Obstructive sleep apnea (OSA) is highly prevalent among patients with hypertension, yet its influence on mortality in critically ill hypertensive individuals admitted to the intensive care unit (ICU) remains unclear. This study aimed to investigate the association between OSA and short-term and long-term mortality in this specific population. We performed a retrospective cohort analysis using the MIMIC-IV database (version 3.1). Adult hypertensive patients with their first ICU admission between 2008 and 2022 were included. OSA was identified using International Classification of Diseases (ICD) codes recorded at discharge for the index hospitalization. The primary outcomes were all-cause in-hospital, 30-day, 90-day, and 365-day mortality. The association was examined using multivariate Cox, multiple imputation for missing data, Kaplan-Meier survival analysis, and landmark analysis. The proportional hazards assumption was assessed using Schoenfeld residual tests. A total of 41,493 patients were enrolled, of whom 4,550 (11.0
We aimed to develop and externally validate an interpretable machine-learning model for predicting overt or endoscopically confirmed gastrointestinal bleeding within 72 hours after EVT. This two-center retrospective study included a development cohort from the First Affiliated Hospital of Guilin Medical University (May 2021 to June 2025; n = 485) and an external validation cohort from Guilin People's Hospital (January 2024 to June 2025; n = 72). The primary outcome was overt or endoscopically confirmed gastrointestinal bleeding occurring within 72 hours after EVT. The intended prediction time was immediately after EVT completion, and all nine final predictors were available at or before that time. Predictors were selected using least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm, and seven supervised algorithms were compared. SHapley Additive exPlanations (SHAP) were used for model interpretation, and a web-based calculator was developed. The primary outcome occurred in 142 of 485 patients (29.3
Human rhinovirus (HRV) infection exhibits vast clinical heterogeneity in children, ranging from mild colds to severe pneumonia. Early identification of children at risk of severe progression remains a major clinical challenge, as traditional scoring systems fail to capture the complex, early-stage pathophysiological changes. This study aimed to leverage eXplainable Artificial Intelligence (XAI) to uncover the core clinical phenotype driving disease severity and develop a high-performance model for early in-hospital risk stratification. This single-center retrospective cohort study included 414 children (214 severe, 200 mild cases) hospitalized with HRV-positive pneumonia. Demographic, clinical, and laboratory data were collected. LASSO regression, applied to an initial pool of over 50 variables, selected 13 key features for model input. Six machine learning models were developed on a training set (80
Acute aortic dissection (AD) is a life-threatening cause of chest pain that can be difficult to recognise. Artificial intelligence (AI) analysis of routinely acquired electrocardiogram (ECG) may provide additional diagnostic information. We developed an AI-ECG model incorporating age and sex and evaluated its discrimination and operating characteristics in internal and external chest pain cohorts. This retrospective two-centre study included 27,160 patients from Zhongshan Hospital, Fudan University, divided at the patient level into development, tuning, and internal test sets. External evaluation included 844 patients from the Qingpu Branch. The full model was compared with ECG-only and age-and-sex models. Classification thresholds were selected in the internal tuning set and applied unchanged to both evaluation cohorts. Calibration, alert burden, and performance in an aortic computed tomography angiography (CTA)-verified subset were also assessed. The internal test set included 5,433 patients with 30 AD events, and the external cohort included 844 patients with 31 AD events. The full model achieved areas under the receiver operating characteristic curve (AUCs) of 0.803 (95
To investigate the types of participation in surgical decision-making and explore core factors associated with surgical decision-making participation among patients with stage I–IIIA non-small cell lung cancer (NSCLC). Methods: A cross-sectional study adopting convenience sampling was conducted. Specifically, a total of 219 stage I–IIIA NSCLC patients who underwent surgical treatment at a tertiary hospital from February to June 2024 were recruited. Electronic questionnaires were administered for data collection, covering sociodemographic information, the Control Preference Scale (CPS), and the Patient's Knowledge of Medical Decision Making Questionnaire. Associations between clinical and sociodemographic factors and types of surgical decision-making participation were assessed via univariate analyses. Multiple logistic regression analysis was performed to examine factors independently associated with participation in surgical decision-making. Among the enrolled NSCLC patients, passive decision-making accounted for 86 cases (39.3
