Citations

OBJECTIVE The Centers for Medicare & Medicaid Services (CMS) requires a low C-peptide level for insulin pump coverage unless the individual is β-cell autoantibody positive, which precludes coverage of automated insulin delivery (AID) systems for many people with type 2 diabetes. RESEARCH DESIGN AND METHODS In the Randomized Trial Evaluating the Efficacy and Safety of Control-IQ+ Technology in Adults With Type 2 Diabetes Using Basal-Bolus Insulin Therapy study evaluating the t:slim X2 insulin pump with Control-IQ+ technology, adults with insulin-treated type 2 diabetes were categorized into high C-peptide (n = 195) and low C-peptide (n = 59) groups based on CMS criteria. RESULTS In the AID group, mean HbA1c decreased from baseline by 0.8%, which was significantly greater than in the control group with both high (P < 0.001) and low (P = 0.02) C-peptide levels. Results were similar in participants ≥65 years old. CONCLUSIONS The benefit of AID is present with high and low C-peptide levels. Thus, requiring a low C-peptide level as a prerequisite for AID therapy is not warranted.

IntroductionAlcohol withdrawal syndrome (AWS) is among the most severe components of alcohol dependence (AD). Severe AWS, especially complicated with seizures and delirium, was a common reason for medical complications and death. The introduction of benzodiazepines decreased but did not eliminate those risks. The definition of AWS evolved in different versions of Diagnostic and Statistical Manual for Mental Disorders (DSM) from having hand tremor plus one or more other symptoms in DSM-III-R to any two or more symptoms in DSM-IV, which was retained in the current version of DSM-5.ObjectivesComparing subgroups of individuals with AUD based on AWS symptoms profile to define phenotypes for investigation of biological underpinnings of AWS phenomenology and treatment response.MethodsTreatment-seeking individuals diagnosed with DSM-IV alcohol dependence (AD; n=473; 35.5% females) were assessed with Psychiatric Research Interview for Substance and Mental Disorders (PRISM), Timeline Follow back (past 90 days) alcohol consumption, Penn Alcohol Craving Scale (PACS), Pittsburgh Sleep Quality Index (PSQI), Patient Health Questionnaire-9 (PHQ9), and General Anxiety Disorder-7 (GAD-7). Latent class analysis was used to classify subjects according to lifetime prevalence of AWS symptoms according to DSM-III-R or DSM-IV/5. Demographic and other clinical variables were compared among clusters by linear model ANOVA and chi-squared test.ResultsFour clusters were identified: subjects with (1) no history of meeting any AWS criteria (n=50); (4) with complete number of AWS symptoms, meeting both DSM definitions (n=259); and two clusters of subjects with smaller number of AWS symptoms: (3) those meeting only DSM-IV/5 criteria (n=94) and (2) those also meeting DSM-III-R criteria (n=70). Compared to cluster 2, the Cluster 3 members had higher frequency of anxiety, insomnia, and restlessness during withdrawal. The clusters also differed in the total number of drinks (p=0.021), average drinks per drinking days (p=0.013), history of anxiety disorder (p=0.006), substance-induced depression (p=0.019), and higher scores of PSQI (p<0.001), PHQ9 (p<0.001), and GAD-7 (p<0.001) with a relative increase in frequency between clusters as follows 1<2<3<4. Group 4 also had the highest percentages of subjects with history of seizure, delirium or hallucinations.ConclusionsDifferent definitions of AWS capture clusters of AD patients with different symptomatology, comorbidity and consumption patterns. Understanding of biological underpinnings behind those differences may guide improvement in personalized treatment selection.Disclosure of InterestNone Declared

