Gao Fuguo, Hou Guangdong, Hou Yan, Chen Jian, Wang Yifeng, Zhao Baoyin, Li Yan, Wang Xinxin, Hua Yiying, Jin Faguang, Gao Yongheng
Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
Department of Pulmonary and Critical Care Medicine, The 940th Hospital of the Joint Logistics Support Force of People's Liberation Army (PLA), Lanzhou, China.
Front Cell Infect Microbiol. 2025 Jun 6;15:1587321. doi: 10.3389/fcimb.2025.1587321. eCollection 2025.
SARS-CoV-2 exhibits rapid transmission with a high susceptibility rate, particularly among the elderly. Pulmonary fibrosis (PF) following SARS-CoV-2 infection is a life-threatening complication. However, predictive models for PF in older patients are lacking.
Data from patients with COVID-19 aged 60 and above, collected retrospectively between November 2022 and November 2023 across two independent hospitals, were analyzed. Patients from Tangdu Hospital were divided into training and validation cohorts using a 7:3 allocation ratio, while those from The 940th Hospital of the Joint Logistics Support Force of the People's Liberation Army (PLA) served as the test cohort. Identify the most valuable predictors (MVPs) for PF using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and construct a nomogram based on their regression coefficients derived from logistic regression. The calibration, clinical utility, and discriminatory ability of the nomogram were evaluated using the Hosmer-Lemeshow test, decision curve analysis (DCA), and Receiver Operating Characteristic (ROC) curve, respectively.
Neutrophil percentage, C-reactive protein (CRP), gender, diagnostic classification, and time from symptom onset to hospitalization were identified as the MVPs for PF. The nomogram was developed based on these predictors, In all the three cohorts, the nomogram showed good calibration, clinical utility and discriminatory ability, with Area Under the Curve (AUC) of 0.777, 0.735 and 0.753, respectively. Furthermore, based on the principle of optimizing the balance between sensitivity and specificity, 131.026 was determined as the optimal cutoff value for the nomogram. Accordingly, patients with a nomogram score of 131.026 or higher were classified into the high-risk group.
This study presents the first nomogram for predicting PF in elderly patients following SARS-CoV-2 infection, which may serve as a clinical tool for risk assessment and early management in this population.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）传播迅速，易感性高，在老年人中尤为明显。SARS-CoV-2感染后的肺纤维化（PF）是一种危及生命的并发症。然而，缺乏针对老年患者PF的预测模型。
分析了2022年11月至2023年11月期间在两家独立医院回顾性收集的60岁及以上COVID-19患者的数据。唐都医院的患者按照7:3的分配比例分为训练组和验证组，而来自中国人民解放军联勤保障部队第940医院的患者作为测试组。使用最小绝对收缩和选择算子（LASSO）回归确定PF的最有价值预测因子（MVP），并根据逻辑回归得出的回归系数构建列线图。分别使用Hosmer-Lemeshow检验、决策曲线分析（DCA）和受试者工作特征（ROC）曲线评估列线图的校准、临床实用性和鉴别能力。
中性粒细胞百分比、C反应蛋白（CRP）、性别、诊断分类以及症状出现至住院的时间被确定为PF的MVP。基于这些预测因子构建了列线图。在所有三个队列中，列线图均显示出良好的校准、临床实用性和鉴别能力，曲线下面积（AUC）分别为0.777、0.735和0.753。此外，基于优化敏感性和特异性之间平衡的原则，确定131.026为列线图的最佳截断值。因此，列线图得分131.026及以上的患者被归类为高危组。
本研究提出了首个用于预测SARS-CoV-2感染后老年患者PF的列线图，可作为该人群风险评估和早期管理的临床工具。
