Citations

CTNNB1- mutated hepatocellular carcinomas are characterized by a distinctive morphology and activation of the Wnt pathway. AXIN1 also plays a key role in the Wnt pathway, but the morphology of AXIN1-mutated tumors has not been examined. In addition, there are ongoing questions on the ability of AXIN1 mutations to activate the Wnt pathway in hepatocellular carcinoma. AXIN1 mutated tumors (N=18) were studied, along with control groups: CTNNB1 (N=17), APC (6), or “Other” genes in the Wnt pathway (5). Wnt pathway activation was studied by immunostains for beta-catenin and glutamine synthetase. Findings were supplemented by gene expression analysis using TCGA data. On histologic examination, the classic morphology associated with beta-catenin mutations was found in all 4 groups: 8/18 AXIN1 (44%), 10/17 CTNNB1 (59%), 4/6 APC (67%), and 1/5 Other (20%). By immunohistochemistry, Wnt pathway activation was found in 11/18 AXIN1 (61%), 15/17 CTTNB1 (88%), 6/6 APC (100%), and 5/5 (100%) of Other. In AXIN1- mutated tumors, the Wnt pathway was weakly activated. Glutamine synthetase stains also highlighted a new “progressed pattern” associated with distinct subnodules of staining. Tertiary lymphoid structures were uncommon except for cases with CTTNNB1 mutations plus additional mutations in the Wnt pathway. In summary, the classic morphology associated with CTNNB1 mutations is found in hepatocellular carcinomas with mutations in AXIN1 , APC , and other Wnt genes. AXIN1 mutated tumors have Wnt activation that is detectable but at lower levels than CTNNB1 mutated tumors. As tumors progress, their level of Wnt activation can change.

Abstract Purpose To develop and implement an in‑house treatment‑management platform to improve communication, data accessibility, and task tracking across a radiation oncology department. Methods We developed Capsule, a custom software platform that centralizes patient data from the ARIA oncology information system (Varian Medical Systems, Palo Alto, United States), and integrates it with data from Outlook (Microsoft, Redmond, United States), spreadsheets, and manual inputs. Built using C#, SQL, and the .NET framework, it features a graphical interface in XAML with twelve modular tools to support workflows across a large multi‐modality department. A formal validation, feedback, and maintenance cycle was established. Its impact on workflow efficiency was evaluated using average CarePath task duration before and after implementation, supplemented by user interviews and surveys. Results The Department Schedule, Treatment Planning and On‐Treatment Whiteboard, Spreadsheet, and various workload tracking modules emerged as the most frequently used among dosimetrists surveyed.The Whiteboard module consolidates planning metadata for streamlined workflows. Workload and scheduling modules help distribute tasks fairly. The Department Schedule module centralizes daily role and contact information. The Spreadsheet module allows dosimetrists to identify a reference for unique plans and provides secure access to large exportable tabulated patient data and clinical trends. Other modules support proton down‐time replanning and special procedure physics assignment. One year after Capsule's implementation, most tasks in the external beam radiation therapy workflow experienced a reduction in average duration. Discussion Capsule streamlines workflows by integrating multiple tools into a single platform, supporting urgent scenarios, and improving task hand‐off. Its modular design allows customization to local workflows, but implementation must be guided by institutional oversight and formal quality management to ensure data security and compliance. Conclusion Capsule can provide a solution for improved workflow efficiency, communication, and interdisciplinary coordination. By democratizing access to routine and retrospective data, it supports treatment planning, scheduling, and quality improvement. Ongoing updates are essential as clinical practices evolve.

Abstract INTRODUCTION Multicomponent non‐pharmacological interventions, such as the HABIT Healthy Action to Benefit Independence & Thinking® program, have shown promise for improving emotional well‐being, functioning, and self‐efficacy in both people with mild cognitive impairment (pwMCI) and their care partners. However, limited research has examined whether outcomes differ based on type of care partner who co‐enrolls with the pwMCI. METHODS We analyzed data from 875 pwMCI−care partner dyads who completed the HABIT program. Multivariate linear mixed‐effects regression models evaluated changes from baseline in outcomes by care partner type (spouse vs. non‐spouse) at post‐intervention, 6‐months, and 12‐ months. RESULTS At post‐intervention, pwMCI with spousal care partners demonstrated significant improvements across all outcomes from baseline, whereas those with non‐spousal partners improved in five out of seven outcomes. Across subsequent follow‐ups, pwMCI with spousal care partners showed sustained mean reductions in anxiety (post‐intervention: −2.0; 12‐month: −0.9) and increased compliance (post‐intervention: 5.7; 12‐month: 0.9). Everyday functioning in memory (post‐intervention: −0.9; 12‐months: 1.8) and executive functioning (post‐intervention: −0.7; 12‐months: 4.8) improved at post‐intervention but declined thereafter. Spousal care partners reported reduced anxiety (−1.2) and depression (−1.3) at post‐intervention, but by 12‐months they experienced increased burden, anxiety, and depression relative to baseline. Non‐spousal pwMCI–care partner dyads did not show significant change at 6 or 12‐months. DISCUSSION The HABIT intervention may have more favorable outcomes for pwMCI and care partners in spousal compared to non‐spousal dyads. These findings highlight the potential impact of care partner characteristics on both immediate and long‐term response to behavioral interventions for MCI. Highlights At post‐intervention, people with mild cognitive impairment (pwMCI) with spousal care partners showed significant improvements across all outcomes, while those with non‐spousal partners improved in five of seven outcomes. Gains attenuated over time for both spousal and non‐spousal dyads Spousal care partners reported reduced anxiety and depression at post‐intervention, but by 12 months they experienced increased burden, anxiety, and depression relative to baseline. Non‐spousal pwMCI–care partner dyads did not show significant change at 6 or 12 months.

ABSTRACT Systemic AL amyloidosis is a clonal plasma cell dyscrasia, where toxic free light chains (FLCs) lead to vital organ damage, with risk of early mortality despite effective therapies. Notably, there is a paucity of data on the incidence and predictors of early mortality in the daratumumab (Dara) era. We conducted a multi‐center retrospective cohort study to develop and validate a risk‐prediction model for early mortality (all‐cause death within 6 months) in patients treated with Dara‐based frontline regimens. We used univariate analysis to identify covariates for a logistic regression model predictive of 6‐month mortality and generated a clinical risk score. Among 339 patients across eight centers, 36 (10.6%) experienced early mortality. Multivariate analysis identified four independent predictors: NT‐proBN P > 5300 pg/mL (odds ratio [OR] 3.83, p = 0.006), A ge > 75 years (OR 2.96, p = 0.073), absence of t(11;14) on C ytogenetics (OR 3.0, p = 0.022), and E COG performance status (OR 1.72 per unit increase, p = 0.044). The resulting PACE score stratified patients into three risk groups with a 6‐month mortality of 1.2% (low‐risk), 11.7% (intermediate‐risk), and 50.0% (high‐risk). The PACE score demonstrated superior discrimination (C‐index 0.78) for early mortality compared to Mayo 2004 (0.69) and Mayo 2012 (0.55) staging systems. External validation confirmed robust discrimination (C‐index 0.73) with corresponding 6‐month mortality rates of 4.2%, 15.25%, and 37.04% across respective risk groups. The PACE score provides a clinically applicable tool for early mortality risk stratification in AL amyloidosis patients receiving Dara‐based therapy, and will enable improved patient counseling, clinical trial design, and identification of ultra‐high‐risk patients requiring intensive monitoring and novel therapies.