ABSTRACT Objectives To explore the clinical impact of social determinants of health (SDoH) stress in bipolar disorder (BD) course of illness and to explore potential gender differences in SDoH stress endorsement. Methods 2280 individuals living with BD type I or II (62% women; 67.5% BD‐I) participating in the Mayo Clinic Bipolar Biobank who completed a questionnaire assessing positive and negative‐valent stressors in the 12 months prior to study enrollment were included for this analysis. Six negative stressors were used as proxies of SDoH. Generalized linear models were used to test the association between SDoH stressors and markers of illness severity as well as potential differences by sex by including an interaction term. Results After adjusting for age, sex, and recruitment site, SDoH‐related stressors were significantly associated with a more severe course of illness in both men and women. Except for unemployment rates, women reported more frequently SDoH‐related stress (i.e., lack of family support, financial problems, health coverage and access problems). Conclusions The study found significant gender differences in both the endorsement of specific SDoH stressors and in the prevalence of specific illness severity markers; however, it did not directly test the predictive relationship between gender differences in SDoH and illness severity markers. Future studies aiming to achieve health equity in mental health care should continue assessing social determinants of health stressors, incorporating a gender perspective to better understand their influence on bipolar disorder.

Abstract Aims Chronotropic incompetence (CI) is a biomarker of cardiac autonomic dysfunction. The aim of the study is to assess the risk stratification value of CI during exercise or pharmacological stress echocardiography in patients with chronic coronary syndromes. Methods and results In a prospective, multicenter, international, observational study, we enrolled 13 445 patients with known or suspected chronic coronary syndromes who underwent stress echocardiography in 19 clinical sites from 10 countries using either exercise (n = 2594), dobutamine (n = 2440), or dipyridamole (n = 8411). Heart rate was automatically measured from the 1-lead ECG in the echocardiography monitor. We considered CI as failure to reach 85% of the maximal predicted (220-age) heart rate for exercise and dobutamine, and heart rate reserve (peak/rest heart rate) ≤ 1.22 (≤1.17 if in permanent atrial fibrillation) for dipyridamole stress. The primary outcome was all-cause death. CI was observed in 5045 patients (38%) and stress-induced regional wall motion abnormalities in 1648 (13%). Over a median follow-up time of 3.4 years (interquartile range, 1.6–9.1 years), there were 2426 (18%) deaths. The 10-year mortality was 39% in patients with and 21% in patients without CI (P &lt; 0.0001). CI was associated with a significant increase in 10-year mortality in all age groups ranging from ≤54 years to ≥75 years (P &lt; 0.0001). In addition, it was associated with increased mortality (P &lt; 0.0001) irrespective of β-blocker use. Cox multivariable analysis revealed that CI was an independent predictor of mortality (HR: 1.60, 95% confidence interval: 1.47–1.74; P &lt; 0.0001) together with age, male sex, diabetes mellitus, left ventricular ejection fraction, and resting heart rate. Conclusion In patients with chronic coronary syndromes, CI during exercise or pharmacological stress is a simple and objective predictor of survival.

Background: Artificial Intelligence (AI)-driven Decision Support Systems (DSSs) promise improvements in diagnostic accuracy and clinical workflow efficiency, but their adoption is hindered by inadequate confidence calibration, limited transparency, and poor alignment with real-world decision processes, which limit clinician trust and lead to high override rates. Methods: We developed and validated a dynamic scoring framework to enhance trust in AI-generated diagnoses by integrating AI confidence scores, semantic similarity measures, and transparency weighting into the override decision process using 6689 cardiovascular cases from the MIMIC-III dataset. Override thresholds were calibrated and validated across varying transparency and confidence levels, with override rate as the primary acceptance measure. Results: The implementation of this framework reduced the override rate to 33.29%, with high-confidence predictions (90–99%) overridden at a rate of only 1.7%, and low-confidence predictions (70–79%) at a rate of 99.3%. Minimal transparency diagnoses had a 73.9% override rate compared to 49.3% for moderate transparency. Statistical analyses confirmed significant associations between confidence, transparency, and override rates (p < 0.001). Conclusions: These findings suggest that enhanced transparency and confidence calibration can substantially reduce override rates and promote clinician acceptance of AI diagnostics. Future work should focus on clinical validation to optimize patient safety, diagnostic accuracy, and efficiency.

