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

作者：Yang Zhou, Jinhua Feng, Shuya Mei, Ri Tang, Shunpeng Xing, Shaojie Qin, Zhiyun Zhang, Qiaoyi Xu, Yuan Gao, Zhengyu He · 发表于：Frontiers in Medicine · 年份：2023 · DOI：10.3389/fmed.2023.1221711 · 被引用次数：10 · 研究领域：COVID-19 diagnosis using AI、COVID-19 Clinical Research Studies、Respiratory Support and Mechanisms

Background: 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 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. Method: The data of included patients were retrospectively collected from the intensive care unit in our hospital from 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 combining the two individual models. The performance of all the 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 used 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 predic...