Efficacy and safety of anti-vascular endothelial growth agents for the treatment of polypoidal choroidal vasculopathy: A systematic review and meta-analysis

There remains limited agreement regarding the efficacy and safety of different antivascular endothelial growth factor (anti-VEGF) agents for the management of polypoidal choroidal vasculopathy (PCV). Our meta-analysis compares different anti-VEGF agents for PCV treatment. Ovid MEDLINE, EMBASE, and Cochrane Library were systematically searched from January 2000 to July 2022. We included articles comparing the efficacy and safety of different anti-VEGF agents, specifically bevacizumab (BEV), ranibizumab (RAN), aflibercept AFL), and brolucizumab (BRO), for patients with PCV. 10,440 studies were identified, 122 underwent full-text review, and seven were included. One study was a randomized trial, and six were observational studies. Ranibizumab and aflibercept were associated with a similar best-corrected visual acuity (BCVA) at the last visit in three observational studies (P = 0.10), similar retinal thickness at the last visit in two observational studies (P = 0.85). One observational study comparing BEV versus RAN found comparable outcomes for final BCVA, retinal thickness, and polyp regression. One randomized trial on BRO versus AFL found comparable outcomes for improvement in BCVA, while anatomical outcomes favored BRO. The available evidence suggests that final BCVA is comparable across different anti-VEGF agents, however, further investigation is warranted due to paucity of evidence.     

Training and teaching of vagus nerve stimulation surgery: Worldwide survey and future perspectives

ObjectiveVagus nerve stimulation (VNS) therapy has been used for more than two decades to treat drug resistant epilepsy and depression and most recently received FDA approval for stroke rehabilitation. Expanding indications will renew the interest in the technique and increase the number of surgeons to be trained. The aim of this study was to survey surgeons with substantial expertise on optimal teaching and training approaches.MethodsAnonymous forms comprising 16 questions were sent by e-mail to surgeons with substantial expertise. Statistical analyses were used to compare the answers of the most experienced surgeons (>5 years) with the less experienced ones (<5 years).ResultsFully-completed forms were collected from 57 experts from 20 countries. The placement of the helical coils was deemed to be the most difficult step by 36 (63.2%) experts, and the use of optical magnification during this step was deemed necessary by 39 (68.4%) experts. Vocal cord palsy should be largely avoidable with proper surgical technique according to 44 (77.2%) experts. The teaching tool considered the most useful was mentoring (38, 66.7%). The future of VNS surgery teaching was deemed to be in anatomical workshops (29, 50.9%) and surgical simulation (26, 45.6%). Overall, answers did not vary significantly according to experience.ConclusionsVNS surgery should be mastered by actively participating in dedicated practical training courses and by individual mentoring during actual surgery, which is still the best way to learn. This study highlights the need for a formal training course and possible specific accreditation.     

The American Association for Thoracic Surgery (AATS) 2022 Expert Consensus Document: The use of mechanical circulatory support in lung transplantation

ObjectiveThe use of mechanical circulatory support (MCS) in lung transplantation has been steadily increasing over the prior decade, with evolving strategies for incorporating support in the preoperative, intraoperative, and postoperative settings. There is significant practice variability in the use of these techniques, however, and relatively limited data to help establish institutional protocols. The objective of the AATS Clinical Practice Standards Committee (CPSC) expert panel was to review the existing literature and establish recommendations about the use of MCS before, during, and after lung transplantation.MethodsThe AATS CPSC assembled an expert panel of 16 lung transplantation physicians who developed a consensus document of recommendations. The panel was broken into subgroups focused on preoperative, intraoperative, and postoperative support, and each subgroup performed a focused literature review. These subgroups formulated recommendation statements for each subtopic, which were evaluated by the entire group. The statements were then developed via discussion among the panel and refined until consensus was achieved on each statement.ResultsThe expert panel achieved consensus on 36 recommendations for how and when to use MCS in lung transplantation. These recommendations included the use of veno-venous extracorporeal membrane oxygenation (ECMO) as a bridging strategy in the preoperative setting, a preference for central veno-arterial ECMO over traditional cardiopulmonary bypass during the transplantation procedure, and the benefit of supporting selected patients with MCS postoperatively.ConclusionsAchieving optimal results in lung transplantation requires the use of a wide range of strategies. MCS provides an important mechanism for helping these critically ill patients through the peritransplantation period. Despite the complex nature of the decision making process in the treatment of these patients, the expert panel was able to achieve consensus on 36 recommendations. These recommendations should provide guidance for professionals involved in the care of end-stage lung disease patients considered for transplantation.     