Retrieval-Augmented Generation (RAG) offers a promising strategy to harness large language models (LLMs) for delivering up-to-date, accurate clinical guidance while reducing physicians’ cognitive burden, yet its effectiveness hinges on query clarity and structure. We propose an adaptive Self-Query Retrieval (SQR) framework that integrates three refinement modules—PICOT (Population, Intervention, Comparison, Outcome, Time), SPICE (Setting, Population, Intervention, Comparison, Evaluation), and Iterative Query Refinement (IQR)—to automatically restructure and iteratively enhance clinical questions until they meet predefined retrieval-quality thresholds. Implemented on Gemini-1.0 Pro, we benchmarked SQR using thirty postoperative rhinoplasty queries, evaluating responses for accuracy and relevance on a three-point Likert scale and for retrieval quality via precision, recall, and F1 score; statistical significance was assessed by one-way ANOVA with Tukey post-hoc testing. The full SQR pipeline achieved 87% accuracy (Likert 2.4 ± 0.7) and 100% relevance (Likert 3.0 ± 0.0), significantly outperforming a non-refined RAG baseline (50% accuracy, 80% relevance; p < 0.01 and p = 0.03). Precision, recall, and F1 rose from 0.17, 0.39 and 0.24 to 0.53, 1.00, and 0.70, respectively, while PICOT-only and SPICE-only variants yielded intermediate improvements. These findings demonstrate that automated structuring and iterative enhancement of queries via SQR substantially elevate LLM-based clinical decision support, and its model-agnostic architecture enables rapid adaptation across specialties, data sources, and LLM platforms.

ABSTRACTBackgroundDifferentiating metastatic melanoma from histologic mimics such as malignant gastrointestinal neuroectodermal tumor (GNET), malignant melanotic nerve sheath tumor (MMNST), and epithelioid malignant peripheral nerve sheath tumor (EMPNST) poses significant diagnostic challenges due to overlapping morphology and immunophenotypes. PRAME is a novel immunohistochemical marker increasingly used to distinguish melanoma from its mimics, but remains underexplored in these rare tumor types.MethodsPRAME immunohistochemistry was performed on four GNETs, seven MMNSTs, 10 EMPNSTs, 16 metastatic melanomas (including eight undifferentiated melanomas), and two MITF‐rearranged melanocytic tumors. PRAME expression was scored from 0 to 4+ based on the percentage of tumor nuclei showing moderate to strong staining.ResultsAll GNETs, MMNSTs, and MITF‐rearranged tumors were PRAME‐negative. EMPNSTs showed variable expression: six were negative (0–1), one equivocal (2+), and three positive (3–4+). Fifteen of 16 melanomas were PRAME‐positive. PRAME scores differed significantly among tumor types (p = 1.99 × 10−5). PRAME demonstrated high sensitivity and specificity for distinguishing metastatic melanoma from primary mimickers including GNET and MMNST, but low specificity in EMPNST (71.4%).ConclusionsPRAME reliably distinguishes metastatic melanoma from GNET and MMNST, supporting its use in this differential. However, its reduced specificity in EMPNST limits its standalone diagnostic value in this context, emphasizing the need for a multimodal approach.

Abstract The combination of enfortumab vedotin and pembrolizumab (EVP) has recently revolutionized care for patients with previously untreated locally advanced and metastatic urothelial carcinoma. However, predictive biomarkers of response, the optimal duration of EVP for patients who respond well to therapy, and the role of circulating tumor DNA to monitor treatment response have not yet been fully elucidated. In this molecular tumor board, we summarize 6 patients from 4 different academic institutions with unresectable or oligometastatic urothelial carcinoma who achieved long-term remission on surveillance after a short duration of EVP treatment that resulted in complete response on follow-up imaging followed by consolidative radical cystectomy. We discuss potential predictive biomarkers for patients with exceptional response to EVP. These findings highlight the need to determine an optimal duration of EVP to maximize efficacy while minimizing adverse events. We aim to further define and explore the value of local therapy with consolidative cystectomy in the setting of unresectable, locally advanced, oligometastatic disease, or those with metastatic disease who achieve complete response of disease outside of the bladder. Identifying this subset of patients who may be candidates for this type of multidisciplinary management may result in durable treatment-free remission.