Official websites use. Share sensitive information only on official, secure websites. COVID mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations.
Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than , COVID 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. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 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 patients globally.
Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. Subject terms: Biomedical engineering, Epidemiology, Risk factors, Infectious diseases. On the last day of , the WHO received information about 44 cases of pneumonia-like disease in Wuhan city, China 1.
By 5 September , more than million cases of SARS-CoV-2 infection had been reported across all continents, regions, and most countries, resulting in nearly 6. It continues to this day to be a challenging global pandemic with significant morbidity and mortality 4. As Knight et al. One such score is the 4C score that includes data about patient comorbidity, abnormal physiology, and inflammation using routinely measured data, bedside observations, and biochemistry tests 6.
