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Objective To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Design Retrospective diagnostic accuracy study. Setting Three sites within a single US tertiary health system. Participants Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 included 1426 patients who underwent transcatheter aortic valve replacement. Primary and secondary outcome measures The reference standard was clinician manual chart adjudication. Outcomes included ischaemic stroke or transient ischaemic attack, myocardial infarction (MI), heart failure (HF) exacerbation or hospitalisation and a composite major adverse cardiovascular events (MACE) outcome. Automated retrieval methods included International Classification of Diseases (ICD) codes, primary diagnosis, problem list and a zero-shot LLM workflow. Area under the (receiver operating characteristic) curve (AUC), sensitivity, specificity and net reclassification improvement were assessed. Results In Cohort 1, the LLM achieved the highest AUC for stroke (0.920; 95% CI 0.881 to 0.958), MI (0.938; 95% CI 0.905 to 0.971) and composite MACE (0.880; 95% CI 0.854 to 0.907), whereas ICD-based retrieval had a higher AUC for HF (0.882; 95% CI 0.845 to 0.918 vs 0.873; 95% CI 0.831 to 0.914). In Cohort 2, the LLM achieved the highest AUC for all evaluated outcomes: stroke (0.915; 95% CI 0.862 to 0.968), MI (0.928; 95% CI 0.839 to 1.000), HF (0.844; 95% CI 0.803 to 0.884) and composite MACE (0.862; 95% CI 0.829 to 0.895). In Cohort 1, differences in AUC between the LLM and ICD methods were not statistically significant across outcomes, whereas in Cohort 2 the LLM showed significantly higher AUC for stroke and composite MACE. Conclusion In this multisite retrospective validation study, the LLM-assisted workflow showed strong but context-dependent performance for identifying cardiovascular events from the EMR. Performance varied by outcome and cohort, and ICD-based retrieval remained competitive for some use cases. These findings support a complementary role for LLM-assisted extraction in retrospective cardiovascular outcomes research.

Abstract Artificial intelligence is spreading through public health agencies and healthcare systems faster than workforces are trained to govern it. We describe RHAMI (Responsible Health AI Readiness and Maturity Index), the first human-centered automated composite index for health organizations to score AI readiness and maturity as one measurable system of science, economics and ethics, and the physician certification-to-Master’s pathway built to teach what it measures—both designed jointly by frontline public health professionals, clinicians, ethicists, engineers, leaders, and policy-makers in low- and middle-income countries and high-income countries.

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.

BACKGROUND: Accurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy management, yet Doppler-based measurements are technically demanding and require expertise. The objective of this work was to develop a multi-view deep learning model capable of classifying LVOT obstruction (>20 mm Hg) using routine 2-dimensional 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 3 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 internally held-out test set (N=275) and a Korean external validation cohort (N=46). Single-view baselines showed limited discrimination (external area under the receiver operating curves, 0.47–0.70). Conversely, the domain-specific foundational model (EchoPrime) achieved superior single-view performance (area under the receiver operating curves, 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 area under the receiver operating curve of 0.84 on the external cohort with significant population-shift. CONCLUSIONS: These results suggest LVOT physiology is encoded in routine 2-dimensional imaging and can be leveraged for clinically relevant gradient classification without Doppler input. The proposed artificial intelligence-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 hypertrophic cardiomyopathy management.

