Rheumatology

Objectives To evaluate whether access to a certified large language model (LLM)-based clinical decision support system improves physician diagnostic performance in rheumatology compared with conventional diagnostic resources alone. Methods In this multicentre, open-label, randomised controlled trial, 82 physicians from seven hospitals in two countries were randomised 1:1 to conventional diagnostic resources plus Prof. Valmed or conventional resources alone. Participants assessed three rheumatology vignettes before and after assistance. The primary outcome was top-1 diagnostic accuracy. Secondary outcomes included top-3 accuracy, diagnostic reasoning, confidence, case-processing time and perceived support quality. Results Top-1 accuracy increased from 22.2% to 33.3% in the intervention group and from 23.3% to 35.0% in the control group, with no between-group difference in improvement (adjusted OR 0.99, 95% CI 0.45 to 2.19; p=0.979). Differences in top-3 accuracy, diagnostic reasoning and confidence were also not significant. Assisted case-processing time was substantially shorter with LLM support (94 vs 206 s; adjusted mean difference -112 s, 95% CI -141 to -83; p<0.001). Information timeliness and perceived diagnostic support quality were rated significantly higher in the intervention group. Exploratory analyses showed persistent overconfidence and substantial AI over-reliance. Conclusions Certified LLM-based diagnostic support did not improve diagnostic accuracy compared with conventional resources, but substantially reduced case-processing time and improved perceived support quality. These findings suggest potential workflow benefits while highlighting overconfidence and over-reliance as important safety considerations.

Objective: The Brief-cope questionnaire explore coping behavior. However, the underlying factor structure remains a subject of ongoing debate. Exploratory factor analyses (EFA) conducted across different populations have identified factor solutions ranging from two to fourteen factors. As of yet, the underlying factor structure of the Brief-cope has not been investigated in patients with seropositive rheumatoid arthritis (RA). Therefore, the aim of this study was to explore the underlying factor structure of the Brief-cope in a German population of seropositive RA. Methods: 216 outpatients with seropositive RA completed the Brief-cope. An EFA with principal axis factoring and Promax rotation was conducted. Results: EFA indicated a five-factor solution. The five-factor solution explained 51.95% of variance. The identified factors were: (1) problem-focused coping (Cronbach's = .851), (2) emotion-focused coping ( = .754), (3) maladaptive coping ( = .747), (4) religious coping ( = .851), and (5) substance-use coping ( = .869). Conclusion: A five-factor solution provided the most appropriate representation of the underlying factor structure of the Brief-cope in patients with seropositive RA. This factor structure may serve as a suitable basis for future analyses of Brief-cope data in comparable RA populations.

Objective: To examine how medial plantar nerve shear wave speed (Cs) and viscosity coefficient (Vi) are associated with the severity of diabetic peripheral neuropathy (DPN), and to assess their ability to differentiate adjacent severity categories. Materials and Methods: Based on TCSS, the 113 patients with type 2 diabetes mellitus were assigned to the non-DPN (n = 33), mild DPN (n = 46), and moderate DPN (n = 34) groups. Medial plantar nerve Cs and Vi were measured using shear wave elastography and viscosity imaging. Receiver operating characteristic analysis evaluated Cs, Vi, and their logistic regression-based combination; areas under the curves (AUCs) were compared using DeLong tests. Results: Cs and Vi increased progressively across the three groups (both P < 0.001). For non-DPN versus mild DPN, the AUCs of Cs, Vi, and the combined model were 0.688 (95% CI, 0.604-0.772), 0.741 (0.660-0.822), and 0.745 (0.665-0.826), respectively, without significant pairwise differences. For mild versus moderate DPN, the corresponding AUCs were 0.707 (0.625-0.789), 0.794 (0.724-0.865), and 0.799 (0.731-0.867). The combined model outperformed Cs (P = 0.045), whereas Cs versus Vi and Vi versus the combined model did not differ significantly (P = 0.162 and 1.000, respectively). Conclusion: Medial plantar nerve Cs and Vi increased with DPN severity. Their combination improved discrimination between mild and moderate DPN compared with Cs alone but not with Vi alone. Quantitative medial plantar nerve viscoelastic assessment may complement clinical severity grading.

