Thaís Lorenna Souza Sales

Pharmacological treatments for COVID-19 remain limited, particularly for severe outcomes. Tenofovir, an inhibitor of the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp), has been proposed as a therapeutic agent to reduce hospitalization, intensive care unit (ICU) admissions, and mortality. To assess the efficacy of tenofovir in COVID-19 patients based on randomized controlled trials (RCTs). A systematic review of RCTs assessing tenofovir in COVID-19 was conducted. Searches in PubMed/MEDLINE, Scopus, Cochrane Library, LILACS, SciELO, and COVID-19 LOVE databases were last updated on April 16, 2025. Risk of bias was evaluated using the Cochrane Risk of Bias 2.0 tool. As a meta-analysis was not feasible, a qualitative analysis was performed. The review protocol was registered in PROSPERO (CRD42023465336). Among 1241 retrieved trials, three met the inclusion criteria. These trials, conducted in 32 hospitals across Colombia, Spain and Iran included 1048 patients. In the Colombian study, the combination of tenofovir disoproxil/emtricitabine with colchicine and rosuvastatin was associated with reduced 28-day mortality (risk difference [RD] = -0.05; 95% CI: -0.07 to -0.04) and lower need for invasive mechanical ventilation (RD = -0.08; 95% CI: -0.11 to -0.04). However, randomization bias and small sample size limit the interpretation of these results. Conversely, the Spanish study was classified as having a low risk of bias, but found no significant benefit of tenofovir disoproxil/emtricitabine in reducing 28-day mortality (risk ratio [RR] = 1.76; 95% CI: 0.52 to 5.91) or for the composite outcome of ICU admission, disease progression, and mortality (RR = 0.95; 95% CI: 0.66 to 1.40). The Iranian study, in turn, demonstrated that tenofovir alafenamide, when combined with standard treatment, significantly reduced the need for mechanical ventilation (0.0% vs. 13.3%, p = 0.038) and ICU length of stay (3.3 days vs. 14.5 days; p = 0.04). However, the presence of a high risk of bias, with major concerns regarding co-interventions and statistical analyses, precludes a definitive conclusion regarding these results. This review identified three clinical trials evaluating the efficacy of tenofovir in COVID-19, with conflicting results. One study suggested a potential benefit in reducing mortality and the need for invasive mechanical ventilation in mild to moderate cases but methodological limitations, including risk of bias and small sample size, weaken its conclusions. The second study found no significant impact on mortality or disease progression. In the third study, no deaths were reported, but he significant reduction in the need for mechanical ventilation and ICU length of stay is extremely limited due to the high risk of bias. Given these inconsistencies and the limitations of available evidence, tenofovir cannot be recommended for COVID-19 treatment.

Introdução: A infecção por HIV é um grave problema de saúde pública em todo o mundo. O número de idosos que vivem com HIV tem aumentado rapidamente, devido ao sucesso da terapia antirretroviral e mudanças no estilo de vida. Porém, a inflamação crônica causada pelo HIV, e os efeitos da terapia antirretroviral têm sido associados ao envelhecimento precoce desses indivíduos. Até o momento, não existe um consenso sobre a idade em que o indivíduo com HIV é considerado idoso. Nesse contexto, o presente estudo tem por objetivo avaliar, por meio de biomarcadores, o envelhecimento em pessoas vivendo com HIV. Método: Trata-se de um estudo transversal desenvolvido com as pessoas que vivem com HIV, atendidas no Serviço de Atendimento Especializado de Divinópolis-MG, no período de outubro de 2019 a março de 2021. Para comparação, utilizou-se um grupo “não HIV”, selecionado a partir do banco de dados da pesquisa nacional em saúde. Os pacientes foram divididos em grupos etários: 18 a 49, 50 a 59 e maiores de 60 anos para algumas análises, outras análises foram realizadas com grupos etários: maiores de 50 anos e maiores de 60 anos. Os grupos foram comparados em termos de variáveis bioquímicas e hematológicas entre si e com os componentes do grupo “não HIV”. Resultados: Ao analisar a função renal do grupo HIV e “não HIV”, acima de 60 anos, não foram encontradas diferenças entre os grupos. Ao comparar perfil metabólico do grupo HIV de 50 a 59 anos e acima de 60 anos, estes também não diferiram. Finalmente, ao analisar dados hematológicos, estes apresentaram padrões diferentes. Para a contagem de hemácias o grupo de 50 a 59 anos foi igual ao grupo acima de 60 anos, em relação ao hematócrito, o grupo de 50 a 59 anos foi igual ao grupo de 18 a 49 anos. Conclusão: Este estudo demonstra que, para a função renal e hematócrito, o corte de idade para que o indivíduo com HIV seja considerado idoso, seria a partir de 60 anos. Diferentemente, para o perfil metabólico e para contagem de hemácias, o corte ideal quanto à idade, seria a partir de 50 anos. Estes resultados podem ser de grande importância para orientar ajuste de dose e escolha de fármacos para pessoas vivendo com HIV em idades avançadas, visando um melhor controle terapêutico.

