Citations

Importance Validation of prognostic tools is an essential component of their integration into clinical practice. Objective To validate 4 delirium risk stratification tools in an independent emergency department (ED) cohort. Design, Setting, and Participants This prognostic study included a retrospective cohort of patients 65 years or older who presented to an academic ED between January 1, 2021, and December 31, 2024. Data were analyzed from February 10 to August 21, 2025. Exposure Delirium screening included the Delirium Triage Screen followed by the brief Confusional Assessment Method as part of the standard of care. Four delirium risk scores were obtained, including the Kennedy rule, the Zucchelli rule, the Mayo Delirium Prediction (MDP) tools, and the Recognizing Delirium in Emergency Medicine (REDEEM) score. Main Outcomes and Measures Main outcomes included risk score performances and diagnostic test accuracy measured using the area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, negative (NPV) and positive (PPV) predictive values, and positive and negative likelihood ratios (LRs) with 95% CIs. Secondary outcomes were discriminative and calibration measures. Results A total of 44 578 patients were included, of whom 1701 (3.8%) were diagnosed with delirium. The median age was 80.0 (IQR, 75.0-85.0) years, and 22 786 patients (51.1%) were female. The Kennedy rule had an AUROC of 0.777 (95% CI, 0.766-0.789). At a cutoff of 5, sensitivity was 0.55 (95% CI, 0.52-0.57); specificity, 0.85 (95% CI, 0.84-0.85); positive LR, 3.54 (95% CI, 3.37-3.72); negative LR, 0.54 (95% CI, 0.51-0.57); PPV, 0.12 (95% CI, 0.12-0.13); and NPV, 0.98 (95% CI, 0.98-0.98). The Zucchelli tool had an AUROC of 0.701 (95% CI, 0.686-0.713). At a cutoff of 5, sensitivity was 0.68 (95% CI, 0.66-0.70); specificity, 0.66 (95% CI, 0.65-0.66); positive LR, 1.99 (95% CI, 1.92-2.06); negative LR, 0.49 (95% CI, 0.45-0.52); PPV, 0.07 (95% CI, 0.07-0.08); and NPV, 0.98 (95% CI, 0.98-0.98). The MDP tool had an AUROC of 0.898 (95% CI, 0.891-0.905). At a 30% cutoff, sensitivity was 0.51 (95% CI, 0.48-0.53); specificity, 0.95 (95% CI, 0.95-0.95); positive LR, 9.69 (95% CI, 9.11-10.31); negative LR, 0.52 (95% CI, 0.50-0.55); PPV, 0.28 (95% CI, 0.26-0.29); and NPV, 0.98 (95% CI, 0.98-0.98). The REDEEM score demonstrated the highest AUROC at 0.921 (95% CI, 0.914-0.929). Using a cutoff of 11 or greater, sensitivity was 0.83 (95% CI, 0.81-0.85); specificity, 0.92 (95% CI, 0.91-0.92), negative LR, 0.18 (95% CI, 0.17-0.20); positive LR, 9.91 (95% CI, 9.54-10.29); PPV, 0.28 (95% CI, 0.27-0.29); and NPV, 0.99 (95% CI, 0.99-0.99). Conclusions and Relevance In this prognostic study comparing 4 delirium tools to improve delirium detection and identify high-risk patients, the REDEEM score and the MDP tool had better performance for delirium detection in the ED. These findings lay the groundwork for integrating validated risk stratification into ED workflows to improve early delirium detection and inform prevention strategies for high-risk older adults.

