Citations

Introduction Increasing publication of fraudulent clinical trials poses a serious threat to evidence-based medicine. In the VITALITY Study I, we demonstrated that contamination by retracted trials significantly distorts evidence synthesis. The upcoming VITALITY Study II will take a step further and investigate how such problematic evidence undermines the credibility of guideline recommendations. Methods and analyses The VITALITY Study II will adopt a retrospective cohort design and will be structured as three work packages (WPs). To start with, a cohort of clinical practice guidelines (CPGs) that were contaminated by retracted trials and/or meta-analyses that included retracted trials will be established based on forward citation searching (WP1). Then, recommendations from these CPGs that used evidence from retracted trials and/or meta-analyses that synthesised these retracted trials will be re-evaluated after removing such problematic evidence, in terms of both the direction and strength of effect sizes (WP2). Finally, the subsequent impact on patient outcomes attributable to distorted recommendations will be estimated on a hypothetical population, measured by the number of expected benefit loss and the number of expected harm increment per 100 000 patients (WP3). Ethics and dissemination Formal ethical approval is not necessary for this study as it does not involve human or animal participants, nor does it involve the collection of primary data. We will disseminate the findings through publication in peer-reviewed journals and, whenever possible, presentations at academic conferences.

Mediastinal radiation potentiates aortic stenosis and complicates its treatment. We compared the short and midterm-outcomes with transcatheter aortic valve replacement (TAVR) versus surgical aortic valve replacement (SAVR) in patients with prior mediastinal radiation. Electronic databases were searched from inception through December 2024. Dichotomous outcomes were pooled as risk ratios (RRs), and continuous outcomes were pooled as mean differences (MDs) with respective 95% confidence intervals (CIs). Six observational studies were identified, including 2458 TAVR patients and 1873 SAVR patients. In the short-term, TAVR was associated with lower rate of mortality (RR: 0.54; 95% CI: 0.34–0.88), atrial fibrillation (RR: 0.31; 95% CI: 0.15–0.65), acute kidney injury (RR: 0.75; 95% CI: 0.59–0.95), bleeding (RR: 0.37, 95% CI: 0.33–0.42), and shorter length of hospital stay (MD: −4.30; 95% CI: −5.45 to −3.15). One-year mortality was comparable between the 2 groups (RR: 1.04; 95% CI: 0.50–2.13). This meta-analysis of observational studies of patients with prior mediastinal radiation demonstrated that TAVR was associated with better short-term outcomes compared to SAVR. While 1-year mortality appeared similar between the 2 interventions, this finding should be interpreted with caution. However, randomized controlled trials are needed to validate these findings.

PURPOSE Artificial intelligence (AI) has been rapidly evolving in medicine. While there are existing data about the perceptions and concerns of health care workers regarding AI, those of the hematology and oncology (HemOnc) workforce remain unknown. We assessed opinions of physicians (faculty and fellows), advanced practice providers (APPs), and nurses in HemOnc on integrating AI in medical education (MedEd) and clinical practice (CP). METHODS Group-specific electronic surveys were developed. The HemOnc workforce from the three Mayo Clinic sites was invited to participate. Participation was anonymous, with data collection between November 7, 2024, and January 20, 2025. Results were reported as proportions, with 95% binomial CI when appropriate. RESULTS A total of 344 participants responded, 118 physicians (77 faculty, 41 fellows), 49 APPs, and 177 nurses. Most had used AI although less than half felt very or moderately knowledgeable about it. A total of 95% of fellows, 97% of faculty physicians, 96% of APP, and 92% of nurses believe that AI will be integrated in MedEd. A total of 94% of physicians and 88% of nonphysicians believe that this integration would be beneficial. In CP, 98% of fellows, 96% of faculty physicians, 100% of APPs, and 92% of nurses believe in AI's integration. However, 51% of physicians and 68% of nonphysicians had some concerns, including technology time, medical costs, and burnout. Nonetheless, 85% of fellows, 94% of faculty physicians, 98% of APPs, and 86% of nurses would embrace AI. CONCLUSION The majority of this single health system HemOnc's workforce endorsed AI integration into MedEd and CP but highlighted relevant concerns. Proper education, effective partnership among all stakeholders, and well-organized integration are essential for optimizing AI's incorporation into HemOnc's MedEd and CP and ensuring that it fully supports the mission of providing high-quality patient care.