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. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. Objective: This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzing changes in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. Methods: The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing–inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Results: Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. Conclusions: We developed a novel methodology combining machine learning and natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimately improving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

Background Warfarin is the only oral anticoagulant available in the Brazilian public health system. Health knowledge and treatment are essential to achieving the desirable therapeutic effect. However, data on these aspects among primary care patients are still lacking. Objective To assess health literacy, patient knowledge, and adherence to oral anticoagulation with warfarin, as well as the medication regimen complexity in primary health units in the municipality of Divinópolis, Minas Gerais, Brazil. Methods This cross-sectional study included patients using warfarin [...] Abstract Background Warfarin is the only oral anticoagulant available in the Brazilian public health system. Health knowledge and treatment are essential to achieving the desirable therapeutic effect. However, data on these aspects among primary care patients are still lacking. Objective To assess health literacy, patient knowledge, and adherence to oral anticoagulation with warfarin, as well as the medication regimen complexity in primary health units in the municipality of Divinópolis, Minas Gerais, Brazil. Methods This cross-sectional study included patients using warfarin

The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48–71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

Background: The assessment of clinical prognosis of pregnant COVID-19 patients at hospital presentation is challenging, due to physiological adaptations during pregnancy. Our aim was to assess the performance of the ABC₂-SPH score to predict in-hospital mortality and mechanical ventilation support in pregnant patients with COVID-19, to assess the frequency of adverse pregnancy outcomes, and characteristics of pregnant women who died. Methods: This multicenter cohort included consecutive pregnant patients with COVID-19 admitted to the participating hospitals, from April/2020 to March/2022. Primary outcomes were in-hospital mortality and the composite outcome of mechanical ventilation support and in-hospital mortality. Secondary endpoints were pregnancy outcomes. The overall discrimination of the model was presented as the area under the receiver operating characteristic curve (AUROC). Overall performance was assessed using the Brier score. Results: From 350 pregnant patients (median age 30 [interquartile range (25.2, 35.0)] years-old]), 11.1% had hypertensive disorders, 19.7% required mechanical ventilation support and 6.0% died. The AUROC for in-hospital mortality and for the composite outcome were 0.809 (95% IC: 0.641–0.944) and 0.704 (95% IC: 0.617–0.792), respectively, with good overall performance (Brier = 0.0384 and 0.1610, respectively). Calibration was good for the prediction of in-hospital mortality, but poor for the composite outcome. Women who died had a median age 4 years-old higher, higher frequency of hypertensive disorders (38.1% vs. 9.4%, p < 0.001) and obesity (28.6% vs. 10.6%, p = 0.025) than those who were discharged alive, and their newborns had lower birth weight (2000 vs. 2813, p = 0.001) and five-minute Apgar score (3.0 vs. 8.0, p < 0.001). Conclusions: The ABC₂-SPH score had good overall performance for in-hospital mortality and the composite outcome mechanical ventilation and in-hospital mortality. Calibration was good for the prediction of in-hospital mortality, but it was poor for the composite outcome. Therefore, the score may be useful to predict in-hospital mortality in pregnant patients with COVID-19, in addition to clinical judgment. Newborns from women who died had lower birth weight and Apgar score than those who were discharged alive.

Background: Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. Methods: This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Results: The median age of the model-derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918–0.939) and validation (temporal AUROC 0.927, 95% CI 0.911–0.941; geographic AUROC 0.819, 95% CI 0.792–0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/). Conclusions: The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.

Background: HIV infection remains a public health concern, especially in low- and middle-income countries. Data regarding exposure of COVID-19 in HIV infected patients remains scarce. We evaluated clinical characteristics and outcomes of COVID-19 patients infected with HIV, and compared with a paired sample without HIV infection. Methods: This is a substudy of a large Brazilian cohort that comprised two periods (2020 and 2021). Data was obtained through the retrospective review of medical records to collect variables of interest and primary outcomes: intensive care admission, mechanical ventilation and death. COVID-19 patients infected with HIV were compared to COVID-19 patients without concomitant diagnosis of HIV infection using the Chi-Square Test and Fisher's exact test for categorical variables and the Wilcoxon test for numerical variables. Both groups were matched for age, sex, number of comorbidities and hospital of origin using the technique of propensity score matching (up to 4:1).Results: Throughout the study, 17,101 COVID-19 patients were hospitalized, 130 (0.76%) of these infected with HIV. The median age was 54 (IQR: 43.0;64.0) years in 2020 and 53 (IQR: 46.0;63.5) years in 2021, with predominance of females in both periods. People living with HIV (PLHIV) and their controls showed similar prevalence for the admission in the ICU and mechanical ventilation requirement in the two periods, with no significant differences. In 2020, in-hospital mortality was higher in the PLHIV compared to the controls (27.9% vs 17.7%; p=0.049), but there was no difference in mortality between groups in 2021 (25.0% vs 25.1%; p>0.999). Conclusion: Our results reiterate that PLHIV were at higher risk of COVID-19 mortality in the early stages of the pandemic, however, this finding did not sustain in 2021, indicating that measures such as large-scale immunization programs have successfully contributed to reducing the excess mortality seen in PLHIV.