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Objectives: This study aims to evaluate COVID-19 in-hospital costs and identify predictors at a patient-level in Brazil. Method(s): This is multicenter, prospective cohort study that applied time-driven activity-based costing (TDABC) method in five Brazilian reference centers for COVID-19 treatment. Patients hospitalized between March and August 2020 (first wave of the disease) and had their COVID-19 status confirmed by reverse transcription-polymerase chain reaction (RT-PCR) at arrival were included in our sample. The cost information was calculated at the patient level and multivariable analyses were applied to identify clinical predictors of cost variability, considering ICU admissions and patient's comorbidities. Result(s): 830 patients were included into the analysis. The median cost per patient was I$4,428 (IQR 2,019;11,464), and patients hospitalized in ICU demonstrated significative higher costs (p<0.001). Patients hospitalized in ICU the median was I$11,596 (IQR 6,016;23,374), while for those who were hospitalized in ward was 1,895 (IQR 1,050;3,317). Median cost per day was I$ 455 (IQR 308;711) for the total sample, I$690 (IQR I,528;1,046) for ICU patients and I$350 (IQR 255;449) for non-ICU. Gender (p<0.001), Obesity (p = 0.005) and Chronic pulmonary diseases (p = 0.044) were identified as clinical predictors for hospital costs. Conclusion(s): By developing a multicenter microcosting study for COVID-19 this study allowed to measure the variability in resource consumption per patients' according their clinical characteristics. These findings can sustain the development of financially sustainable health policies in middle-income countries such as Brazil.Copyright © 2023
ABSTRACT
Introduction: Fast and efficient assessment of prognosis of coronavirus disease 19 (COVID-19) is needed to optimize the allocation of health care and human resources, to empower early identification and intervention of patients at higher risk of poor outcome. A proper assessment tool may guide decision making, to develop an appropriate plan of care for each patient. Although different scores have been proposed, the majority of them are limited due to high risk of bias, and there is a lack of reliable prognostic prediction models. Purpose: To develop and validate an easy applicable rapid scoring system that employs routinely available clinical and laboratory data at hospital presentation, to predict in-hospital mortality in patients with COVID-19, able to discriminate high vs non-high risk patients. Additionally, we aimed to compare this score with other existing ones. Method: Cohort study, conducted in 36 Brazilian hospitals in 17 cities. Consecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Primary outcome was in-hospital mortality. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Results: Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality 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) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2- SPH score is implemented in a freely available online risk calculator. Conclusions: 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.
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Background: Underlying comorbidities have been widely associated with a worse prognosis for COVID-19 patients, since viral infections could act as triggers for worsening of chronic diseases. Although Chagas disease (CD) is endemic in Latin America, it has been recognized that the disease is now a worldwide concern. Information on the interplay between COVID-19 and CDis lacking. Purpose: To assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients. Methods: Patients with COVID-19 diagnosis were selected from the Brazilian COVID-19 Registry, a prospective multicenter cohort, from March to September, 2020. CD diagnosis was based on hospital record at the time of admission. Study data were collected by trained hospital staff using Research Electronic Data Capture (REDCap) tools. Genetic matching for sex, age, hypertension, DM and hospital was performed in a 4:1 ratio. Results: Of the 7,018 patients who had confirmed infection with SARSCoV- 2 in the registry, 31 patients with CD and 124 matched controls were included. Overall, the median age was 72 (64.-80) years-old and 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 for both). 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). Seventy-two (46.5%) patients required admission to the intensive care unit. In-hospital management, outcomes and complications were similar between the groups (Table 1). Conclusions: 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. (Figure Presented).
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Background: A lot of attention has been drawn to the identification of predictors of VTE in COVID-19 patients, and an accurate clinical prediction model is still lacking in this context. Aims: To develop a clinical prediction model using artificial intelligence techniques, to predict VTE in COVID-19 patients, using variables easily available upon hospital admission. Methods: This multicenter cohort included consecutive adult patients (≥ 18 years-old) with laboratory-confirmed COVID-19 from 37 Brazilian hospitals from 17 cities, between March and September 2020. Study data were collected from medical records using Research Electronic Data Capture (REDCap) tools. We trained multiple machine learning models on various combinations of structured and non-structured features, calibrated to reflect a probability distribution while predicting the desired clinical outcome. Subsequently, we analyzed the relationship between this model ' s predicted confidence score and the fraction of false negatives in the test sample to devise a splitting point where no false negatives would occur, thus calibrating for sensitivity over specificity. The study was approved by the National Research Ethics Commission waiving off the application of informed consent. Results: The dataset included 6421 patients (median age 61 [P25-75 48-73] years-old, 54.8% men), 4.5% of them developed venous thromboembolic disease. Patient ' s age, sex and comorbidities, as well as their list of household prescription drugs, history of recent surgery and laboratory tests were significant predictors. Given a proper confidence level, our model predicted 100% of the true positive cases while eliminating a significant portion of the true negatives (Figure 1). (Figure Presented) Conclusions: This study suggests that an ensemble of decision rules can effectively predict COVID patients with high risk of VTE. It might be possible to decrease the use of anticoagulants while still treating patients with an appreciable likelihood of thromboembolism.