Publications by authors named "L Seijo"
Objective: The LungFlag risk prediction model uses individualized clinical variables to identify individuals at high-risk of non-small cell lung cancer (NSCLC) for screening with low-dose computed tomography (LDCT). This study evaluates the cost-effectiveness of LungFlag implementation in the Spanish setting for the identification of individuals at high-risk of NSCLC.
Methods: A model combining a decision-tree with a Markov model was adapted to the Spanish setting to calculate health outcomes and costs over a lifetime horizon, comparing two hypothetical scenarios: screening with LungFlag versus non-screening, and screening with LungFlag versus screening the entire population meeting 2013 US Preventive Services Task Force (USPSTF) criteria.
Lung transplantation aims to improve health-related quality of life (HRQL) and survival. While lung function improvements are associated with these outcomes, the association between physical functioning and these outcomes is less clear. We investigated the association between changes in patient-reported physical functioning and HRQL, chronic lung allograft dysfunction (CLAD), and survival after lung transplantation.
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- * An analysis of plasma samples from 40 COPD patients revealed 363 proteins, with 31 showing significant differences in levels between those who survived and those who did not after four years.
- * The study found that predictive models based on proteomic data achieved high accuracy for mortality prediction (90%) and suggested that specific protein groups related to immune response, hemostasis, and inflammation could enhance prognostic capabilities for managing COPD.
Lung cancer remains the leading cause of cancer-related deaths worldwide, mainly due to late diagnosis and the presence of metastases. Several countries around the world have adopted nation-wide LDCT-based lung cancer screening that will benefit patients, shifting the stage at diagnosis to earlier stages with more therapeutic options. Biomarkers can help to optimize the screening process, as well as refine the TNM stratification of lung cancer patients, providing information regarding prognostics and recommending management strategies.
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