Citations

Abstract Background Adults with congenital heart disease (ACHD) represent a rapidly expanding population with evolving mortality patterns. Despite improved survival, excess mortality persists. Objective To evaluate the incidence, causes, and predictors of mortality in a contemporary ACHD cohort. Methods We performed a retrospective cohort study of adults (≥18 years) first evaluated at Mayo Clinic from 2002–2023. Baseline clinical, imaging, and electrocardiographic data were analyzed. Vital status was determined using institutional records and the Accurint national mortality database. Kaplan-Meier analysis and Cox proportional hazard models were used to evaluate mortality and identify independent predictors of mortality Results A total of 7,678 ACHD patients were included, with median age of 36.8 years and median follow-up of 11.4 years. During 78,768 patient-years of follow-up, 1,116 patients died (median age at death 57.2 years), corresponding to an annual mortality rate of 1.4%. The cumulative rate of all-cause mortality at 5, 10, 15, and 20 years was 6.9%, 12.0%, 19.0%, and 26.2%, respectively. When stratified by CHD complexity, the annual death rate in patients with severe CHD (2.4%/year) was twice that of patients with moderate or mild CHD (both 1.2%/year). Older age and ACHD subtypes, specifically, cyanotic heart disease (HR 3.9, 95% CI 2.9–5.3) and Fontan physiology (HR 3.2, 95% CI 2.3–4.4), were strongly associated with increased mortality. Additional independent predictors included male sex, ventricular dysfunction, advanced NYHA class, prior heart failure hospitalization, hypertension, smoking, coronary artery disease, renal dysfunction, and abnormal hemoglobin levels. Cardiovascular causes accounted for 57.7% of deaths with known etiology, predominantly heart failure (48.9%) and sudden cardiac death (21.9%), while non-cardiovascular causes were driven mainly by infection and malignancy. Conclusions In this large contemporary ACHD cohort, mortality was driven by ventricular dysfunction, heart failure, and systemic end-organ involvement in addition to the underlying congenital anatomy. Both cardiovascular and non-cardiovascular causes contributed significantly to mortality. These findings underscore the need for comprehensive multidisciplinary ACHD care focused on early recognition of cardiac functional decline, management of acquired comorbidities, and end-organ dysfunction.

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.

ABSTRACT Background Radiotherapy is a cornerstone of treatment for pediatric central nervous system (CNS) tumors, but dose selection remains uniform despite interpatient variability in tumor radiosensitivity. This is consequential in children, for whom toxicity can have lifelong effects. Genomic-adjusted radiation dose (GARD) integrates tumor genomics with delivered radiation dose to quantify the biological effect of radiotherapy and has been validated across adult malignancies. Its relevance in pediatric CNS tumors is unknown. Methods We performed a cohort study using gene expression and data from 246 pediatric patients with high-grade glioma, medulloblastoma, or ependymoma from the Children’s Brain Tumor Network. GARD was calculated using a sequencing-adapted radiosensitivity index integrated with radiation dose through the linear–quadratic model. Associations of GARD and physical radiation dose with event-free and overall survival were evaluated using Cox proportional hazards models stratified by tumor type and anatomic location. Patients who did not receive radiotherapy comprised a negative control cohort (sham-GARD). Results Among patients receiving radiotherapy, physical dose was uniform, whereas GARD showed substantial interpatient variability in biological effect. Higher GARD was associated with improved event-free survival (hazard ratio [HR] 0·90, 95% CI 0·83–0·97; p=0·004) and overall survival (HR 0·90, 95% CI 0·83–0·99; p=0·018). Physical dose was not associated with either endpoint. Among patients who did not receive radiotherapy, sham-GARD was not associated with outcomes, supporting GARD as a treatment-specific predictor rather than a prognostic biomarker. Conclusions In pediatric CNS tumors, the biological effect of radiotherapy quantified by GARD was associated with outcomes, whereas physical dose was not. These findings challenge uniform radiotherapy dosing and support genomically informed dose individualization. Prospective evaluation of GARD-guided radiotherapy is warranted to optimize tumor control while minimizing long-term toxicity in children.

