Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

Thomas Wetere Tulu; Tsz Kin Wan; Ching Long Chan; Chun Hei Wu; Peter Yat Ming Woo; Cee Zhung Steven Tseng; Asmir Vodencarevic; Cristina Menni; Kei Hang Katie Chan

doi:10.1186/s44247-022-00001-0

Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

BMC Digit Health. 2023;1(1):6. doi: 10.1186/s44247-022-00001-0. Epub 2023 Feb 3.

Authors

Thomas Wetere Tulu^#^{1

2}, Tsz Kin Wan^#³, Ching Long Chan^#¹, Chun Hei Wu^#¹, Peter Yat Ming Woo⁴, Cee Zhung Steven Tseng⁵, Asmir Vodencarevic⁶, Cristina Menni⁷, Kei Hang Katie Chan^{1

3

8}

Affiliations

¹ Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China.
² Computational Data Science Program, Addis Ababa University, Addis Ababa, Ethiopia.
³ Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.
⁴ Department of Neurosurgery, Kwong Wah Hospital, Hong Kong SAR, China.
⁵ Department of Medicine and Geriatrics, Kwong Wah Hospital, Hong Kong SAR, China.
⁶ Innovative Medicines, Novartis Pharma GmbH, 90429 Nuremberg, Germany.
⁷ Department of Twin Research, King's College London, London, UK.
⁸ Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI USA.

^# Contributed equally.

Abstract

COVID-19 mortality prediction Background COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. Method To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong's public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. Result We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). Conclusion We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study.

Supplementary information: The online version contains supplementary material available at 10.1186/s44247-022-00001-0.

Keywords: Biomarkers; COVID-19; Deep neural network; Machine learning; Random forest classifier.