Muradil • Mardan ¹ Huoliang Zheng ¹ Qingyin Xu ¹ Shaokuan Song ¹ Zeyu Lu ¹ Hui Deng ¹ Hao Cai ¹ Qizhu Chen ¹ Bingyi Yang ¹ Kudelaiti • Abuduwufuer ² Pengbo Chen ¹ Bo Li ¹ Shengdan Jiang ^1* Leisheng Jiang ^1* Xin-feng Zheng ^1*

• ¹ Clinic of Spine Center, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
• ² Department of Oncology, Shanghai Traditional Chinese Medicine Hospital, Shanghai, China., Shanghai, China

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.