The majority of available scores to assess mortality risk of coronavirus disease 19 (COVID-19) patients in the emergency department have high risk of bias. Therefore, our aim was to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients, and to compare this score with other existing ones. Consecutive patients (≥18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833 to 0.885]) and Spanish (0.894 [95% CI 0.870 to 0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.

Visceral leishmaniasis is a severe and potentially fatal vector-borne disease. The most typical symptoms are fever, hepatosplenomegaly, weight loss, bleeding and bacterial infections. Neurological changes are rarely reported. This paper describes a child who presented with neurological signs as the first symptoms of leishmaniasis; tone was diminished and tremors in the extremities were observed. A diagnosis of visceral leishmaniasis was confirmed by parasite detection in the bone marrow. Symptoms were reversed by specific treatment. The nature of a possible mechanism of neurological involvement in visceral leishmaniasis remains unexplained.

Several studies show that the prevalence of multidrug-resistant HIV-1 virus is declining over time. A retrospective cohort study was carried out to evaluate the trends of drug resistance in antiretroviral treatment-exposed individuals in a state of a middle-income country, Minas Gerais, southeast region of Brazil. We analyzed 2115 HIV-1 sequences from 2002 up to 2012, from 52 cities of Minas Gerais. The groups were analyzed according to the definitions: "IAS - 3 class mutations", if ≥1 drug resistance mutation from IAS 2015 list (DRM) was present in each class; "No fully susceptible drugs" as the absence of any fully susceptible drug in Stanford algorithm; and "GSS≥2″, when a maximum calculated GSS (genotypic susceptibility score) was ≥2 or ≥3, counting only drugs available in Brazil and USA at given calendar years. Time trends of resistance were analyzed by Cochran-Armitage test. We observed a decrease in the rate resistance mutations for PI, NRTI, "IAS - 3 class mutations", and "No fully susceptible drugs" over these 11 years, from 69.2% to 20.7%, 92.3% to 90.2%, 46.2% to 22.5%, and 12.8% to 5.7%, respectively (p<0.05). Resistance to NNRTI increased from 74.4% to 81.6%, mainly because of K103N mutation. The GSS score ≥2 increased during the years from 35.9% to 87.3% (p<0.001). We demonstrate that resistance to PI and to the three main classes simultaneously are declining, although the number of patients on of antiretroviral therapy has doubled in the last ten years in Brazil (125,000 in 2002 to 400,000 in 2014). Broader resistance testing and the availability of more therapeutic options might have influenced this decline. The increase in NNRTI resistance can limit this class as first line treatment in Brazil in the future.

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.

Chagas disease (CD) continues to be a major public health burden in Latina America. Information on the interplay between COVID-19 and CD is lacking. Our aim was to assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients. Consecutive patients with confirmed COVID-19 were included from March to September 2020. Genetic matching for sex, age, hypertension, diabetes mellitus and hospital was performed in a 4:1 ratio. Of the 7018 patients who had confirmed COVID-19, 31 patients with CD and 124 matched controls were included (median age 72 (64–80) years-old, 44.5% were male). At baseline, heart failure (25.8% vs. 9.7%) and atrial fibrillation (29.0% vs. 5.6%) were more frequent in CD patients than in the controls (p < 0.05). C-reactive protein levels were lower in CD patients compared with the controls (55.5 [35.7, 85.0] vs. 94.3 [50.7, 167.5] mg/dL). In-hospital management, outcomes and complications were similar between the groups. In this large Brazilian COVID-19 Registry, CD patients had a higher prevalence of atrial fibrillation and chronic heart failure compared with non-CD controls, with no differences in-hospital outcomes. The lower C-reactive protein levels in CD patients require further investigation.

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.