Prediction of multidrug-resistant bacteria (MDR) hospital-acquired infection (HAI) and colonisation: A systematic review.

Background: Hospital-Acquired Infections (HAI) represent a public health priority in most countries worldwide. Our main objective was to systematically review the quality of the predictive modeling literature regarding multidrug-resistant gram-negative bacteria in Intensive Care Units (ICUs).

Methods: We conducted and reported a Systematic Literature Review according to the recommendations of the PRISMA statement.

Evolving changes in mortality of 13,301 critically ill adult patients with COVID-19 over 8 months.

Purpose: Clinical characteristics and management of COVID-19 patients have evolved during the pandemic, potentially changing their outcomes. We analyzed the associations of changes in mortality rates with clinical profiles and respiratory support strategies in COVID-19 critically ill patients.

Methods: A multicenter cohort of RT-PCR-confirmed COVID-19 patients admitted at 126 Brazilian intensive care units between February 27 and October 28, 2020.

App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning.

Background: Tests are scarce resources, especially in low and middle-income countries, and the optimization of testing programs during a pandemic is critical for the effectiveness of the disease control. Hence, we aim to use the combination of symptoms to build a predictive model as a screening tool to identify people and areas with a higher risk of SARS-CoV-2 infection to be prioritized for testing.

Materials And Methods: We performed a retrospective analysis of individuals registered in "Dados do Bem," a Brazilian app-based symptom tracker.

Progression of confirmed COVID-19 cases after the implementation of control measures.

Objective: To analyse the measures adopted by countries that have shown control over the transmission of coronavirus disease 2019 (COVID-19) and how each curve of accumulated cases behaved after the implementation of those measures.

Methods: The methodology adopted for this study comprises three phases: systemizing control measures adopted by different countries, identifying structural breaks in the growth of the number of cases for those countries, and analyzing Brazilian data in particular.

Results: We noted that China (excluding Hubei Province), Hubei Province, and South Korea have been effective in their deceleration of the growth rates of COVID-19 cases.

Analysis of COVID-19 under-reporting in Brazil.

Objective: To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states.

Methods: We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil's expected case-fatality ratio was also adjusted by the population's age pyramid.

Public hospitalizations for stroke in Brazil from 2009 to 2016.

Background: Stroke is the third major cause of death in the world and the second in Brazil. The purpose of this work was to assess the stroke-related hospitalization, in-hospital mortality, and case fatality rates under the Brazilian Unified Health System (SUS) from 2009 to 2016.

Methods: We evaluated the hospital admissions for stroke and their associated outcomes using data from the Hospital Information available at the Informatics Department of SUS.

Predicting Patient No-show Behavior: a Study in a Bariatric Clinic.

Purpose: No-shows of patients to their scheduled appointments have a significant impact on healthcare systems, including lower clinical efficiency and higher costs. The purpose of this study was to investigate the factors associated with patient no-shows in a bariatric surgery clinic.

Materials And Methods: We performed a retrospective study of 13,230 records for 2660 patients in a clinic located in Rio de Janeiro, Brazil, over a 17-month period (January 2015-May 2016).

No-shows in appointment scheduling - a systematic literature review.

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed.