Huang Chen Lu, Fei Ling, Li WeiXia, Xu Wei, Xie Xu Dong, Li Qiang, Chen Liang
Department of Liver Diseases, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
Infect Dis Ther. 2021 Jun;10(2):897-909. doi: 10.1007/s40121-021-00437-3. Epub 2021 Mar 31.
Due to the lack of clear direction (evidence) on the duration of viral shedding and thus potential for transmission, this retrospective study aimed to come up with a prediction model of prolonged coronavirus disease-19 (COVID-19) transmission or infection-spreading potential.
A total of 1211 non-severe patients with COVID-19 were retrospectively enrolled. Multivariate Cox regression was performed to identify the risk factors associated with long-term SARS-CoV-2 RNA shedding, and a prediction model was established.
In the training set, 796 patients were divided into the long-term (> 21 days) group (n = 116, 14.6%) and the short-term (≤ 21 days) group (n = 680, 85.4%) based on their viral shedding duration. Multivariate analysis identified that age > 50 years, comorbidity, CD4-positive T-lymphocytes count (CD4 + T cell) ≤ 410 cells/ul, C-reactive protein (CRP) > 10 mg/L, and the corticosteroid use were independent risk factors for long-term SARS-CoV-2 RNA shedding. Incorporating the five risk factors, a prediction model, named as the CCCCA score, was established, and its area under the receiver operator characteristic curve (AUROC) was 0.87 in the training set and 0.83 in the validation set, respectively. In the validation set, using a cut-off of 8 points, we found sensitivity, specificity, positive predictive value, and negative predictive value of 51.7%, 92.2%, 33.3%, and 96.2%, respectively. Long-term SARS-CoV-2 RNA shedding increased from 14/370 (3.8%) in patients with CCCCA < 8 points to 15/45 (33.3%) in patients with CCCCA ≥ 8 points.
Using the CCCCA score, clinicians can identify patients with long-term SARS-CoV-2 RNA shedding.
由于缺乏关于病毒 shedding 持续时间以及因此潜在传播可能性的明确指导（证据），这项回顾性研究旨在得出一个预测模型，用于预测新冠病毒疾病-19（COVID-19）的长期传播或感染传播潜力。
回顾性纳入了总共1211例非重症COVID-19患者。进行多变量Cox回归以确定与长期SARS-CoV-2 RNA shedding相关的风险因素，并建立了一个预测模型。
在训练集中，796例患者根据其病毒 shedding持续时间被分为长期（>21天）组（n = 116，14.6%）和短期（≤21天）组（n = 680，85.4%）。多变量分析确定年龄>50岁、合并症、CD4阳性T淋巴细胞计数（CD4 + T细胞）≤410个细胞/微升、C反应蛋白（CRP）>10毫克/升以及使用皮质类固醇是长期SARS-CoV-2 RNA shedding的独立风险因素。纳入这五个风险因素，建立了一个名为CCCCA评分的预测模型，其在训练集中的受试者操作特征曲线下面积（AUROC）分别为0.87，在验证集中为0.83。在验证集中，使用8分的截断值，我们发现敏感性、特异性、阳性预测值和阴性预测值分别为51.7%、92.2%、33.3%和96.2%。长期SARS-CoV-2 RNA shedding从CCCCA<8分的患者中的14/370（3.8%）增加到CCCCA≥8分的患者中的15/45（33.3%）。
使用CCCCA评分，临床医生可以识别出长期SARS-CoV-2 RNA shedding的患者。
