Respiratory and Critical Care Department, The First Hospital of China Medical University, Shenyang, China.
Department of Respiratory Disease, Liaoning Province Peoples' Hospital, Shenyang, China.
BMC Infect Dis. 2022 Apr 11;22(1):366. doi: 10.1186/s12879-022-07338-x.
COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding time and disease progression, then develop and validate two prognostic discriminant models.
This study included 125 hospitalized patients with COVID-19, for whom 44 parameters were recorded, including age, gender, underlying comorbidities, epidemiological features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. Fisher's exact test and Mann-Whitney test were used for feature selection. All models were developed with fourfold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L regularization, prognostic discriminant models were built to predict postponed viral shedding time and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models' sensitivity and specificity.
Sixty-nine patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Six and eleven demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding time and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y (postponed viral shedding time) = - 0.244 + 0.2829x (the interval from the onset of symptoms to antiviral treatment) + 0.2306x (age) + 0.234x (Urea) - 0.2847x (Dual-antiviral therapy) + 0.3084x (Treatment with antibiotics) + 0.3025x (Treatment with Methylprednisolone); y (disease progression) = - 0.348-0.099x (interval from Jan 1st,2020 to individualized onset of symptoms) + 0.0945x (age) + 0.1176x (imaging characteristics) + 0.0398x (short-term exposure to Wuhan) - 0.1646x (lymphocyte counts) + 0.0914x (Neutrophil counts) + 0.1254x (Neutrphil/lymphocyte ratio) + 0.1397x (C-Reactive Protein) + 0.0814x (Procalcitonin) + 0.1294x (Lactic dehydrogenase) + 0.1099x (Creatine kinase).The output ≥ 0 predicted postponed viral shedding time or disease progressing to severe/critical state. These two models yielded the maximum AUROC and faired best in terms of prognostic performance (sensitivity of78.6%, 75%, and specificity of 66.7%, 88.9% for prediction of postponed viral shedding time and disease severity, respectively).
The two discriminant models could effectively predict the postponed viral shedding time and disease severity and could be used as early-warning tools for COVID-19.
COVID-19 感染可导致危及生命的呼吸道疾病。本研究旨在充分描述与病毒脱落时间延迟和疾病进展相关的临床特征，然后开发和验证两种预后判别模型。
本研究纳入了 125 例住院 COVID-19 患者，记录了 44 个参数，包括年龄、性别、合并症、流行病学特征、实验室指标、影像学特征和治疗方案等。采用 Fisher 确切检验和 Mann-Whitney 检验进行特征选择。所有模型均采用四折交叉验证进行开发，并通过接收者操作特征曲线下面积（AUROC）比较各模型的最终性能。通过 L 正则化优化参数后，建立预后判别模型以预测 COVID-19 感染的病毒脱落时间延迟和疾病进展。然后使用测试集通过评估模型的灵敏度和特异性来检测预测值。
69 例患者病毒脱落时间延迟（>14 天），125 例患者中有 28 例进展为重症病例。6 个和 11 个人口统计学、临床特征和治疗方案与病毒脱落时间延迟和疾病进展分别显著相关（p<0.05）。最佳判别模型为：y（病毒脱落时间延迟）=-0.244+0.2829x（从症状发作到抗病毒治疗的时间间隔）+0.2306x（年龄）+0.234x（尿素）-0.2847x（双抗病毒治疗）+0.3084x（抗生素治疗）+0.3025x（甲泼尼龙治疗）；y（疾病进展）=-0.348-0.099x（2020 年 1 月 1 日至个体化发病的时间间隔）+0.0945x（年龄）+0.1176x（影像学特征）+0.0398x（短期武汉暴露）-0.1646x（淋巴细胞计数）+0.0914x（中性粒细胞计数）+0.1254x（中性粒细胞/淋巴细胞比值）+0.1397x（C 反应蛋白）+0.0814x（降钙素原）+0.1294x（乳酸脱氢酶）+0.1099x（肌酸激酶）。输出≥0 预测病毒脱落时间延迟或疾病进展为严重/危急状态。这两个模型产生了最大的 AUROC，并在预后性能方面表现最佳（预测病毒脱落时间延迟和疾病严重程度的灵敏度分别为 78.6%和 75%，特异性分别为 66.7%和 88.9%）。
这两种判别模型可有效预测病毒脱落时间延迟和疾病严重程度，可作为 COVID-19 的预警工具。
