Publications by authors named "Renato Camargos Couto"
Objective: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting.
Methods: Three machine learning models (random forest, extreme gradient boosting and neural networks) and three traditional regression models (Poisson regression, linear regression, negative binomial regression) were trained in a dataset of 9,584 patients who underwent coronary artery bypass grafting between January 2017 and December 2021. The data were collected from hospital discharges from 133 centers in Brazil.
Objective: Defining priority vaccination groups is a critical factor to reduce mortality rates.
Methods: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021.
Objective: To assess the benefit of using procedure-specific alternative cutoff points for National Nosocomial Infections Surveillance (NNIS) risk index variables and of extending surgical site infection (SSI) risk prediction models with a postdischarge surveillance indicator.
Design: Open, retrospective, validation cohort study.
Setting: Five private, nonuniversity Brazilian hospitals.
Objective: We examined the usefulness of a simple method to account for incomplete postdischarge follow-up during surveillance of surgical site infection (SSI) by use of the National Nosocomial Infections Surveillance (NNIS) system's risk index.
Design: Retrospective cohort study that used data prospectively collected from 1993 through 2006.
Setting: Five private, nonuniversity healthcare facilities in Belo Horizonte, Brazil.