Citations

Background: The Accreditation Council for Graduate Medical Education (ACGME) case log system establishes minimum clinical experiences for anesthesiology residency. While core program requirements were last updated in 2016, case log minimums themselves have not undergone a major revision in approximately two decades. Consequently, current requirements may not fully reflect changes in anesthetic practice and the evolving scope of perioperative medicine. In anticipation of major ACGME revisions in 2027, this study aimed to develop consensus-driven recommendations for modernizing residency case logs. Methods: A three-round modified Delphi study was conducted with a nationally representative panel of 60 stakeholders, including anesthesiology residency program directors and academic and private practice anesthesiologists. Panelists evaluated a comprehensive list of potential case categories across three domains: technical skills, surgical case characteristics, and patient demographics. In Round 1, panelists proposed minimum numbers or suggested that no requirement was necessary. Rounds 2 and 3 utilized iterative feedback to determine consensus on the necessity of requirements and specific minimum thresholds. Consensus was defined as at least two-thirds agreement. Results: Sixty invited experts completed all three rounds (100% response rate). The panel reached consensus to recommend new minimum requirements across several areas not currently tracked with minimums, including arterial lines (40), central lines (20), fiberoptic intubation (10), one-lung ventilation (10), neuromonitoring (10), POCUS (10 Cardiac, 10 Lung), and NORA (20 cases). Regarding existing requirements, consensus was reached to recommend increasing total peripheral nerve blocks from 40 to 60, with added requirements for anatomic specificity, and cardiac case minimums from 20 to 25. The panel also established consensus definitions for life-threatening pathologies to standardize reporting. The panel did not achieve consensus on several areas, including, for example, whether a minimum requirement for patients younger than 3 months should be maintained. Conclusion: These findings provide stakeholder-validated recommendations to inform upcoming ACGME requirement revisions.

Importance Patients undergoing cardiac surgery with cardiopulmonary bypass often require platelet transfusions for bleeding. Platelets are routinely stored at room temperature (20-24 °C) for up to 5 to 7 days; however, cold storage (1-6 °C) may allow for increased storage duration without loss of hemostatic function. Objective To determine the maximum cold-storage duration, up to 21 days, for which cold-stored platelets (CSPs) are noninferior or superior in hemostatic efficacy to room-temperature platelets (RTPs) when transfused in actively bleeding patients undergoing cardiac surgery with cardiopulmonary bypass. Design, Setting, and Participants A phase 3, multicenter, randomized, partially blinded, adaptive, noninferiority, storage duration–ranging trial conducted in pediatric and adult patients undergoing cardiac surgery with planned cardiopulmonary bypass at 27 sites in the US and Australia from December 2021 to March 2025. Statistical analysis was conducted from September 9 to October 3, 2025. Intervention Patients were randomized 2:1 to receive CSPs with a maximum of 21 days of storage vs RTPs with a maximum of 7 days of storage. Main Outcomes and Measures The primary outcome was a hemostatic efficacy score with values ranging from 1 to 5 and higher values indicating greater bleeding. The noninferiority margin was 1 point. The success criterion was met if at least 1 cold-storage duration of 7 days or more had a posterior probability of noninferiority of at least 97.5%. The secondary outcome was 24-hour chest tube output. Results Of the 1000 patients who underwent platelet transfusion, 989 were included in the primary analysis (650 CSPs and 339 RTPs). The cohort had a mean (SD) age of 42.1 (30.1) years and 67.8% were male. CSPs were noninferior to RTPs for the primary outcome, with a probability of greater than 99.9% for all cold-storage durations. Pooled across storage durations, CSPs had a mean difference from RTPs of 0.09 (95% credible interval, −0.06 to 0.23). Median (IQR) chest tube output at 24 hours (8.9 mL/kg [5.2-15.4] for CSPs vs 8.4 mL/kg [5.5-15.9] for RTPs; difference in medians, 0.4 [95% CI, −1.0 to 1.5]) was not statistically different between study groups. In a post hoc analysis, as all storage durations were noninferior, considering the CSPs as a single group, the hemostatic efficacy score was similar between both groups: mean (SD) of 3.08 (1.15) CSPs vs 2.99 (1.10) RTPs. There were no differences in venous or arterial thrombotic events, transfusion-associated adverse events, acute respiratory distress syndrome, kidney failure, septic shock, and mortality between the study groups, except for increased reexploration rates in the CSP group. Conclusions and Relevance For patients undergoing cardiac surgery, CSPs stored up to 21 days are noninferior to RTPs for the control of active surgical bleeding. Use of CSPs may allow for increased availability of platelets by reducing wastage and shortages and may allow for incorporation of platelets into inventory at locations where platelets cannot be maintained due to the 5- to 7-day shelf life of RTPs. Trial Registration ClinicalTrials.gov Identifier: NCT04834414

This study examines the patient- and hospital-level variation in time to initiation of inpatient management among patients admitted to general medical services.

