Níveis disfuncionais de ansiedade relacionada ao Coronavírus em estudantes de medicina: Dysfunctional levels of Coronavirus-related anxiety in medical students

As preocupações com a saúde mental das pessoas afetadas pela pandemia de coronavírus não foram abordadas adequadamente. Isso é surpreendente, uma vez que tragédias em massa, particularmente aquelas que envolvem doenças infecciosas, muitas vezes desencadeiam ondas de medo e ansiedade elevados que são conhecidos por causar perturbações maciças no comportamento e no bem-estar psicológico de muitos na população. Assim, o objetivo desse trabalho é demonstrar os níveis disfuncionais de ansiedade relacionada ao coronavírus em estudantes de medicina. Para isso, foi realizado uma revisão sistemática sobre a temática

Perspectivas epidemiológicas, clínicas e terapêuticas do transtorno bipolar em comorbidade com o uso de drogas: revisão de sistemática: Epidemiological, clinical and therapeutic perspectives of bipolar disorder in comorbidity with drug use: a systematic review

Conhecida como transtorno maníaco-depressivo, atualmente possui um novo nome: Transtorno Afetivo Bipolar, visto que com o passar do tempo foi se percebendo que esse transtorno não se tratava de uma alteração psicótica, e mais de um prejuízo afetivo. O transtorno bipolar possui alguns tipos, não se caracterizando em apenas uma forma, sua manifestação varia conforme o indivíduo e suas tendências, disforia e/ou euforia porém independente da forma expressa o paciente bipolar pode ter sua vida social comprometida, se não tratada, visto a irregularidade no estado de humor; bem como pode fazer uso de substâncias psicoativas, o que prejudica a sua condição clínica. Objetivo central da pesquisa é de apresentar a correlação do transtorno bipolar com o uso de drogas, mediante uma revisão de literatura integrativa realizada entre os meses de março de 2022 a julho de 2022, através da busca de artigos científicos nos bancos de dados online PubMed, Scielo e Google Acadêmico, utilizando como critério de refinamento de pesquisa artigos de todas as línguas publicados entre os anos 2000 e 2022

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use