence, emphasizing the need for a precision medicine approach that accounts for the continuous interaction of pathophysiological processes in order to refine patient selection, ablation strategies, and post-procedural management. Full article

Deep venous thrombosis (DVT) is a pathological condition that develops when a thrombus forms within the deep venous system. Typically, it involves the lower limbs and, less frequently, the upper extremities or other unusual districts such as cerebral or splanchnic veins. While leg DVT itself is rarely fatal and occasionally can lead to limb-threatening implications, its most fearsome complication, namely pulmonary embolism, is potentially fatal and significantly contributes to increased healthcare costs and impaired quality of life in affected patients and caregivers. Thanks to its high accuracy, ease of use, and safety profile, duplex ultrasound (DUS), particularly compression ultrasound (CUS), has emerged as the first-line imaging modality for DVT diagnosis. The evaluation of suspected DVT needs a multifaceted approach, and in this context, CUS rapidly became a key diagnostic tool owing to its many unique advantages. Its central role in the diagnostic algorithm of suspected DVT is clearly established in the latest clinical practice guidelines from the European Society for Vascular Surgery and the American Society of Haematology. Indeed, DUS effectively visualizes blood flow and identifies abnormalities like clot formation with high sensitivity (typically exceeding 90% for proximal DVT) and specificity (often approaching 100% for proximal DVT). Additionally, CUS is non-invasive, readily available at the bedside, and avoids radiation exposure, resulting in an ideal method for various clinical settings. CUS has been shown to have a substantial role not only in the diagnosis of an acute DVT but also in the follow-up of its management. Moreover, this method can provide a prognostic assessment, mostly in terms of risk stratification for recurrent thrombosis and/or for potential complications, such as post-thrombotic syndrome. In summary, given its established benefits, CUS is a technique that many physicians should be familiar with, especially those working in emergency departments, intensive care units, or general wards. When needed, healthcare operators with more advanced US skills (such as radiologists, angiologists, or vascular surgeons) may be called upon to provide a second look in case of uncertainty and/or need for additional information. Full article

A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new “DR-Insight” dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system’s goal is to augment optometrists’ expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR). Full article