Yang Yinghao, Li Dong, Nie Jinqiu, Wang Junxue, Huang Huili, Hang Xiaofeng
Department of Infectious Diseases, Changzheng Hospital, Naval Medical University, Shanghai, People's Republic of China.
Department of Infectious Diseases, the 988th Hospital of the Joint Logistic Support Force, Zhengzhou, People's Republic of China.
Infect Drug Resist. 2025 Apr 25;18:2093-2104. doi: 10.2147/IDR.S509178. eCollection 2025.
Patients with severe SARS-CoV-2 omicron variant pneumonia pose a serious challenge. This study aimed to develop a nomogram for predicting survival using chest computed tomography (CT) imaging features and laboratory test results based on admission data.
A total of 436 patients with SARS-CoV-2 pneumonia (323 and 113 in the training and validation groups, respectively) were enrolled. Pneumonitis volume, assessed on chest CT scans at admission, was used to identify low- and high-risk groups. Risk analysis was performed using clinical symptoms, laboratory findings, and chest CT imaging features. A predictive algorithm was developed using Cox multivariate analysis.
The high-risk group had a shorter survival duration than the low-risk group. Significant differences in mortality rate, neutrophil and lymphocyte counts, C-reactive protein (CRP) concentration, and urea nitrogen level were observed between the two groups. In the training group, age, pneumonia volume, total bilirubin, and blood urea nitrogen were independent prognostic factors. In the validation group, age, pneumonia volume, neutrophil count, and CRP were independent prognostic factors. A personalized prediction model for survival outcomes was developed using independent predictors.
A personalized prediction model was created to forecast the 5-, 10-, 15-, 20-, and 30-day survival rates of patients with COVID-19 omicron variant pneumonia based on admission data, and can be used to determine the survival rate and early treatment of severe patients.
严重的新型冠状病毒奥密克戎变异株肺炎患者带来了严峻挑战。本研究旨在基于入院数据，利用胸部计算机断层扫描（CT）影像特征和实验室检查结果开发一种预测生存的列线图。
共纳入436例新型冠状病毒肺炎患者（训练组323例，验证组113例）。入院时胸部CT扫描评估的肺炎体积用于识别低风险和高风险组。使用临床症状、实验室检查结果和胸部CT影像特征进行风险分析。采用Cox多变量分析开发预测算法。
高风险组的生存时间短于低风险组。两组在死亡率、中性粒细胞和淋巴细胞计数、C反应蛋白（CRP）浓度及尿素氮水平方面存在显著差异。在训练组中，年龄、肺炎体积、总胆红素和血尿素氮是独立的预后因素。在验证组中，年龄、肺炎体积、中性粒细胞计数和CRP是独立的预后因素。利用独立预测因子开发了生存结局的个性化预测模型。
基于入院数据创建了一个个性化预测模型，用于预测新型冠状病毒奥密克戎变异株肺炎患者的5天、10天、15天、20天和30天生存率，并可用于确定重症患者的生存率及早期治疗。