Background:The development of symptoms or left ventricular dysfunction in severe aortic stenosis (AS) is associated with high morbidity and mortality. While elective transcatheter aortic valve replacement (TAVR) is supported by robust randomized evidence, data on TAVR performed in non-elective settings (urgent, emergent, or salvage) remain limited and heterogeneous. In these settings, outcomes may be influenced more by baseline clinical severity than by procedural factors. This study aimed to systematically evaluate the safety and effectiveness of non-elective TAVR and to compare the associated outcomes with those of elective TAVR.Methods:This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and registered with PROSPERO. The PubMed, Embase, and Scopus databases were searched for studies published after 2002 evaluating urgent, emergent, or salvage TAVR in adult patients with severe AS. Eligible studies were required to include elective TAVR as a comparator. Outcomes included mortality and major procedural complications. Prespecified era-based analyses and sensitivity analyses excluding studies at critical risk of bias were performed. Risk of bias was assessed using the ROBINS-I tool.Results:A total of 17 observational studies published between 2015 and 2025 were included, comprising 215,141 patients. Compared with elective patients, those undergoing non-elective TAVR had more advanced heart failure, higher NYHA class, greater comorbidity burden, and higher surgical risk scores, whereas baseline echocardiographic severity of AS was similar. Pooled outcomes for non-elective TAVR showed in-hospital, 30-day, and 1-year mortality rates of 5.1%, 12.4%, and 26.7%, respectively. Non-elective TAVR was associated with higher mortality at all time points compared with elective procedures; meanwhile, rates of stroke, vascular complications, and permanent pacemaker implantation were similar, while major bleeding and acute kidney injury were more frequent. Era-based analyses showed a stable relative mortality risk over time.Conclusions:Although non-elective TAVR is associated with worse outcomes than elective procedures, these differences appear to be largely driven by baseline clinical severity rather than by the transcatheter intervention. In unstable patients with severe AS, non-elective TAVR remains the most effective definitive treatment option compared with balloon aortic valvuloplasty or conservative management.The PROSPERO Registration:CRD420251239620, https://www.crd.york.ac.uk/PROSPERO/view/CRD420251239620.

Introduction: Co-stimulatory proteins are master regulators of the immune system. We identified the co-stimulatory CD40-CD40L dyad as a key driver of inflammation during atherosclerosis and have shown that CD40 and CD40L exert cell-type divergent functions. Hypothesis: CD40 is abundantly expressed on endothelial cells (EC) undergoing endothelial mesenchymal transition (EndMT). We hypothesize that EC CD40 drives atherosclerosis via EndMT. Methods: We analyzed scRNAseq data from human carotid plaques, performed multiplex IHC, and used human aortic endothelial cells (HAECs) for in vitro studies. A tamoxifen-inducible EC specific CD40 deficient atherosclerotic mouse model (cadherin5 (cdh5)ERT2-CD40 flfl -ApoE -/- ) was generated. Results: Besides its presence on immune cells, CD40 is expressed on endothelial cells in both human and mouse atherosclerotic plaques. Analysis of scRNAseq data of human carotid atherosclerotic plaques showed that CD40 is highly expressed in COL1A2/FN1 expressing endothelial cells (ECs), a subset undergoing EndMT. Multiplex staining revealed that CD40 colocalized with the EndMT markers αSMA, NOTCH3, TWIST and VCAM1 in human plaques. In vitro , stimulation of human arterial endothelial cells (HAECs) with CD40L resulted in an increase in the EndMT markers markers fibronectin, vimentin, sm22α, FSP1 and snail. Using phosphoproteomics, CD40L was found to decrease phosphorylation of the junctional integrity proteins CTNNA1 [S652; S655] and ARHGAP21 [S1383]. To investigate the endothelial-specific effects of CD40 in atherosclerosis, 8-week-old cdh5ERT2-CD40 flfl -ApoE -/- mice and their CD40 flfl -ApoE -/- littermate controls were treated with tamoxifen and fed a high cholesterol diet for 10 wks. Deletion of EC-CD40 resulted in a significant reduction in plaque size. This reduction was accompanied by a shift from fibrous cap atheromas (FCA) towards intimal xanthoma (IX) and pathological intimal thickening (PIT). Flow cytometry analysis of the spleen showed that CD40 deletion in ECs results in a significant decrease in CD8 and CD4 effector T cells. This decrease in CD8 and CD4 effector T cells was accompanied by a diminished activation status, shown by a decrease in CD69 + CD8 + CD62 - CD44 + T cells, CD40L + CD8 + CD62 - CD44 + T cells and CD40L + CD4 + CD62 - CD44 + T cells. These findings demonstrate that endothelial CD40 drives atherosclerosis by inducing EndMT-driven plaque inflammation.

