COVID-19 and isolation: Risks and implications in the scenario of new variants

Abstract With the emergence of new variants of SARS-CoV-2, questions about transmissibility, vaccine efficacy, and impact on mortality are important to support decision-making in public health measures. Modifications related to transmissibility combined with the fact that much of the population has already been partially exposed to infection and/or vaccination, have stimulated recommendations to reduce the isolation period for COVID-19. However, these new guidelines have raised questions about their effectiveness in reducing contamination and minimizing impact in work environments. Therefore, a collaborative task force was developed to review the subject in a non-systematic manner, answering questions about SARS-CoV-2 variants, COVID-19 vaccines, isolation/quarantine periods, testing to end the isolation period, and the use of masks as mitigation procedures. Overall, COVID-19 vaccines are effective in preventing severe illness and death but are less effective in preventing infection in the case of the Omicron variant. Any strategy that is adopted to reduce the isolation period should take into consideration the epidemiological situation of the geographical region, individual clinical characteristics, and mask for source control. The use of tests for isolation withdrawal should be evaluated with caution, due to results depending on various conditions and may not be reliable.

Active surveillance of carbapenem-resistant Gram-negative healthcare-associated infections in a low-middle-income country city

ABSTRACT Background: Carbapenem-resistance in healthcare-associated infections (HCAIs) is of great concern, and it is urgent to improve surveillance. We aimed to describe and analyze HCAIs trends on Gram-negative antimicrobial susceptibility in a city from a developing country, following the implementation of an active surveillance program. Methods: This is an aggregated study describing data from 24 hospitals with intensive care units, including a trend analysis by Joinpoint regression between January 2012 and December 2017. Results: There were 23,578 pathogens in 39,832 HCAIs, from which 16,225 were Gram-negatives (68.8%). Carbapenem susceptibility was lowest in A. baumannii (15.4-25.9%), K. pneumoniae (51.0-55.9%), and P. aeruginosa (64.9-84.1%) and highest in E. coli (96.5-99.2%). Only K. pneumoniae showed a significant Joinpoint at 95% confidence interval: −10.71% (−18.02; −2.75) from 2012 to 2014, p = 0.02, and 6.54% (−2.00; 15.83) from 2015 to 2017, p = 0.12, which was most influenced by urinary tract infections: −9.98% (−16.02; −3.48) from 2012 to 2014, p = 0.01, and 9.66% (−1.75; 22.39) from 2015 to 2017, p = 0.09. Conclusion: Although we found a significant change toward an improvement in carbapenem susceptibility in K. pneumoniae, resistance is high for most pathogens. These data should encourage health institutions to improve their prevention and control strategies.

Early Identification of Patients at Risk of Sepsis in a Hospital Environment

Abstract Sepsis is a systematic response to an infectious disease, being a concerning factor because of the increase in the mortality ratio for every delayed hour in the identification and start of patient's treatment. Studies that aim to identify sepsis early are valuable for the healthcare domain. Further, studies that propose machine learning-based models to identify sepsis risk are scarce for the Brazilian scenario. Hence, we propose the early identification of sepsis considering data from a Brazilian hospital. We developed a temporal series based on LSTM to predict sepsis in patients considering a three-day timestep. The patients were selected using both criteria, ICD-10, and qSOFA, where we supplemented qSOFA with the additional identification of words referring to infections in the clinical texts. Additionally, we tested a Random Forest classifier to classify patients with sepsis with a single timestep before the sepsis event, evaluating the most relevant features. We achieved an accuracy of 0.907, a sensitivity of 0.912, and a specificity of 0.971 when considering a three-day timestep with LSTM. The Random Forest classifier achieved an accuracy of 0.971, a sensitivity of 0.611, and a specificity of 0.998. The features age, blood glucose, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and admission days had the most influence over the algorithm classification, with age being the most relevant feature. We achieved satisfactory results compared with the literature considering a scenario of spaced measures and a high amount of missing data.

Sepsis risk assessment: a retrospective analysis after a cognitive risk management robot (Robot Laura®) implementation in a clinical-surgical unit

Abstract Introduction This study aimed at evaluating the impact of the implementation of a cognitive robot (Robot Laura™) on processes related to the identification and care of patients with risk of sepsis in a clinical-surgical unit of a private hospital in Curitiba-PR. Methods The study data were obtained from the retrospective review of medical records of patients identified with infection and/or sepsis, in the period of six months before and after the implementation of such technology in the hospital. In addition, the Average Attendance Time (AAT) was obtained from the autonomous reading of the robot. Results The average time/median until antibiotic prescription from the first identified sign of infection, with or without sepsis, was 390/77 and 109/58 minutes, respectively, in the six months before and after implementation of the technology. However, this difference was not statistically significant (p = 0.85). Regarding AAT, it was possible to observe a reduction from 305 to 280 minutes when comparing the periods of six months before and after the implementation of the technology (p = 0.02). Conclusion Technologies such as this may be promising in helping healthcare professionals to identify risky situations for patients, as well as in assisting them to optimize the care required. However, further studies, with a greater number of subjects and with different scenarios, are necessary to consistently validate the results found.