ABSTRACT Background Sunlight has profound impacts on physical and mental health, beyond vision, including effects on circadian rhythms, alertness, mood, and sleep. A family history of any mood disorders is strongly associated with psychiatric disorders including bipolar disorder. The purpose of this study was to evaluate the association between a family history of any mood disorder in patients with bipolar I disorder and solar insolation at varied international onset locations. Methods Data for this analysis were available from 5842 patients with a diagnosis of bipolar I disorder obtained at 83 collection sites in both hemispheres. This included 4752 patients from 71 collection sites in the northern hemisphere and 1090 patients from 12 collection sites in the southern hemisphere. Patient data variables were obtained from records or interviews. Solar insolation data were obtained from The National Aeronautics and Space Administration (NASA) Power database for each onset location, and the ratio of the mean monthly minimum/mean monthly maximum solar insolation was calculated. Typically, the ratio is largest near the equator (little yearly change in solar insolation) and smallest near the poles (large yearly change in solar insolation). Results A significant relationship was found between a family history of any mood disorder, the ratio of the mean monthly minimum/mean monthly maximum solar insolation, and gender. The odds of a family history of mood disorder increased as patient location nears the poles and decreased near the equator. Female gender also increased the odds of having a family history of a mood disorder. Conclusions This study highlighted the association between family history, solar insolation, and gender in international patients with bipolar I disorder. Given the profound effects of sunlight on human health, the family of patients with bipolar disorder who live in the same location with the same solar insolation, and especially females, may be at increased risk for a mood disorder.

ABSTRACT Background Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diastolic function (DF) and filling pressure (FP) is often infeasible. We evaluated whether artificial intelligence–enabled electrocardiography (AI-ECG) could provide scalable DF grading and FP estimation in hospitalized AHF patients. Methods We retrospectively studied adults hospitalized for AHF across Mayo Clinic sites (2013–2023) who received ≥1 dose of intravenous loop diuretic and had paired 12-lead ECG and TTE. The previously validated AI-ECG DF model was applied without retraining to generate four DF grades and a continuous FP probability. Clinical outcomes were all-cause mortality and heart failure rehospitalization. Associations with clinical severity markers and echocardiographic indices were examined. Kaplan–Meier survival analysis and adjusted multivariable Cox proportional hazards models were performed. Exploratory analyses examine the kinetics of change in FP probability and impact on mortality. Results Among 11,513 patients (median age 75 years, 39% female), AI-ECG DF grading was feasible in 100%, whereas echocardiographic DF was indeterminate in 44% of clinically eligible patients. In 2,582 patients with determinate echocardiographic DF, AI-ECG FP probability discriminated TTE Grade 2–3 dysfunction with AUC 0.85 (95% CI 0.83 – 0.86). Higher AI-ECG DF grades were associated with higher comorbidity burden, worse NYHA class, elevated NT-proBNP, higher MAGGIC scores, elevated PCWP, and more advanced structural remodeling. After multivariable adjustment, AI-ECG DF remained independently associated with mortality (hazard ratio [HR] 1.25, 95% CI 1.16–1.35 for Grade 2; HR 1.44, 95% CI 1.33–1.56 for Grade 3 versus Normal/Grade 1). Combining AI-ECG DF with MAGGIC scores yielded ordered risk gradients, with highest mortality in patients with both high MAGGIC and Grade 2–3 DF. Among patients with serial ECGs, improvement in FP probability was independently associated with lower mortality (HR 0.85, 95% CI 0.79–0.91), whereas worsening did not show a consistent adverse gradient beyond baseline DF. Conclusions In a large, geographically diverse AHF cohort, AI-ECG DF grading was universally feasible, correlated with established hemodynamic severity markers, and provided independent prognostic information beyond established risk factors, supporting its role as a pragmatic, scalable diastolic biomarker in AHF. CLINICAL PERSPECTIVE What Is New? In 11,513 hospitalized acute heart failure (HF) patients, artificial intelligence-enabled electrocardiography provided diastolic function grading in 100% of patients from a single 12-lead ECG without requiring additional clinical variables, compared with 56% feasibility for guideline-based echocardiography grading. AI-ECG diastolic function grades correlated with established marker of severity (NYHA functional class, NT-proBNP, MAGGIC risk scores, and pulmonary capillary wedge pressure) and remained independently associated with both mortality and HF rehospitalization after multivariable adjustment. Serial AI-ECG measurements identified post-discharge filling pressure trajectories, with improvement independently associated with 15% lower mortality, a first demonstration that longitudinal ECG assessment can track post-discharge hemodynamic recovery. What Are the Clinical Implications? AI-ECG transforms the universally obtained 12-lead ECG into an actionable hemodynamic biomarker that addresses the critical gap when echocardiographic diastolic function assessment is indeterminate or unavailable in acute HF patients. Despite markedly different hemodynamic severity and long-term outcomes across AI-ECG diastolic function grades, hospitalization length of stay did not differ, suggesting advanced diastolic dysfunction represents occult risk not easily recognized during routine acute care and highlighting the need for improved post-discharge risk stratification. The continuous filling pressure probability metric enables longitudinal monitoring of post-discharge hemodynamic status using serial routine ECGs, potentially identifying patients requiring intensified follow-up or specialist referral.