Purpose: The primary objective of the Stanford Knee Osteoarthritis PET/MRI Evaluation (SKOPE) study is to develop and evaluate a multimodal, dynamic [18F]NaF PET-MRI framework for characterizing whole-joint physiology and its relationship to osteoarthritis (OA) risk, pain, and disease progression. Specifically, we aim to integrate dynamic PET with quantitative and anatomical MRI, to characterize structural, compositional, and metabolic features across the knee and surrounding musculoskeletal system, evaluate acute tissue responses to exercise, and identify imaging biomarkers associated with OA risk, pain, and disease progression. Methods: The SKOPE study includes multimodal PET-MRI of the knee and surrounding musculoskeletal tissues, with imaging of the knee, tibia, ankle, thigh, hip, pelvis, and lumbosacral spine. Dynamic [18F]NaF PET is combined with conventional anatomical MRI and quantitative MRI techniques, including quantitative double-echo steady-state (qDESS) T2 mapping of cartilage, Dixon fat-fraction imaging, ultrashort echo time (UTE) T2* mapping of short-T2 tissues, UTE imaging of tibial bone, and zero echo time (ZTE) imaging for bone morphology and pseudo-CT generation. Additional MRI sequences characterize muscle composition, bone and joint anatomy, intervertebral discs, and regional vascular anatomy. Selected scans are acquired before and after a standardized exercise protocol to assess the acute physiological response of the joint. Automated segmentation is used to generate subject-specific masks of muscles, bones, vertebrae, and intervertebral discs. A subset of the MRI protocol is repeated at 1- and 2-year follow-up to assess longitudinal changes. Expected Impact: By combining dynamic bone metabolic imaging with quantitative measures of cartilage, menisci, muscle, bone, fat, vascular structures, and the spine and hip, the SKOPE protocol provides a whole-joint and multijoint framework for studying the structural, metabolic, and physiological processes associated with OA and pain. Exercise and longitudinal imaging further enable assessment of acute tissue responses and changes over time, supporting the development of quantitative imaging biomarkers for OA risk, pain, and disease progression.

Objectives To analyse the long-term effects of a cruciate ligament (CL) injury on health and socioeconomic outcomes. Methods We used a comprehensive national injury insurance database to identify CL injuries occurring in New Zealand between 2009 and 2022, and employed a doubly robust staggered difference-in-differences research design to identify the effects of these injuries on outcomes up to 10 years after injury. The outcomes of interest were healthcare use (hospitalisations, emergency department visits, medications, knee replacement surgery for osteoarthritis), associated healthcare costs, and labour market outcomes (employment rates, income, and government benefit payments). Results We identified 61 344 CL injuries for inclusion in the analysis. Over 10-year follow-up, a CL injury resulted in increased healthcare use (0.6 more hospitalizations [95%CI 0.4 to 0.7], 1.7 more days spent in hospital [95%CI 1.3 to 2.1], 0.4 more emergency department visits [95%CI 0.3 to 0.6], 2.5 more outpatient visits [95%CI 1.8 to 3.2], and 4.7 more medications dispensed [95%CI -1.8 to 11.2]) and public healthcare costs ($7 537; 95%CI 5 888 to 9 186), reduced income (-$6 060; 95%CI -11 644 to -475), and increased benefit payments ($1 152; 95%CI 542 to 1 761). Conclusion CL injuries have long-term impacts on healthcare use and socioeconomic outcomes. Strategies to reduce the incidence of CL injuries have the potential to realise large health and economic benefits.

