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PURPOSE In immunoglobulin light chain (AL) amyloidosis, amyloid fibrils cause organ dysfunction. Anselamimab, a monoclonal antibody, selectively binds to amyloid fibrils, accelerating their clearance. METHODS Newly diagnosed patients with European modification of Mayo 2004 stage IIIa or IIIb AL amyloidosis were randomly assigned to receive anselamimab or placebo with cyclophosphamide, bortezomib, and dexamethasone (with or without daratumumab). The primary end point was a hierarchical composite of time to all-cause mortality (ACM) and frequency of cardiovascular hospitalizations (CVHs), analyzed using the Finkelstein-Schoenfeld test and win ratio estimation. RESULTS Among 406 randomly assigned patients, the primary end point win ratio for anselamimab versus placebo was 1.11 (95% CI, 0.83, 1.50; P = .332) and the hazard ratio (HR) for ACM was 0.80 (95% CI, 0.57, 1.13; P = .290). CVH rates in the anselamimab and placebo groups were 0.59 (95% CI, 0.42, 0.83) and 0.89 (95% CI, 0.55, 1.43), respectively ( P = .145). Among patients with kappa isotype (n = 72), the win ratio was 2.06 (95% CI, 0.98, 4.31; P = .100) and anselamimab reduced ACM by 62% versus placebo (HR, 0.38; 95% CI, 0.17 to 0.86; nominal P = .012), and CVH by 71% (IRR, 0.29; 95% CI, 0.10 to 0.87; nominal P = .028) with improvements in Kansas City Cardiomyopathy Questionnaire-Overall Score. There was no clinical benefit observed in the lambda population. Anselamimab was generally well-tolerated; safety data were comparable between treatment arms overall. CONCLUSION Treatment with anselamimab, an antifibril antibody used alongside antiplasma cell dyscrasia therapy, while not meeting the primary end point in the overall population, significantly improved ACM and CVH in patients with kappa AL, potentially offering a new therapeutic option for this isotype.

7532 Background: BsAbs are highly effective, off-the-shelf T-cell engagers recently approved for relapsed/refractory multiple myeloma (MM) but remain underutilized in real-world settings. In partnership with the Florida Society of Clinical Oncology (FLASCO), a statewide initiative was launched to expand BsAb access through peer-led regional discussions focused on operational barriers, care transitions, and therapy management strategies. This study assessed the real-world impact of these interventions on BsAb utilization in FL. Methods: Multidisciplinary discussion forums were held in 4 FL metropolitan areas (Jan-Apr 2024). We conducted single and controlled interrupted time series (ITS) analyses using IQVIA eLAAD claims data (Apr 2023 - Sep 2025) including adult patients with a diagnosis of MM, evidence of claim activity, and provider state information. Fifth line or later (5L+) status was estimated as 3.3% of MM prevalence (Nikolaou et al.) BsAb utilization (% of MM 5L+ patients receiving a BsAb) was evaluated using segmented regression to estimate pre- and post-intervention trends, excluding the intervention period. We reported BsAb utilization by comparing FL with all other states, between community versus academic settings, and assessing the number of sites administering BsAb. Results: Before the intervention, BsAb utilization among MM 5L+ patients in FL rose from 8.2% (Apr 2023) to 11.2% (Dec 2023) (0.3 pp/mo; p=0.23), compared with a rise from 6.7% to 16.3% (1.3 pp/mo; p<0.0001) across all other states over the same time period. Post-intervention, BsAb utilization in FL increased from 22.8% (May 2024) to 35.9% (Sep 2025), reflecting a 7.0% immediate increase and 0.8 pp/mo sustained growth, exceeding the post-intervention trend observed across all other states (23.5% to 30.3%; 0.5 pp/mo). Overall, although pre-intervention BsAb utilization in FL was lower than all other states, FL demonstrated a 1.2 pp/month higher post-intervention utilization trend (p<0.001; Table). Post-intervention, FL community sites showed a 0.13 pp/mo higher BsAb utilization trend than academic sites, and the number of community sites administrating BsAb increased from 5 to 9, with no change among academic sites. Conclusions: This study suggests that the FLASCO-led, collaborative model increased BsAb utilization in FL community oncology sites and offers a framework for broader community adoption across other states. Parameter estimates from ITS model(s). FL (%) p-value Other US States (%) p-value Difference CITS:FL - Other US States (%) p-value Intercept (Apr 2023) 8.6 <.0001 7.7 <.0001 0.9 0.52 Monthly change pre-intervention 0.3 0.23 1.3 <.0001 -0.9 0.002 (Apr - Dec 2023) Immediate change post-intervention 7 0.01 0.2 0.87 6.8 0.01 (May 2024) Monthly change post-intervention 0.8 <.0001 0.5 <.0001 1.2 0.0003 (May 2024 – Sep 2025)