Development of a Semiautomated Database for Patients With Adult Congenital Heart Disease

BackgroundDatabases for Congenital Heart Disease (CHD) are effective in delivering accessible datasets ready for statistical inference. Data collection hitherto has, however, been labour and time intensive and has required substantial financial support to ensure sustainability. We propose here creation and piloting of a semiautomated technique for data extraction from clinic letters to populate a clinical database.MethodsPDF formatted clinic letters stored in a local folder, through a series of algorithms, underwent data extraction, preprocessing, and analysis. Specific patient information (diagnoses, diagnostic complexity, interventions, arrhythmia, medications, and demographic data) was processed into text files and structured data tables, used to populate a database. A specific data validation schema was predefined to verify and accommodate the information populating the database. Unsupervised learning in the form of a dimensionality reduction technique was used to project data into 2 dimensions and visualize their intrinsic structure in relation to the diagnosis, medication, intervention, and European Society of Cardiology classification lists of disease complexity. Ninety-three randomly selected letters were reviewed manually for accuracy.ResultsThere were 1409 consecutive outpatient clinic letters used to populate the Scottish Adult Congenital Cardiac Database. Mean patient age was 35.4 years; 47.6% female; with 698 (49.5%) having moderately complex, 369 (26.1%) greatly complex, and 284 (20.1%) mildly complex lesions. Individual diagnoses were successfully extracted in 96.95%, and demographic data were extracted in 100% of letters. Data extraction, database upload, data analysis and visualization took 571 seconds (9.51 minutes). Manual data extraction in the categories of diagnoses, intervention, and medications yielded accuracy of the computer algorithm in 94%, 93%, and 93%, respectively.ConclusionsSemiautomated data extraction from clinic letters into a database can be achieved successfully with a high degree of accuracy and efficiency.     

Patient engagement in the Continuing Professional Development programs within the department of radiation oncology at the University of Toronto (UTDRO): A qualitative study

IntroductionThere is little research regarding patient engagement (PE) in Continuing Professional Development (CPD) programs in radiation oncology. This study aims to understand the barriers and enablers to PE in the design and implementation process of CPD programs, and advance PE in these programs moving forward.MethodsThis qualitative study involved 17 semi-structured interviews, with 5 cancer patients and 12 educators, conducted from June 2019 to April 2020. Interview data identified common themes, such as: the current state of PE in CPD programming, and key barriers and recommendations on how to engage patients in meaningful and practical ways.ResultsSix themes were identified related to PE: the concept of PE, ethical considerations, barriers, key considerations in planning resources, and the anticipated impact of PE on curriculum planning.ConclusionBoth patients and educators emphasized that creating and sustaining meaningful educator-patient relationships and giving patients an active and effective role in CPD planning would improve curriculum content. The University of Toronto Department of Radiation Oncology (UTDRO) should consider building this initiative into its strategic CPD priorities and ensure the appropriate infrastructure is in place.     

Anomaly segmentation in retinal images with poisson-blending data augmentation

Diabetic retinopathy (DR) is one of the most important complications of diabetes. Accurate segmentation of DR lesions is of great importance for the early diagnosis of DR. However, simultaneous segmentation of multi-type DR lesions is technically challenging because of 1) the lack of pixel-level annotations and 2) the large diversity between different types of DR lesions. In this study, first, we propose a novel Poisson-blending data augmentation (PBDA) algorithm to generate synthetic images, which can be easily utilized to expand the existing training data for lesion segmentation. We perform extensive experiments to recognize the important attributes in the PBDA algorithm. We show that position constraints are of great importance and that the synthesis density of one type of lesion has a joint influence on the segmentation of other types of lesions. Second, we propose a convolutional neural network architecture, named DSR-U-Net++ (i.e., DC-SC residual U-Net++), for the simultaneous segmentation of multi-type DR lesions. Ablation studies showed that the mean area under precision recall curve (AUPR) for all four types of lesions increased by >5% with PBDA. The proposed DSR-U-Net++ with PBDA outperformed the state-of-the-art methods by 1.7%-9.9% on the Indian Diabetic Retinopathy Image Dataset (IDRiD) and 67.3% on the e-ophtha dataset with respect to mean AUPR. The developed method would be an efficient tool to generate large-scale task-specific training data for other medical anomaly segmentation tasks.