  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. 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.
Our machine learning model developed from an international cohort can serve to
better regulate hospital risk prioritisation of at-risk patients.
