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A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.
J Med Internet Res
January 2025
Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Background: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.
Objective: This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients.
Methods: In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University.
Length of Hospital Stay and Its Predictions Among Patients With Exacerbations of Chronic Respiratory Diseases.
J Eval Clin Pract
February 2025
Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, University of Jordan, Amman, Jordan.
Background: Chronic respiratory disorders such as asthma and chronic obstructive pulmonary disease (COPD) may deteriorate into acute exacerbations requiring hospitalization. Assessing the predictors of prolonged hospital stays could help identify potential interventions to reduce the burden on patients and healthcare systems.
Aim: This study aimed to identify the risk factors attributed to prolonged hospital stays among patients admitted with acute exacerbations of chronic respiratory disorders in Jordan.
Impact of Frailty on Surgical Outcomes of Patients With Cushing Disease Using the Multicenter Registry of Adenomas of the Pituitary and Related Disorders Registry.
Neurosurgery
February 2025
Global Neurosciences Institute, Philadelphia , Pennsylvania , USA.
Background And Objectives: Despite growing interest in how patient frailty affects outcomes (eg, in neuro-oncology), its role after transsphenoidal surgery for Cushing disease (CD) remains unclear. We evaluated the effect of frailty on CD outcomes using the Registry of Adenomas of the Pituitary and Related Disorders (RAPID) data set from a collaboration of US academic pituitary centers.
Methods: Data on consecutive surgically treated patients with CD (2011-2023) were compiled using the 11-factor modified frailty index.
Impact of sodium-glucose cotransporter inhibitors in acute coronary syndrome patients on endothelial function and atherosclerosis related-biomarkers: ATH-SGLT2i pilot study.
Medicine (Baltimore)
November 2024
Department of Cardiology, Rabta Teaching Hospital, University of Medicine Tunis, Tunis, Tunisia.
Little is known about the effects of sodium-glucose co-transporter 2 inhibitors (SGLT2i) on atherosclerosis. We aimed to determine if a 90-day intake of Dapagliflozin could improve atherosclerosis biomarkers (namely endothelial function assessed by flow-mediated dilatation [FMD] and carotid intima-media thickness [CIMT]) in diabetic and non-diabetic acute coronary syndrome (ACS) patients when initiated in the early in-hospital phase. ATH-SGLT2i was a prospective, single-center, observational trial that included 113 SGLT2i naive patients who were admitted for ACS and who were prescribed Dapagliflozin at a fixed dose of 10 mg during their hospital stay for either type 2 diabetes or for heart failure.
View Article and Find Full Text PDFArtificial Intelligence-Guided Lung Ultrasound by Nonexperts.
JAMA Cardiol
January 2025
Department of Emergency Medicine, Rush University Medical Center, Chicago, Illinois.
Importance: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.
Objective: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).
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