Differential diagnosis of illness in travelers arriving from sierra Leone, Liberia, or guinea: A cross-sectional study from the Geosentinel surveillance network

Background: The largest-ever outbreak of Ebola virus disease (EVD), ongoing in West Africa since late 2013, has led to export of cases to Europe and North America. Clinicians encountering ill travelers arriving from countries with widespread Ebola virus transmission must be aware of alternate diagnoses associated with fever and other nonspecific symptoms. Objective: To define the spectrum of illness observed in persons returning from areas of West Africa where EVD transmission has been widespread. Design: Descriptive, using GeoSentinel records. Setting: 57 travel or tropical medicine clinics in 25 countries. Patients: 805 ill returned travelers and new mmigrants from Sierra Leone, Liberia, or Guinea seen between September 2009 and August 2014. Measurements: Frequencies of demographic and travelrelated characteristics and illnesses reported. Results: The most common specific diagnosis among 770 nonimmigrant travelers was malaria (n = 310 [40.3%]), with Plasmodium falciparum or severe malaria in 267 (86%) and non–P. falciparum malaria in 43 (14%). Acute diarrhea was the second most common diagnosis among nonimmigrant travelers (n= 95 [12.3%]). Such common diagnoses as upper respiratory tract infection, urinary tract infection, and influenza-like illness occurred in only 26, 9, and 7 returning travelers, respectively. Few instances of typhoid fever (n = 8), acute HIV infection (n = 5), and dengue (n = 2) were encountered. Limitation: Surveillance data collected by specialist clinics may not be representative of all ill returned travelers. Conclusion: Although EVD may currently drive clinical evaluation of ill travelers arriving from Sierra Leone, Liberia, and Guinea, clinicians must be aware of other more common, potentially fatal diseases. Malaria remains a common diagnosis among travelers seen at GeoSentinel sites. Prompt exclusion of malaria and other life-threatening conditions is critical to limiting morbidity and mortality

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