Predictive Value of Hematologic Inflammatory Indices for Early Dialysis Requirement After Kidney Transplantation
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND Delayed graft function remains a common and clinically relevant complication following kidney transplantation, yet reliable early prediction tools are limited. This small-scale, retrospective, exploratory study investigated whether routine hematologic inflammatory indices, such as the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and prognostic inflammatory value (PIV), when considered alongside donor-recipient demographic characteristics, predict the need for early posttransplant dialysis. MATERIAL AND METHODS Retrospective analysis of 33 cadaveric kidney transplant recipients was performed. Catorigization into dialysis (n=14) and non-dialysis (n=19) groups was based on early posttransplant dialysis requirement. SII, SIRI, and PIV were calculated from pretransplant laboratory parameters. Given the exploratory study design, analyses were primarily descriptive, supported by univariate comparisons and logistic regression with bootstrap resampling. RESULTS Recipients requiring dialysis tended to receive kidneys from older donors, although this did not reach statistical significance. Monocyte count was significantly lower in the dialysis group (P=0.032). Inflammatory indices, including SII, SIRI, and PIV, showed no significant differences (P>0.05). Logistic regression analyses did not identify SII, SIRI, or PIV as independent dialysis predictors. However, bootstrap resampling showed consistent, although non-significant, directional trends, suggesting higher donor age and inflammatory burden among recipients requiring dialysis. CONCLUSIONS This study provides preliminary insights into the potential role of combined hematologic inflammatory indices in the context of delayed graft function, underscoring the need for larger, prospective studies to clarify whether inflammatory burden and donor-related factors can be integrated into clinically useful prediction models for early dialysis after kidney transplantation.
Source: Annals of Transplantation
DOI: 10.12659/aot.951843
Institutions: Sivas Cumhuriyet Üniversitesi, Aksaray University
Language: English
Subfield: Transplantation
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7129285156