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Background: Obicetrapib is a next-generation cholesteryl ester transfer protein (CETP) inhibitor. It has emerged as a promising agent for improving lipid parameters and potentially reducing risk of atherosclerotic cardiovascular disease (ASCVD). Methods: A systematic search of databases, including PubMed, Scopus, Embase, Web of Science, Clinical Trials, and Cochrane Library, was conducted to identify relevant randomized controlled trials (RCTs). Outcomes included the assessment of LDL-C reduction, changes in ApoB, non-HDL-C, HDL-C, triglycerides, Lp(a), MACE, as well as the safety and tolerability profile of Obicetrapib in patients with dyslipidemia or ASCVD. Standardized mean differences (SMD) with 95% confidence intervals (CI) were pooled using random-effects. Results: Seven RCTs encompassing 3483 patients were included. Obicetrapib significantly improved lipid parameters. It reduced LDL-C (MSD: –35.43; 95% CI: –40.14 to –30.17; I2 = 93.26%), non-HDL-C (MSD: –36.42; 95% CI: –42.77 to –30.07; I2 = 99.06%), ApoB (MSD: –23.38; 95% CI: –30.68 to –16.08; I2 = 88.56%), and Lp(a) (MSD: –23.95; 95% CI: –35.26 to –12.63; I2 = 77.14%). Obicetrapib also led to a reduction in TG levels (MSD: –0.12; 95% CI: –0.19 to –0.05; I2 = 0.00%). In contrast, favorable increases were observed in HDL-C (MSD: 77.35; 95% CI: 64.56 to 90.13; I2 = 95.82%) and ApoA1 (MSD: 63.93; 95% CI: 53.65 to 74.21; I2 = 81.89%). Despite high heterogeneity, these results support Obicetrapib’s effectiveness in improving atherogenic lipid markers. Obicetrapib demonstrated a safety profile comparable to placebo. The overall risk of any adverse event was slightly reduced in the Obicetrapib group compared to placebo, but the 95% CI included 1, (RR: 0.96; 95% CI: 0.76 to 1.21; I2 = 66.3%; p = 0.01). Specific adverse events such as diarrhea (OR: 1.56; 95% CI: 0.34 to 7.11; I2 = 5.16%; p = 0.57), headache (OR: 1.17; 95% CI: 0.74 to 1.84; I2 = 0.00%; p = 0.50), and back pain (RR: 0.36; 95% CI: 0.11 to 1.16; I2 = 18.3%; p = 0.26) were also not significantly different between groups. Overall, Obicetrapib appears well-tolerated without increased adverse events. Conclusion: Obicetrapib significantly improved lipid parameters, notably reducing LDL-C, non-HDL-C, ApoB, Lp(a), and triglycerides, while increasing HDL-C and ApoA1, with a favorable and comparable safety profile.

Importance The effect of cornea donor diabetes on endothelial cell density (ECD) and morphometry after Descemet membrane endothelial keratoplasty (DMEK) is not known. Objective To determine whether endothelial cell loss (ECL) and morphometric changes 1 year after successful DMEK are related to cornea donor’s diabetes status. Design, Setting, and Participants This was a multicenter, double-masked, randomized clinical trial conducted from February 2022 to July 2024 at 28 US clinical sites (46 surgeons) and 13 eye banks. Included in the trial were the eyes of recipients (some of whom received tissue from donors without diabetes and others who received tissue from donors with diabetes) who underwent successful DMEK, primarily for Fuchs endothelial corneal dystrophy, and had at least 1 analyzable postoperative endothelial image. Intervention DMEK performed with a cornea from a donor without or with diabetes, assigned using a minimization procedure to achieve an approximate 2:1 distribution. Main Outcomes and Measures ECD, ECL, coefficient of variation in cell area (CV), and percentage of hexagonal cells (HEX) at 1 year from eye bank and postoperative specular central endothelial images. Results A total 1274 eyes of 982 recipients (mean [SD] age, 70 [8] years; 569 female [57.9%]; 816 [64.1%] with tissue from donors without diabetes and 458 [35.9%] with tissue from donors with diabetes) were included in the study. Preoperatively, mean (SD) central ECD in tissue from donors without diabetes and with diabetes were 2676 (290) cells/mm 2 and 2671 (286) cells/mm 2 , respectively. At 1 year, mean (SD) ECL was 28.3% (16.1%) and 28.0% (17.0%), in the donor groups without and with diabetes, respectively (adjusted mean difference = −0.4%; 95% CI, −2.3% to 1.4%), resulting in a mean (SD) 1-year ECD of 1927 (498) cells/mm 2 and 1920 (496) cells/mm 2 , respectively (adjusted mean difference = 10 cells/mm 2 ; 95% CI, −36 to 56; P = .95). No difference in ECD at 1 year associated with diabetes severity was noted ( P = .97). Mean (SD) CV did not differ at 1 year between the 2 groups of eyes (31.5% [4.1%] vs 31.4% [4.1%]; adjusted mean difference = −0.4%; 95% CI, −0.9% to 0.1%; P = .51), and mean (SD) HEX did not differ at 1 year between the 2 groups of eyes (57.7% [5.8%] vs 57.2% [5.8%]; adjusted mean difference = 0.1%, 95% CI, −0.8% to 0.9%; P = .33). Conclusions and Relevance This randomized clinical trial found that ECL and morphometry 1 year after DMEK were not affected by cornea donor diabetes status. With comparable 1-year graft success with tissue from donors with and without diabetes demonstrated in this trial, these findings support the use of corneas from donors with diabetes for endothelial keratoplasty procedures. Trial Registration ClinicalTrials.gov Identifier: NCT05134480