Importance Primary brain and central nervous system cancer (collectively referred to as CNS cancer) comprises 2% of all human cancers and poses significant health and economic challenges in the United States. Objective To analyze CNS cancer burden in the US, stratified by time, location (state and division), sex, age group, and Sociodemographic Index (SDI). Design, Setting, and Participants This cross-sectional study involved a repeated analysis of Global Burden of Disease Study (GBD) 2021 data in 2024. Using data from 183 sources, CNS cancer metrics in the US were estimated across states and years. US CNS cancer metrics across all sexes and age groups were included in the GBD. Exposure CNS cancer diagnosis. Main Outcomes and Measures Overall and age-standardized estimates of the incidence, prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost, and years lived with disability per 100 000 population, including 95% uncertainty intervals (UIs), and time trends. Results In 2021, for all age groups and sexes across the US, there were 31 780 incident cases (95% UI, 29971.1 to 32843.9). Age-standardized incidence, DALYs, and mortality rates per 100 000 population were 6.91 (95% UI, 6.58 to 7.12), 134.38 (95% UI, 129.83 to 137.95), and 4.1 (95% UI, 3.87 to 4.22), respectively. Despite no significant change observed in the overall incidence between 1990 and 2021, DALY and mortality rates decreased by 15.77% (95% UI, −17.75% to −13.68%) and 8.41% (95% UI, −11.09% to −6.22%), respectively. Substantial geographic variability was noted. Mississippi, Alabama, Kentucky, and Kansas (West North Central and East South Central divisions) and West Virginia faced persistently high burdens over the past 30 years. Sex differences were evident; disease burden was consistently higher in males compared with females. Age-specific estimates showed a bimodal distribution: the youngest group (<5 years) showed a significant decrease in incidence rate (−34.42% to −11.56%), whereas older age groups (>70 years) experienced increasing trends. DALYs and mortality rates were negatively correlated with SDI (ρ = −0.6860 and ρ = −0.6391; P < .001). Conclusions and Relevance These findings provide valuable insights into the CNS cancer burden across the US by age, sex, location, and SDI, enabling better public health status assessments, health care policy restructuring, and resource redistribution for improved care.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

Background: Atrial fibrillation (AF) significantly impacts quality of life (QoL) and is often observed in patients with transthyretin amyloid cardiomyopathy (ATTR-CM). Acoramidis achieves near-complete (≥90%) transthyretin (TTR) stabilization and is approved in the USA, the UK, Europe and Japan for the treatment of ATTR-CM in adults. In the phase 3 ATTRibute-CM study (NCT03860935), acoramidis treatment significantly reduced the time to all-cause mortality (ACM) or first cardiovascular-related hospitalization (CVH) at Month 30, and resulted in improved 6-minute walk distance (6MWD), Kansas City Cardiomyopathy Questionnaire-Overall Summary (KCCQ-OS) scores and N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels relative to placebo. The clinical efficacy of acoramidis has not yet been reported in patients with ATTR-CM and with an AF or atrial flutter (AFL) diagnosis at baseline. Research Question: How does acoramidis treatment affect ACM or first CVH, 6MWD, KCCQ-OS scores, and NT-proBNP levels at Month 30 in participants with ATTR-CM with or without AF/AFL diagnosis at baseline? Methods: The ATTRibute-CM study design has been previously published. This post hoc analysis included participants in the modified intention-to-treat population (N=611) grouped by AF/AFL diagnosis at baseline (defined as recorded AF medical history or the presence of AF or AFL on an ECG at enrollment). The rate of ACM or first CVH at Month 30 was compared between groups using a stratified Cox proportional hazards model. Changes from baseline to Month 30 in 6MWD, KCCQ-OS scores and NT-proBNP levels were summarized descriptively. Results: Overall, 62.8% (384/611) of participants had an AF/AFL diagnosis at baseline (acoramidis: 255/409, placebo: 129/202). These participants had lower mean 6MWD and KCCQ-OS scores and higher median NT-proBNP levels than those without AF/AFL diagnosis at baseline. Treatment with acoramidis reduced ACM or first CVH, slowed the decline in 6MWD and KCCQ-OS scores and blunted the rise in NT-proBNP compared with placebo, regardless of AF/AFL diagnosis at baseline ( Table ). Conclusions: Acoramidis improved clinical outcomes (ACM, CVH), functional status, QoL and NT-proBNP levels relative to placebo in participants with ATTR-CM, regardless of AF/AFL diagnosis at baseline.

Background: Adults with congenital heart disease (CHD) face ongoing physical, emotional, and psychosocial challenges that impact long-term outcomes. Research is limited on how their health topic interests vary by levels of empowerment. The 15-item Gothenburg Empowerment Scale (GES) assesses CHD patient empowerment across five domains. Objective: Examine the association between patient empowerment levels and patient-prioritized health education topics among adults with CHD. Methods: This cross-sectional study included adults (≥18) with self-reported CHD enrolled in the Empower My Congenital Heart digital study. Participants completed the GES survey and responded to open-ended questions regarding information of interest to them in managing their healthcare. GES scores were divided into tertiles, and responses were grouped into patient-prioritized topics. Descriptive analysis examined associations between empowerment levels and topic preferences. Results: Of 292 eligible patients, mean age was 45.6 ± 14.7 years, 91.4% were Caucasian, and 66.4% were female. The median (25th, 75th) GES score was 62 (57, 68). The most frequently observed patient prioritized topic was CHD health knowledge (73%), followed by interest in building CHD community (52.7%), lifestyle management (23.6%), health care navigation (7.2%), research and representation (5.1%). Participants in the lower and middle GES tertiles more frequently expressed interest in foundational sub-topics such as understanding CHD and surgical history, finding ACHD specialists, exercise and nutrition. In contrast, participants in the highest GES tertile prioritized sub-topics related to aging with CHD, managing menopause, CHD research and legislative activities. Peer support was a common topic across all groups (Figure 1). Patient quotes were stratified by tertile and patient prioritized health topics and subtopics as summarized in table 1. Conclusion: Adults with CHD prioritized varied health education topics depending on their baseline empowerment levels. Those with lower GES scores prioritized foundational information and guidance, while those with higher scores prioritized community-oriented and research-related topics. These findings could help tailor CHD patient-centered education strategies based on individual empowerment profiles.