Noncirrhotic portal hypertension has historically been described using heterogeneous and region-specific terminology—such as idiopathic portal hypertension (IPH), noncirrhotic portal fibrosis (NCPF), obliterative portal venopathy, and nodular regenerative hyperplasia—leading to substantial variability in diagnosis, reporting, and international research collaboration. Differences in guideline definitions from major societies (AASLD, EASL, and APASL), together with the presence of characteristic histologic lesions in patients without clinically overt portal hypertension, have further complicated disease classification. To address these challenges, a large, multisociety, international initiative was convened to harmonize nomenclature and diagnostic criteria. Representatives from liver, pathology, and pediatric hepatology societies across the Americas, Europe, and Asia participated in a structured consensus process that included specialized working groups and external Delphi validation. The initiative produced a globally harmonized and implementable diagnostic framework. Consensus was reached that the terms porto-sinusoidal vascular disorder (PSVD) and NCPF may be used interchangeably when identical diagnostic criteria are applied, and that they should be written as PSVD or NCPF . The diagnosis was defined as fundamentally clinicopathological, requiring integrated assessment. Core principles include the need for a high-quality liver biopsy (≥10 mm), mandatory exclusion of cirrhosis, and systematic exclusion of specific alternative conditions. Importantly, the consensus recognizes that PSVD or NCPF may be diagnosed even without clinical portal hypertension and may coexist with other liver diseases, provided cirrhosis is excluded. Standardized major and minor histologic criteria were developed collaboratively by expert pathologists and externally validated. Features of portal hypertension were harmonized into specific and nonspecific categories applicable to routine clinical practice. An integrated diagnostic scoring system incorporating histology, clinical features, associated conditions, and concommitant etiologies was developed and validated using the Delphi method. This consensus provides the first internationally endorsed, unified framework for the diagnosis of PSVD or NCPF. Its global implementation is expected to reduce diagnostic variability, improve comparability across regions, and facilitate the development of robust, internationally harmonized clinical and translational research cohorts.

PURPOSE Integrating external control data into clinical trial designs and analyses has the potential to accelerate drug development processes. We reanalyzed the three experimental arms of the Individual Screening Trial of Innovative Glioblastoma Therapy (INSIGhT), a randomized phase II platform trial in newly diagnosed O 6 -methylguanine-DNA methyltransferase-unmethylated glioblastoma (ClinicalTrials.gov identifier: NCT02977780 ). To evaluate the validity of using external data sets, we compared treatment effect estimates based on internal INSIGhT control data and matched external control data. METHODS The three experimental arms of INSIGhT (abemaciclib [n = 72], neratinib [n = 80], and CC-115 [n = 12]) did not improve survival compared with internal controls (standard chemoradiation [n = 70]). We derived external control patient-level data from multiple real-world and clinical trial data sets. We applied propensity score matching and Cox proportional hazards models to estimate treatment effects with external controls. Additionally, using this glioblastoma (GBM) data collection, we specified simulation scenarios to evaluate trial designs that integrate external controls. RESULTS After matching to external controls, no survival benefit was observed for patients receiving abemaciclib (hazard ratio [HR], 1.00 [95% CI, 0.75 to 1.34]), neratinib (HR, 0.93 [95% CI, 0.70 to 1.24]), or CC-115 (HR, 0.88 [95% CI, 0.41 to 1.88]). Simulations, together with the INSIGhT data and a collection of GBM data sets, allowed us to examine efficiencies and risks of clinical trial designs that leverage external control data. CONCLUSION The use of carefully matched external controls, to replace or augment the internal controls of INSIGhT, produced treatment effect estimates that were similar to previously published analyses. Single-arm trial designs and hybrid randomized designs incorporating propensity score–matched external control data evaluated treatment effects in the early-phase testing of experimental therapies in newly diagnosed GBM. The validity of this approach and risks of bias depended on the availability of comprehensive and accurate data on all potential confounders, in the absence of unmeasured confounding.