Background Little evidence is available on the epidemiology of different knee injuries at a whole-of-population level. The objective of this article is to provide accurate estimates of knee injury incidence by harnessing the unique comprehensive, population-wide data of New Zealand's universal no-fault injury insurance provider, the Accident Compensation Corporation (ACC). Methods We obtained insurance claims data from ACC covering all knee injury insurance claims approved between 2015 and 2024. We calculated the number of injuries and the incidence rate per 100 000 population, by injury type, year, sex, ethnicity, and age. Results The total number of injuries increased from 184 710 (4 067 per 100 000 population) in 2015 to 244 155 (4 701 per 100 000) in 2024. The most common injuries were other/unspecified ligament sprains, contusions, and collateral ligament sprains. Ligament and cartilage injuries were more common for males than for females, while contusions were more common for females. Ligament tears and dislocations were more common in younger people (15 to 35 years of age), while cartilage injuries were more common at older ages (40 to 65 years). Discussion and Conclusions The rate of knee injuries observed in this study was higher than previously reported in other settings, probably due to broader coverage of injuries treated in primary and community care settings. A broad range of injuries were common, including those that have received less attention in the epidemiological literature to date. More research is needed on the prevention, burden, and outcomes of different knee injuries, beyond a narrow focus on cruciate ligament injuries.

Introduction Acute lower respiratory tract disease (aLRTD) is a leading cause of hospitalisation and death, particularly in older adults and adults with comorbidities, with acute lower respiratory tract infection (aLRTI; pneumonia and non-pneumonic LRTI) being a major component. Non-pulmonary complications and functional decline after aLRTI are recognised, but their pathogen-specific burden is poorly described. We aimed to quantify renal, hepatic, thromboembolic and functional complications, and mortality, after aLRTI hospitalisation, by clinical phenotype and pathogen. Methods We conducted a cohort study of adults (>18 years) admitted with aLRTD to two hospitals in Bristol, UK (01 August 2022-31 July 2024). aLRTD was classified as pneumonia, non-pneumonic LRTI (NP-LRTI) or no diagnosis of aLRTI. Pathogens were identified from standard-of-care and research microbiology. Outcomes were acute kidney injury (AKI), acute liver dysfunction, venous thromboembolism (VTE), in-hospital falls, reduced mobility at discharge, increased care requirements, and 30-day and 1-year mortality. Analyses were descriptive. Results Among 246,797 adult admissions, 21,456 aLRTD hospitalisations were included: 10,239 (47.7%) pneumonia, 7,742 (36.1%) NP-LRTI and 3,475 (16.2%) with no evidence of aLRTI. Of 19,152 tested aLRTD admissions, 8,503 (44.4%) had a positive microbiological/virological test, yielding 9,204 pathogen detections; 1,194 (6.2%) had co-infections, and SARS-CoV-2 was most frequent, with influenza the second most common in pneumonia and NP-LRTI. Pneumonia had greater severity than NP-LRTI and no diagnosis of aLRTI (median length of stay 6 vs 4 vs 4 days; ICU admission 3.4% vs 0.7% vs 0.5%, respectively). Overall, 22.2% developed AKI, 6.1% acute liver dysfunction, 0.6% DVT and 2.4% PE; 1.8% had a fall, 11.5% reduced mobility, and 16.6% required increased care at discharge. 30-day and 1-year mortality were highest for pneumonia (14.0% and 32.0%, respectively). Pathogen-specific analyses showed longer stays and higher complications and mortality rates for SARS-CoV-2 and Streptococcus pneumoniae, and shorter stays with lower complication and mortality rates for influenza and Haemophilus influenzae. Conclusions Non-cardiovascular complications and functional decline after aLRTI were common, particularly in pneumonic and SARS-CoV-2 or pneumococcal disease. These findings support routine surveillance for renal, hepatic, thromboembolic events, early mobilisation and rehabilitation, and consideration of multi-system outcomes when evaluating public health and economic value of vaccines and therapies.

