Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China.
Sci Rep. 2024 Aug 6;14(1):18197. doi: 10.1038/s41598-024-68946-y.
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.
随着新型冠状病毒（COVID-19）的迅速传播，一种持续的全球大流行已经出现。在全球范围内，累计死亡人数已达数百万人。COVID-19 感染和死亡人数的不断增加，严重影响了全球人民的生活、医疗系统和经济发展。我们对 COVID-19 患者的特征进行了回顾性分析。该分析包括初次住院时的临床特征、相关实验室检查结果和影像学发现。我们旨在确定重症的危险因素，并构建预测模型以评估 COVID-19 重症的风险。我们收集并分析了 2022 年 12 月 18 日至 2023 年 2 月 28 日期间入住江苏大学附属医院（镇江）的确诊 COVID-19 患者的电子病历。根据世界卫生组织（WHO）对新型冠状病毒的诊断标准，我们将患者分为重症和非重症两组，并比较了他们的临床、实验室和影像学数据。使用逻辑回归分析、最小绝对收缩和选择算子（LASSO）回归以及受试者工作特征（ROC）曲线分析来确定 COVID-19 重症患者的相关危险因素。患者被分为训练队列和验证队列。使用 R 软件中的“rms”包构建了一个列线图模型。在 346 名患者中，重症组的呼吸频率、呼吸困难、意识改变、中性粒细胞与淋巴细胞比值（NLR）和乳酸脱氢酶（LDH）水平明显高于非重症组。影像学表现表明，重症组双侧肺部炎症和磨玻璃影的比例明显高于非重症组。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 联合使用时，诊断性能达到最大。根据统计分析结果，我们开发了一种 COVID-19 严重程度风险预测模型。通过将每个 12 个独立变量的得分相加来计算总分。通过将总分映射到最低分值，可以估计 COVID-19 严重程度的风险。此外，校准图和 DCA 分析表明，列线图对预测 COVID-19 严重程度具有更好的判别能力。我们的研究结果表明，预测列线图的开发和验证对 COVID-19 重症具有良好的预测价值。
