Citations

Abstract Aims Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is estimated that a large proportion of those with early systolic dysfunction remain asymptomatic at a stage when guideline-directed medical therapies have been shown to prevent disease progression. To develop an artificial intelligence (AI) model capable of predicting abnormal left ventricular ejection fraction (EF) directly from static, non-gated, non-contrast chest computed tomography (CT) scans as a form of opportunistic screening, Methods and results Using a multi-institutional dataset of 34 058 paired non- contrast CT images and echocardiogram reports from two academic centres, we trained our model of classification for predicting left-ventricle ejection fraction (LVEF) categories: abnormal EF (EF < 50%) vs. normal on 25 948 studies. We validated the model on 8110 paired chest CT and echocardiogram results from a separate institution. The model achieved an area under the receiver operating characteristic (AUROC) curve of 0.786 on the hold-out test set and 0.762 on external validation to detect an abnormal EF (<50%). Beyond strong predictive performance, the AI model surpassed expert radiologists in both accuracy and efficiency and provided interpretable visualizations highlighting imaging features linked to reduced LVEF. Conclusion In this study, we developed and validated an AI model capable of predicting abnormal LVEF directly from static, non-gated, non-contrast chest CT scans, a novel application for an imaging modality typically used for unrelated indications as a form of opportunistic screening. This technology holds significant promise for early detection of systolic HF, reducing the diagnostic gap, and improving outcomes in asymptomatic HF patients.

Background: Artificial intelligence (AI), particularly generative and large language models, is being used in nursing education, practice, and scholarly writing. Generative AI applications have been specifically examined for their use in conducting literature reviews with evidence supporting reduced production time of scholarly work. However, there has been limited investigation of their levels of accuracy with identifying references for a literature review. Objective: The purpose of this study was to compare human-generated citations of literature reviews with AI literature-review generated citations. Methods: Using a comparative exploratory design, references from 4 human-written literature reviews, 2 published and 2 unpublished, on 4 different topics, were compared to references derived from 2 AI literature applications, Consensus and Elicit. Three prompting strategies were utilized, including prompts generated using ChatGPT-4. Agreement between the AI and human references was evaluated. Results: The percent of agreement between AI and human generated reference lists ranged from 0% to 63.6%. The Consensus application had a greater overall mean rate of match (21.3%) as compared to Elicit (3.7%). The use of a ChatGPT-4 prompt did not significantly impact results, and there were no differences based on published or unpublished literature reviews. Conclusion: The 2 literature-based applications examined in this study offered a glimpse of their potential use and limitations. The use of an AI literature review application may support but not replace human work.

ABSTRACT Background This study aimed to explore the effect of family history (FH) and polygenic risk scores (PRS) for multiple psychiatric disorders on lithium response in bipolar disorder (BD). Method The sample included patients from the Mayo Clinic BD Biobank. FH in first‐degree relatives was obtained via the patient questionnaire. Linear regression models tested the association of FH and PRS for BD, major depressive disorder (MDD), schizophrenia (SCZ), alcohol use disorder (AUD), anxiety disorder (ANX), and attention‐deficit/hyperactivity disorder (ADHD) with lithium response measured by the Alda A score adjusted for the Alda B sum score. Post hoc models additionally adjusted for clinical covariates (age at onset, rapid cycling, and BD subtype). Results 716 patients had Alda scores for lithium response (61.9% females, mean age = 43.4 years). FH‐BD ( β = −0.57, p = 0.015), AUD ( β = −0.44, p = 0.045), and ANX ( β = −0.61, p = 0.050) were associated with worse lithium response. After adjusting for clinical covariates, associations for FH‐BD and FH‐ANX were no longer significant. PRS‐BD was associated with better response ( β = 0.22, p = 0.041), while PRS‐MDD ( β = −0.28, p = 0.01) and PRS‐ADHD ( β = −0.24, p = 0.028) were associated with worse response, with effects largely unchanged after adjusting for clinical covariates. In the combined model considering all FHs simultaneously, only FH‐BD remained significantly associated with worse lithium response ( β = −0.71, p = 0.039). In the combined model for PRS, only PRS‐BD ( β = 0.31, p = 0.008; better response) and PRS‐MDD ( β = −0.29, p = 0.020; worse response) showed significant associations. Conclusions In this exploratory study, FH‐BD, AUD, and ANX were associated with poor response, though only FH‐AUD remained significant after adjusting for clinical factors. PRS‐BD predicted better response, while PRS‐MDD was associated with worse response. These findings suggest independent and potentially complementary roles of FH and PRS. Integrating both familial and genetic risk measures may improve personalized treatment strategies and outcomes for BD patients.