Objective. To determine the publication rate of abstracts presented at the American Diabetes Association Scientific Sessions and to evaluate the association between statistical significance of study results and subsequent publication. Research Design and Methods. We conducted a retrospective cohort study of abstracts presented at the 2018 American Diabetes Association Scientific Sessions. The primary exposure was study result category (statistically significant vs. non-statistically significant findings), and the primary outcome was publication in an indexed journal within 5 years after conference presentation. Publication status was determined through PubMed/MEDLINE and Scopus searches. Adjusted relative risks (RRs) and 95% CIs were estimated using generalized linear models with Poisson distribution and robust variance. Results. Among 541 included abstracts, 321 (59.3%) were subsequently published in indexed journals. Abstracts reporting statistically significant findings had a higher publication rate than those reporting non-statistically significant findings (61.9% vs. 42.3%; p=0.002). In the adjusted analysis, abstracts with non-statistically significant findings had a lower likelihood of publication compared with those reporting statistically significant findings (adjusted RR 0.71 [95% CI 0.55-0.93]; p=0.013). Conclusions. Approximately four in ten abstracts presented at the ADA Scientific Sessions were not published within 5 years. Abstracts reporting non-statistically significant findings had a lower likelihood of subsequent publication, suggesting persistent publication bias in diabetology research. Future initiatives promoting the interpretation of effect estimates, confidence intervals and clinical relevance, rather than statistical significance alone, may help reduce selective dissemination of evidence

Rationale: Pulmonary hypertension is defined by pulmonary hemodynamics, but diagnostic and prognostic biomarkers remain limited. Nuclear magnetic resonance (NMR) spectroscopy provides detailed insights, particularly in the lipid metabolism. Objectives: To explore circulating NMR-derived metabolites and lipoprotein-related parameters for their association with pulmonary hemodynamics and to analyse their prognostic properties in pulmonary arterial hypertension (PAH). Methods: Retrospective analysis of a PAH cohort with complete diagnostic workup including right heart catheterization and baseline serum samples, from the prospective GRaz Pulmonary Hypertension-Metabolism (GRAPH-M) registry. Measurements: NMR-derived metabolites and lipoprotein-related parameters were analyzed for their association with clinically relevant parameters of PAH. We defined PHIHDL, a score derived from high-density lipoprotein (HDL) related measures based on their strong association with pulmonary hemodynamics, and evaluated its prognostic value. Results: We included 100 patients with PAH treated at the PH clinic of LKH University Hospital, Medical University of Graz, between 2011 and 2021. Age was 61{+/-}15 years, female/male ratio 2.5, BMI 26 {+/-}7 kg/m2, mPAP 41{+/-}16 mmHg, PAWP 8.8{+/-}3.2 mmHg, PVR 8.0{+/-}4.9 WU, and median survival was 8.0 years. During follow-up, 46 patients died. We identified a cluster of 12 HDL-related measures that showed significant inverse association to pulmonary hemodynamics and derived PHIHDL from the reversed scaled average of these particles. PHIHDL was associated with all-cause mortality after adjustment for age and sex (HR 2.96, 95% CI 1.52-5.70), independent of the clinical risk scores COMPERA 2.0 and REVEAL Lite2. Conclusion: PHIHDL, a pulmonary hemodynamics-based metabolomic score, provides independent prognostic information beyond established risk scores in PAH.

