Ye Jiawei, Huang Yingying, Chu Caiting, Li Juan, Liu Guoxiang, Li Wenjie, Gao Chengjin
Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People's Republic of China.
Dementia Research Centre, Faculty of Medicine, Health and Human Sciences, Macquarie University Sydney, Australia.
J Inflamm Res. 2024 May 14;17:2977-2989. doi: 10.2147/JIR.S456440. eCollection 2024.
Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients.
From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality.
Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001-1.005), APACHE II score (OR = 1.387, 95% CI 1.216-1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939-2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947-0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131-2.068), urea nitrogen (OR = 1.587, 95% CI 1.222-2.062), SOFA score(OR = 3.333, 95% CI 1.476-7.522), qSOFA score(OR = 15.197, 95% CI 3.281-70.384), PSI score(OR = 1.053, 95% CI 1.018-1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615-114,953.586) independently linked to COVID-19 patient severity.
一些新冠病毒感染患者会迅速发展为呼吸衰竭或死亡，这凸显了早期识别易发展为重症患者的必要性。众多研究聚焦于临床和实验室特征，但仅有少数关注胸部计算机断层扫描。本研究旨在通过人工智能算法结合临床和实验室数据对早期胸部CT扫描进行数值量化，以帮助临床医生早期识别一组新冠病毒感染患者中重症和死亡的发展情况。
2022年12月15日至2023年1月30日，连续纳入了191名入住上海交通大学医学院附属新华医院的确诊新冠病毒感染患者。所有患者在入院前48小时内均接受了胸部CT扫描和血清检测。单因素分析中与危重症或死亡显著相关的变量在进行共线性评估后纳入多因素逻辑回归模型。计算调整后的比值比、95%置信区间、敏感性、特异性、约登指数、受试者工作特征（ROC）曲线和曲线下面积（AUC）以预测病情严重程度和院内死亡率。
多因素逻辑分析显示，肌红蛋白（OR = 1.003，95%CI 1.001 - 1.005）、急性生理与慢性健康状况评分系统II（APACHE II）评分（OR = 1.387，95%CI 1.216 - 1.583）以及感染的CT区域百分比（OR = 113.897，95%CI 4.939 - 2626.496）与新冠病毒感染患者的院内死亡率独立相关。前白蛋白是一个独立的保护因素（OR = 0.965，95%CI 0.947 - 0.984）。中性粒细胞计数（OR = 1.529，95%CI 1.131 - 2.068）、尿素氮（OR = 1.587，95%CI 1.222 - 2.062）、序贯器官衰竭评估（SOFA）评分（OR = 3.333，95%CI 1.476 - 7.522）、快速序贯器官衰竭评估（qSOFA）评分（OR = 15.197，95%CI 3.281 - 70.384）、肺炎严重指数（PSI）评分（OR = 1.053，95%CI 1.018 - 1.090）以及感染的CT区域百分比（OR = 548.221，95%CI 2.615 - 114,953.586）与新冠病毒感染患者的病情严重程度独立相关。
