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Importance Thirty-day hospital readmission is used to guide postdischarge care; however, a single binary 30-day estimate does not distinguish when risk occurs from when intervention is most actionable, with implications for allocation of postdischarge resources. Objective To evaluate time-structured hospital readmission risk within 30 days after discharge and implications for clinical utility. Design, Setting, and Participants Retrospective prognostic study of adults with hospital discharges from 2017 to 2024 (from 19 hospitals within a US health system for the development cohort) and 2008 to 2019 (from Medical Information Mart for Intensive Care IV for the external cohort) with 30-day follow-up. Analyses were conducted from November 2025 through June 2026. Exposure Postdischarge prediction window, including landmark intervals of 0 to 7, 8 to 14, 15 to 21, and 22 to 30 days and cumulative horizons through days 7, 14, 21, and 30 after discharge. Main Outcomes and Measures First all-cause hospital readmission before death during each landmark interval and by each cumulative horizon. Risk estimates were derived using clinical information available at hospital discharge. Performance was assessed using discrimination, calibration, decision curves, and capacity-constrained analyses. Results Among 617 841 discharges in the development cohort, the median (IQR) patient age was 65 (50-76) years, and 315 220 discharges (51.0%) occurred among female patients. The 30-day readmission rate was 16.3% (95% CI, 16.2%-16.5%), with 68 797 readmissions (68.2%) occurring within 14 days. Cumulative-horizon analyses characterized readmission risk through days 7, 14, 21, and 30, whereas landmark interval-specific analyses showed lower positive predictive values (PPVs) beyond day 7. At 5% intervention capacity, landmark PPVs were 58.3% (95% CI, 57.4%-59.1%) for days 0 to 7, 16.0% (95% CI, 15.5%-16.6%) for days 8 to 14, 14.5% (95% CI, 13.9%-15.0%) for days 15 to 21, and 12.9% (95% CI, 12.4% to 13.5%) for days 22 to 30; numbers needed to prevent 1 readmission were 8.6, 31.3, 34.6, and 38.6, respectively. Conclusions and Relevance In this prognostic study of adult hospital discharges, hospital readmission risk varied over time and was not captured by a single binary 30-day estimate. These findings suggest that time-structured readmission modeling can characterize when readmission risk occurs, but additional work is needed before such models can reliably guide interventions during specific postdischarge intervals.

ABSTRACT Background Percutaneous left atrial appendage occlusion (LAAO) is an alternative strategy for stroke prevention in patients with nonvalvular atrial fibrillation (AF) with contraindications to long‐term oral anticoagulation therapy. However, evidence comparing the long‐term outcomes of LAAO with those of direct oral anticoagulants (DOAC) remains limited. Methods A systematic search was conducted across PubMed, Scopus, and the Cochrane databases through April 2026. Co‐primary outcomes were major adverse cardiovascular and cerebrovascular events (MACCE; composite of cardiac death, stroke, or systemic embolism) and net adverse clinical events (NACE; composite of MACCE and non‐procedure‐related bleeding). Secondary endpoints included individual components of co‐primary endpoints, ischemic and hemorrhagic stroke, all‐cause death, major or clinically significant bleeding (procedure + non‐procedure related), and stroke or systemic embolism. The random‐effects model was used to generate risk ratios (RRs) and 95% CIs. Results Four randomized controlled trials comprising 5890 AF patients and a median follow‐up of 3 years were included. There was no significant difference between LAAO and DOAC therapy in the risk of MACCE (RR 1.17; 95%CI 0.96–1.41; I 2 = 0%) or NACE (RR 0.89; 95%CI 0.65–1.24; I 2 = 85%). Both LAAO and DOAC therapy were similar in risk of cardiac death, stroke, systemic embolism, hemorrhagic stroke, ischemic stroke, all‐cause mortality, stroke or systemic embolism, and pericardial effusion. LAAO was associated with a lower risk of non‐procedure‐related bleeding (RR 0.60; 95%CI 0.47–0.76; I 2 = 59%). Conclusion LAAO demonstrated efficacy comparable to DOAC therapy in reducing the risk of MACCE, NACE, and ischemic events, while significantly reducing the risk of non‐procedure‐related bleeding. Our findings support a tailored approach to stroke prevention in AF, wherein LAAO serves as an effective alternative for patients with contraindications to DOAC therapy.

