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.
ABSTRACT
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.
ABSTRACT
Background : COVID-19 patients are at increased risk of venous thromboembolism (VTE), and this complication leads to a worse prognosis. However, to diagnose VTE on COVID-19 patients is a challenge to physicians, as the symptoms of pulmonary embolism can often be mistaken for the overlapped viral pneumonia. Herein, there is still little information on VTE incidence and associated risk factors specifically for this population. Aims : To assess the incidence and associated risk factors for VTE in hospitalized COVID-19 patients in Brazilian hospitals. Methods : Retrospective multicenter cohort in 15 Brazilian hospitals. Consecutive adult patients (≥ 18 years-old) with laboratoryconfirmed COVID-19 between March and September 2020 were included. Study data were collected from medical records using Research Electronic Data Capture (REDCap) tools. The study was approved by the National Research Ethics Commission waiving off the application of informed consent. Results : Of 4,021 patients included, 234 (5.8%) had VTE. When comparing VTE and non-VTE groups (Tables 1 and 2), there was no statistical difference in terms of sex and age between groups. The median age was 63 years-old (IQR 51-72 years-old) in VTE group. The most common comorbidities for both groups were hypertension and diabetes. Obesity, chronic pulmonary obstructive disease, previous VTE and recent surgery were more frequent in VTE group. D-dimer, C-reactive protein, lactate dehydrogenase levels and lymphocyte count were higher in the VTE group. Admission to intensive care units (37.6% vs 69.7%;P < 0,001) and in-hospital mortality (19.0% vs 28.3%;P < 0,01) were significantly higher in those who had VTE. Conclusions : Overall, 5.8% of COVID-19 hospitalized patients had VTE. Elevated laboratory values were associated with increased risk of this condition. VTE was associated with higher rates of intensive care admission and in-hospital mortality.