Importance If the success of Descemet membrane endothelial keratoplasty (DMEK) is not affected by whether the donor has diabetes, then the donor pool should expand. Objective To determine whether the 1-year DMEK success rate is affected by the presence of diabetes in the donor. Design, Setting, and Participants This was a multicenter, double-masked, randomized clinical trial conducted from February 2022 to July 2025 at 28 clinical sites (46 surgeons), with donor corneas provided by 13 eye banks in the US. Included in the study were individuals undergoing low to moderate risk DMEK (95% for Fuchs endothelial corneal dystrophy). Study data were analyzed from April to September 2025. Intervention DMEK performed with a cornea from a donor without or with diabetes, assigned using a minimization procedure (comparable with randomization) to achieve an approximate 2:1 distribution, respectively. Main Outcomes and Measures Graft success at 1 year Results A total of 1097 individuals (1421 study eyes; median [IQR] age, 71 [66-76] years; 631 female [57.5%]) were included in the study. The 1-year cumulative probability of graft success was 96.3% (95% CI, 95.0%-97.5%) among 912 study eyes (64.2%) receiving tissue from donors without diabetes and 97.1% (95% CI, 95.5%-98.4%) among 509 study eyes (35.8%) receiving tissue from donors with diabetes (difference between groups = 0.7 percentage points; 95% CI, −1.2 to 2.6; P = .63). The 1-year cumulative probability of graft success was 96.5% (95% CI, 93.6%-98.9%) in the mild donor diabetes severity subgroup (n = 173) and 97.3% (95% CI, 95.4%-98.8%) in the moderate to severe donor diabetes severity subgroup (n = 336), using a diabetes severity rating scale based on medical history. The rates of primary donor failure, early failure related to surgical complications, and subsequent failure were as follows: 2.5% (23 of 912), 0.7% (6 of 912), and 0.3% (3 of 912), respectively, in recipients of tissue from a donor without diabetes, and 2.6% (13 of 509), 0.4% (2 of 509), and 0%, respectively, among recipients of tissue from a donor with diabetes. There were no failures due to graft rejection. Conclusions and Relevance The 1-year success rate in eyes undergoing DMEK with successfully prepared tissue was very high regardless of donor diabetes status. These results, supported by the separately reported finding that endothelial cell loss and cornea morphometry after 1 year were not affected by donor diabetes status, provide strong support for having no restrictions on the use of tissue from donors with diabetes for DMEK. Trial Registration ClinicalTrials.gov Identifier: NCT05134480

