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Updated: May 10, 2026

Flow Cytometry Analysis of Immune Cell Subsets within the Murine Spleen, Bone Marrow, Lymph Nodes and Synovial Tissue in an Osteoarthritis Model
Published on: April 24, 2020
Association Between Pan-Immune-Inflammation Value and Mortality in Osteoarthritis: Evidence for Elevated Risk at
Donghai Wang1,2, Fang Li2, Yantong Liu1
1Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Objective:
Systemic inflammation plays a key role in the progression of osteoarthritis (OA). The aim of this study was to explore the association of an emerging systemic inflammation marker, the pan-immune-inflammation value (PIV), with all-cause and cardiovascular disease (CVD) mortality in patients with OA.
Methods:
OA participants from the National Health and Nutrition Examination Survey 1999-2018 were included. PIV was calculated according to the following formula: PIV = (platelet count × neutrophil count × monocyte count)/lymphocyte count. OA was assessed through self-report, and mortality was obtained by matching to the National Death Index.
Results:
A total of 3903 participants with OA were included. During the follow-up period, 1032 individuals died, 359 of whom died of CVD. After adjusting for all confounders, lnPIV was positively associated with all-cause and CVD mortality in the OA population (HR 1.273 and 1.364, respectively; p=0.002 and p=0.004). Participants with PIV at Q4 had a significantly increased risk of mortality compared to Q1 (HR 1.439 and 1.506, respectively). These associations all demonstrated nonlinear relationships and were significant only after the inflection point, suggesting a threshold effect rather than a bidirectional pattern. Alcohol consumption influenced the association between PIV and CVD mortality. However, receiver operating characteristic analyses demonstrated the limited predictive value of PIV for long-term mortality.
Conclusions:
Higher PIV was associated with increased risk of all-cause and CVD mortality in US adults with OA. High-quality studies are needed to explore the value of PIV in clinical mortality prediction models.
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