Background: Ventricular tachycardia (VT) is a major contributor to death among ischemic heart disease (IHD) patients. In this systematic review and meta-analysis, we evaluate the efficacy of catheter ablation in patients with ischemic cardiomyopathy. Methods: We conducted a meta-analysis by searching the electronic database up to April 2025. Seven studies, including 1192 patients, were identified. A random-effect model was used to estimate pooled risk ratios (RRs) and 95% confidence intervals (CIs). Trial sequential analysis was conducted to assess sample size adequacy. Results: Among Ischemic heart disease patients with ICDs, VT ablation was associated with a significant reduction in ICD shocks (RR = 0.50, 95% CI [0.34–0.74]), a trend toward reduced VT storm (RR = 0.64, 95% CI [0.40–1.04], p = 0.07), and lower rates of cardiovascular hospitalization (RR = 0.73, 95% CI [0.53–1.01], p = 0.06) compared to ICD alone. However, no significant differences were observed when compared to patients receiving ICD plus antiarrhythmic drugs (AADs). Additionally, VT ablation showed no significant impact on mortality or VT/VF recurrence compared to either ICD alone or ICD with AADs. Trial Sequential Analysis provided conclusive evidence for VT/VF recurrence outcomes, while further data are required for other outcomes. Conclusion: Among ischemic heart disease patients with ICD, VT ablation reduces ICD shocks, VT storm, and cardiovascular hospitalizations compared to ICD-only, but offers no significant advantage over ICD combined with antiarrhythmic drugs. VT ablation does not impact mortality or the recurrence of VT/VF. Trial Sequential Analysis confirmed conclusive evidence for VT/VF recurrence, while additional data is needed for other outcomes.

Background: Atrial fibrillation (AF) is the most common sustained arrhythmia with prevalence increasing over the last decade. However, data on recent temporal trends in incidence of AF have been inconsistent, with some reports of increasing trends over time and others indicating no change in incidence over time. Therefore, we aimed to determine how the incidence of AF has changed over the last decade in a community in the Midwest USA. Methods: Between 2013 and 2023, adults (aged 18 and older) with incident AF were identified in a 27-county region in the Midwest. AF was identified using diagnostic codes by employing an electronic algorithm requiring 1 inpatient code or 2 outpatient codes separated by >7 days but within 1 year. For total rates, age - and sex-adjusted incidence rates were standardized to the 2010 US total population. Poisson regression was used to calculate the incidence rate ratio of AF for 2023 vs 2013. Results: We identified 44,930 individuals with incident AF from 2013 to 2023. Of these, 56.4% were male, the mean (SD) age was 72.8 (13.5) years, and 95.8% were non-Hispanic white. The age-and sex-adjusted incidence rates (per 1,000) and 95% confidence intervals standardized to the 2010 US total population were 5.24 (5.07-5.41) in 2013, 5.73 (5.56-5.91) in 2018, and 5.44 (5.28-5.61) in 2023. Although a drop in incidence was observed corresponding to the COVID-19 pandemic, a small increase in the overall incidence of AF was observed over time. The overall incidence rate ratio (95% CI) for 2023 vs 2013 was 1.07 (1.02-1.11), P=0.003. The incidence rates were higher in men compared to women and increased with older age ( Figure 1 ). However, differences in AF incidence by sex were greatest in the youngest age group and attenuated with older age (incidence rate ratio (95% CI): 2.18 (2.10-2.28) for ages 18-64 years, 1.76 (1.70-1.83) for ages 65-74 years, 1.38 (1.33-1.42) for ages 75-84 years, and 1.23 (1.18-1.28) for ages 85 years and older). Conclusion: In this 27-county region in the Midwest, although small in magnitude, a statistically significant increase in AF incidence was observed over time from 2013 to 2023. Furthermore, the incidence of AF was higher in men compared with women, especially in younger age groups.