Mesothelioma is a rare cancer that originates from the mesothelial surfaces of certain sites within the body. Pleural mesothelioma is the most common type and represents approximately 85% of mesotheliomas. The NCCN Guidelines for Mesothelioma: Pleural provide recommendations for evaluation and treatment in patients with pleural mesothelioma. The NCCN Guidelines will continue to be updated annually based on available clinical evidence and panel consensus.

Stellate ganglion blockade (SGB) is an uncommon but increasingly relevant therapy used to modulate the autonomic nervous system and reduce the risk of atrial fibrillation (AF). This narrative review focuses specifically on the potential therapeutic role of SGB in new-onset perioperative atrial fibrillation (POAF), a complication associated with adverse long-term outcomes that may diminish the overall benefits of surgery. SGB may reduce AF through several inflammation-mediated mechanisms, including attenuation of direct sympathetic activation and modulation of perioperative inflammatory responses. Because the clinical effects of SGB may persist beyond the expected duration of local anesthetic nerve blockade, we emphasize immunomodulation as a potential mechanism sustaining its beneficial effects and influencing longer-term outcomes. In this review, we examine the immunological effects of SGB in the context of postoperative AF, with particular emphasis on human studies most relevant to clinical practice. Available evidence suggests that, beyond nonspecific inflammation, several less commonly studied cellular components, including mast cells and T cells, may contribute to the initiation and persistence of POAF. These findings support the hypothesis that SGB may reduce POAF by limiting stellate ganglionitis driven by perioperative localized leukocyte activation. Future studies should define the extent, timing, and frequency of this inflammatory process and determine whether specific inflammatory or autonomic phenotypes can identify patients most likely to benefit from SGB.

BACKGROUND AND OBJECTIVES: Medical management options for nontraumatic subdural hematoma (SDH) remain limited, with only atorvastatin demonstrating benefit in a single phase 2 randomized clinical trial and mixed evidence for dexamethasone. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may benefit SDH outcomes. The objective of this study was to evaluate the association between GLP-1 RA use and rescue surgery rates and mortality in patients with medically managed nontraumatic SDH. METHODS: We conducted a retrospective cohort study using the TriNetX database from 2016 to 2025, including adults with SDH who were managed conservatively without surgical evacuation or middle meningeal artery embolization. Patients prescribed GLP-1 RAs were compared with nonusers. After propensity matching, rescue surgical evacuation, all-cause mortality at 1 year, and GLP-1 RA–associated adverse events were analyzed. RESULTS: A total of 134 901 patients with conservatively managed nontraumatic SDH were identified, of whom 1470 (1.1%) were GLP-1 RA users. After propensity score matching, 2922 patients remained, with 1461 in each group. At 1 year, GLP-1 RA users experienced significantly lower rates of rescue surgery (2.23% vs 3.51%; P = .046), culminating in an overall 38% reduction in relative hazard. All-cause mortality was also significantly lower for GLP-1 RA users at 1 year (16.1% vs 23.7%, P < .001), reflecting a 33% reduction in hazards. GLP-1 RA users did not experience different rates of adverse events, such as pancreatitis, gallbladder disease, or ileus/bowel obstruction (all P > .05). CONCLUSION: Among patients with medically managed nontraumatic SDH, GLP-1 RA use was associated with significantly lower rates of rescue surgery and all-cause mortality without significantly increased risk of common GLP-1 RA–related complications.

