What question did this study set out to answer?

To develop a predictive model for infection risk in patients with ANCA-associated vasculitis using baseline inflammation indices.

February 12, 2026Open Access

The predictive value of baseline systemic inflammation response index and systemic immune-inflammation index for the risk of infection within 6 months following initial immunosuppressive treatment in patients with ANCA-associated vasculitis

Key Points

To develop a predictive model for infection risk in patients with ANCA-associated vasculitis using baseline inflammation indices.
Conducted a retrospective cohort analysis of 185 ANCA-associated vasculitis patients.
Collected clinical and laboratory data at diagnosis, including SII and SIRI calculations.
Utilized univariate and multivariable logistic regression to identify infection risk factors.
Implemented ROC curve analysis to evaluate predictive performance and developed a nomogram.
41.6% of patients developed infections within six months, primarily respiratory in nature.
Identified five independent predictors of infection, including older age and higher SII and SIRI values.
The combined model showed an improved AUC of 0.825 compared to a clinical-only model (AUC = 0.741).
Nomogram provides a quantitative estimate of infection risk based on identified predictors.

Abstract

Background Patients with ANCA-associated vasculitis (AAV) face a high risk of severe infections, particularly within the first six months of immunosuppressive therapy, contributing significantly to early mortality. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are integrative biomarkers reflecting inflammatory and immune status. This study aims to develop a model based on baseline assessment and early-treatment parameters to predict infection risk in AAV patients. Methods In this retrospective cohort study, 185 AAV patients from a single center were enrolled. Clinical and laboratory data at diagnosis, along with treatment details were collected, and the systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) were calculated. The primary endpoint was the occurrence of infection within 6 months. Univariate and multivariable logistic regression were used to identify independent risk factors. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were applied to assess the predictive performance. A nomogram was developed based on the multivariable model. Results Within 6 months, 77 patients (41.6%) developed infections, primarily respiratory (77.9%). Multivariable analysis identified five independent predictors: older age (OR = 1.040, 95% CI: 1.006-1.076, p=0.021), higher SII (OR = 1.000, 95% CI: 1.000-1.001, p=0.037), higher SIRI (OR = 1.293, 95% CI: 1.039-1.610, p=0.021), lower serum albumin (OR = 0.898, 95% CI: 0.826-0.975, p=0.011), and a higher cumulative dose of prednisone within the first month (OR = 1.002, 95% CI: 1.001-1.004, p=0.007). The combined model incorporating all five factors showed an improved area under the curve (AUC = 0.825) compared to the “clinical-only” model comprising age, albumin, and cumulative dose of prednisone within the first month (AUC = 0.741). By integrating the five independent predictors, the nomogram yields a quantitative estimate of infection risk through the summation of points assigned to each variable. Conclusion Pre-treatment SII and SIRI are independent predictors of early infection in AAV patients. The developed nomogram, which integrates these indices with age, albumin and cumulative dose of prednisone within the first month, serves as an exploratory assessment tool for individualized risk.

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