Publications by authors named "Filipe C Aguiar"
A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.
JMIR Med Inform
October 2024
Background: Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died.
View Article and Find Full Text PDFReal-Life Data on Hydroxychloroquine or Chloroquine with or Without Azithromycin in COVID-19 Patients: A Retrospective Analysis in Brazil.
Arq Bras Cardiol
September 2023
Background: Despite no evidence showing benefits of hydroxychloroquine and chloroquine with or without azithromycin for COVID-19 treatment, these medications have been largely prescribed in Brazil.
Objectives: To assess outcomes, including in-hospital mortality, electrocardiographic abnormalities, hospital length-of-stay, admission to the intensive care unit, and need for dialysis and mechanical ventilation, in hospitalized COVID-19 patients who received chloroquine or hydroxychloroquine, and to compare outcomes between those patients and their matched controls.
Methods: A retrospective multicenter cohort study that included consecutive laboratory-confirmed COVID-19 patients from 37 Brazilian hospitals from March to September 2020.
The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions.
View Article and Find Full Text PDFArticle Synopsis
- - The study investigated acute kidney injury (AKI) in hospitalized COVID-19 patients, focusing on predicting the need for kidney replacement therapy (KRT) as a measure of disease severity.
- - Researchers used a large cohort of 5212 adult patients, employing statistical methods like generalized additive models and LASSO regression to develop a prognostic score named MMCD, identifying four key predictors for KRT.
- - The MMCD score demonstrated high accuracy in predicting KRT requirements across various cohorts and is accessible via an online risk calculator, potentially aiding healthcare professionals in managing COVID-19 patients.
Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot.
J Aging Res
August 2020
Background: Renal replacement therapy (RRT) is a public health problem worldwide. Kidney transplantation (KT) is the best treatment for elderly patients' longevity and quality of life.
Objectives: The primary endpoint was to compare elderly versus younger KT recipients by analyzing the risk covariables involved in worsening renal function, proteinuria, graft loss, and death one year after KT.