Background VEXAS syndrome is a late onset autoinflammatory disease caused by somatic UBA1 mutations and characterized by heterogeneous systemic and hematologic manifestations. We aimed to describe all identified Belgian cases through a national multicenter cohort. Methods We conducted a retrospective study across four Belgian tertiary centers. Clinical, biological, genetic, therapeutic, and outcome data were collected using standardized anonymized case report forms. Analyses were descriptive. Results Twenty-one male patients were identified between January 2018 and May 2025. General symptoms such as Fatigue, weight loss and sweating occurred in 95% of cases. The most frequent manifestations were cutaneous (85.7%), hematologic (76.2%), articular (66.7%), thromboembolic (57.1%), chondritis (42.9%), ophthalmologic (38.1%), pulmonary (38.1%). Other manifestations also included vasculitis (61.9%). At diagnosis, 95% had anemia, macrocytic in 57%, and 28.6% had thrombocytopenia. Corticosteroids were the main first line therapy. Second line treatments included anti IL 6 agents (46.7%), JAK inhibitors (20%), and azacitidine (14.3%). Complete remission occurred in 50% of patients receiving anti IL 6 therapy and in 33% treated with either JAK inhibitors or azacitidine. Two patients underwent allogeneic stem cell transplantation, one died from infectious complications. Twenty six infectious episodes were recorded, including opportunistic infections. Six patients (28.6%) died during follow-up, four from infectious complications. Conclusion This first Belgian national cohort confirms the clinical heterogeneity of VEXAS syndrome and highlights substantial infectious morbidity and mortality. Access to targeted second-line therapies, particularly anti IL-6 agents and JAK inhibitors, remains challenging despite apparent clinical benefit.

Biological aging occurs heterogeneously across individuals and organs. However, current measures of biological age incompletely capture organ-specific differences in health and disease risk. Because chest CT visualizes multiple thoracic organs, it offers an opportunity to quantify structural aging across organ systems. Here, we developed MOSAIC-Age, a framework characterizing eight organ-specific aging clocks on chest CT. The clocks were developed and validated using 9,971 CT scans from CT-RATE and MIDRC, and subsequently locked and applied to two independent prospective cohorts with 35,293 participants from the National Lung Screening Trial and Genetic Epidemiology of COPD study. CT-derived biological age gaps (BAGs) were examined in relation to lifestyle and socioeconomic factors, prevalent comorbidities, incident chronic diseases, and all-cause and cause-specific mortality. Higher BAGs, indicating organs that appeared older on CT than expected for their chronological age, were broadly associated with adverse health characteristics, chronic disease burden, and increased mortality risk. Multiple disease outcomes were associated with aging across several organs, whereas in multivariable analyses including all eight organ-specific BAGs, the remaining associations were more organ specific. A greater number of markedly older-appearing organs and a faster pace of aging were each associated with higher mortality. Together, these findings demonstrate that routine chest CT captures both shared and organ-specific patterns of biological aging and establish CT-derived organ aging as a quantitative imaging biomarker for assessing multi-organ health and long-term disease risk.

Background Elexacaftor-tezacaftor-ivacaftor (ETI) has been demonstrated to improve lung function in clinical trials; however, evidence describing effects on trajectories and whether long-term improvements are sustained (>1-year) is lacking. We estimated within-person lung clearance index (LCI) trajectories before and after ETI initiation, assessing changes in level and rate of change, alongside acute LCI change, up to three years after ETI initiation. Methods Prospective observational study of children at a tertiary hospital. Children aged 3-17 years with [&ge;]2 LCI testing occasions (i) before and (ii) after starting ETI were used to describe lung function trajectories. Children with [&ge;]1 pre-ETI and [&ge;]1 post-ETI LCI occasion(s) were used to describe acute LCI change after ETI initiation. Age-adjusted LCI trajectories for time periods (i) before and (ii) after ETI initiation were estimated using linear mixed-effects models, and pre- and post-ETI LCIs were compared using paired Wilcoxon tests. Results Mean pre-ETI and post-ETI longitudinal changes in LCI were -0.007 (95% CI: -0.28, 0.27; n=35) and 0.12 (95% CI: -0.17, 0.41; n=20) turnovers per year, respectively. Before ETI initiation, 57% (30/53) of patients had an LCI[&ge;]7.1 turnovers (indicating impaired lung function), compared to 26% (14/53) post-ETI, with a median LCI difference of -0.70 (95% CI -0.84, -0.46; p<0.001) turnovers. Within-individual variability in LCI decreased post-ETI. Conclusions Our real-world data within a unique longitudinal study provide a comprehensive picture of ETI benefit by outlining not only acute improvement in LCI but maintained stability in LCI trajectories and improved LCI stability sustained up to three years post-initiation.

Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System IISDS, an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [&ge;]0.011 Dice score, reducing lesion volume estimation error by [&ge;]0.3 average symmetric surface distance (ASSD), and increasing classification performance by [&ge;]0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation.

Background: The Centiloid (CL) scale standardizes global amyloid PET quantification and is widely used to define amyloid positivity. As a global summary measure, however, CL may not fully reflect the regional distribution of amyloid deposition, which can carry additional prognostic information about the rate of cognitive decline. Objective: To develop and externally validate a fixed, regional amyloid PET composite score that complements CL for predicting cognitive decline in Alzheimer's disease. Methods: The Regional Amyloid PET Score (RAPS) was derived from 82 FreeSurfer regions using machine learning with bootstrap stability selection to predict the rate of change in CDR-Sum of Boxes (CDR-SB) in 433 amyloid-positive ADNI [18F]florbetapir participants. The fixed nine-region weights were applied without retraining in a cross-tracer ADNI [18F]florbetaben subset (N = 71; largely overlapping the discovery participants) and two external validation cohorts, NACC SCAN (N = 1531; four tracers) and OASIS-3 (N = 428). Results: RAPS comprised nine regions. In ADNI, RAPS correlated more strongly with CDR-SB slope than CL and showed higher discrimination of rapid decliners (AUC 0.813 vs 0.713). Performance was directionally consistent across validation cohorts; in NACC SCAN, RAPS and CL independently predicted clinical progression. Cross-cohort meta-analysis of the three independent cohorts supported incremental discrimination beyond CL (pooled {Delta}AUC +0.066; I2 = 0%). Conclusions: RAPS, a fixed regional amyloid PET-derived score, may complement CL for prognostic stratification in Alzheimer's disease research.

Background: Endometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidate-variant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program. Methods: Among 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test. Results: Discrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67/0.65) versus near-zero sensitivity at a default threshold. Conclusions: Combining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.

Objectives To validate two computational fluid dynamics (CFD) models derived from computed tomography angiography (CTA) for estimating trans-stenotic pressure gradients, using invasive intra-arterial pressure measurements as the reference standard in patients with renal artery stenosis (RAS). Background We assessed whether non-invasive assessment of the pressure gradient using CFD could be a reliable alternative to intra-arterial measurements for identifying hemodynamically significant RAS. Methods We performed intra-arterial measurements at rest and during dopamine-induced hyperemia to assess the trans-stenotic pressure gradient in 28 patients with RAS. A pre-intervention CTA scan was used to simulate the pressure gradient with a CFD model using a strategy based on Murray's law (CFD-Mu) and cortical volume (CFD-C). The agreement between the simulated and measured pressure gradients was assessed using intraclass correlation coefficients (ICC), Bland-Altman analysis and diagnostic agreement on the presence of a hemodynamically significant stenosis. Results In 20 patients, successful measurements and simulations were obtained. The ICC between measured pressure gradient and the CFD pressure gradient was 0.78 and 0.94 during baseline and 0.86 and 0.72 during hyperemia, for CFD-Mu and CFD-C, respectively. The sensitivity of CFD-Mu and CFD-C was 70% for both models at rest and 100% compared to the hyperemic measurements, whereas the specificity was 90% and 70% at rest and 79% and 72% during hyperemia, respectively. Conclusions The results support the use of individualized CFD simulations for hemodynamic assessment of RAS using CTA as input. The CFD models demonstrated high accuracy for the identification of a hemodynamically significant stenosis.

