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BackgroundIt is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19. The study aims to compare clinical characteristics and outcomes of COVID-19 patients with and without hypothyroidism. MethodsThe study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis, including data collected from 37 hospitals. Matching for age, sex, number of comorbidities and hospital was performed to select the patients without hypothyroidism for the paired analysis. ResultsFrom 7,762 COVID-19 patients, 526 had previously diagnosed hypothyroidism (50%) and 526 were selected as matched controls. The median age was 70 (interquartile range 59.0-80.0) years-old and 68.3% were females. The prevalence of underlying comorbidities were similar between groups, except for coronary and chronic kidney diseases, that had a higher prevalence in the hypothyroidism group (9.7% vs. 5.7%, p=0.015 and 9.9% vs. 4.8%, p=0.001, respectively). At hospital presentation, patients with hypothyroidism had a lower frequency of respiratory rate > 24 breaths per minute (36.1% vs 42.0%; p=0.050) and need of mechanical ventilation (4.0% vs 7.4%; p=0.016). D-dimer levels were slightly lower in hypothyroid patients (2.3 times higher than the reference value vs 2.9 times higher; p=0.037). In-hospital management was similar between groups, but hospital length-of-stay (8 vs 9 days; p=0.029) and mechanical ventilation requirement (25.4% vs. 33.1%; 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). ConclusionIn this large Brazilian COVID-19 Registry, 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, and should not be considered among the comorbidities that indicate a risk factor for COVID-19 severity.
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ObjectiveTo provide a thorough comparative study among state-of-the-art machine learning methods and statistical methods for determining in-hospital mortality in COVID-19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and MethodsDe-identified data were obtained from COVID-19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID-19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross-validation procedure, from which we assessed performance and interpretability metrics. ResultsThe Stacking of machine learning models improved over the previous state-of-the-art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macro F1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the why. ConclusionThe best results were obtained using the meta-learning ensemble model - Stacking. State-of the art explainability techniques such as SHAP-values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine-learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions.
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IntroductionChildren and adolescents with Covid-19 have been shown lower mortality less intense symptoms when compared to adults, but studies in Brazil have been based on the compulsory notifying system only. ObjectiveTo analyse clinical, laboratory, radiological characteristics and outcomes of hospitalized patients under 20 years with Covid-19. MethodsCases series of hospitalized patients with confirmed Covid-19 under 20 years, obtained from a cohort study in 37 hospitals from five states of Brazil. ResultsFrom 36 patients, 20 (55.5%) were adolescentes, 20 (55.5%) were male, 18 (50.0%) had comorbidities, 2 were pregnant and in 7 (19.4%), initial symptoms occurred during hospitalization for other causes, of whom 3 were possibly infected in the hospital. Fever (61.1%), dyspnea (33.3%) and neurological symptoms (33.0%) were the most common complaints. C-reactive protein was higher than 50mg/L in 16.7% and D-dimer was above the reference limit in 22.2%. Chest X-rays were performed in 20 (55.5%) patients, 9 had abnormalities, and chest tomography in 5. Hospital length of stay ranged from 1-40 days (median 5 [interquartile range 3-10]), 16 (44.4%) needed intensive therapy, 6 (16.7%) required mechanical ventilation and one patient (2.8%) died. ConclusionIn case series patients under 20 years from hospitals from 5 states of Brazil, comorbidities were frequent, and most common symptoms were fever, dyspnea and neurological symptoms. Forty-four percent required intensive therapy, showing that the disease was not as mild as it was expected, and one patient died.
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Around 5% of coronavirus disease 2019 (COVID-19) patients develop critical disease, with severe pneumonia and acute respiratory distress syndrome (ARDS). In these cases, extracorporeal membrane oxygenation (ECMO) may be considered when conventional therapy fails. This study aimed to assess the clinical characteristics and in-hospital outcomes of COVID-19 patients with ARDS refractory to standard lung-protective ventilation and pronation treated with ECMO support and to compare them to patients who did not receive ECMO. Patients were selected from the Brazilian COVID-19 Registry. At the moment of the analysis, 7,646 patients were introduced in the registry, eight of those received ECMO support (0.1%). The convenience sample of patients submitted to ECMO was compared to control patients selected by genetic matching for gender, age, comorbidities, pronation, ARDS and hospital, in a 5:1 ratio. From the 48 patients included in the study, eight received ECMO and 40 were matched controls. There were no significant differences in demographic, clinical and laboratory characteristics. Mortality was higher in the ECMO group (n = 7; 87.5%) when compared with controls (n = 17; 42.5%), (p=0.048). In conclusion, COVID 19 patients with ARDS refractory to conventional therapy who received ECMO support had worse outcomes to patients who did not receive ECMO. Our findings are not different from previous studies including a small number of patients, however there is a huge difference from Extracorporeal Life Support Organization results, which encourages us to keep looking for our best excellence.
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ObjectiveTo develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones. DesignCohort study SettingThe Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. 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. ParticipantsConsecutive symptomatic patients ([≥]18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay. Main outcome measuresIn-hospital mortality ResultsMedian (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 (https://abc2sph.com/). ConclusionsWe 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. Summary boxesWhat is already known on this topic? O_LIRapid scoring systems may be very useful for fast and effective assessment of COVID-19 patients in the emergency department. C_LIO_LIThe majority of available scores have high risk of bias and lack benefit to clinical decision making. C_LIO_LIDerivation and validation studies in low- and middle-income countries, including Latin America, are scarce. C_LI What this study adds O_LIABC2-SPH employs seven well defined variables, routinely assessed upon hospital presentation: age, number of comorbidities, blood urea nitrogen, C reactive protein, Spo2/FiO2 ratio, platelets and heart rate. C_LIO_LIThis easy-to-use risk score identified four categories at increasing risk of death with a high level of accuracy, and displayed better discrimination ability than other existing scores. C_LIO_LIA free web-based calculator is available and may help healthcare practitioners to estimate the expected risk of mortality for patients at hospital presentation. C_LI