Abstract Background: Clostridioides difficile infection (CDI) requiring colectomy carries substantial mortality risk, with optimal timing of surgery remaining poorly defined. We examined temporal trends in colectomy among inpatients with CDI, identified predictors of surgical intervention and postoperative mortality, and evaluated the association between surgical timing and patient outcomes. Methods: A retrospective cohort study was conducted using the National Inpatient Sample database from 2018 to 2022. We compared patients undergoing colectomy with those managed medically. To minimize confounding by hospital-onset cases, the analysis of surgical timing and mortality was restricted to patients undergoing colectomy within 8 days of admission. Predictors were identified using survey-weighted logistic regression and LASSO regression models. Results: Among 240,564 CDI hospitalizations (representing 1,207,995 weighted nationally), 717 patients underwent colectomy (3,585 weighted). CDI prevalence declined from 0.99% (2018) to 0.76% (2022), while colectomy rates increased from 0.28% to 0.34%. Peritonitis (OR 5.42; 95% CI, 4.46–6.59), coagulopathy (OR 4.96; 95% CI, 3.76–6.55), and sepsis/septic shock (OR 3.89; 95% CI, 3.39–4.47) were the strongest predictors of colectomy. Among patients undergoing colectomy within 8 days (2,830 weighted), in-hospital mortality was 26.5% overall, increasing from 21.0% (2018) to 30.7% (2022). Sepsis/septic shock (OR 8.20; 95% CI 2.92–23.07) and coagulopathy (OR 7.27; 95% CI 3.31–15.97) predicted mortality. Each additional day from admission to colectomy was associated with a 16% (OR 1.16; 95% CI 1.04–1.28) increased mortality risk. Conclusions: In this nationally representative cohort, surgical timing was an independent and modifiable determinant of survival in patients with CDI requiring colectomy. Our findings underscore the importance of early surgical consultation for CDI patients with peritonitis, sepsis, and coagulopathy.

Abstract Background Ceftriaxone-resistant Escherichia coli infections are increasingly common, partially because of the emergence of E. coli sequence type 131 (ST131), including its subclade C2/H30Rx, which produce extended-spectrum β-lactamases. Methods A prospective cohort including 14 US sites, which enrolled monomicrobial ceftriaxone-resistant and susceptible E. coli bloodstream infection (BSI) cases in a 1:1 ratio, was used to compare ST131 versus non-ST131 E. coli BSI, with specific attention to E. coli ST131 C2/H30Rx. Desirability of outcome ranking was determined at 30 days after infection onset. Results This analysis included 282 patients with E. coli BSI; 43% (121/282) were E. coli ST131 and 23% (66/282) belonged to the C2/H30Rx subclade. Resistance to ceftriaxone was present in 79% (96/121) ST131, 86% (57/66) E. coli ST131 C2/H30Rx, and 27% (43/161) E. coli non-ST131. Compared to patients with non-ST131 E. coli BSI, patients with ST131 BSI were older (median 70 years, [Q1 62, Q3 76] years vs 65 years, [51, 74]; P = .005) and more often admitted from long-term care facilities (21/121 [17%] vs 7/161 [4%], P < .001). Overall and empiric carbapenem use was more frequent in the treatment of patients with ST131 BSI compared with non-ST131 BSI (overall 89/121 [74%] vs 50/161 [31%]; empiric: 58/121 [48%] vs 31/161 [19%], P < .001). Desirability of outcome ranking outcomes were similar between groups. Conclusions Most ceftriaxone-resistant E. coli from US patients with E. coli BSI belong to ST131, particularly E. coli ST131 C2/H30Rx, serving as an important driver of carbapenem use.