Comprehensive analysis of Pan-Immune Inflammation and all-cause mortality in rheumatoid arthritis: a database-driven approach, 1999-2018.

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by systemic inflammation and immune dysregulation, leading to a higher risk of all-cause mortality. The Pan-Immune Inflammation Value (PIV), a novel biomarker capturing immune-inflammatory activity, has shown prognostic value in various diseases. However, its role in predicting outcomes in RA patients remains largely unexplored.

Objectives: This study aimed to evaluate the association between PIV and all-cause mortality in RA patients, investigate nonlinear relationships, and identify threshold effects.

Methods: Data from the 1999-2018 National Health and Nutrition Examination Survey (NHANES) were used, including 1,882 RA patients. PIV was calculated as (neutrophil count×platelet count×monocyte count)/lymphocyte count and categorized into quartiles (Q1-Q4). Multivariable Cox proportional hazards models were applied to assess the relationship between PIV and mortality, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Restricted cubic splines (RCS) explored nonlinear trends, and segmented Cox regression identified threshold effects. Kaplan-Meier survival curves and subgroup analyses validated the findings and assessed potential modifiers.

Results: Elevated PIV levels were strongly associated with increased all-cause mortality. Compared to Q1, adjusted HRs for Q2, Q3, and Q4 were 1.60 (95% CI: 1.01-2.53, P = 0.047), 1.70 (95% CI: 1.10-2.63, P = 0.016), and 2.12 (95% CI: 1.33-3.37, P = 0.002), respectively (P for trend < 0.001). RCS analysis revealed a nonlinear relationship with a threshold at PIV = 302. Below this threshold, increasing PIV was associated with higher mortality risk (HR = 1.67, 95% CI: 1.07-2.61, P = 0.024). Conversely, above the threshold, further increases in PIV were linked to reduced mortality risk (HR = 0.98, 95% CI: 0.97-0.99, P = 0.026). Kaplan-Meier survival curves showed a clear decline in survival probability with increasing PIV quartiles (P < 0.001). Subgroup analyses confirmed consistent findings, with a notable interaction observed in diabetic patients (P for interaction = 0.002).

Conclusions: PIV is a significant and independent predictor of all-cause mortality in RA patients, characterized by a nonlinear association and a distinct threshold effect. These findings highlight the potential of PIV as a pragmatic biomarker for stratifying mortality risk and informing personalized treatment strategies in RA.