Background: Estimating operative time with low accuracy and precision leads to suboptimal scheduling of available operating rooms (ORs) and waste of healthcare resources. We aimed to develop a predictive machine learning (ML) model for operative time using our institutional data to improve the scheduling of neurosurgical ORs, minimize waste, and maximize resource utilization. Methods: We developed a predictive model using our institution’s multicenter registry, which included diverse neurosurgical cases across three tertiary hospitals. We applied multiple ML techniques, including linear regression, support vector regression, deep neural networks, and extreme gradient boosting regression (XGBR). The best model was combined with the first fit decreasing bin-packing algorithm to create a practical OR scheduling system based on prior data. Results: The XGBR model exhibited the best performance, with lower mean absolute errors (MAEs) (root mean squared error: 52.24, MAE: 37.25) and higher R2 (0.76) values than other models. Implementing this model in a simulated OR scheduling scenario using institutional data, we successfully scheduled 30 random procedures with minimal residual time, such that the model ran out of procedures for the week with 86 and 171 min remaining in the cranial and spinal ORs, respectively, demonstrating potential efficiency gains. The cranial procedures were scheduled over 4 days and spinal procedures over 6 days, optimizing available OR time. Conclusion: Integrating ML models into operative time prediction can significantly improve the accuracy and efficiency of surgical schedules. This approach minimizes wasted OR time and enhances resource allocation, potentially improving patient outcomes and satisfaction by reducing waiting times.

OBJECTIVE Artificial intelligence (AI) is increasingly capable of academic writing, with large language models such as ChatGPT showing potential to assist or even generate scientific manuscripts. However, concerns remain regarding the quality, reliability, and interpretive capabilities of AI-generated content. The authors’ study aimed to compare the quality of a human-written versus an AI-generated scientific manuscript to evaluate the strengths and limitations of AI in the context of academic publishing. METHODS Two manuscripts were developed using identical titles, abstracts, and tables of a simulated analysis: one authored by a physician with multiple publications, and the other generated by ChatGPT-4o. Three independent and blinded reviewers—two human and one AI—assessed each manuscript across five domains: clarity and readability, coherence and flow, technical accuracy, depth, and conciseness and precision. Each category was scored on a 10-point scale, and qualitative feedback was collected to highlight specific strengths and weaknesses. Additionally, all reviewers were asked to deduce authorship of the manuscripts. RESULTS The AI-generated manuscript scored higher in clarity and readability (mean 9.0 vs 7.2), but lower in technical accuracy (mean 6.3 vs 9.3) and depth (mean 5.5 vs 7.5). However, reviewers noted that the AI version lacked depth, critical analysis, and contextual interpretation. All reviewers accurately identified the authorship of each manuscript and tended to rate the version more favorably when it aligned with their own origin (human or AI); i.e., human reviewers assigned higher scores to the human-written manuscript, while the AI reviewer scored the AI-generated manuscript higher. CONCLUSIONS Although AI models can improve some aspects of scientific writing, particularly clarity and readability, they fall short in critical reasoning and contextual understanding. This reinforces the importance of human authorship and oversight in maintaining the critical analysis and scientific accuracy essential for academic publishing. AI may be used as a complementary tool to support, rather than replace, human-led scientific writing.

Abstract Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI‐driven tools are being developed to integrate diverse data sources, including multi‐omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image analysis and histopathology interpretation. AI also offers opportunities to refine treatment planning, optimize radiation therapy, and personalize systemic therapy selection. Furthermore, AI is explored for its potential to improve survivorship care by tailoring interventions and to enhance end‐of‐life care through improved symptom management and prognostic modeling. Beyond care delivery, AI augments clinical workflows, streamlines the dissemination of up‐to‐date evidence, and captures critical patient‐reported outcomes for clinical decision support and outcomes assessment. However, the successful integration of AI into clinical practice requires addressing key challenges, including rigorous validation of algorithms, ensuring data privacy and security, and mitigating potential biases. Effective implementation necessitates interdisciplinary collaboration and comprehensive education for health care professionals. The synergistic interaction between AI and clinical expertise is crucial for realizing the potential of AI to contribute to personalized and effective cancer care. This review highlights the current state of AI in oncology and underscores the importance of responsible development and implementation.

