Citations

Objective: To establish benchmark cutoffs for robotic liver resection (R-LR), encompassing both major and minor resections, and to determine the impact of patient selection on outcomes. Background: R-LR is a key advancement in minimally invasive liver surgery but lacks standardized benchmarks, especially for minor resections. While guidelines endorse R-LR, its role in optimizing outcomes remains unclear. This study establishes the first benchmarks for R-LR, enabling comparisons across surgical modalities and refining patient selection. Methods: This retrospective, multicenter study analyzed consecutive adult patients undergoing R-LR at 30 international centers (2020–2023). Benchmark centers had an annual case volume of ≥15 R-LR. Benchmark criteria included ASA ≤2, no major comorbidities, no prior liver resections, and Child-Pugh A status. Benchmark cutoffs for 14 key outcomes were set at the 50th and 75th percentiles of median values across benchmark centers. Multivariable logistic regression identified predictors of textbook outcomes. Results: Eighteen high-volume centers contributed 4028 cases with 2632 (65.3%) meeting benchmark criteria. Malignancy was the indication in 29.6%, most commonly hepatocellular carcinoma followed by colorectal liver metastases. Major liver resection was performed in 42.6%. The distribution of Iwate difficulty scores was low (25.4%), intermediate (53.9%), advanced (15.9%), and expert (4.9%). Benchmark cutoffs were established for minor and major resection, and stratified by Iwate low (0-3), intermediate (4-6) and high (7-12): Open conversion (≤6.5% minor, ≤10.5% major), major complications within 90 days (≤5.2% minor, ≤16.7% major), R1 resection (≤9.2% minor, ≤6.7% major). Benchmark cases performed at low-volume centers were able to achieve outcomes within the corresponding benchmark cutoffs. In fact, patient selection reflected by the proportion of benchmark patients, rather than case volume, was associated with textbook outcomes. Conclusions: This study defines R-LR benchmarks, emphasizing patient selection over center volume for optimal outcomes. Benchmark cutoffs guide training and support the expansion of R-LR.

Abstract Background Robotic Whipple holds the promise to overcome safety concerns associated with laparoscopy, paving the way for widespread implementation of minimal-invasive surgery in this complex procedure. However, randomized data comparing robot vs. open Whipple demonstrate more pancreas-specific complications and R1-resections in the robotic arm. Recent international consensus identified establishing benchmarks as critical to ensure safe adoption of the robot. Benchmarking is a validated quality improvement tool, enabling comparison of surgical performance. Aims The aim was to define benchmarks for outcome parameters in robotic Whipple. Methods We analyzed consecutive patients undergoing robotic Whipple from January 2020 until December 2023 in 11 centers across 4 continents, with a minimum one-year follow-up. Centers had to perform ≥15 cases/year and have mounted their learning curve. Benchmark criteria included benign or resectable malignant disease without neoadjuvant therapy, arterial resection, major co-morbidities, or significant previous abdominal surgery. Medians across centers represented benchmark cutoffs. Results Eleven centers performed 1’037 Whipple procedures, of which 603 (58%) were benchmark cases. One third (n=192) were pancreatic ductal adenocarcinoma (PDAC) patients. Key benchmarks at 6 months included ≤1.2% mortality, ≤24.2% major complications, and ≤ 8.7 points Comprehensive Complication Index®. Pancreas-specific cutoffs included ≤13.0% postoperative pancreatic fistula (POPF) B/C and ≤3.4% post-pancreatectomy hemorrhage B/C, with 100% R0-resection and ≥19 harvested lymph nodes in PDAC patients. One-year actuarial overall and recurrence-free survival was 87% and 77%. In the entire cohort POPF B/C occurred in 16% (n=195). Independent POPF predictors included duct diameter ≤4mm (OR 1.79 95%CI [1.27-2.55]), anticoagulation (OR 3.68 95%CI [2.14-6.24]), and indication other than PDAC (OR 3.17, 95%CI [2.13-4.85]). Conclusion This study establishes benchmarks for key outcomes in robotic Whipple, demonstrating oncologic adequacy and morbidity comparable to open surgery. Risk factors for POPF in open surgery also hold true in the robotic approach.

Background Secondary aortoenteric fistulas (SAEF) following endovascular aortic repair (EVAR) is an extremely rare event but life threatening. Our review offers comprehensive knowledge on pathophysiology, clinical presentation, diagnosis, and treatment options. Aim To summarize the current literature regarding pathophysiology, clinical, diagnostic and therapeutic approach of aortoenteric fistulas secondary to EVAR. Methods We performed a literature search in Pubmed/MEDLINE to identify the literature published about SAEF after EVAR. Cases were summarized in a table and prevalences. Other relevant literature was included in the results sections. Results A total of 35 reports (single cases and small series) with 45 patients were included. SAEF after EVAR can result from infection, inflammation, or mechanical factors. Clinical presentation is often non-specific, ranging from a gastrointestinal herald bleed to hemorrhagic shock, or malaise and general infection-related symptoms. Cross-sectional imaging plays a critical role in diagnosing SAEF. The treatment approach involves a multidisciplinary team approach and requires broad-spectrum intravenous antibiotics, endovascular intervention for urgent hemorrhage control, and open surgical intervention for definitive repair. Long-term antimicrobial therapy is essential to avoid reinfection. Conclusions SAEF following EVAR represents a complex, life-threatening condition with limited evidence-based management strategies. Given the growing prevalence of endovascular procedures, comprehensive knowledge of SAEF is crucial for all health care providers to improve early diagnosis and outcomes.

