Citations

Abstract Purpose: Knowledge of prognostic factors and long-term survival in patients with diffuse hemispheric glioma, H3 G34–mutant (DHG, H3 G34), remains limited in this tumor with a poor prognosis. Experimental Design: This retrospective, multi-institutional study investigated prognostic variables for patients with DHG, H3 G34, and their association with progression-free survival (PFS) and overall survival (OS). Uni- and multivariable Cox proportional hazard models were applied with multiple imputed datasets. Results: A total of 153 patients (142 G34R, 9 G34V, 2 via DNA methylation) were included. The median age at diagnosis was 17 years (range, 2–45). Initial gross/near total resection (GTR/NTR) was achieved in 43% of patients. Radiation was given in 91% (85% focal irradiation), and initial chemotherapy was given in 87% [70% temozolomide-based (TMZ), 25% TMZ/lomustine, 5% non-TMZ]. Median OS was 24 months [interquartile range (IQR), 22–28] with a median PFS of 14 months (IQR, 12–19). Twelve patients (8%) were found to be long-term survivors (≥5 years). Exploratory multivariable analysis showed that adjuvant radiotherapy [HR, 0.076; 95% confidence interval (CI), 0.033–0.17] and achieving GTR/NTR compared with < NTR (HR, 0.51; 95% CI, 0.33–0.78) were associated with improved PFS. Multivariable analysis showed improved OS with increasing age at diagnosis (HR, 0.70; 95% CI, 0.57–0.87), initial radiotherapy (HR, 0.38; 95% CI, 0.15–0.96), and initial GTR/NTR compared with < NTR (HR, 0.60; 95% CI, 0.37–0.97). Conclusions: This cohort highlights prognostic factors for patients with DHG, H3 G34, and describes relapse patterns and therapy approaches. Clinical trials and prospective registries are needed to improve outcomes.

Purpose: Delirium is a serious underrecognized health condition in hospitalized older adults. The current qualitative study used dimensional analysis to increase understanding of the clinical reasoning processes used by acute care nurses to assess for delirium. Method: Ten focus groups with experienced medical, surgical, and intensive care nurses ( N = 39) from quaternary, community, and academic medical centers discussed their application of clinical reasoning processes to assess/detect delirium symptoms. Transcripts were inductively analyzed. Trustworthiness was checked with a subset of participants ( n = 12). Results: Findings illustrate how nurses apply clinical reasoning to recognize and respond to delirium symptoms and navigate clinical and social structures to maintain patient safety. Clinical context and social structures are important conditions influencing nurse reasoning and judgment. Conclusion: Further process improvement and research efforts should explore strategies to enhance interdisciplinary shared knowledge within a supportive environment to improve the detection and care to lessen the severity and duration of delirium for vulnerable patients.

Background: Reoperative metabolic and bariatric surgery (MBS), consisting of revision and conversion surgery, carries higher morbidity and mortality compared with primary procedures, yet robust long-term data are limited. Methods: A retrospective review was conducted of all patients 18 years of age or older who underwent first-time reoperative MBS between 2000 and 2024 at a single large quaternary referral center. Data were stored securely and analyzed in R (v4.4.2) using standard descriptive statistics and group tests ( t tests/Wilcoxon/Kruskal-Wallis; χ²/Fisher). Sparse late-complication data precluded pairwise tests. Results: Three hundred sixty patients, 86% female, mean age 50 years, underwent reoperative MBS. Conversion to Roux-en-Y gastric bypass (49%) was the most common surgery. The majority were performed for weight recidivism (37%). Length of stay, estimated blood loss, length of surgery, complication rates, readmission rates, and reintervention rates varied by type of reoperative MBS performed and indication. Most early complications were Clavien-Dindo 2, and most late complications were Clavien-Dindo 3b for all approaches. There was one procedure-related death in the study following an open conversion. Conclusion: In this 24-year single-center cohort, weight recidivism was the most frequent indication for reoperation and was associated with improved perioperative outcomes and durable long-term weight loss. As the landscape of bariatric surgery continues to evolve, we recommend early referral to centers with reoperative MBS expertise.

ABSTRACT Background Diabetic ketoacidosis (DKA) is a severe diabetes complication managed with fluids, insulin and electrolyte correction. This review evaluates the debated effectiveness of bicarbonate therapy on DKA outcomes. Methods Following PRISMA guidelines, we systematically reviewed studies on bicarbonate therapy in DKA. We searched PubMed, Google Scholar, the Cochrane Library and ClinicalTrials.gov (as of August 2024), including studies on patients with DKA. Meta‐analysis was conducted using RevMan. Bias risk was assessed with the Newcastle‐Ottawa Scale (NOS) for cohort studies, Cochrane's ROB‐2 for RCTs and the Joanna Briggs Institute (JBI) Scale for case series. Prospero ID: CRD42024581810. Results Eight studies, including 646 patients, met the inclusion criteria. Participants' mean ages spanned from approximately 9.7 years to 45.8 years. Bicarbonate therapy did not significantly improve pH levels (mean difference = −0.02, 95% CI [−0.13, 0.09], p = 0.7), time to resolution of acidosis (mean difference = 0.09 h, 95% CI [−2.6, 2.79], p = 0.95) or potassium levels (mean difference = −0.10, 95% CI [−0.49, 0.29], p = 0.61). Bicarbonate therapy was associated with a marginally longer duration of hospital stay (mean difference = 13.63 h, 95% CI [0.23, 27.03], p = 0.05), although the overall effect size was small. No significant difference was observed in the incidence of hypoglycemia (odds ratio = 2.62, 95% CI [0.59, 11.63], p = 0.20). High heterogeneity was observed across most outcomes, mainly due to variability in study protocols and patient populations. Conclusion Bicarbonate therapy provided no meaningful clinical benefit in the routine management of DKA and was associated with potential harm, including worsened hyperglycemia. Consistent with current guidelines, its use should be restricted to cases of severe acidemia (pH < 6.9). Further high‐quality studies are needed to clarify its role in extreme acidosis and to explore alternative buffering strategies.

