Xu Yongjie, Tang Minjie, Guo Zhaopei, Lin Yanping, Guo Hongyan, Fang Fengling, Lin Lin, Shi Yue, Lai Lu, Pan Yan, Tang Xiangjun, You Weiquan, Li Zishun, Song Jialin, Wang Liang, Cai Weidong, Fu Ya
Department of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Front Cell Infect Microbiol. 2025 Apr 3;15:1499154. doi: 10.3389/fcimb.2025.1499154. eCollection 2025.
COVID-19 caused by the coronavirus SARS-CoV-2 has resulted in a global pandemic. Considering some patients with COVID-19 rapidly develop respiratory distress and hypoxemia, early assessment of the prognosis for COVID-19 patients is important, yet there is currently a lack of research on a comprehensive multi-marker approach for disease prognosis assessment. Here, we utilized a large sample of hospitalized individuals with COVID-19 to systematically compare the clinical characteristics at admission and developed a nomogram model that was used to predict prognosis. In all cases, those with pneumonia, older age, and higher PT-INR had a poor prognosis. Besides, pneumonia patients with older age and higher PT-INR also had a poor prognosis. A nomogram model incorporating presence of pneumonia, age and PT-INR could evaluate the prognosis in all patients with SARS-CoV-2 infections well, while a nomogram model incorporating age and PT-INR could evaluate the prognosis in those with pneumonia well. Together, our study establishes a prognostic prediction model that aids in the timely identification of patients with poor prognosis and helps facilitate the improvement of treatment strategies in clinical practice in the future.
由冠状病毒SARS-CoV-2引起的COVID-19已导致全球大流行。鉴于一些COVID-19患者会迅速发展为呼吸窘迫和低氧血症，对COVID-19患者的预后进行早期评估很重要，但目前缺乏关于用于疾病预后评估的综合多标志物方法的研究。在此，我们利用大量住院的COVID-19患者样本系统地比较了入院时的临床特征，并开发了一种用于预测预后的列线图模型。在所有病例中，患有肺炎、年龄较大且PT-INR较高的患者预后较差。此外，年龄较大且PT-INR较高的肺炎患者预后也较差。纳入肺炎、年龄和PT-INR的列线图模型可以很好地评估所有SARS-CoV-2感染患者的预后，而纳入年龄和PT-INR的列线图模型可以很好地评估肺炎患者的预后。总之，我们的研究建立了一个预后预测模型，有助于及时识别预后不良的患者，并有助于促进未来临床实践中治疗策略的改进。
