Comparing Different Donor Sites After Vascularized Lymph Node Transfer to the Lymphedematous Upper Limb: A Systematic Review and Meta-analysis of Clinical Outcomes.

BACKGROUND: Vascularized lymph node transfer (VLNT) involves the microvascular transplantation of functional lymph nodes from a donor site into a limb affected by lymphedema to restore the normal flow of lymphatic fluid. Despite the increasing clinical experience with VLNT, there remains insufficient data to support its routine use in clinical practice. Here, we aim to evaluate the effectiveness and safety of VLNTs for upper limb lymphedema and compare clinical outcomes when using different donor sites. METHODS: We carried out a systematic search of the literature through PubMed and Scopus databases for studies on VLNT for upper limb lymphedema. Primary and secondary outcomes included circumference reduction rate (CRR) and infection reduction rate by postoperative cellulitis episodes for the efficacy and safety of VLNT. Pooled analysis was performed using the inverse variance weighting meta-analysis of single means using the meta package in R software. Subgroup analyses were performed for donor and recipient sites, age groups, follow-ups, and symptom durations. Quality assessment was performed using the Newcastle-Ottawa Scale for nonrandomized studies. RESULTS: A total of 1089 studies were retrieved from the literature, and 15 studies with 448 upper limb lymphedema patients who underwent VLNT were included after eligibility assessment. The mean CRR was 34.6 (18.8) and the mean postoperative cellulitis episodes per year was 0.71 (0.7). The pooled analysis of CRR was 28.4% (95% confidence interval, 19.7-41.1) and postoperative cellulitis episodes showed a mean of 0.59 (95% confidence interval, 0.36-0.95) using the random-effect model. Subgroup analyses showed significant group differences in recipient site for CRR and postoperative cellulitis episodes with the wrist comprising the highest weights, and patients younger than 50 years showing a lower postoperative infection. CONCLUSIONS: Vascularized lymph node transfer using gastroepiploic flaps at the wrists has shown a significant difference in reductions of limb circumference and cellulitis episodes in upper limb lymphedema patients when compared with other donor sites. However, further prospective studies are needed to consolidate this finding.

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review.

PURPOSE OF REVIEW: This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to understand the role of AI and ML in healthcare, specifically in medical diagnosis, statistics, and precision medicine, and to explore their applications in predicting and managing sudden cardiac arrest outcomes, especially in the context of prehospital emergency care. RECENT FINDINGS: The role of AI and ML in healthcare is expanding, with applications evident in medical diagnosis, statistics, and precision medicine. Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There's a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). Furthermore, the combination of AI with automated external defibrillators (AEDs) shows potential in better detecting shockable rhythms during cardiac arrest incidents. AI and ML hold immense promise in revolutionizing the prediction and management of sudden cardiac arrest, hinting at improved survival rates and more efficient healthcare interventions in the future. Sudden cardiac arrest (SCA) continues to be a major global cause of death, with survival rates remaining low despite advanced first responder systems. The ongoing challenge is the prediction and prevention of SCA. However, with the rise in the adoption of AI and ML tools in clinical electrophysiology in recent times, there is optimism about addressing these challenges more effectively.