Abstract BACKGROUND [177Lu]Lu-PSMA-617 improves survival and quality of life in men with metastatic castration-resistant prostate cancer but requires substantial healthcare involvement. Our analysis evaluated the time toxicity associated with [177Lu]Lu-PSMA-617, representing the first dedicated radioligand time toxicity assessment. MATERIALS AND METHODS We conducted a multi-institutional retrospective study of patients initiating [177Lu]Lu-PSMA-617 between April 2022 and March 2023. Patients were followed from first [177Lu]Lu-PSMA-617 administration until 3 months after the final cycle, initiation of new systemic therapy, or death. Time toxicity was quantified as healthcare contact days during the at-risk interval. Associations with baseline characteristics were evaluated using negative binomial regression models. Univariate and multivariable models were performed. RESULTS Among 135 patients, median age was 69 years and median prior systemic therapies was 4. Patients completed a median of 5 [177Lu]Lu-PSMA-617 cycles over 7.6 months. Median healthcare contact days were 20 (IQR, 15–28), representing 9.5% (IQR, 6.4–13.9) of at-risk days. Fifteen patients (11%) experienced high time toxicity (&gt;1 contact day per 5 days), while 42 (31%) had low time toxicity (≤1 contact day per 14 days). Among the 57 patients who received both cycle 1 and cycle 6, mean per cycle healthcare contact days were 4 and 3.6, respectively (paired Wilcoxon signed-rank test, p = 0.04). Routine oncology visits comprised 32.7% of contact days, compared with 8.8% from hospitalizations. Lower baseline hemoglobin was independently associated with greater time toxicity (RR = 0.87; 95% CI, 0.79–0.96; p = 0.004). CONCLUSION In heavily pretreated patients, [177Lu]Lu-PSMA-617 demonstrated relatively low time toxicity, supporting it as a time-efficient therapy.

This systematic review evaluates current computer vision models and their applications in laparoscopic cholecystectomy. Using PRISMA guidelines, we analyzed studies reporting on primary computer vision model applications in laparoscopic cholecystectomy, from inception to March 2026. A total of 85 studies were included: workflow analysis (n = 26), anatomy recognition and segmentation (n = 14), safety assessment (n = 14), instrument detection (n = 12), tissue/image characterization (n = 7), event detection and scene understanding (n = 6), and surgical performance assessment (n = 6). The performance of the computer vision models in the studies was heterogeneous, with accuracy, F1-score, mean average precision, Dice coefficient, mean absolute error, and area under the receiver operating characteristic curve being commonly reported. Despite strong model performances in several studies, implementation in the clinical setting remains a challenge. We highlight the need for standardized model performance reporting, broader external and multi-institutional validation of models, and coordinated frameworks for safe and effective implementation of computer vision models in laparoscopic cholecystectomy.

Abstract: Background Artificial intelligence (AI)-assisted colonoscopy reduces costs in colorectal cancer screening, but its incremental value relative to non-AI resect-and-discard (RD) strategies for diminutive polyps (≤ 5 mm) remain unclear. We aimed to compare the clinical, economic, and environmental outcomes of AI-assisted and non-AI RD strategies with standard of care (SoC). Methods In our Markov model simulating 6 million individuals aged ≥45 years undergoing screening colonoscopy, 4 strategies were evaluated: SoC, non-AI RD, CADe-assisted RD (CADe-RD), and CADe+CADx-assisted RD (CADe+CADx-RD). The primary outcome was total quality-adjusted life years (QALYs), with secondary outcomes including cost and carbon dioxide (CO₂) emissions. Results Non-AI RD achieved the lowest total cost ($36.6B) and carbon emissions (0.90 million kg CO₂), while yielding the highest QALYs (51.37 million), although differences in QALYs across strategies were small (<0.01%). CADe-RD and CADe+CADx-RD remained cost-saving relative to SoC ($36.9B and $37.4B vs $37.8B, respectively) but were associated with higher costs than non-AI RD. CADe modestly increased adenoma detection without improving QALYs, while the addition of CADx increased costs and emissions and resulted in slightly lower QALYs. Sensitivity analysis showed that CADe+CADx-RD matched or exceeded CADe-RD QALYs only within a limited region of high diagnostic performance. At currently reported CADx performance levels, CADe+CADx-RD yielded lower QALYs and higher costs than CADe-RD. CADe+CADx-RD achieved non-inferiority in QALYs relative to CADe-RD only within a limited region of high diagnostic performance. Conclusions In our non-deterministic model, non-AI RD provided the optimal balance of clinical benefit, cost savings, and lowest environmental impact.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.