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.

Background: The economic impact associated with the treatment strategies of coronavirus disease-2019 (COVID-19) patients by hospitals and health-care systems in Brazil is unknown and difficult to estimate. This research describes the investments made to absorb the demand for treatment and the changes in occupation rates and billing in Brazilian hospitals. Methods: This research covers the initial findings of “COVID-19 hospital costs and the proposition of a bundled reimbursement strategy for the health-care system,” which includes 10 hospitals. The chief financial officer, the chief medical officer, and hospital executives of each participating hospital provided information regarding investments attributed to COVID-19 patient treatment. The analysis included variations in occupation rates and billing from 2019 to 2020 observed in each institution, and the investments for medical equipment, individual protection materials and building construction per patient treated. Results: The majority of hospitals registered a decrease in hospitalization rates and revenue from 2019 to 2020. For intensive care units (ICUs), the mean occupancy rate ranged from 88% to 83%, and for wards, it ranged from 85% to 73%. Monthly average revenue decreased by 10%. The mean hospital investment per COVID-19 inpatient was I$6800 (standard deviation 7664), with the purchase of ventilators as the most common investment. For this item, the mean, highest and lowest acquisition cost per ventilator were, respectively, I$31 468, I$48 881 and I$17 777. Conclusion: There was significant variability in acquisition costs and investments by institution for responding to the COVID-19 pandemic. These findings highlight the importance of continuing microeconomic studies for a comprehensive assessment of hospital costs. Only with more detailed analyses, will it be possible to define and drive sustainable strategies to manage and reimburse COVID-19 treatment in health-care systems.

Systemic arterial hypertension and diabetes mellitus, and their related morbidity and mortality, are currently the most common public health problems and also a higher burden of disease in Brazil. They represent a real challenge for primary health care. This study describes the methodology and baseline data of an adult population with hypertension and diabetes attending in primary health care. It is a cross sectional study which presents data from a longitudinal research. 3784 adults were randomly selected from the registry of a health service in Porto Alegre, Brazil. The eligibility criteria were: confirmed diagnosis of hypertension and/or diabetes, consulted at least once in the prior 3 years and 18 years of age or older. Home data collection consisted of a questionnaire with information on demographic, medical history, life style and socio-economic factors. A total of 2482 users were interviewed (response rate of 71 %). The median age was 64 (IQR = 55.7) and the majority were women (68 %), and married (52 %). Whereas 66.5 % (CI 95 % 64.5-68.3) of the sample had only hypertension, 6.5 % (CI 95 % 5.5-7.5) had diabetes and 27.1 % (CI 95 % 25.3-28.8) had both diseases. The prevalence of diseases increased with age and with fewer years of study (p < 0.05). Subjects with both diseases had significantly more associated comorbidities. Hypertension and diabetes are more prevalent in older individuals, especially women, and less educated people. People suffering with both chronic conditions simultaneously are more likely to have additional comorbidities.

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.

It is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19. The study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis from 37 hospitals. Matching for age, sex, number of comorbidities, and hospital was performed for the paired analysis. Of 7,762 patients with COVID-19, 526 had previously diagnosed hypothyroidism and 526 were matched controls. The median age was 70 years, and 68.3% were females. The prevalence of comorbidities was similar, except for coronary and chronic kidney diseases that were higher in the hypothyroidism group (p=0.015 and p=0.001). D-dimer levels were lower in patients with hypothyroid (p=0.037). In-hospital management was similar, but hospital length-of-stay (p=0.029) and mechanical ventilation requirement (p=0.006) were lower for patients with hypothyroidism. There was a trend of lower in-hospital mortality in patients with hypothyroidism (22.1% vs 27.0%; p=0.062). Patients with hypothyroidism had a lower requirement of mechanical ventilation and showed a trend of lower in-hospital mortality. Therefore, hypothyroidism does not seem to be associated with a worse prognosis.