CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients
CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients
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DOI：
10.7150/thno.45985
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发表时间：
2020-01-01
期刊：
影响因子：
12.4
通讯作者：
Shi, Yuxin
中科院分区：
文献类型：
--
作者：
Liu, Fengjun;Zhang, Qi;Shi, Yuxin
基本原理：部分2019冠状病毒病（COVID-19）患者迅速发展为呼吸衰竭甚至死亡，这强调了早期识别重症风险升高的患者的必要性。本研究旨在通过计算机断层扫描（CT）在早期量化肺炎病变，以预测COVID-19患者队列中进展为严重疾病。方法：本回顾性队列研究包括确诊的COVID-19患者。使用人工智能算法自动计算肺炎病变的三个定量CT特征，代表双肺中磨玻璃样阴影体积（PGV）、半实变体积（PSV）和实变体积（PCV）的百分比。CT特征、急性生理学和慢性健康评估2第0天的（APACHE-II）评分、嗜中性粒细胞与淋巴细胞比率（NLR）和d-二聚体（入院）和第4天的数据，使用逻辑回归和考克斯比例风险模型预测28天随访内严重疾病的发生。我们纳入了134例患者，其中19例（14.2%）出现任何严重疾病。第0天和第4天的CT特征，以及它们从第0天到第4天的变化，显示出预测能力。第0天至第4天的CT特征变化在预测中表现最佳（受试者工作特征曲线下面积= 0.93，95%置信区间[CI] 0.87与0.99相似; C指数=0.88，95% CI 0.81与0.95相似）。PGV和PCV的风险比分别为1.39（95%CI 1.05相似于1.84，P=0.023）和1.67（95%CI 1.17相似于2.38，P=0.005）。在第4天以及从第0天到第4天的变化方面，根据年龄和性别调整的CT特征优于APACHE-II，NLR和d-dimer.Conclusions：肺炎病变的CT定量可以早期和无创地预测病情进展，为COVID-19的临床管理提供有希望的预后指标。
Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients.Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models.Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87 similar to 0.99; C-index=0.88, 95% CI 0.81 similar to 0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05 similar to 1.84, P=0.023) and 1.67 (95% CI 1.17 similar to 2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer.Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.