Affiliations 

  • 1 Department of Engineering Science, University of Oxford, Oxford, UK. munib.mesinovic@jesus.ox.ac.uk
  • 2 Digital Health Research and Innovation Unit, Institute for Clinical Research, National Institutes of Health (NIH), Shah Alam, Malaysia
  • 3 Queen Elizabeth II Hospital, Ministry of Health, Kota Kinabalu, Malaysia
  • 4 Pandemic Sciences Institute, ISARIC, University of Oxford, Oxford, UK
  • 5 Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
  • 6 Nuffield Department of Population Health, University of Oxford, Oxford, UK
Sci Rep, 2024 Jul 16;14(1):16387.
PMID: 39013928 DOI: 10.1038/s41598-024-63212-7

Abstract

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.