Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ejpn.2023.04.003 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Improved Risk Adjustment for Comorbid Diagnoses in Administrative Claims Analyses of Orthopaedic Surgery.
J Bone Joint Surg Am
January 2025
Department of Orthopaedic Surgery, Stanford University, Redwood City, California.
Background: The accurate inclusion of patient comorbidities ensures appropriate risk adjustment in clinical or health services research and payment models. Orthopaedic studies often use only the comorbidities included at the index inpatient admission when quantifying patient risk. The goal of this study was to assess improvements in capture rates and in model fit and discriminatory power when using additional data and best practices for comorbidity capture.
View Article and Find Full Text PDFA comparative analysis of risk stratification tools in systemic sclerosis-associated pulmonary arterial hypertension: a EUSTAR analysis.
Rheumatology (Oxford)
January 2025
Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Objectives: The 2022 European Society of Cardiology and European Respiratory Society (ESC/ERS) Guidelines for pulmonary arterial hypertension (PAH) recommend risk stratification to optimize management. However, the performance of generic PAH risk stratification tools in patients with systemic sclerosis (SSc)-associated PAH remains unclear. Our objective was to identify the most accurate approach for risk stratification at SSc-PAH diagnosis.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Prognostic impact of one-year permanent pacemaker implantation after mitral valve surgery with the Cox-maze procedure.
Eur J Cardiothorac Surg
January 2025
Department of Thoracic and Cardiovascular Surgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Objectives: This study aimed to evaluate the prognostic impact of permanent pacemaker (PPM) implantation within the first year after mitral valve (MV) surgery combined with the Cox-maze procedure, focusing on long-term outcomes, including overall mortality, infective endocarditis (IE), and ischaemic stroke.
Methods: We conducted a retrospective cohort study using data from the National Health Insurance Service (NHIS) in South Korea, identifying 10,127 patients who underwent MV surgery with the Cox-maze procedure between 2005 and 2020. Patients were classified into the PPM and non-PPM groups based on PPM implantation within one year postoperatively.
Developing a novel model for predicting overall survival in late-onset colon adenocarcinoma patients based on LODDS: a study based on the SEER database and external validation.
Discov Oncol
January 2025
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230000, China.
Aim: To construct a predictive model based on the LODDS stage established for patients with late-onset colon adenocarcinoma to enhance survival stratification.
Methods: Late-onset colon adenocarcinoma data were obtained from the public database. After determining the optimal LODDS truncation value for the training set via X-tile software, we created a new staging system by integrating the T stage and M stage.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!