Rib-cage morphology is a determinant of thoracic biomechanics, ventilation, and injury response, yet statistical shape models (SSMs) of the rib cage have relied on small cohorts (~100s of individuals) imaged by clinical computed tomography, which over-represents injury and disease. We constructed a surface-based SSM of the complete 24-rib cage from 26,275 standardised whole-body magnetic resonance imaging (MRI) scans of adults aged 19-74 years from the population-based German National Cohort (NAKO). Ribs were segmented with a deep-learning pipeline (a rib-extended SPINEPS model), reconstructed as per-rib surface meshes, and brought into dense vertex-wise correspondence by Gaussian-process morphable registration in Scalismo; the aligned ensemble was summarised by generalised Procrustes analysis and principal component analysis (PCA). Fourteen per-rib geometric descriptors provided a quantitative cross-walk between the abstract PCA modes and named shape features, and associations with sex, age, body size and composition (including body-fat percentage), and smoking exposure were estimated by multivariable regression with Benjamini-Hochberg false-discovery-rate control. Shape variation was strongly concentrated: 28 modes captured 95% of the total variance, and the first three alone accounted for 69.4% (PC1, 42.6%; PC2, 16.3%; PC3, 10.5%) and admitted consistent anatomical readings - a sexually dimorphic axis (PC1), a slender-versus-stout body-habitus contrast (PC2), and a free-rib-size axis at ribs 11-12 (PC3). The sexes were nearly fully separated along PC1 (Cohen's d = 2.52). Body mass and body-fat percentage were the dominant modifiable correlates of rib-cage shape, whereas the association with cumulative smoking exposure was comparatively small. The model is released as a population-representative geometric reference for benchmarking and morphing donor-derived finite-element human-body models and for further large-cohort shape analysis.

Background: Hippocampal sclerosis (HS) is a common cause of drug-resistant focal epilepsy (DRFE) and amenable to neurosurgical treatment. Detection relies on MRI but can be challenging. 7 Tesla (T) ultra-high field MRI and automated MRI post-processing tools have independently been shown to improve radiological diagnosis of HS. However, combining these approaches remains underexplored. This study evaluated whether AID-HS, a tool for HS detection developed using 3T MRI, generalises to 7T MRI data. Methods: We collated a dataset of paired 3T and 7T T1-weighted MRI from four epilepsy centres, including 23 patients with HS, 39 healthy controls, and 23 individuals with focal cortical dysplasia as disease controls. Histopathology served as the gold standard for defining HS where available (n=7), otherwise radiological findings (n=16). AID-HS was applied to images acquired at both field strengths, and sensitivity and specificity for detection and lateralisation of HS were compared. Additionally, agreement of hippocampal features across 3T and 7T was evaluated. Results: We found no evidence of a difference in performance of AID-HS between 3T and 7T. Sensitivity for detection of unilateral HS was 63% (12/19) at 3T and 68% (13/19) at 7T (McNemar's exact test p=1.0). Specificity in controls was 97% (60/62) at 3T and 100% (62/62) at 7T (p=0.5). Bilateral HS was correctly flagged in 3 of 4 cases using feature-based criteria, with high specificity in controls. Quantitative hippocampal features showed moderate to good agreement across field strengths (ICC 0.70 to 0.98), with small differences observed for volume and thickness estimates. Conclusion: AID-HS provides robust detection and lateralisation of HS across multiple 7T MRI centres, highlighting its potential to enhance lesion detection. Future work is needed to investigate whether models trained on 7T data can leverage the improved image quality for further gains in HS detection performance.