ABSTRACT Background Anemia is an established marker of cardiovascular disease severity and risk which leads to elevations in resting myocardial blood flow (MBF) and impaired myocardial flow reserve (MFR) in patients without obstructive coronary artery disease (CAD). Anemia can potentially be detected opportunistically from blood pool density changes on computed tomography (CT) imaging. Objectives We evaluated relationships between chamber density measurements with hemoglobin, positron emission tomography (PET) findings, and cardiovascular events. Methods We included 33460 patients from 13 sites in the REFINE-PET who underwent PET and 24368 patients undergoing lung cancer screening chest CT. A deep learning model segmented cardiac chambers from CT images, then quantified chamber density. We evaluated the relationship between chamber density measures with resting MBF and MFR, as well as associations with death or myocardial infarction (MI). Results We included a total of 57,828 patients. A higher density in myocardium compared to left ventricle blood pool was associated with reduced MFR (adjusted odds ratio 3.02 per SD increase, 95% confidence interval[CI] 2.72 – 3.38) and an increased risk of death or MI in (adjusted hazard ratio[HR] 1.38 per SD increase, 95% CI 1.26-1.51). Having myocardial density higher than blood pool density was also associated with cardiovascular death in patients undergoing low-dose chest CT (adjusted HR 1.73, 95% CI 1.20-2.52). Conclusions In a large multimodality dataset, lower cardiac chamber density is associated with impaired MFR and independently associated with cardiovascular events. These biomarkers can be automatically extracted from CT to provide physiologic insights and potentially guide patient care. CONDENSED ABSTRACT Anemia is an important marker of cardiovascular disease severity and predictor of cardiovascular events. We utilized deep learning to measure cardiac chamber density on computed tomography imaging as a surrogate for anemia in 57,828 patients from two large multicenter studies. A higher density in myocardium compared to left ventricle blood pool was associated with reduced MFR and an increased risk of death or MI in patients undergoing positron emission tomography, and an increased risk of cardiovascular death in patients undergoing low-dose computed tomography. These biomarkers can provide additionally physiologic insights which could be used to guide patient care.

ABSTRACT Aims Exercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition “fitness” score, then validated its utility in two external populations. Methods and Results We included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing ≥7 minutes on a Bruce protocol) then developed a body composition “fitness” score. We then assessed the associations of “fitness” score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 – 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition “fitness” score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 – 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition “fitness” score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 – 0.79, p<0.001). Conclusions We demonstrated that a comprehensive body composition “fitness” score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed. Graphical Abstract Overview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition “fitness” score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.

