Hangzhou Xiaoshan District Center for Disease Control and Prevention, Hangzhou, China.
Hangzhou Wowjoy Information Technology Co., Ltd, Hangzhou, China.
Sci Rep. 2021 Feb 3;11(1):2933. doi: 10.1038/s41598-021-82492-x.
COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.
新型冠状病毒肺炎（COVID-19）是一种新发传染病，普遍易感，给人类健康带来了巨大损失。急性呼吸窘迫综合征（ARDS）是严重 COVID-19 的常见临床表现之一，也是目前全球呼吸机短缺的原因之一。本研究旨在分析 COVID-19 合并 ARDS 患者的临床特征，并建立基于人工智能（AI）方法的诊断系统，预测 COVID-19 患者 ARDS 的概率。我们收集了来自中国 11 个地区的 659 例 COVID-19 患者的临床数据。详细比较了 COVID-19 合并 ARDS 组和非 ARDS 组患者的临床特征，并采用传统机器学习算法和基于深度学习的方法构建了预测模型。结果表明，ARDS 患者的中位年龄为 56.5 岁，比非 ARDS 患者大 7.5 岁。男性和 BMI>25 的患者更容易发生 ARDS。ARDS 患者的临床特征包括咳嗽（80.3%）、呼吸急促（59.2%）、肺部实变（53.9%）、继发细菌感染（30.3%）和高血压等合并症（48.7%）。淋巴细胞计数、CK、NLR、AST、LDH 和 CRP 等异常生化指标均与 ARDS 加重密切相关。此外，通过基于上述危险因素的各种 AI 方法进行建模和预测效果评估，决策树在识别容易发生 ARDS 的轻度患者方面取得了最佳 AUC、准确性、敏感性和特异性，这无疑有助于提供适当的护理并优化有限资源的利用。
