PDF(Pubmed)
Abstract：
With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the \"rms\" package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.
摘要：
随着新型冠状病毒（COVID-19）的迅速传播，持续的全球流行病已经出现。全球范围内,累计死亡人数以百万计。不断上升的COVID-19感染和死亡人数严重影响了全世界人民的生活，医疗保健系统,和经济发展。我们对COVID-19患者的特征进行了回顾性分析。该分析包括初次入院时的临床特征，相关实验室测试结果，和成像发现。我们旨在确定严重疾病的危险因素，并构建评估严重COVID-19风险的预测模型。我们收集并分析了江苏大学附属医院（镇江，中国）2022年12月18日至2023年2月28日。根据世界卫生组织对新型冠状病毒的诊断标准，我们将患者分为两组：重度和非重度，并比较了他们的临床，实验室,和成像数据。Logistic回归分析,最小绝对收缩和选择算子(LASSO)回归，采用受试者工作特征(ROC)曲线分析确定重症COVID-19患者的相关危险因素。将患者分为训练队列和验证队列。使用R软件中的\"rms\"软件包构建列线图模型。在346名患者中，严重组表现出明显更高的呼吸频率，呼吸困难,改变了意识,中性粒细胞与淋巴细胞比率（NLR），和乳酸脱氢酶（LDH）水平与非严重组相比。影像学检查结果表明，与非严重组相比，严重组的双侧肺部炎症和磨玻璃混浊的比例更高。NLR和LDH被确定为重症患者的独立危险因素。当NLR，呼吸频率（RR），和LDH合并。根据统计分析结果,我们建立了COVID-19严重程度风险预测模型。总分通过将十二个独立变量中的每一个的分数相加来计算。通过将总分映射到最低比例，我们可以估计COVID-19严重程度的风险。此外,校准图和DCA分析显示,列线图对预测COVID-19严重程度具有较好的判别力.我们的结果表明，预测列线图的开发和验证对严重COVID-19具有良好的预测价值。