The indications for immune checkpoint inhibitor (ICI) use in cancer treatment continue to expand. This is attributable to their proven anticancer activity in addition to their tolerability and favorable toxicity profile as compared to conventional chemotherapeutic agents. ICIs work by blocking the inhibitory signals between tumor cells and T-cells, thereby enhancing the T-cell cytotoxic activity to inhibit tumor growth. Because of their immune-stimulating effect, ICIs are linked to adverse renal outcomes in both native and transplanted kidneys. The risk of kidney allograft rejection in the setting of ICI use has been reported to be around 40%, leading to an increased risk of graft loss. In this report, we review the literature examining outcomes in kidney transplant recipients receiving ICIs for various oncologic indications.

e24024 Background: Patients with active cancer undergoing trans catheter aortic valvular replacement (TAVR) face a higher risk of adverse outcomes due to the interplay between cancer and cardiovascular disease. Although TAVR is increasingly used in high-risk populations, data on outcomes for these patients remain limited. This systematic review and meta-analysis aim to evaluate the clinical outcomes associated with TAVR in patients with active cancer compared to non-cancer controls, providing insights to inform clinical decision-making in this unique group. Methods: A systematic search of major databases (PubMed, Embase, OVID, Web of Science, Scopus) was conducted to identify studies comparing clinical outcomes of TAVR between patients with active cancer and those without cancer. Key outcomes included measures of mortality, procedural complications, and post-procedure adverse events. The Mantel-Haenszel method was used for pooled analysis, applying random-effects or fixed-effects modeling based on heterogeneity assessed by Higgins I². Statistical significance was set at a p-value < 0.05. Results: Twelve studies involving 5,73,192 patients (67,684 active cancer, 505508 control) were included. Patients with active cancer undergoing TAVR have significantly higher risks of short-term mortality (OR 1.31, 95% CI [1.15, 1.49], p < 0.0001), long-term mortality (OR 2.24, 95% CI [1.85, 2.70], p < 0.0001), and major bleeding complications (OR 1.80, 95% CI [1.68, 1.92], p < 0.0001). There were no statistically significant differences in cardiovascular mortality (OR 1.33, 95% CI [0.94, 1.89], p = 0.11), myocardial infarction (OR 1.60, 95% CI [0.82, 3.11], p = 0.17), stroke (OR 0.61, 95% CI [0.28, 1.35], p = 0.22), AKI (OR 0.92, 95% CI [0.49, 1.73], p = 0.80), or the need for permanent pacemaker implantation (OR 1.12, 95% CI [0.85, 1.48], p = 0.42). Conclusions: Patients with active cancer undergoing TAVR face significantly higher risks of mortality and major bleeding compared to non-cancer controls. However, the risks of cardiovascular mortality, MI, stroke, AKI, and the need for permanent pacemaker implantation are comparable to those of non-cancer controls. Careful risk assessment and management are crucial for optimizing outcomes in this vulnerable population, and further studies are needed to refine treatment strategies. Odds ratios of all outcomes. Outcomes OR Confidence Interval (95% CI) p-value Short term mortality 1.31 [1.15, 1.49] < 0.0001 Long term mortality 2.24 [1.85, 2.70] < 0.00001 Major bleeding 1.8 [1.68, 1.92] < 0.00001 Cardiovascular mortality 1.33 [0.94, 1.89] 0.11 Myocardial Infarction 1.6 [0.82, 3.11] 0.17 Stroke 0.61 [0.28, 1.35] 0.22 Acute Kidney Injury 0.92 [0.49, 1.73] 0.8 Need for permanent pacemaker implantation 1.12 [0.85, 1.48] 0.42

