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Systemic immune inflammation index and systemic inflammation response index predict frailty progression in older
Guanghan Gao1, Zitian Zheng2, Fei Wang2
1Department of Orthopedics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Introduction:
The frailty status of older patients undergoing elective orthopedic surgery significantly influences their surgical benefits. Evaluating the progression of postoperative frailty assists clinicians in making informed clinical decisions. The biomarkers systemic immune inflammation index (SII) and systemic inflammation response index (SIRI), which reflect chronic inflammation and immune status, may play a positive role in predicting the progression of frailty.
Methods:
We conducted a single-center prospective cohort study, including patients aged 65 and older who underwent elective orthopedic surgery for chronic degenerative conditions between January 2020 and January 2022. Basic patient information, laboratory results, and frailty assessments were collected. LASSO regression was used to identify important predictive variables, and multivariate logistic regression was employed to assess the associations between SII/SIRI and frailty progression. Restricted cubic splines (RCS) were used to detect potential non-linear relationships between them. ROC curves and AUC values were utilized to assess their predictive performance. Finally, we presented stratified analyses and interaction tests of covariates.
Results:
A total of 546 patients were included, with 109 (19.5%) experiencing postoperative frailty progression. Multivariate regression analysis revealed that ln.SII and ln.SIRI were positively correlated with frailty progression in the fully adjusted model, with odds ratios (OR) of 3.449 and 3.084, respectively. These findings were consistent across various subgroups. The linear trend between SII-SIRI pattern/SII/SIRI levels and frailty progression was statistically significant. However, the RCS curve indicated that the non-linear model significantly outperformed the linear model. The AUC values for ln.SII, ln.SIRI, and their combined model were 0.686, 0.710, and 0.723, respectively. The cutoff values for ln.SII and ln.SIRI were 5.93 and 0.10, respectively.
Conclusion:
SII and SIRI can effectively serve as non-invasive preoperative screening tools for identifying older patients with chronic degenerative orthopedic diseases who are at high risk of frailty progression following elective surgical procedures.
Clinical Trial Registration:
https://www.chictr.org.cn/, identifier chiCTR1800018840 (Date: 2018-10-13).
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