Publications by authors named "Tiago P F Lima"
In the backdrop of the global obesity pandemic, recognized as a notable risk factor for coronavirus disease 2019 (COVID-19) complications, the study aims to explore clinical and epidemiological attributes of hospitalized COVID-19 patients throughout 2021 in Brazil. Focused on four distinct age cohorts, the investigation scrutinizes parameters such as intensive care unit (ICU) admission frequency, invasive mechanical ventilation (IMV) usage, and in-hospital mortality among individuals with and without obesity. Using a comprehensive cross-sectional study methodology, encompassing adult COVID-19 cases, data sourced from the Influenza Epidemiological Surveillance Information System comprises 329 206 hospitalized patients.
View Article and Find Full Text PDFObjective: To determine the clinical profile of COVID-19 inpatients who were vaccinated prior to hospitalization and to compare the risk factors for death and the 28-day survival rate of between those inpatients vaccinated with one, two, or three doses and unvaccinated COVID-19 inpatients.
Methods: This was a retrospective observational cohort study involving COVID-19 patients admitted to a referral hospital in the city of Recife, Brazil, between July of 2020 and June of 2022.
Results: The sample comprised 1,921 inpatients, 996 of whom (50.
COVID-19 in children and adolescents has low frequency, severity, and fatality rate all over the world. A cross-sectional study was conducted to assess the epidemiological and clinical aspects of COVID-19 in patients younger than 20 years in Pernambuco (Brazil), with cases confirmed by reverse-transcriptase-PCR SARS-CoV-2 between 13 February and June 19, 2020, reported on information systems. Data regarding age (< 30 days, 1-11 months, 1-4 years, 5-9 years, 10-14 years, and 15-19 years), gender, color/race, symptoms, pregnancy or puerperium, comorbidities, hospitalization, and death were investigated.
View Article and Find Full Text PDFArticle Synopsis
- The study focuses on classifying elderly cancer patients using machine learning (ML) algorithms and comprehensive geriatric assessments (CGA) to gauge their risk of early death.
- The research evaluated the ability of various ML models to predict mortality within six months of diagnosis, assessing a cohort of 608 patients through a combination of questionnaires related to mental health, physical activity, and overall health status.
- Results indicated that specific subsets of CGA questionnaires could effectively predict early death, often outperforming models that used the full set of questionnaires.