Retrospective cohort analysis on predicting pulmonary fibrosis in elderly SARS-CoV-2-infected patients

TL;DR

This study develops a predictive tool to identify elderly patients at risk of developing pulmonary fibrosis after SARS-CoV-2 infection.

Contribution

The study introduces the first nomogram for predicting pulmonary fibrosis in elderly SARS-CoV-2 patients.

Findings

Neutrophil percentage, CRP, gender, diagnostic classification, and time to hospitalization were key predictors of pulmonary fibrosis.

The nomogram demonstrated good calibration and discriminatory ability across three patient cohorts with AUC values above 0.7.

A cutoff score of 131.026 was identified to classify patients into high-risk groups.

Abstract

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.

Figures