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By September, 2022, more than 600 million cases of SARS-CoV-2 infection havebeen reported globally, resulting in over 6.5 million deaths. COVID-19mortality risk estimators are often, however, developed with smallunrepresentative samples and with methodological limitations. It is highlyimportant to develop predictive tools for pulmonary embolism (PE) in COVID-19patients as one of the most severe preventable complications of COVID-19. Usinga dataset of more than 800,000 COVID-19 patients from an international cohort,we propose a cost-sensitive gradient-boosted machine learning model thatpredicts occurrence of PE and death at admission. Logistic regression, Coxproportional hazards models, and Shapley values were used to identify keypredictors for PE and death. Our prediction model had a test AUROC of 75.9% and74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortalityrespectively on a highly diverse and held-out test set. The PE prediction modelwas also evaluated on patients in UK and Spain separately with test results of74.5% 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.Our machine learning model developed from an international cohort can serve tobetter regulate hospital risk prioritisation of at-risk patients.

At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19  Patients Using Statistical and Machine Learning Methods: An International  Cohort Study