Abstract Idecabtagene vicleucel (ide-cel) is a chimeric antigen receptor (CAR) T-cell therapy approved for relapsed and refractory multiple myeloma (RRMM). Out-of-specification (OOS) CAR T cells not meeting criteria for commercial release are considered an investigational product; the expanded access protocol (EAP) allows release of OOS product for administration. The manufacturing analysis of capability, reliability, and timely delivery of ide-cel in the United States over >3 years since its approval included patients with RRMM who underwent leukapheresis for ide-cel treatment after ≥2 prior lines of therapy between 23 February 2021 and 1 November 2024. Additionally, the BB2121-EAP-001 study evaluated safety and efficacy of OOS ide-cel and included patients who had undergone leukapheresis for commercial ide-cel treatment not meeting commercial release specifications, most commonly due to high CD137 activation or low-infused dose. Primary end point was safety. Secondary end points were overall response rate (ORR) and complete response rate (CRR). At data cutoff (1 November 2024), the manufacturing success rate of ide-cel improved from 95.3% in 2021 to 96.8% in 2024. Turnaround time also improved from 31 days in 2021 to 24 days in 2024. In BB2121-EAP-001, 68 (72.3%) patients experienced ≥1 cytokine release syndrome event (mostly grade 1/2) and 15 (16.0%) had neurological toxicities (2 grade 3, 13 grade 1/2). ORR was 75.4% (95% confidence interval [CI], 63.1-85.2), and CRR was 35.4% (95% CI, 23.9-48.2). The manufacturing success of ide-cel is reliable and consistently high, and when OOS ide-cel products emerged, they demonstrated consistent clinical efficacy and safety. The BB2121-EAP-001 study was registered at www.clinicaltrials.gov as NCT04771078.

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Objective Identifying eligible patients for precision oncology clinical trials is challenging, particularly for rare molecular subpopulations. To address this challenge, A2 Biotherapeutics developed BASECAMP-1 ( NCT04981119 ), a non-interventional master screening study to identify patients eligible for interventional studies of logic-gated Tmod chimeric antigen receptor T-cell therapies. Eligible patients for these interventional trials have an advanced solid malignancy and are germline human leucocyte antigen (HLA)-A*02 heterozygous, with tumour-associated HLA-A loss of heterozygosity (LOH). HLA-A LOH occurs in ~16% of advanced solid malignancies; therefore, an efficient screening strategy is required. This report describes BASECAMP-1; compares the efficiency of two screening methods; and discusses the broader advantages of BASECAMP-1 beyond efficient enrolment. Methods and analysis Patients are identified for BASECAMP-1 using two approaches. In the traditional approach, common for clinical trials, investigators consent and screen all patients who might be good candidates for cell therapy trials, with no prior knowledge of patient HLA-A type or LOH status. To further optimise our approach, we co-developed with Tempus AI (Tempus) the bioinformatic programme Aware, which identifies potentially eligible patients with tumour-associated HLA-A*02 LOH within a clinico-genomic database that includes linked genomic and transcriptomic sequencing and clinical data collected during routine care. Results Over 42 months of using a traditional approach to identify eligible patients, 1918 patients at 13 study sites were consented and screened for BASECAMP-1; of these, 30 patients with tumour-associated HLA-A*02 LOH were enrolled (~0.7 participants per month). Over the last 30 months of that same period, Tempus Aware screening was implemented and 55 patients with tumour-associated HLA-A*02 LOH were enrolled (~1.8 participants per month). The bioinformatic approach identified more patients than the traditional approach and used sequencing results produced as part of the standard clinical tumour sequencing workflow, reducing resource use and study staff burden. Additional advantages of using a screening study, such as BASECAMP-1, include manufacturing efficiencies and collection of a large dataset of molecular and clinical parameters that can be used to supplement trial analyses. Conclusions The BASECAMP-1 study demonstrates a clinico-genomic screening approach can more efficiently identify patients for precision oncology trials. Furthermore, precision oncology can be enhanced through collaborative data-sharing. Trial registration number NCT04981119 .

