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Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study.
JMIR Perioper Med
January 2025
Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States.
Background: Postoperative delirium (POD) is a common complication after major surgery and is associated with poor outcomes in older adults. Early identification of patients at high risk of POD can enable targeted prevention efforts. However, existing POD prediction models require inpatient data collected during the hospital stay, which delays predictions and limits scalability.
View Article and Find Full Text PDFConjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.
Transl Vis Sci Technol
January 2025
Institute of the Electrical and Biomedical Engineering, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tyrol, Austria.
Purpose: To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline.
Methods: Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity.
Senescence-related Genes as Prognostic Markers for STEMI Patients: LASSO Regression-Based Bioinformatics and External Validation.
J Cardiovasc Transl Res
January 2025
Clinical Laboratory of Tianjin Chest Hospital, 261 Taierzhuang South Road, Tianjin, 300222, Jinnan District, China.
The prognostic value of differentially expressed senescence-related genes(DESRGs) in ST-segment elevation myocardial infarction(STEMI) patients is unclear. We used GEO2R to identify DESRGs from GSE60993 and performed functional enrichment analysis. We built an optimal prognostic model with LASSO penalized Cox regression via GSE49925.
View Article and Find Full Text PDFRisk prediction for elderly cognitive impairment by radiomic and morphological quantification analysis based on a cerebral MRA imaging cohort.
Eur Radiol
January 2025
Institute of PLA Geriatric Medicine, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Objective: To establish morphological and radiomic models for early prediction of cognitive impairment associated with cerebrovascular disease (CI-CVD) in an elderly cohort based on cerebral magnetic resonance angiography (MRA).
Methods: One-hundred four patients with CI-CVD and 107 control subjects were retrospectively recruited from the 14-year elderly MRA cohort, and 63 subjects were enrolled for external validation. Automated quantitative analysis was applied to analyse the morphological features, including the stenosis score, length, relative length, twisted angle, and maximum deviation of cerebral arteries.
Prediction Models for Post-Stroke Cognitive Impairment: A Systematic Review and Meta-Analysis.
Public Health Nurs
January 2025
School of Nursing, Evidence-Based Nursing Center, Lanzhou University, Lanzhou, China.
Background: Stroke is one of the most serious illnesses worldwide and is the primary cause of acquired disability among adults. Post-stroke cognitive impairment (PSCI) is a complication of stroke that significantly impacts patients' daily activities and social functions. Therefore, developing a risk prediction model for PSCI is essential for identifying and preventing disease progression.
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