Financial toxicity is a significant but often overlooked consequence of cancer for adolescents and young adults (AYAs, 15–39 years old), who face limited savings, unstable employment, and insurance gaps that heighten financial stress. As part of a larger initiative to determine whether our cancer center can use an existing social drivers of health (SDOH) screening process to identify patients who may benefit from targeted supportive interventions, we sought to characterize financial strain, toxicity, and SDOH needs in AYAs treated in our cancer center and compare them to both younger and older cancer patients. This is a cross-sectional observational study at Mayo Clinic of patients with an oncology-related department visit between 8 September 2023–4 June 2024 (N = 81,731) who completed routine institutional electronic screenings for financial strain and SDOH. Survey items assessed financial strain, transportation barriers, and food insecurity; institutional billing data captured financial toxicity (unresolved balances, collection referrals, write-offs). AYAs (n = 5655) were more likely than non-AYAs to report medium/high financial strain, have unpaid debt, or have an unresolved balance, referral to a collection agency, or bad debt write-off, with nearly double the odds of medium/high financial strain compared to patients over 40 (OR 2.02, 95% CI 1.84–2.23, p < 0.001). Within AYAs, ages 26–32 bore the greatest burden: 24.2% in the medium/high-risk category, 9.4% with unresolved billing issues or collections, and 11.6% reporting food insecurity. These findings identify AYAs—particularly those 26–32—as a distinct high-risk group for financial toxicity and health-related social needs, underscoring the need for targeted interventions, tailored financial navigation, and policy efforts to reduce economic barriers to care. Future research should evaluate the effectiveness of these interventions in mitigating financial toxicity, addressing health-related social needs, and improving patient outcomes in this population.

Objective To compare the diagnostic accuracy of four available automated electronic medical record (EMR) retrieval methods, including a large language model (LLM)-assisted workflow, against manual chart adjudication for identifying cardiovascular events. Design Retrospective diagnostic accuracy study. Setting Three sites within a single US tertiary health system. Participants Two adult cohorts with previously adjudicated cardiovascular outcomes were included. Cohort 1 included 2258 patients treated with immune checkpoint inhibitors, and Cohort 2 included 1426 patients who underwent transcatheter aortic valve replacement. Primary and secondary outcome measures The reference standard was clinician manual chart adjudication. Outcomes included ischaemic stroke or transient ischaemic attack, myocardial infarction (MI), heart failure (HF) exacerbation or hospitalisation and a composite major adverse cardiovascular events (MACE) outcome. Automated retrieval methods included International Classification of Diseases (ICD) codes, primary diagnosis, problem list and a zero-shot LLM workflow. Area under the (receiver operating characteristic) curve (AUC), sensitivity, specificity and net reclassification improvement were assessed. Results In Cohort 1, the LLM achieved the highest AUC for stroke (0.920; 95% CI 0.881 to 0.958), MI (0.938; 95% CI 0.905 to 0.971) and composite MACE (0.880; 95% CI 0.854 to 0.907), whereas ICD-based retrieval had a higher AUC for HF (0.882; 95% CI 0.845 to 0.918 vs 0.873; 95% CI 0.831 to 0.914). In Cohort 2, the LLM achieved the highest AUC for all evaluated outcomes: stroke (0.915; 95% CI 0.862 to 0.968), MI (0.928; 95% CI 0.839 to 1.000), HF (0.844; 95% CI 0.803 to 0.884) and composite MACE (0.862; 95% CI 0.829 to 0.895). In Cohort 1, differences in AUC between the LLM and ICD methods were not statistically significant across outcomes, whereas in Cohort 2 the LLM showed significantly higher AUC for stroke and composite MACE. Conclusion In this multisite retrospective validation study, the LLM-assisted workflow showed strong but context-dependent performance for identifying cardiovascular events from the EMR. Performance varied by outcome and cohort, and ICD-based retrieval remained competitive for some use cases. These findings support a complementary role for LLM-assisted extraction in retrospective cardiovascular outcomes research.

Abstract Provided herein are novel tetrahydropyridinyl-6,6-bicyclic compounds as 5-HT2A receptor agonists, pharmaceutical compositions, use of such compounds in treating depression, anxiety, substance abuse and headaches, and processes for preparing such compounds.

Purpose of review Artificial intelligence applied to electrocardiography (AI-ECG) has rapidly been investigated in adult cardiovascular medicine, yet translation into pediatric and congenital heart disease populations has lagged. This review summarizes contemporary AI-ECG methodologies and emerging applications in pediatric and congenital heart disease (PCHD), with emphasis on current clinical utility, technical challenges, and future opportunities for implementation. Recent findings Recent studies demonstrate that deep learning models can accurately identify arrhythmias, ventricular dysfunction, and CHD from standard ECG. Convolutional neural networks remain the dominant architecture, although transformer-based foundation models and self-supervised learning approaches are increasingly being explored. AI-ECG applications in PCHD have expanded from automated interpretation toward proactive risk stratification, including prediction of ventricular dysfunction, mortality, and sudden cardiac death risk. Additional work has investigated wearable monitoring, telemetry analysis, and integration with longitudinal clinical data. Despite promising performance, most studies remain retrospective and single-center, with limited external validation and challenges related to small datasets, physiologic heterogeneity, and age-dependent ECG variation. Summary AI-ECG has the potential to transform PCHD by improving diagnostic accuracy, enabling earlier disease detection, and enhancing longitudinal risk assessment. Broader clinical implementation will require multicenter collaboration, prospective validation, standardized datasets, and careful attention to ethical, regulatory, and equity considerations.

