A composite risk model predicts disease progression in early stages of COVID-19: A propensity score-matched cohort study
A composite risk model predicts disease progression in early stages of COVID-19: A propensity score-matched cohort study
复制标题
综合风险模型预测 COVID-19 早期疾病进展：倾向评分匹配队列研究
DOI：
10.1177/00045632211011194
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发表时间：
2021
影响因子：
2.2
通讯作者：
Zheng Qichang
中科院分区：
文献类型：
--
作者：
Xu Jianjun;Gao Yang;Hu Shaobo;Li Suzhen;Wang Weimin;Wu Yuzhe;Su Zhe;Zhou Xing;Cheng Xiang;Zheng Qichang
背景最近，关于COVID-19的研究主要集中在疾病的流行病学和患者的临床特征，以及与危重COVID-19病例住院期间死亡率相关的风险因素。然而，在COVID-19的早期阶段，对特定患者群体的疾病进展进行预测的研究很少。方法该研究包括2019年12月至2020年3月在中国武汉两家医院接受治疗的338名COVID-19患者。通过logistic回归分析选择COVID-19从轻度到重度阶段进展的预测因素。结果78例（23.1%）患者确诊为COVID-19进展至重度和危重期。疾病进展组患者的嗜中性粒细胞与淋巴细胞比率（NLR）平均值高于改善组。多因素Logistic回归分析显示NLR、LDH和IL-10升高是疾病进展的独立预测因素。NLR的最佳临界值为3.75。反映NLR预测COVID-19进展准确性的曲线下面积值为0.739（95%CI：0.605-0.804）。基于NLR、LDH和IL-10的风险模型具有最高的ROC曲线下面积。结论高浓度NLR、LDH和IL-10是预测COVID-19早期患者疾病进展的独立危险因素。结合NLR、LDH和IL-10的风险模型提高了对COVID-19早期患者疾病进展预测的准确性。
Background Recently, studies on COVID-19 have focused on the epidemiology of the disease and clinical characteristics of patients, as well as on the risk factors associated with mortality during hospitalization in critical COVID-19 cases. However, few research has been performed on the prediction of disease progression in particular group of patients in the early stages of COVID-19. Methods The study included 338 patients with COVID-19 treated at two hospitals in Wuhan, China, from December 2019 to March 2020. Predictors of the progression of COVID-19 from mild to severe stages were selected by the logistic regression analysis. Results COVID-19 progression to severe and critical stages was confirmed in 78 (23.1%) patients. The average value of the neutrophil-to-lymphocyte ratio (NLR) was higher in patients in the disease progression group than in the improvement group. Multivariable logistic regression analysis revealed that elevated NLR, LDH and IL-10 were independent predictors of disease progression. The optimal cut-off value of NLR was 3.75. The values of the area under the curve, reflecting the accuracy of predicting COVID-19 progression by NLR was 0.739 (95%CI: 0.605–0.804). The risk model based on NLR, LDH and IL-10 had the highest area under the ROC curve. Conclusions The performed analysis demonstrates that high concentrations of NLR, LDH and IL-10 were independent risk factors for predicting disease progression in patients at the early stage of COVID-19. The risk model combined with NLR, LDH and IL-10 improved the accuracy of the prediction of disease progression in patients in the early stages of COVID-19.