e17155 Background: Localized PCa is classified into six NCCN risk categories, each with specific treatment recommendations. However, treatment concordance across these categories and its variation by race in clinical practice remain unexplored. Therefore, we aim to describe patterns of guideline-concordant treatment receipt for localized PCa across risk groups and race. Methods: Surveillance, Epidemiology, and End Results (SEER) database (2018–2020) was queried to identify patients with localized PCa from 17 registries. Data regarding clinical T stage, Gleason patterns, PSA level, biopsy cores and initial treatment received (radiation, radical prostatectomy, systemic therapy) was obtained. Each patient was then stratified into one of six NCCN-based risk categories using a rule-based algorithm. Using the stratified risk category of each patient, treatment recommendations were obtained from the NCCN guidelines. Concordance was assessed by comparing the received treatment and guideline-recommended treatment. A focused bi-variate logistic regression analysis was performed adjusting for risk groups, and race. Odds ratio (OR) with 95% confidence intervals (CI) were computed to quantify the magnitude of guideline-discordant treatment receipt. Results: This analysis included 69,584 patients with localized PCa (White: 53,875 [77.4%]; Black: 11,265 [16.2%]; Asian/Pacific Islander: 4,133 [5.9%]; American Indian/Alaska Native: 311 0.5%]). In terms of risk groups, patients in higher risk categories had increased odds of discordance compared to the lower risk groups (Table). In terms of race, Black men had significantly higher odds of receiving guideline-discordant therapy (OR:1.67, 95% CI:1.58–1.76), while no statistically significant difference was observed with American Indian/Alaska Native men (OR:1.21, 95% CI: 0.92–1.61) and Asian/Pacific Islander men (OR:1.05, 95% CI:0.97–1.14) compared to White men. Conclusions: Population level data, based on limited sample size in some groups, suggests that treatment concordance with NCCN guidelines varies by risk group and race in localized PCa. Black patients, and those with higher risk groups were more likely to receive guideline-discordant therapy. These findings highlight the need for strategies and prospective studies at individual patient level to evaluate and enhance guideline adherence to promote personalized care for these patients. Variable Discordant Treatment (%) OR (95% CI) White 22168 (41.1) REF Black 5653 (47.5) 1.67 (1.58-1.76) * Asian/Pacific Islander 1822 (44.1) 1.05 (0.97-1.14) American Indian/Alaska Native 191 (61.4) 1.22 (0.92- 1.61) Very Low Risk 770 (9) REF Low Risk 146 (2) 0.16 (0.13-0.19) * Favorable Risk 35 (0.4) 0.03 (0.02-0.04) * Unfavorable Risk 15125 (83.2) 36.30 (33.72-39.25) * High/Very High Risk 13441 (50) 7.32 (6.83-7.86) * * p <0.05.

e17089 Background: Prostate cancer (PC) is often treated with androgen-deprivation therapy (ADT), but progression remains a challenge. Darolutamide, a next-generation androgen receptor inhibitor, may improve outcomes when combined with ADT. This systematic review and meta-analysis evaluates the efficacy and safety of darolutamide plus ADT in hormone-sensitive (HSPC) and castration-resistant prostate cancer (CRPC). Methods: A comprehensive search of PubMed, Cochrane, Clinicaltrials.gov, Embase, and Scopus was conducted through January 11, 2025. Randomized controlled trials (RCTs) comparing darolutamide plus ADT vs. placebo or control in prostate cancer were included. Data on overall survival, metastatic progression, antineoplastic therapy initiation, pain progression, bone fractures, hypertension, adverse events and serious adverse events were analyzed. Outcomes were pooled as risk ratios (RR) with 95% confidence intervals (CI) using a random-effects model. Heterogeneity was assessed via Higgins I², with subgroup analyses for HSPC and CRPC. Results: Four RCTs with 4,992 patients (darolutamide + ADT=3007; control=1985) were further included for quantitative analysis. Our pooled analysis showed a statistically significant decrease in the development of metastatic CRPC across both HSPC and CRPC (RR: 0.38 [0.19, 0.77], p=0.007). Similarly, progression to pain (RR: 0.81 [0.71, 0.92], p=0.001) and the use of antineoplastic therapy (RR: 0.46 [0.38, 0.57], p<0.0001) was significantly delayed across all PC, indicating slower disease progression. While a slightly higher overall survival was noted in the CRPC subgroup (RR: 1.08 [1.01, 1.14], p=0.007), no significant differences were reported on the use of combination therapy across HSPC and overall PC (RR: 0.71 [0.39, 1.31], p=0.28). While a significantly higher risk of developing adverse events (RR: 1.08 [1.03, 1.14], p=0.002) and serious adverse events (RR:1.2 [0.99, 1.45], p=0.07) was noted in the CRPC subgroup, a slight increase was reported in the overall cohort and HSPC, although with statistical insignificance. Bone fractures (RR: 1.52 [1.12, 2.07], p=0.008) and hypertension risk (RR: 1.28 [1.03, 1.59], p=0.03) were elevated, while rates of rash, fatigue, falls, and mental impairment were similar. Conclusions: Combination therapy of darolutamide and ADT reduced metastatic progression and slowed tumor growth but did not significantly improve overall survival. Adverse events were more common, particularly in CRPC cohort. Longer follow-up studies are needed to assess long-term efficacy and weigh tumor control against adverse event risks.