Background: The relative merits of infrainguinal bypass and primary major lower limb amputation (MLLA) for chronic limb-threatening ischaemia (CLTI) remain uncertain, and the baseline profiles of patients selected for each strategy are poorly described. Methods: A systematic review and meta-analysis were undertaken in accordance with PRISMA 2020 and prospectively registered (PROSPERO: CRD42022356094). MEDLINE, Embase, CENTRAL, and CINAHL were searched from inception to March 2025. Prospective studies of adults with CLTI undergoing primary infrainguinal bypass or primary MLLA were eligible. Mortality, major adverse cardiovascular events (MACE) and subsequent amputation outcomes were synthesised using random-effects meta-analysis of proportions. Baseline comorbidity profiles were also extracted. Results: Twenty-seven studies involving 6,576 patients were included: 5,779 underwent infrainguinal bypass and 797 underwent MLLA. After bypass, pooled mortality was 3.7% at 30 days (95% CI 2.8%-4.9%, I2 = 49.4%), 18.5% at 1 year (95% CI 15.6%-21.9%, I2 = 62.3%), and 54.3% at 5 years (95% CI 50.5%-58.0%, I2 = 0%). After MLLA, pooled mortality was 9.2% at 30 days (95% CI 4.1%-19.3%, I2 = 73.5%), 28.5% at 1 year (95% CI 13.3%-51.0, I2 = 70.8%), and 39.9% at 2 years (95% CI 0.3%-99.3, I2 = 90.5%), although longer-term estimates were limited by sparse data and marked heterogeneity. Thirty-day MACE was 6.5% (95% CI 4.3%-9.7, I2 = 63.5%) after bypass and 2.8% after MLLA (95% CI 0.1%-37.6%, I2 = 0%). Early subsequent major amputation after bypass occurred in 3.9% of patients (95% CI 2.0%-7.7%, I2 = 91.2%), rising to 16.2% at 1 year (95% CI 12.6%-20.5%, I2 = 82.0%) and 33.3% at 3 years (95% CI 20.1%-49.8%, I2 = 0%). Early re-amputation after MLLA occurred in 10.9% of patients (95% CI 4.5%-24.4%, I2 = 40.3%). Baseline comorbidity burden was high in both groups, with substantial heterogeneity across studies. Conclusions: CLTI carries a poor prognosis regardless of treatment strategy. Infrainguinal bypass is associated with lower early mortality and better early limb preservation than primary MLLA, but long-term survival remains poor and later limb failure is common. Primary MLLA is not a low-risk alternative. Better contemporary comparative evidence utilising modern causal inference approaches is needed to support individualised decision-making.

Background: Lumbar disc herniation, with or without concomitant disc degeneration, is a major cause of lumbar radiculopathy and low back pain, which also a key public musculoskeletal disorder without an exact pathophysiology. Studies have suggested that inflammatory cells and biochemical markers of inflammation also play an important role in lumbar radiculopathy in addition to nerve compression. The aim of the present study was to assess the association of selected circulatory inflammatory markers (CRP, hs-CRP and E-selectin) in patients with lumbar disc herniation without radiological degeneration (LDH) and lumbar disc herniation with radiological degeneration (LDHD). Materials & methods: This case-control study included 208 participants, comprising 104 patients with lumbar disc pathology and 104 controls. Patients were further stratified into LDH (n=67) and LDHD (n=37). Serum CRP, hs-CRP and E-selectin concentrations were measured. Results: Among the patients, 35.6 % presented with LDHD while 64.4 % had only LDH. Significantly increased median hs-CRP (p<0.001) and CRP (p<0.001) were observed in patients groups compared to controls, while CRP showing a consistent independent association across the combined disease (OR=1.68, 95% CI=1.33-2.14, p<0.001), LDHD (OR=1.62, 95% CI=1.16-2.20, p=0.005) and LDH (OR=1.69, 95% CI=1.30-2.20, p<0.001) multivariable models. No significant difference was observed in serum E-selectin between the study groups. Multivariable models incorporating inflammatory and clinical variables demonstrated substantially greater discriminatory performance than individual biomarkers alone. Conclusion: Elevated circulating CRP and hs-CRP concentrations were associated with lumbar disc pathology, with CRP showing a consistent independent association across the combined disease, LDH and LDHD multivariable models, whereas E-selectin showed no significant association. Multivariable models incorporating inflammatory and clinical variables demonstrated greater discriminatory performance than individual biomarkers. These findings support a potential systemic inflammatory component in lumbar disc pathology, although the cross-sectional nature of the measurements does not establish causality or a local inflammatory response within the disc.