Zhang Hanfei, Zhong Feiyang, Wang Binchen, Liao Meiyan
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Future Virol. 2022 Jan. doi: 10.2217/fvl-2020-0193. Epub 2022 Feb 21.
This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893-0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.
本研究旨在构建一个易于使用的列线图，以预测新型冠状病毒肺炎（COVID-19）的严重程度。2019年12月至2020年1月，纳入我院确诊为COVID-19的患者。提取其初始临床和影像学特征。采用单因素和多因素逻辑回归分析确定列线图的变量。共纳入104例患者。基于统计分析，确定年龄、中性粒细胞计数、肌酐、降钙素原水平及受累肺段数用于构建列线图。曲线下面积为0.939（95%CI：0.893-0.984）。校准曲线显示列线图预测与初始队列中的观察结果具有良好的一致性。构建了一个具有良好鉴别能力的易于使用的列线图，以预测COVID-19的严重程度。