Abstract Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

Key Points In phospholipase A2 receptor-membranous nephropathy, clinical variables and antibody levels over 3 or 6 months of treatment were predictors of remission at 1 year. Prediction models at 3 and 6 months had similar performance predicting remission, with the 3-month model allowing for earlier assessment of response. The 3-month and 6-month prediction models could be applied in those treated with supportive therapy, rituximab, calcineurin inhibitors, or cyclophosphamide. Background In patients with antiphospholipase A2 receptor antibody–associated membranous nephropathy, there is currently no accepted method to predict an individual's probability of remission after treatment with immunosuppression or supportive therapy using changes in antibody levels and clinical variables during the first 3–6 months of therapy. Methods Using a cohort of 187 patients from the Glomérulopathie extramembraneuse rituximab, Membranous Nephropathy Trial of Rituximab, and Sequential Treatment with Tacrolimus and Rituximab Versus Alternating Corticosteroids and Cyclophosphamide in primary Membranous Nephropathy clinical trials with antibody levels at baseline ≥14 RU/ml, we derived logistic regression models to predict proteinuria remission at 12 months that can be used at baseline or after 3 or 6 months of treatment. Treatment exposures in the trials included supportive therapy, rituximab, calcineurin inhibitors, and cyclophosphamide. Predictors in the models included male sex and baseline and changes in serum albumin, proteinuria, and antibody levels, with or without changes in eGFR. Results Proteinuria remission at 12 months was achieved in 107 patients. Compared with the model at baseline, the 3-month and 6-month models had better model fit with lower Akaike information criterion (186/158 versus 225) and higher R 2 (52.7%/62.4% versus 25.8%), better discrimination with higher C-statistic (0.87 and 0.91 versus 0.75, P < 0.001), and better calibration with lower integrated calibration index (0.89%/2.22% versus 2.51%). The 3-month and 6-month models had no consistent difference in prediction performance, and decision curve analysis demonstrated similar net benefit for treatment decisions based on either model up to a threshold probability of 31%. Prediction performance was similar after internal validation using optimism correction. Prediction performance was maintained within subgroups of different treatment regimens, including supportive therapy, rituximab, calcineurin inhibitors, and cyclophosphamide. Conclusions Either the 3-month or 6-month models can be used in patients with antiphospholipase A2 receptor antibody associated membranous nephropathy after 3 or 6 months of treatment with a variety of immunosuppression or supportive therapy to predict remission status at 12 months.

Abstract Background The Global Leadership Initiative on Malnutrition (GLIM) introduced an approach for malnutrition diagnosis in 2019 that comprised screening followed by assessment of three phenotypic criteria (weight loss, low body mass index [BMI], and low muscle mass) and two etiologic criteria (reduced food intake/assimilation and inflammation/disease burden). This planned update reconsiders the GLIM framework based on published knowledge and experience over the past 5 years. Methods A working group ( n = 43 members) conducted a literature search spanning 2019–2024 using the keywords “Global Leadership Initiative on Malnutrition or GLIM.” Prior GLIM guidance activities for using the criteria on muscle mass and inflammation were reviewed. Successive rounds of revision and review were used to achieve consensus. Results More than 400 scientific reports were published in peer‐reviewed journals, forming the basis of 10 systematic reviews, some including meta‐analyses of GLIM validity that indicate strong construct and predictive validity. Limitations and future priorities are discussed. Working group findings suggest that assessment of low muscle mass should be guided by experience and available technological resources. Clinical judgment may suffice to evaluate the inflammation/disease burden etiologic criterion. No revisions of the weight loss, low BMI, or reduced food intake/assimilation criteria are suggested. After two rounds of review and revision, the working group secured 100% agreement with the conclusions reported in the 5‐year update. Conclusion Ongoing initiatives target priorities that include malnutrition risk screening procedures, GLIM adaptation to the intensive care setting, assessment in support of the reduced food intake/assimilation criterion, and determination of malnutrition in obesity.

Abstract Background and Aims Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk. Methods Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single-view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analysing colour Doppler videos was developed to predict which patients with mild, mild–moderate, and moderate MR were most likely to progress to moderate–severe or severe MR with analysis by Kaplan–Meier and Cox proportional hazards models. Results A total of 71 660 TTEs with 1 203 980 colour Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach taking all colour Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR-progression risk even when controlled for clinical factors known to be associated with MR progression [internal test set hazard ratio 4.1 (95% confidence interval 2.5–6.6)]. Conclusions An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.

Abstract Excessively restrictive inclusion and exclusion criteria in clinical trials are one of many barriers to clinical trial enrollment for patients with myelodysplastic syndromes/neoplasms (MDSs). Many organizations are developing efforts to increase clinical trial eligibility; yet, several recent publications focused on patients with MDS suggest that many patients with this disease may be excluded from clinical trials unnecessarily. Clinical trial eligibility should reflect the phase of the study and risks of the agent being studied. Phase 3 trials should be less restrictive than early-phase trials to represent the real-world population as closely as possible. We hypothesize that many clinical trials, particularly phase 3 trials, have unnecessarily restrictive eligibility criteria. This study aims to evaluate the most common eligibility criteria according to phase of trial and to determine whether criteria correspond with drug safety signals. We identified MDS clinical trials registered on ClinicalTrials.gov from 1 January 2000 to 1 September 2023 and analyzed the eligibility criteria of 191 therapeutic MDS trials. We found that categorical inclusion and exclusion criteria are remarkably similar in representation across trial phases. Additionally, only 13% of trials are concordant with drug safety signals, suggesting that the eligibility criteria are often arbitrary. On behalf of the icMDS (International Consortium for Myelodysplastic Syndromes), an association of international MDS experts, we provide a position statement on restrictive eligibility criteria for MDS clinical trials that should be avoided with the aim of removing barriers to clinical trial enrollment.