Zakariaee Seyed Salman, Salmanipour Hossein, Naderi Negar, Kazemi-Arpanahi Hadi, Shanbehzadeh Mostafa
Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran.
Department of Radiology, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran.
Clin Transl Imaging. 2022;10(6):663-676. doi: 10.1007/s40336-022-00512-w. Epub 2022 Jul 21.
Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis.
Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes.
This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157-1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307-9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both > 0.2). The shapes of Begg's funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg's test = 0.945 and 0.356, respectively).
The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.
胸部计算机断层扫描（CT）是一种用于描绘间质性肺炎的高灵敏度诊断工具，在评估肺部受累的严重程度和范围方面可能发挥关键作用。在本研究中，我们旨在通过系统评价和荟萃分析评估胸部CT严重程度评分（CT-SS）与COVID-19患者死亡率之间的关联。
使用科学网、PubMed、Embase、Scopus和谷歌学术搜索原始文章。采用随机效应模型进行荟萃分析，并计算具有95%置信区间（95%CI）的比值比（OR）作为效应量。
该荟萃分析共纳入7106例COVID-19患者。CT-SS与COVID-19患者死亡率之间关联的合并估计值计算为1.244（95%CI 1.157-1.337）。CT-SS与最佳临界值及COVID-19患者死亡率之间关联的合并估计值计算为7.124（95%CI 5.307-9.563）。纳入研究的结果不存在发表偏倚。放射科医生的经验和研究地点不是研究间异质性的潜在来源（两者均>0.2）。对于评估CT-SS与COVID-19患者有无最佳临界值及死亡率之间关联的研究，Begg漏斗图的形状似乎对称（Begg检验分别为0.945和0.356）。
本研究结果表明CT-SS与COVID-19患者死亡率之间存在关联。在肺部受累的视觉评分中，使用最佳CT-SS临界值可以准确预测COVID-19患者的死亡几率。
