Author:
Wang Yueying,Wang Zhao,Liu Yaqing,Yu Qiong,Liu Yujia,Luo Changfan,Wang Siyang,Liu Hongmei,Liu Mingyou,Zhang Gongyou,Fan Yusi,Li Kewei,Huang Lan,Duan Meiyu,Zhou Fengfeng
Abstract
Abstract
Background
Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts.
Methods
We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients.
Results
Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values.
Conclusions
Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php.
Funder
Jilin Provincial Key Laboratory of Big Data Intelligent Computing
Jilin Senior and Junior Technological Innovation Team
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC