Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, OH.
AJR Am J Roentgenol. 2021 Jul;217(1):83-92. doi: 10.2214/AJR.20.24044. Epub 2020 Sep 9.
Chest CT findings have the potential to guide treatment of hospitalized patients with coronavirus disease (COVID-19). The purpose of this study was to assess a CT visual severity score in hospitalized patients with COVID-19, with attention to temporal changes in the score and the role of the score in a model for predicting in-hospital complications. This retrospective study included 161 inpatients with COVID-19 from three hospitals in China who underwent serial chest CT scans during hospitalization. CT examinations were evaluated using a visual severity scoring system. The temporal pattern of the CT visual severity score across serial CT examinations during hospitalization was characterized using a generalized spline regression model. A prognostic model to predict major complications, including in-hospital mortality, was created using the CT visual severity score and clinical variables. External model validation was evaluated by two independent radiologists in a cohort of 135 patients from a different hospital. The cohort included 91 survivors with nonsevere disease, 55 survivors with severe disease, and 15 patients who died during hospitalization. Median CT visual lung severity score in the first week of hospitalization was 2.0 in survivors with non-severe disease, 4.0 in survivors with severe disease, and 11.0 in nonsurvivors. CT visual severity score peaked approximately 9 and 12 days after symptom onset in survivors with nonsevere and severe disease, respectively, and progressively decreased in subsequent hospitalization weeks in both groups. In the prognostic model, in-hospital complications were independently associated with a severe CT score (odds ratio [OR], 31.28), moderate CT score (OR, 5.86), age (OR, 1.09 per 1-year increase), and lymphocyte count (OR, 0.03 per 1 × 10/L increase). In the validation cohort, the two readers achieved C-index values of 0.92-0.95, accuracy of 85.2-86.7%, sensitivity of 70.7-75.6%, and specificity of 91.4-91.5% for predicting in-hospital complications. A CT visual severity score is associated with clinical disease severity and evolves in a characteristic fashion during hospitalization for COVID-19. A prognostic model based on the CT visual severity score and clinical variables shows strong performance in predicting in-hospital complications. The prognostic model using the CT visual severity score may help identify patients at highest risk of poor outcomes and guide early intervention.
胸部 CT 表现有可能指导冠状病毒病(COVID-19)住院患者的治疗。本研究的目的是评估 COVID-19 住院患者的 CT 视觉严重程度评分,重点关注评分的时间变化以及评分在预测住院并发症模型中的作用。这项回顾性研究纳入了来自中国 3 家医院的 161 例 COVID-19 住院患者,这些患者在住院期间接受了系列胸部 CT 扫描。使用视觉严重程度评分系统评估 CT 检查。使用广义样条回归模型描述住院期间系列 CT 检查中 CT 视觉严重程度评分的时间模式。使用 CT 视觉严重程度评分和临床变量创建预测主要并发症(包括住院死亡率)的预后模型。两名独立的放射科医生对来自另一家医院的 135 例患者的队列进行了外部模型验证。该队列包括 91 例非重症疾病幸存者、55 例重症疾病幸存者和 15 例住院期间死亡的患者。非重症疾病幸存者在住院第一周的中位 CT 视觉肺部严重程度评分为 2.0,重症疾病幸存者为 4.0,非幸存者为 11.0。在症状出现后分别约 9 天和 12 天,非重症和重症疾病幸存者的 CT 视觉严重程度评分达到峰值,随后在两组后续住院周中逐渐下降。在预后模型中,住院并发症与严重 CT 评分(比值比[OR],31.28)、中度 CT 评分(OR,5.86)、年龄(OR,每增加 1 岁增加 1.09)和淋巴细胞计数(OR,每增加 1×10/L 增加 0.03)独立相关。在验证队列中,两位读者对预测住院并发症的准确性为 85.2%-86.7%、敏感性为 70.7%-75.6%、特异性为 91.4%-91.5%,C 指数值分别为 0.92-0.95。CT 视觉严重程度评分与临床疾病严重程度相关,并且在 COVID-19 住院期间以特征性方式演变。基于 CT 视觉严重程度评分和临床变量的预后模型在预测住院并发症方面具有出色的性能。使用 CT 视觉严重程度评分的预后模型可能有助于识别预后不良风险最高的患者,并指导早期干预。