Retrospective study identifies preoperative inflammation markers predicting SIRS in older adults after cardiac surgery, suggesting early intervention for high-risk cases.
Background Systemic inflammatory response syndrome (SIRS) is a significant postoperative complication in older patients leading to higher morbidity and mortality rates. This study investigates the predictive value of preoperative inflammation markers for postoperative SIRS in older adults undergoing cardiac surgery with cardiopulmonary bypass. Methods This retrospective study screened 290 patients ≥ 65 years. Patients were categorized into two groups based on the development of postoperative SIRS. Inflammatory indices including neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory index (SII) and systemic inflammation response index (SIRI) were calculated based on preoperative blood routine examination. The discriminatory power of each marker was evaluated with ROC curve analysis, and the odds ratio (OR) was used to examine the relationship between inflammatory indices and postoperative SIRS. Results Ninety patients (31.0%) fulfilled SIRS criteria within 3 days after surgery. Patients with postoperative SIRS showed significant variations in NLR, SII, and SIRI compared to those without. ROC analysis showed that NLR ≥ 2.8, SII ≥ 1066, and SIRI ≥ 1.05 effectively predicted postoperative SIRS. Multivariate analysis identified NLR ≥ 2.8 (OR = 1.94, 95% CI: 1.06–3.77), SII ≥ 1066 (OR = 2.74, 95% CI 1.53–4.92), and SIRI ≥ 1.05 (OR = 2.46, 95% CI 1.30–4.67) as independent predictors for postoperative SIRS development. The predictive value of these inflammatory indices for postoperative SIRS was consistent across subgroups. Patients complicated by postoperative SIRS had longer ICU stays, extended mechanical ventilation duration, higher ICU readmission rates, and greater long-term all-cause mortality. Conclusion We showed that NLR ≥ 2.8, SII ≥ 1066, and SIRI ≥ 1.05 can effectively predict postoperative SIRS in older cardiac surgery patients, helping clinicians identify high-risk individuals for early intervention.
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Wang et al. (2026) studied this question.
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