Abstract The liver is organized into tightly regulated zones with distinct metabolic functions but zonation erodes with age. Cellular senescence contributes to aging and liver diseases, however, its impact on aging biology is ill-defined. As part of The Cellular Senescence Network Consortium, we used multiple spatial transcriptomics approaches (GeoMx, Visium, CosMx) with snRNA-seq to profile senescence signatures, zonation markers, and metabolic pathways in livers from wild-type (WT) mice of multiple ages. We observed a loss of canonical zone signatures in aged mouse livers characterized by “expansion” of midlobular (zone 2) marker gene expression, accompanied by diminished expression of zone 3 marker genes by middle-age (18 months), indicative of loss of cell identity. Multiple analytic approaches identified distinct age-, zone- and sex-specific senescence signatures, which were significantly associated with zonation markers changes. This was recapitulated in Ercc1 mutant models of accelerated senescence, supporting a causal role of senescent cells in liver aging. A “no-zone” hepatocyte-like cluster expanded with age and with the strongest Senescence-Associated Secretory Phenotype (SASP) profile. Gene expression profiles from senescent hepatocytes implicate decreased WNT signaling and increased BMP as contributing to age-related loss of zonation. Together, these data elucidate the role of senescent cells in driving aging biology in non-diseased liver through disruption of cell:cell signaling and the loss of metabolic and cell identity gene expression necessary for hepatocyte function.

Home enteral nutrition (HEN) is essential for patients with a functioning gastrointestinal tract who cannot meet nutritional needs orally, yet outpatient management remains complex, resource-intensive, and supported by a limited evidence base. Artificial intelligence (AI) may augment HEN care by extending monitoring, education, risk assessment, documentation support, and operational coordination into the home environment. Consistent with the narrative review format, this article uses a pragmatic, transparent synthesis of influential HEN-specific literature, relevant clinical nutrition evidence, and background knowledge from adjacent fields, including home healthcare, chronic disease management, oncology nutrition, telehealth, and software regulation. Direct evidence in established HEN populations remains scarce; therefore, most AI applications should be considered hypotheses or early implementation opportunities rather than proven standards of care. The strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation. Predictive analytics, smart pumps, and precision enteral prescription tools are promising but require prospective HEN-specific validation, interoperability with electronic health records and home-infusion systems, reimbursement pathways, and governance safeguards. Key barriers include dataset bias, limited external validation, alert fatigue, privacy and regulatory concerns, unclear accountability, digital equity, cost uncertainty, and the risk of dehumanizing care. AI should be viewed as a complement to multidisciplinary HEN expertise. Priorities for the near future include HEN registries, standardized outcomes, prospective validation, pragmatic implementation trials, health-economic evaluation, and transparent oversight that preserves clinician accountability and patient-centered care.

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.

Abstract Background Biological sex and endocrine signaling influence cancer biology, immune response, and therapeutic outcomes. Recent evidence suggests that testosterone signaling may exert brain-context-dependent protective effects in glioblastoma through the hypothalamic-pituitary-adrenal axis, reduced glucocorticoid-mediated immune suppression, and altered tumor-immune interactions. We assessed whether testosterone replacement therapy (TRT) exposure was associated with survival in solid tumor central nervous system (CNS) metastases and glioblastoma (GBM, IDH-wildtype, WHO grade 4), settings in which post-diagnosis survival and TRT timing can be clinically defined. Methods We performed a retrospective Mayo Clinic cohort study of adult patients with molecularly confirmed glioblastoma and solid tumor CNS metastases confirmed from neuroimaging reports using large language model-assisted adjudication. TRT exposure was defined by testosterone-specific prescription evidence within prespecified peri-diagnostic windows. Overall survival was evaluated using propensity score-matched Cox models, 24-month administratively censored Cox models, time-dependent Cox sensitivity analyses, and 24-month restricted mean survival time. Results In the pooled solid tumor CNS metastasis cohort, TRT exposure was associated with improved overall survival after propensity score matching (HR 0.80, 95% CI 0.65–0.98, p=0.029) and a 3.22-month improvement in 24-month restricted mean survival time. In glioblastoma, TRT exposure was similarly associated with improved overall survival after propensity score matching (HR 0.56, 95% CI 0.38–0.82, p=0.003) and a 5.81-month improvement in 24-month restricted mean survival time. Conclusions TRT exposure was associated with improved survival in CNS metastases and glioblastoma. These hypothesis-generating findings support prospective studies incorporating TRT timing, hormone levels, corticosteroid exposure, immune correlates, and tumor-specific stratification. Key points TRT exposure is associated with improved survival in aggressive brain tumors. Survival associations persist after propensity score matching. Prospective studies should define TRT timing, hormone levels, and immune effects. Importance of Study This study evaluates testosterone replacement therapy (TRT) in aggressive intracranial tumors with clinically interpretable survival timelines. Building on recent evidence that androgen signaling may have protective brain-specific effects in glioblastoma, we extend this question to CNS metastases. In a multi-site Mayo Clinic cohort, TRT exposure was associated with improved survival in CNS metastases and glioblastoma, supporting androgen biology as an underrecognized factor in neuro-oncology outcomes. These findings provide rationale for prospective clinical trials incorporating TRT timing, hormone levels, corticosteroid exposure, immune profiling, and tumor-specific stratification.