The predictive value of systemic immune-inflammation index for vascular access survival in chronic hemodialysis patients.

Front Immunol

Department of Nephrology and Institute of Nephrology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan Clinical Research Centre for Kidney Diseases, Chengdu, China.

Published: June 2024


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Article Abstract

Objective: To examine the prognostic values of systemic immune-inflammation indices of hemodialysis (HD) vascular access failure and develop a prediction model for vascular access failure based on the most pertinent systemic immune-inflammation index.

Study Design: A prospective cohort study.

Setting & Participants: Patients undergoing autogenous HD vascular access surgeries or arteriovenous graft as a permanent hemodialysis access in a tertiary center in southwest China from January 2020 to June 2022.

Predictors: Systemic immune-inflammation indices, including NLR, dNLR, AAPR, SIRI, SII, PNI, PLR, and LIPI, and clinical variables.

Outcomes: The outcome was defined as survival of the hemodialysis access, with both occluded and stenotic access being considered as instances of access failure.

Analytical Approach: Cox proportional hazard regression model.

Results: 2690 patients were included in the study population, of whom 658 experienced access failure during the follow-up period. The median duration of survival for HD vascular access was 18 months. The increased systemic immune-inflammation indices, including dNLR, NLR, SII, PNI, SIRI, PLR, and LIPI, are predictive of HD access failure, with SII demonstrating the strongest prognostic value. A simple SII-based prediction model for HD access failure was developed, achieving C-indexes of 0.6314 (95% CI: 0.6249 - 0.6589) and 0.6441 (95% CI: 0.6212 - 0.6670) for predicting 6- and 12-month access survival, respectively.

Conclusions: Systemic immune-inflammation indices are significantly and negatively associated with HD vascular access survival. A simple SII-based prediction model was developed and anticipates further improvement through larger study cohort and validation from diverse centers.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140091PMC
http://dx.doi.org/10.3389/fimmu.2024.1382970DOI Listing

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