Mehrabi Nejad Mohammad-Mehdi, Abkhoo Aminreza, Salahshour Faeze, Salehi Mohammadreza, Gity Masoumeh, Komaki Hamidreza, Kolahi Shahriar
Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Department of Infectious Diseases and Tropical Medicines, Tehran University of Medical Sciences, Tehran, Iran.
Radiol Res Pract. 2022 Feb 26;2022:4732988. doi: 10.1155/2022/4732988. eCollection 2022.
Providing efficient care for infectious coronavirus disease 2019 (COVID-19) patients requires an accurate and accessible tool to medically optimize medical resource allocation to high-risk patients.
To assess the predictive value of on-admission chest CT characteristics to estimate COVID-19 patients' outcome and survival time.
Using a case-control design, we included all laboratory-confirmed COVID-19 patients who were deceased, from June to September 2020, in a tertiary-referral-collegiate hospital and had on-admission chest CT as the case group. The patients who did not die and were equivalent in terms of demographics and other clinical features to cases were considered as the control (survivors) group. The equivalency evaluation was performed by a fellowship-trained radiologist and an expert radiologist. Pulmonary involvement (PI) was scored (0-25) using a semiquantitative scoring tool. The PI density index was calculated by dividing the total PI score by the number of involved lung lobes. All imaging parameters were compared between case and control group members. Survival time was recorded for the case group. All demographic, clinical, and imaging variables were included in the survival analyses.
After evaluating 384 cases, a total of 186 patients (93 in each group) were admitted to the studied setting, consisting of 126 (67.7%) male patients with a mean age of 60.4 ± 13.6 years. The PI score and PI density index in the case vs. the control group were on average 8.9 ± 4.5 vs. 10.7 ± 4.4 ( value: 0.001) and 2.0 ± 0.7 vs. 2.6 ± 0.8 ( value: 0.01), respectively. Axial distribution ( value: 0.01), cardiomegaly ( value: 0.005), pleural effusion ( value: 0.001), and pericardial effusion ( value: 0.04) were mostly observed in deceased patients. Our survival analyses demonstrated that PI score ≥ 10 ( value: 0.02) and PI density index ≥ 2.2 ( value: 0.03) were significantly associated with a lower survival rate.
On-admission chest CT features, particularly PI score and PI density index, are potential great tools to predict the patient's clinical outcome.
为2019冠状病毒病（COVID-19）患者提供高效护理需要一种准确且易于获取的工具，以便从医学角度优化对高危患者的医疗资源分配。
评估入院时胸部CT特征对COVID-19患者预后和生存时间的预测价值。
采用病例对照设计，我们纳入了2020年6月至9月在一家三级转诊大学医院死亡的所有实验室确诊的COVID-19患者，这些患者入院时进行了胸部CT检查，作为病例组。未死亡且在人口统计学和其他临床特征方面与病例组相当的患者被视为对照组（幸存者）。等效性评估由一名经过专科培训的放射科医生和一名专家放射科医生进行。使用半定量评分工具对肺部受累情况（PI）进行评分（0-25分）。PI密度指数通过将总PI评分除以受累肺叶数来计算。比较病例组和对照组成员之间的所有成像参数。记录病例组的生存时间。所有人口统计学、临床和成像变量都纳入生存分析。
在评估了384例病例后，共有186例患者（每组93例）被纳入研究，其中包括126例（67.7%）男性患者，平均年龄为60.4±13.6岁。病例组与对照组的PI评分和PI密度指数平均分别为8.9±4.5 vs. 10.7±4.4（P值：0.001）和2.0±0.7 vs. 2.6±0.8（P值：0.01）。轴位分布（P值：0.01）、心脏扩大（P值：0.005）、胸腔积液（P值：0.001）和心包积液（P值：0.04）在死亡患者中最为常见。我们的生存分析表明，PI评分≥10（P值：0.02）和PI密度指数≥2.2（P值：0.03）与较低的生存率显著相关。
入院时胸部CT特征，尤其是PI评分和PI密度指数，是预测患者临床结局的潜在有力工具。
