AUTHOR=Zhou Yang , Feng Jinhua , Mei Shuya , Tang Ri , Xing Shunpeng , Qin Shaojie , Zhang Zhiyun , Xu Qiaoyi , Gao Yuan , He Zhengyu 

TITLE=A deep learning model for predicting COVID-19 ARDS in critically ill patients

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1221711

DOI=10.3389/fmed.2023.1221711

ISSN=2296-858X

ABSTRACT=The coronavirus disease 2019 (COVID-19) is an acute infectious pneumonia caused by a severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection previously unknown to humans. However, predictive studies of acute respiratory distress syndrome (ARDS) in patients with coronavirus disease 2019 (COVID-19) are limited. In this study, we attempted to establish predictive models to predict ARDS caused by COVID-19 via a thorough analysis of patients' clinical data and CT images.The data of included patients were retrospectively collected from the intensive care unit in our hospital form April 2022 to June 2022. The primary outcome was the development of ARDS after ICU admission. We first established two individual predictive models based on extreme gradient boosting (XGBoost) and convolutional neural network (CNN) respectively, then an integrated model was developed by combing the two individual models. The performance of all predictive models was evaluated using the area under receiver operating characteristic curve (AUC), confusion matrix and calibration plot.Results: A total of 103 critically ill COVID-19 patients were included in this research, of which 23 patients (22.3%) developed ARDS after admission. Five predictive variables were selected and further utilized to establish the machine learning models and the XGBoost model yielded the most accurate predictions with the highest AUC (0.94, 95% CI: 0.91-0.96). The AUC of the CT-based convolutional neural network predictive model and the integrated model was 0.96 (95% CI: 0.93-0.98) and 0.97 (95% CI: 0.95-0.99) respectively.An integrated deep learning model could be utilized to predict COVID-19 ARDS in critically ill patients.