COVID-19 knowledge, attitudes, and practices among health care workers in Latin America

Objective: To evaluate COVID-19 knowledge, attitudes, and practices among health care workers (HCWs) practicing in Latin American countries during the first surge of the COVID-19 pandemic. Methods: This was a multinational cross-sectional survey study, using an online self-administered questionnaire. The final version of the questionnaire comprised 40 questions, organized in five sections: demographic and professional characteristics; COVID-19 knowledge; attitudes toward COVID-19; COVID-19 practices; and institutional resources. Results: The study involved 251 HCWs from 19 Latin American countries who agreed to participate. In our sample, 77% of HCWs participated in some sort of institutional training on COVID-19, and 43% had a low COVID-19 knowledge score. COVID-19 knowledge was associated with the type of health center (public/ private), availability of institutional training, and sources of information about COVID-19. Concerns about not providing adequate care were reported by 60% of the participants. The most commonly used ventilatory strategies were protective mechanical ventilation, alveolar recruitment maneuvers, and prone positioning, and the use of drugs to treat COVID-19 was mainly based on institutional protocols. Conclusions: In this multinational study in Latin America, almost half of HCWs had a low COVID-19 knowledge score, and the level of knowledge was associated with the type of institution, participation in institutional training, and information sources. HCWs considered that COVID-19 was very relevant, and more than half were concerned about not providing adequate care to patients. © 2022 Sociedade Brasileira de Pneumologia e Tisiologia

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. © The Author(s) 2024