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

• URL: http://arxiv.org/abs/2305.11199v1
• Date: Thu, 18 May 2023 14:55:27 GMT
• Title: At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study
• Authors: Munib Mesinovic, Xin Ci Wong, Giri Shan Rajahram, Barbara Wanjiru Citarella, Kalaiarasu M. Peariasamy, Frank van Someren Greve, Piero Olliaro, Laura Merson, Lei Clifton, Christiana Kartsonaki, ISARIC Characterisation Group
• Abstract summary: It is highly important to develop predictive tools for pulmonary embolism in COVID-19 patients. We propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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