Abstract INTRODUCTION Centiloid (CL) scaling standardizes amyloid positron emission tomography (PET) quantification across tracers and platforms; however, variability across software implementations may affect diagnostic classification. This study evaluated inter‐software variability and diagnostic performance across five platforms using identical 1 8 F‐florbetapir datasets. METHODS Retrospectively, 192 patients undergoing 1 8 F‐florbetapir PET/computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. CL values were generated using four US Food and Drug Administration (FDA) ‐cleared platforms and an in‐house Centiloid standard pipeline. Agreement was assessed using intraclass correlation coefficient, with bias and limits of agreement evaluated by linear modeling and Bland–Altman analysis. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and classification against visual interpretation. RESULTS Agreement was excellent (intraclass correlation coefficient [ICC] 0.969; 95%CI 0.961–0.975). Some platforms produced systematically higher CL values versus others slightly lower. LoA reached ± 30 CL. Diagnostic accuracy was high (area under the curve [AUC]: 0.949–0.975), with sensitivity 0.923–0.968 and specificity 0.667–0.806. DISCUSSION Despite excellent agreement, systematic differences persist and may affect classification near thresholds, supporting consistent use of a single processing pipeline.

ABSTRACT Background Post‐pancreatectomy exocrine insufficiency is common after resection for pancreatic cancer and requires pancreatic enzyme replacement therapy (PERT). We sought to determine the out‐of‐pocket cost of PERT and the association between PERT and survival. Methods The SEER‐linked Medicare database was queried for patients with pancreatic cancer who underwent pancreaticoduodenectomy (PD) or distal pancreatectomy (DP) from 2010 to 2019. Cumulative out‐of‐pocket costs were calculated and stratified by procedure and radiation. Results 11,854 Medicare beneficiaries with resected pancreatic cancer were identified, 4,653 (39%) required PERT and 7,201 (61%) did not. Median age was 73.0, 50.6% were female and 83.6% White. 25.9% underwent DP and 74.1% underwent PD; 7% received neoadjuvant radiation while 23.5% received adjuvant radiation. Logistic regression demonstrated that female sex (OR 1.25, 95% CI 1.16‐1.35, p < 0.001), neoadjuvant radiation (OR 1.93, 95% CI 1.67‐2.24, p < 0.001), and PD (OR 2.45, 95% CI 2.23‐2.7, p < 0.001) were risk factors for requiring PERT. Patients without subsidies who underwent DP paid a cumulative $4,736 out‐of‐pocket for PERT after 5 years, those who underwent PD paid $5,601 out‐of‐pocket. Conclusions Nearly 40% of Medicare beneficiaries took PERT after resection for pancreatic cancer and paid thousands in out‐of‐pocket costs. While a conservative estimate, these findings highlight the importance of addressing this financial burden to patients.

Objectives: To examine the effects of anterior load carriage on dynamic sagittal spinal alignment during the gait cycle in younger (<65 y) and older (≥65 y) adults. Design: Cross-sectional study of 47 participants aged 20–80 years categorized as young and older adults. Participants walked at self-selected speeds with and without a 4.5 kg anterior chest load. Whole-body kinematics were recorded using a 19-camera infrared motion capture system. Outcome measures included lumbar lordosis (LL), thoracic kyphosis (TK), trunk inclination (TI), sagittal trunk shift (STS), and pelvic tilt (PT). Gait-cycle–dependent differences were analyzed using statistical parametric mapping. Results: The mean age was 41 years in young adults (n=28) and 70 years in older adults (n=19). Anterior load carriage increased LL during portions of the gait cycle and reduced TI throughout the entire gait cycle. Younger adults demonstrated greater LL and lower TI than older adults. A significant age-by-load interaction for TI was observed during portions of the gait cycle. Conclusions: Anterior load carriage alters spinal alignment by increasing LL and reducing TI during walking, with distinct age-related patterns. These results provide valuable reference data and a foundation for future studies on load management and ergonomic interventions across the adult lifespan.