Introduction: V-domain Immunoglobulin Suppressor of T-cell Activation (VISTA), a co-inhibitory immune checkpoint protein, is known for regulating adaptive immune responses. Here, we investigated the role of VISTA in atherosclerosis. Hypothesis: VISTA is constitutively expressed on hematopoietic cells, particularly T cells. VISTA functions as both a ligand and receptor to maintain T cell quiescence. We hypothesize that VISTA expression is protective against plaque formation by inhibiting CD4 + T cell activation. Methods: Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) was performed on aortic CD45 + cells of HCD fed ApoE-/- mice. Spatial transcriptomics (VISIUM) was performed on carotid plaques from patients with either asymptomatic or symptomatic cerebrovascular disease. Plaque burden, characterization, and flow cytometry of blood and lymphoid organs were performed on VISTA -/- ApoE -/- (VISTA KO) and VISTA +/+ ApoE -/- (WT) mice that were fed a HCD for 10 weeks. ApoE -/- mice treated with VISTA agonist 8G8 or IgG control (200ug, intra-peritoneal, 3 times per week for the last 6/10 weeks of HCD feeding). In vitro proliferation, migration, and functional T cell assays were performed. Results: We found that VISTA is expressed on plaque T cell populations, particularly regulatory T cells, gamma-delta T cells, and naïve T cells. In humans, individuals with symptomatic CVD have significantly reduced VISTA gene expression in carotid plaques. VISTA KO mice have increased total plaque area and necrotic core area compared to littermate WT controls. VISTA KO mice have increased peripheral CD4 + and CD8 + effector memory T (T EM ) cells with a corresponding decrease in CD4 + and CD8 + naïve T (T N ) cells and CD4 + regulatory T (T reg ) cells. Further, VISTA KO mice have a higher frequency of CD4 + T H 1 (T-bet + IFNy + ) cells and lower frequencies of IL-10 + CD4 + T cells and CD4 + T H 2 (GATA-3 + IL-13 + ) cells. In vitro assays showed that VISTA suppresses CD4 + T cell proliferation, IFNγ production, and migration. Finally, VISTA agonist treatment significantly decreased plaque and necrotic core areas in atherosclerotic mice. Conclusion: Our data reveals a novel role for VISTA in reducing plaque development and in suppressing T cell activation, proliferation, and migration. Further characterization of T-cell specific VISTA-deficient mouse models and mechanistic insights into how VISTA modulates T cells are currently being investigated.

Background Randomized trials have shown equivocal outcomes between a direct aspiration as first-pass technique (ADAPT) and stent retriever (SR) stroke thrombectomy, yet whether specific patient subgroups benefit differentially from one technique over the other remains unknown. Methods In this retrospective multicenter cohort study, patients with large vessel occlusion were included, and those who underwent ADAPT versus SR first-pass thrombectomy (either as standalone or combined with aspiration) were compared. The primary outcome was a composite of first-pass effect (FPE, modified Thrombolysis In Cerebral Infarction (mTICI) 2c or better with one attempt) with no symptomatic intracranial hemorrhage (sICH). A causal forest model was used to estimate the conditional average treatment effect (CATE) for each patient, with larger CATEs indicating that ADAPT is expected to be more advantageous. Patients were stratified by CATEs, and outcomes were compared between treatment groups. Results Among 6700 included patients, ADAPT (n=3695, 55%) was associated with a higher rate of FPE with no sICH (39.9% vs 26.9%; adjusted OR (aOR) 2.07, 95% CI 1.77 to 2.41, P<0.001). The causal forest identified significant treatment effect heterogeneity (interaction P<0.001), where ADAPT was more advantageous in more recent years and among patients with M1 occlusions presenting in earlier time windows. Low-CATE patients showed no statistically significant difference between techniques (aOR 0.91, 95% CI 0.57 to 1.45, P=0.70), while medium- and high-CATE patients demonstrated progressively larger ADAPT advantages (aOR 2.06, 95% CI 1.67 to 2.55, and aOR 2.99, 95% CI 2.24 to 3.98, respectively, both P<0.001). Conclusions ADAPT may be superior to SR for a majority of patients, particularly in recent years, and among patients with M1 occlusions presenting in earlier time windows.