ABSTRACT Imaging mass spectrometry has made significant advancements in recent years owing to its ability to study the spatial localization and abundance of proteins, peptides, lipids, glycans, metabolites, and drugs. In this study, we employed an imaging mass spectrometry‐based workflow called MALDI‐IHC, which uses antibodies conjugated with photocleavable mass‐tags, to investigate the detection of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) along with pan‐cytokeratin in formalin‐fixed paraffin‐embedded (FFPE) sections from cases with breast cancer. The expression of ER, PR, and HER2 was consistent with the clinical diagnosis based on conventional immunohistochemistry, lacking signal in triple‐negative cases and concordant signals in the cases that were classified as ER+/PR+/HER2+, ER+/PR+/HER2−, and ER−/PR−/HER2+ groups, respectively. In addition, the potential utility of a seconday antibody‐based MALDI‐IHC approach for HER2 expression was evaluated. In agreement with immunohistochemistry results, HER2 expression co‐localized within tumor regions in a HER2+ breast cancer case, while no expression was observed in a HER2− case. Collectively, these results highlight the potential of imaging mass spectrometry‐based MALDI‐IHC workflow for multiplexed detection of proteins in clinically relevant tissue sections.

ABSTRACT Cohort studies are vulnerable to confounding and time‐related biases. Recent advances emphasize the importance of emulating a hypothetical target trial to minimize several of these biases. The target trial framework is a critical tool for designing cohort studies. It requires explicit specification of protocol components: eligibility criteria, treatment strategies, outcomes, timing of follow‐up (time zero), and the causal contrast to be estimated. Critical to this framework is defining “time zero” when treatment assignment takes place and follow‐up begins, which prevents prevalent user and immortal time bias and ensures proper temporal sequencing of confounders, exposure, and outcome. By guiding design, data collection, and analysis, the target trial framework helps align observational studies with the principles of randomized trials, improving the validity and interpretability of their findings. While it cannot eliminate unmeasured confounding, this approach promotes transparent, bias‐conscious cohort design, minimizes time‐related bias, and supports more robust causal inference with observational data.

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 Rare Mendelian disorders affect 300-400 million people globally. Although genetic testing has become widely adopted, gene-specific evidence for tailored variant interpretation remains scattered across resources. We present Gene Portals, a framework for gene-centered multimodal knowledge bases that co-localize expert-harmonized clinical data, functional assays, population variation, structural annotations and gene-specific ACMG/AMP specifications within a single resource. A modular interface integrates this unified evidence with VCEP-refined ACMG specifications to enable automated gene-specific variant classification, infer molecular mechanisms, and support cross-gene analyses. We demonstrate the framework’s utility across five Gene portals spanning eleven neurodevelopmental disorder-associated genes, integrating data from 4,423 individuals with 2,838 unique variants, 36,149 ClinVar submissions, and 1,044 expert-curated molecular readouts. By organizing evidence that is otherwise dispersed across multiple sources into a unified, queryable framework, the SCN, GRIN, CACNA1A, SATB2 and SLC6A1 Gene Portals became widely used community resources and provide an extensible template for standardized rare-disease variant interpretation and mechanism-aware discovery.

Background: Tumor mutational burden (TMB) is an FDA-approved biomarker for immune checkpoint inhibitor (ICI) therapy. However, its predictive value varies among tumor types and molecular contexts. We investigated whether a very high TMB identifies a biologically distinct subset and whether a higher cutoff provides additional clinical insights beyond the conventional high TMB threshold. Methods: We analyzed 133 patients with advanced solid tumors and TMB ≥ 10 mutations/Mb (mut/Mb) who underwent tumor genomic profiling using a 523-gene DNA/RNA next-generation sequencing panel. Tumors were stratified into prespecified TMB categories: 10–20 mut/Mb (TMB-H) and >20 mut/Mb (TMB-VH). The clinical characteristics, ICI outcomes (in the treated subset), and pathway-level genomic features were compared between groups. Results: TMB-VH was observed in 42/133 (31.6%) patients and spanned more than 20 tumor types. MSI was markedly more prevalent in TMB-VH than in TMB-H tumors (38.1% vs. 2.2%; Fisher’s exact p = 8.9 × 10−8). Pathway-level comparisons did not identify statistically significant differences after false discovery rate correction (all q > 0.05), and the observed patterns were descriptive in nature. In the ICI-treated subset with complete follow-up, objective response did not differ according to the TMB group. Overall survival (OS) was also similar between groups, whether measured from metastatic diagnosis (log-rank p = 0.937) or from ICI initiation (log-rank p = 0.814), although OS was numerically longer in the TMB-VH group in both analyses without reaching statistical significance. Conclusions: In this cohort study, TMB-VH was strongly associated with MSI but not independently associated with improved ICI outcomes. Larger multicenter cohorts are needed to validate pathway-oriented patterns and clarify the clinical utility of extreme TMB thresholds across various histologies. Integrating the functional context (e.g., MSI status, gene-level context, and pathway-level features) with TMB magnitude may enable more robust, tumor-aware biomarker models for immunotherapy selection.