Symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil.

BACKGROUND: COVID-19 has shown a broad clinical spectrum, ranging from asymptomatic to mild, moderate, and severe infections. Many symptoms have already been identified as typical of COVID-19, but few studies show how they can be useful in identifying clusters of patients with different severity of illness. This interpretation may help to recognize the different profiles of symptoms of COVID-19 expressed in a population at certain time. The aim of this study was to identify symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil. The clusters were evaluated based on sociodemographic characteristics, admission to the Intensive Care Unit (ICU), use of respiratory support, and outcome. METHODS: The Multiple Correspondence Analysis (MCA)-based cluster analysis was applied to symptoms presented before admission. Pearson's chi-square test was used to compare the proportions of symptoms between the clusters and to examine differences in the calculated rates for the following variables: sex, age group, race, Brazilian region, use of respiratory support, admission to the ICU and outcome. RESULTS: Three COVID-19 clusters with distinct symptom profiles were identified by MCA-based cluster analysis. Cluster 1 had the mildest severity profile, with the lowest frequencies for most symptoms investigated. Cluster 2 had a severe respiratory profile, with the highest frequencies of patients with dyspnea, respiratory discomfort and O2 saturation< 95%. Cluster 2 was also the most prevalent in all Brazilian regions and had the highest percentages of patients who used invasive respiratory support (27.4%) (p-value<0.001), were admitted to the ICU (42.6%) (p -value<0.001) and died (39.0%) (p-value<0.001). Cluster 3 had a prominent profile of gastrointestinal symptoms. CONCLUSIONS: The study identified three distinct COVID-19 clusters based on the symptoms presented by patients with severe acute respiratory illness by SARS-CoV-2, but without distinction in their prevalence in the Brazilian regions.

Biomechanical and Anthropometric Factors That Differentiate National- and Regional-Level Judo Players.

PURPOSE: The study aimed to identify the variables that differentiate judo athletes at national and regional levels. Multivariable analysis was applied to biomechanical, anthropometric, and Special Judo Fitness Test (SJFT) data. METHOD: Forty-two male judo athletes from 2 competitive groups (14 national and 28 state levels) performed the following measurements and tests: (1) skinfold thickness, (2) circumference, (3) bone width, (4) longitudinal length, (5) stabilometric tests, (6) dynamometric tests, and (7) SJFT. The variables with significant differences in the Wilcoxon rank-sum test were used in stepwise logistic regression to select those that better separate the groups. The authors considered models with a maximum of 3 variables to avoid overfitting. They used 7-fold cross validation to calculate optimism-corrected measures of model performance. RESULTS: The 3 variables that best differentiated the groups were the epicondylar humerus width, the total number of throws on the SJFT, and the stabilometric mean velocity of the center of pressure in the mediolateral direction. The area under the receiver-operating-characteristic curve for the model (based on 7-fold cross validation) was 0.95. CONCLUSION: This study suggests that a reduced set of anthropometric, biomechanical, and SJFT variables can differentiate judo athlete's levels.

EXPLORING CORTICAL BONE DENSITY THROUGH THE ULTRASOUND INTEGRATED REFLECTION COEFFICIENT.

OBJECTIVE: This work evaluates the relationship between ultrasonic reflection and bone density from fourteen cylindrical bovine cortical bone samples (3.0-cm thick). METHODS: Twenty US reflection signals per sample were acquired along the bone surface (2.0-mm step). The Integrated Reflection Coefficient (IRC) from each signal was compared to Quantitative Computed Tomography (QCT). RESULTS: Seven IRC and QCT curves presented Pearson's Correlation R-values above 0.5. For weak correlation curves, QCT and IRC showed similar trends in several segments. CONCLUSION: IRC was sensitive to bone density variation. Level of Evidence: Experimental Study, Investigating a Diagnostic Test.

OBJETIVO: Este estudo avalia a relação entre a reflexão ultrassônica e a densidade óssea de 14 amostras cilíndricas de osso cortical bovino (3,0 cm de espessura). MÉTODOS: Foi realizada a aquisição de 20 sinais de reflexão ultrassônica por amostra (passo de 2,0 mm), ao longo da superfície óssea. O Coeficiente de Reflexão Integrado (IRC) de cada sinal foi comparado por Tomografia Computadorizada Quantitativa (QCT). RESULTADOS: Sete curvas de IRC e QCT apresentaram valor de Correlação R de Pearson acima de 0,5. Para curvas de correlação fraca, QCT e IRC apresentaram tendências semelhantes em vários segmentos. CONCLUSÃO: O IRC foi sensível à variação da densidade óssea. Nível de evidência: Estudo Experimental, Investigação de Exame Diagnóstico.

Exploring cortical bone density through the ultrasound integrated reflection coefficient / Densidade óssea cortical analisada pelo coeficiente de reflexão ultrassônica

ABSTRACT Objective: This work evaluates the relationship between ultrasonic reflection and bone density from fourteen cylindrical bovine cortical bone samples (3.0-cm thick). Methods: Twenty US reflection signals per sample were acquired along the bone surface (2.0-mm step). The Integrated Reflection Coefficient (IRC) from each signal was compared to Quantitative Computed Tomography (QCT). Results: Seven IRC and QCT curves presented Pearson's Correlation R-values above 0.5. For weak correlation curves, QCT and IRC showed similar trends in several segments. Conclusion: IRC was sensitive to bone density variation. Level of Evidence: Experimental Study, Investigating a Diagnostic Test.

RESUMO Objetivo: Este estudo avalia a relação entre a reflexão ultrassônica e a densidade óssea de 14 amostras cilíndricas de osso cortical bovino (3,0 cm de espessura). Métodos: Foi realizada a aquisição de 20 sinais de reflexão ultrassônica por amostra (passo de 2,0 mm), ao longo da superfície óssea. O Coeficiente de Reflexão Integrado (IRC) de cada sinal foi comparado por Tomografia Computadorizada Quantitativa (QCT). Resultados: Sete curvas de IRC e QCT apresentaram valor de Correlação R de Pearson acima de 0,5. Para curvas de correlação fraca, QCT e IRC apresentaram tendências semelhantes em vários segmentos. Conclusão: O IRC foi sensível à variação da densidade óssea. Nível de evidência: Estudo Experimental, Investigação de Exame Diagnóstico.