Li Qiu-Yu, An Zhuo-Yu, Pan Zi-Han, Wang Zi-Zhen, Wang Yi-Ren, Zhang Xi-Gong, Shen Ning
Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing 100191, China.
Department of Education, Peking University People's Hospital, Beijing 100044, China.
World J Clin Cases. 2023 Apr 26;11(12):2716-2728. doi: 10.12998/wjcc.v11.i12.2716.
Early identification of severe/critical coronavirus disease 2019 (COVID-19) is crucial for timely treatment and intervention. Chest computed tomography (CT) score has been shown to be a significant factor in the diagnosis and treatment of pneumonia, however, there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.
To develop a severe/critical COVID-19 prediction model using a combination of imaging scores, clinical features, and biomarker levels.
This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients. The study also took into consideration the general clinical indicators such as dyspnea, oxygen saturation, alternative lengthening of telomeres (ALT), and androgen suppression treatment (AST), which are commonly associated with severe/critical COVID-19 cases. The study employed lasso regression to evaluate and rank the significance of different disease characteristics.
The results showed that blood oxygen saturation, ALT, IL-6/IL-10, combined score, ground glass opacity score, age, crazy paving mode score, qsofa, AST, and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases. The study established a COVID-19 severe/critical early warning system using various machine learning algorithms, including XGBClassifier, Logistic Regression, MLPClassifier, RandomForestClassifier, and AdaBoost Classifier. The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.
The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.
早期识别重症/危重型冠状病毒病2019（COVID-19）对于及时治疗和干预至关重要。胸部计算机断层扫描（CT）评分已被证明是肺炎诊断和治疗的一个重要因素，然而，目前缺乏基于动态CT演变的针对重症/危重型COVID-19的有效预警系统。
利用影像评分、临床特征和生物标志物水平相结合的方法开发一种重症/危重型COVID-19预测模型。
本研究采用一种改进的评分系统来提取和描述COVID-19患者的胸部CT特征。该研究还考虑了诸如呼吸困难、血氧饱和度、端粒酶逆转录酶（ALT）和雄激素抑制治疗（AST）等一般临床指标，这些指标通常与重症/危重型COVID-19病例相关。该研究采用套索回归来评估和排序不同疾病特征的重要性。
结果表明，血氧饱和度、ALT、白细胞介素-6/白细胞介素-10、综合评分、磨玻璃影评分、年龄、铺路石征评分、快速序贯器官衰竭评估（qSOFA）、AST和总体肺受累评分是预测重症/危重型COVID-19病例的关键因素。该研究使用包括XGBClassifier、逻辑回归、多层感知器分类器（MLPClassifier）、随机森林分类器和自适应增强分类器在内的各种机器学习算法建立了一个COVID-19重症/危重型早期预警系统。该研究得出结论，基于改进的CT评分和机器学习算法的预测模型是早期检测重症/危重型COVID-19病情演变的一种可行方法。
本研究结果表明，基于改进的CT评分和机器学习算法的预测模型在检测重症/危重型COVID-19的预警信号方面是有效的。