OBJECTIVE Ruptured, large pediatric arteriovenous malformations (AVMs) pose a significant management challenge due to high rerupture risk and the morbidity of conventional treatments. Volume-staged stereotactic radiosurgery (VS-SRS) has emerged as an alternative; however, evidence for its use in this specific population is limited. This study aimed to evaluate the long-term efficacy and safety of VS-SRS for ruptured, large AVMs in a dedicated pediatric cohort. METHODS This international, multicenter, retrospective cohort study from 21 centers analyzed outcomes for 42 pediatric patients (age < 18 years) with previously ruptured, large AVMs treated with VS-SRS. The primary outcome was complete AVM obliteration, and secondary outcomes included post-SRS hemorrhage, radiation-induced changes (RICs), and favorable outcome. Favorable outcome in this study was defined as obliteration without post-SRS hemorrhage or permanent RIC. RESULTS At the initial SRS, the median patient age was 14.5 years, and the median AVM volume was 15.0 cm 3 ; most AVMs were high grade (Spetzler-Martin grades IV and V). With a median follow-up of 41.5 months, complete AVM obliteration was achieved in 21 patients (50.0%). The cumulative obliteration rate was 37% at 5 years and 56% at 10 years. Patients with a nidus volume of ≤ 10 cm 3 had significantly higher cumulative obliteration rates than those with a nidus volume > 10 cm 3 (log-rank test, p = 0.021). Similarly, patients treated with a prescription dose > 17 Gy showed significantly higher cumulative obliteration rates compared to those treated with a dose ≤ 17 Gy (log-rank test, p = 0.012). The cumulative 5-year incidences of hemorrhage and RICs following VS-SRS were 19% and 7%, respectively. In multivariable analysis, only larger total AVM volume was an independent predictor of a lower likelihood of favorable outcome (hazard ratio 0.89, 95% CI 0.80–0.99; p = 0.036). CONCLUSIONS In this multicenter pediatric cohort, VS-SRS for ruptured, large AVMs provides a reasonable chance of long-term obliteration, but generally acceptable risks of post-SRS hemorrhage and RICs exist. VS-SRS should be considered as an option for pediatric patients with large, ruptured AVMs.

Background: Prior research has revealed gender disparities in the financial relationships between industry and orthopaedic surgeons. Despite attempts to level the playing field, it remains unclear whether these efforts have reduced or eliminated gender disparities in financial relationships between industry and orthopaedic surgeons. Methods: The Centers for Medicare and Medicaid Services (CMS) Open Payments database was queried from January 1, 2024, to December 31, 2024 for orthopaedic surgeons receiving “consulting fee” or “royalty or license” payments of $1,000 or more. Surgeon characteristics were recorded from the CMS database and public sources. Total payment amounts and number of payments were calculated. Characteristics were compared by gender between those receiving any industry payment, consulting payment, and royalty payment. Multivariable linear regression analyses were used to identify factors associated with payment amount and number of payments. Results were compared with a study using similar data from 2016 to 2017. Results: A total of 3,714 surgeons received industry payments, with 3,514 (95%) being men. Of those receiving royalty payments (n = 1,374), 1.5% (n = 21) were women. Of those receiving consulting payments (n = 3,122), 6.2% (n = 194) were women. Men (β = 1.57 [95% confidence interval [CI]: 0.41-2.73, p = 0.008), years of experience (β = 0.09 [95% CI: 0.07-0.12], p < 0.001), and adult reconstruction (β = 1.77 [95% CI: 0.76-2.79], p = 0.001) were associated with a greater total number of payments. Hand surgery (β = −2.57 [95% CI: 3.97 to −1.17], p < 0.001), pediatrics (β = −2.62 [95% CI: 4.60 to −0.64], p = 0.01), and sports (β = −1.12 [95% CI: 2.08 to −0.16], p = 0.02) were associated with the fewer total number of payments. Men (β = 0.34 [95% CI: 0.10-0.57], p = 0.004), years of experience (β = 0.051 [95% CI: 0.046-0.056], p < 0.001), and adult reconstruction (β = 0.47 [95% CI: 0.27-0.68], p < 0.001) were associated with greater payment amounts, while hand (β = −0.96 [95% CI: 1.24 to −0.68], p < 0.001) and pediatrics (β = −0.79 [95% CI: 1.18 to −0.39], p < 0.001) were associated with lower payment amounts. When examining trends over time, men received 1.57 (2024) vs. 5.17 (2016-2017) additional payments and 40% (2024) vs. 197% (2016-2017) larger payments compared with women. Conclusions: Gender disparities in financial relationships (i.e., number of payments and payment amounts) between industry and orthopaedic surgeons persist, though are improved compared with prior research from about a decade ago.

Imaging-based, single-cell, spatial transcriptomics (iSCST) of FFPE tissue enables comprehensive analysis of archived specimens while preserving spatial context, critical to an understanding of ulcerative colitis (UC) pathology. Here, we deployed a robust framework for applying iSCST to clinical FFPE mucosal biopsies from patients with UC or immune checkpoint inhibitor–induced colitis, as well as patients serving as healthy controls. iSCST using custom Xenium gene panels enabled precise detection of diverse cell subsets and disease-specific genes. We mapped transcriptionally distinct fibroblast subsets within mucosal niches, including inflammation-associated fibroblasts (IAFs), and identified colitis-specific neighborhoods formed by IAFs, monocytes, and neutrophils. Transcriptional signatures and spatial neighborhoods uncovered through iSCST were associated with vedolizumab (VDZ) response, with nonresponders exhibiting either an innate IAF-monocyte-neutrophil signature or adaptive gut-associated lymphoid tissue signature, while responders showed enrichment of an epithelial cellular neighborhood. These signatures were validated in an internal and an external dataset, supporting the existence of 2 distinct archetypes of treatment resistance to VDZ in UC. This iSCST framework provides a powerful approach for analyzing FFPE tissues, offering insights into colitis-associated cellular networks and identifying biomarkers to enhance patient risk stratification in routine clinical workflows.