Retrospective Cohort Analysis on Predicting Pulmonary Fibrosis in Elderly SARS-CoV-2-Infected Patients

Fuguo Gao¹Guangdong Hou¹Yan Hou²Jian Chen¹Yifeng Wang¹Baoyin Zhao²Yan Li¹Xinxin Wang¹Yiying Hua¹Faguang Jin^1*Yongheng Gao^3*

• ¹Air Force Medical University, Xi'an, Shaanxi Province, China
• ²People's Liberation Army Joint Logistics Support Force 940th Hospital, Lanzhou, Gansu Province, China
• ³Tangdu Hospital, Air Force Medical University, Xi'an, China

Background: 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 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.Results: 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.