Background: Digital health offers a potential solution to support adults with congenital heart disease (CHD), filling the critical gap to help maintain recommended lifelong care with adult CHD specialists. However, its design and implementation require a rigorous scientific approach to make it scalable and usable for the target population. Objective: To pilot test the acceptability and feasibility of a digital health intervention to support lifelong healthcare for adults with CHD. Methods: Using a community-based participatory research and a behavioral theory-based approach, we developed and piloted a digital tool, Empower My Congenital Heart (EmpowerMyCH), within the web—and mobile-based Eureka Digital Research platform at UCSF. Intervention components within EmpowerMyCH include exhibits (expert and peer-developed learning modules on CHD-related topics such as finding specialists or navigating visits, tests, procedures, etc.), peer support, and a digital medical passport (Figure 1). Participants receive an exhibit with study measure surveys every two months, with a two-month completion window. Participant satisfaction with the digital tool is measured at six months using the Client Satisfaction Questionnaire-8 (CSQ-8), a validated survey with scores ranging from 8 to 32; higher scores indicate higher satisfaction with the digital tool. Results: Of the 519 participants recruited, 495 were eligible adults with CHD, 445 consented, and 6 withdrew. The median age was 43 (ranging from 20 to 85) years, and 30.1% were males. Survey completion rate was the highest at 88.7% during the first two months after enrollment and 53.5% at 8 months. Of the five exhibits delivered so far, over 87% of participants rated the exhibits to be helpful (Figure 2). Participants found the exhibits readable, easily accessible, motivational, useful, and educational (Figure 3). The median (25th, 75th) CSQ-8 score was 21 (18, 25) among 144 participants who completed the survey. Conclusions: EmpowerMyCH, the digital health tool, appears to be acceptable to most adults with CHD to support and educate them in navigating their healthcare. The tool and its contents are accessible, user-friendly, and responsive to the needs of the CHD community, resulting in high satisfaction rates. The feasibility rates and satisfaction scores are similar to or higher for EmpowerMyCH than those of prior digital health studies. Future hybrid intervention trials can demonstrate EmpowerMyCH‘s effectiveness.

Background: Heart failure with preserved ejection fraction (HFpEF) accounts for approximately half of all heart failure hospitalizations; yet, tools for individualized 1-year mortality risk stratification remain limited. We conducted a retrospective study to develop and validate a machine learning model using genetic programming and automated model search to predict 1-year all-cause mortality in HFpEF patients admitted for decompensated HF. Methods: Electronic medical records from Mayo Clinic (2010-2020) were queried to identify 7,840 adult patients hospitalized with HF exacerbation and a left ventricular ejection fraction>50% on echocardiograms performed between six months pre-admission and seven days post-discharge. Demographic, clinical, laboratory, medication, comorbidity, and imaging data available at the time of index hospitalization were extracted. The cohort was split into 80% training (n=6,272) and 20% held-out test (n=1,568) sets. After standard preprocessing, we performed an evolutionary AutoML search. The final pipeline applied a RobustScaler, two sequential feature-union blocks, and an XGBoost classifier. Model performance on the test set was assessed by area under the receiver operating characteristic curve (AUC), Brier score, accuracy, precision, recall, and F1 score, each with bootstrap-derived 95% confidence intervals (CIs). For interpretability, SHAP (SHapley Additive exPlanations) values were computed using the full scaled training set as background. Results: On the held-out test set, the XGBoost-based model achieved an AUC of 0.7595 (95% CI: 0.7307-0.7870) and a Brier score of 0.1764 (95% CI: 0.1652-0.1890). Accuracy was 0.7406 (95% CI: 0.7143-0.7645), precision 0.6349 (95% CI: 0.5759-0.6942), recall 0.4135 (95% CI: 0.3636-0.4609), and F1 score 0.5008 (95% CI: 0.4529-0.5444). The global SHAP summary plot (Figure 1) ranked age, serum albumin, blood urea nitrogen (BUN), presence of renal failure, and NT-proBNP as the top 5 predictors. Conclusions: In this large HFpEF cohort, an AutoML-derived XGBoost pipeline achieved robust discrimination (AUC≈0.76) and calibration (Brier≈0.18) for 1-year mortality prediction. SHAP-based explanations highlighted the importance of age, nutritional status (albumin), renal biomarkers (BUN, renal failure), and natriuretic peptides (NT-proBNP) as principal drivers of risk. These interpretable machine learning findings may guide personalized risk stratification and identify therapeutic targets in HFpEF.