Precis: This retrospective multicentre study of 77 NTG eyes undergoing phacoemulsification and Hydrus implantation showed a sustained reduction in medication burden, while maintaining IOP levels. Complications were rare and nonsight-threatening. Purpose: To evaluate 36-month outcomes of combined phacoemulsification and Hydrus Microstent implantation (Phaco-Hydrus) in eyes with normal tension glaucoma (NTG). Methods: This multicentre retrospective study included 77 NTG eyes from Singapore and Canada that underwent Phaco-Hydrus between 2014 and 2023. Primary outcomes were changes in intraocular pressure (IOP) and medication burden, and cumulative success probabilities from Kaplan-Meier (KM) analyses. Longitudinal changes were analysed using generalized estimating equations. Results: Of 77 eyes, 31 completed 36 months of follow-up. Mean baseline IOP and medications was 13.5±2.5 mm Hg and 1.7±0.9, respectively. No significant reductions in IOP were observed. A sustained reduction in medication use was observed at all follow-up timepoints ( P ≤0.001). At 36 months, medication use decreased by −0.7±0.2 [adjusted mean difference±SE of the mean [SEM]]. Proportion of medication-free eyes increased from 7.8% (n=6/77) at baseline to 45.2% (n=14/31) at 36 months. Cumulative success probabilities varied widely based on definitions. When defined as a decrease in ≥1 medication from baseline without an increase in IOP, or IOP ≤15 mm Hg and reduced ≥20% from baseline while medication-free, and no additional laser or incisional glaucoma surgery, this was 40.7% at 36 months. No visual field mean deviation progression was detected. Adverse events were infrequent and nonsight-threatening. Conclusion: In eyes with NTG, Phaco-Hydrus may offer a greater role in medication reduction or achieving a medication-free status over 36 months, rather than absolute IOP reduction.

The accurate prediction of motor progression in Parkinson’s disease (PD) remains a major clinical challenge that limits personalized treatment planning and efficient clinical trial design. In this study, we developed and validated a machine-learning framework integrating a targeted panel of plasma proteins measured by Olink proximity extension assays with clinical variables to stratify patients according to their progression risk. We analyzed baseline plasma samples from 211 early-stage PD patients enrolled in the Parkinson’s Progression Markers Initiative (PPMI) cohort using four targeted Olink panels, from which 28 circulating proteins were retained after quality-control filtering. Patients were classified as rapid or slow progressors based on their annualized change in MDS-UPDRS Part III scores. Among the algorithms tested, Random Forest achieved the highest discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.751 (95% CI: 0.684–0.811), which exceeded that of clinical predictors alone (AUC 0.666). The integration of targeted proteomic and clinical features further improved model performance (AUC 0.773; p = 0.009). Nested cross-validation confirmed minimal optimistic bias (AUC 0.743). To enhance clinical interpretability, we applied SHapley Additive exPlanations (SHAP) analysis, which identified interleukin-6 (IL-6), brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor A (VEGF-A) as the most influential predictors. SHAP feature rankings were highly stable across cross-validation folds (mean Spearman ρ = 0.91). The robustness of these findings was confirmed through sensitivity analyses using extreme quartile comparisons (AUC 0.823), treatment-naïve subgroup analysis (AUC 0.738), and a clinically anchored outcome definition based on the minimal clinically important difference (AUC 0.739). A decision curve analysis demonstrated a net clinical benefit across threshold probabilities of 0.25–0.70. Our results establish targeted plasma protein profiling combined with interpretable machine learning as a promising tool for PD motor progression risk stratification, with potential applications in individualized patient counseling regarding motor prognosis and the selection of candidates for disease-modifying trials.