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.

The goal in selecting these recent articles was to help identify literature that may change the clinical practice of women’s health for practitioners in the primary care setting. Articles were identified by reviewing the high-impact medical and women’s health journals, national guidelines, ACP JournalWise, and NEJM Journal Watch. In this Clinical Update, we selected recent publications relevant to the cardiovascular health of women.

Importance The Breast Cancer Index (BCI) previously identified premenopausal patients with tumors in which the ratio of expression of HOXB13 relative to IL17BR (hereafter, BCI [H/I]–low tumors) as likely to derive greatest benefit from ovarian function suppression (OFS)–containing adjuvant therapy in the Suppression of Ovarian Function Trial (SOFT) trial. Objectives To assess BCI as a predictive biomarker of benefit from exemestane plus OFS vs tamoxifen plus OFS and to validate BCI as a prognostic biomarker for premenopausal patients. Design, Setting, and Participants This prognostic study used a prospective-retrospective translational design within the Tamoxifen and Exemestane (TEXT) and SOFT trials (enrolled November 2003 to April 2011). Blinded BCI testing in all available tumor samples was completed in March 2024. Premenopausal women with hormone receptor–positive breast cancer randomized to tamoxifen plus OFS or exemestane plus OFS who had BCI assessed were included. Analysis occurred from March to August 2024. Exposure 5 years of adjuvant tamoxifen plus OFS or exemestane plus OFS. Main Outcomes and Measures The primary outcomes were breast cancer-free interval (BCFI) for predictive analyses and distant recurrence-free interval (DRFI) for prognostic analyses after a median follow-up of 13 years in the TEXT cohort. Secondary objectives examined the predictive performance of BCI (H/I) in the combined TEXT and SOFT cohort overall and in prespecified clinical subgroups. Results Of 1782 patients in the TEXT study, 1034 (58.0%) had BCI (H/I)–low tumors; 915 (51.3%) of patients had N0 disease and 1077 (60.4%) were younger than 45 years. Patients with BCI (H/I)–low tumors had a 6.6% absolute benefit in 12-year BCFI (HR, 0.61; 95% CI, 0.44-0.85) for exemestane plus OFS vs tamoxifen plus OFS, while those with BCI (H/I)–high tumors had a 6.3% absolute benefit (HR, 0.78; 95% CI, 0.57-1.07; P for interaction = .29). Results were consistent in the combined TEXT plus SOFT cohort (2896 patients) and adjusting for clinicopathological variables. Clinical subgroup analyses consistently showed benefit of exemestane plus OFS vs tamoxifen plus OFS for BCI (H/I)–low tumors, and more variable relative treatment effects among BCI (H/I)–high tumors, including by age. Post hoc exploratory time-varying estimates suggested the treatment × BCI associations may differ in years 0 to 5 vs greater than 5 years. BCI and BCI N+ as continuous indices were prognostic for distant recurrence in N0 (HR, 1.27; 95% CI, 1.11-1.44; P < .001) and N1 (HR, 1.58; 95% CI, 1.21-2.05; P < .001) cancers. The 12-year DRFI was 96.3%, 90.3%, and 84.9% for BCI low-, intermediate-, and high-risk N0 cancers, respectively. Conclusions and Relevance In this study of premenopausal women with hormone receptor–positive breast cancer, BCI (H/I) status did not clearly predict greater benefit of adjuvant exemestane plus OFS vs tamoxifen plus OFS for women with BCI (H/I)–low tumors than for those with BCI (H/I)–high tumors; BCI continuous indices were reconfirmed as prognostic for premenopausal women. These findings support prior results of SOFT, which compared tamoxifen-alone vs OFS with either exemestane or tamoxifen, indicating premenopausal patients with BCI (H/I)–low tumors may benefit from more intensive endocrine therapy.

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.

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual.

Brain tumor-related epilepsy (BTRE) is a common and debilitating symptom of central nervous system (CNS) tumors. The epileptogenic zone, defined as cortex responsible for seizure generation, is located at the peritumoral region for most tumors, and lower-grade intrinsic brain tumors have the highest seizure incidence. Surgery is often the most effective treatment for the reduction in seizures in BTRE. However, surgical decisions have historically often been made exclusively for oncologic reasons, with less emphasis on seizure control. Surgical approaches for all tumor types are reviewed, highlighting relevant risk factors. Adjunctive tools during surgery, such as intraoperative electrocorticography (ECoG), may help identify and remove surrounding brain areas which are epileptogenic. Minimally invasive surgery is also gaining traction, given its utility in treating seizures deep-seated tumors. This review explores epileptogenic brain tumors, surgery for BTRE, and emerging strategies to better achieve seizure control.

Purpose of review The rapid growth and integration of artificial intelligence in healthcare has the potential to revolutionize all fields of medicine, including anesthesiology. This review summarizes the role of artificial intelligence in anesthesiology, highlighting how it can enhance patient safety, workflow efficiency, and research capabilities, while also examining the potential risks and ethical issues of introducing this new technology. Recent findings Evolving applications in anesthesiology include closed-loop anesthetic delivery, risk stratification, intraoperative monitoring, natural language processing for literature synthesis, and predictive modeling for adverse events. While these tools offer significant promise for improving efficiency and safety, emerging risks, such as algorithmic bias, lack of transparency, hallucinations, and automation complacency – must be carefully addressed. Regulatory frameworks, clinician education, and transparent model development are necessary to ensure responsible integration. Summary Artificial intelligence is poised to transform anesthetic care, but it must be developed and deployed with caution. Clinician oversight, robust validation, and ethical safeguards are essential to ensure artificial intelligence enhances, rather than replaces, clinical judgment. Strategic adoption can improve patient outcomes, reduce preventable harm, and streamline perioperative workflows.