Coronary microvascular dysfunction (CMD) has emerged as a crucial contributor to cardiovascular morbidity and mortality, particularly in patients with ischemia and non-obstructive coronary arteries (INOCA). The condition arises from a complex interplay of structural and functional abnormalities within the small coronary vessels, driven by underlying molecular mechanisms including endothelial nitric oxide synthase (eNOS) uncoupling, oxidative stress, and chronic inflammation. Lipid metabolism plays a central role in this pathology, especially in the setting of elevated low-density lipoprotein cholesterol (LDL-C). Furthermore, the protective capacity of high-density lipoprotein (HDL) is increasingly understood to depend on its functionality rather than absolute levels, as it can become dysfunctional and pro-inflammatory in pathological states. Emerging evidence has identified lipoprotein(a) [Lp(a)] and triglyceride-rich lipoproteins as significant, independent contributors to microvascular injury. Comprehensive clinical assessment of microvascular dysfunction therefore requires integration of advanced lipid profiling, including apolipoprotein B (ApoB), [Lp(a)], and the triglyceride-glucose (TyG) index with invasive and non-invasive measures of coronary flow reserve to more precisely stratify risk. In this narrative review, we synthesize current observational, mechanistic, and early interventional data linking diverse lipid phenotypes to coronary microvascular dysfunction. We propose a concept of lipid-driven CMD endotypes, such as ApoB-/particle overload, dysfunctional HDL, Lp(a)-mediated risk, and metabolic/TyG-high states, and map these to a practical, mechanism-informed management framework. While intensive LDL-C lowering with high-intensity statins and combination therapy remains guideline-directed care for high-risk patients, evidence for dedicated microvascular benefit from newer lipid and cardiometabolic agents is still largely hypothesis-generating. A personalized approach that aligns lipid phenotyping, CMD endotyping, and existing guideline-based therapies may help refine risk assessment and inform future trials.

Abstract INTRODUCTION Sleep and circadian rest–activity rhythm (RAR) disruption may bidirectionally relate to Alzheimer's disease (AD). Down syndrome (DS), the most common genetic cause of AD, presents sleep disorders, yet RAR patterns across the DS‐associated AD continuum remain uncharacterized. METHODS We analyzed 7‐day wrist actigraphy in 140 adults with DS (108 asymptomatic; 32 AD dementia) and 41 unimpaired controls. General linear models, adjusted for age, sex, sleep efficiency, and obstructive sleep apnea (OSA) severity, tested group differences, with interaction terms included to evaluate group‐specific associations. RESULTS DS showed lower relative amplitude and higher nocturnal activity, already in asymptomatic individuals. Rhythm strength declined further with AD progression, while regularity and phase timing remained preserved until dementia. Findings were independent of sleep duration and OSA. DISCUSSION Adults with DS showed early RAR disturbance that progressed across the AD continuum, paralleling sporadic AD. Circadian RAR features may be scalable biomarkers of AD progression.

Abstract Background Accurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy (HCM) management, yet Doppler-based measurements are technically demanding and require expertise. Objective To develop a multi-view deep learning model capable of classifying LVOT obstruction (> 20mmHg) using routine 2D echocardiographic windows without reliance on Doppler imaging. Methods We trained and externally validated a cross-attention-based video-to-video fusion framework that integrated EchoPrime-derived video representations from three standard transthoracic echocardiographic views to classify LVOT gradients. Results Training was performed on a derivation cohort (N = 1833) from a tertiary care system in the United States, with model performance evaluated on an internal held-out test set (N = 275) and a Korean external validation cohort (N = 46). Single-view baselines showed limited discrimination (external AUROCs 0.47–0.70). Conversely, domain-specific foundational model (EchoPrime) achieved superior single-view performance (AUROCs 0.75–0.80 internal; 0.79–0.83 external), highlighting the importance of echo-specific pretraining and temporal modeling. The proposed multi-view fusion further enhanced predictive performance, with the late fusion model reaching an AUROC of 0.84 on the external cohort with significant population-shift. Conclusions These results suggest LVOT physiology is encoded in routine 2D imaging and can be leveraged for clinically relevant gradient classification without Doppler input- proposed AI-guided strategy demonstrates substantial cost savings compared with the screen-all approach. By integrating complementary spatial-temporal information across multiple views, our approach generalizes robustly across populations and may enable real-time decision support, extend LVOT assessment to portable or resource-limited settings, and complement Doppler-based evaluation for longitudinal HCM management.