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Background <p>Sepsis remains a leading cause of mortality in intensive care units (ICUs), highlighting the need for reliable and accessible prognostic biomarkers. The systemic immune-inflammation (SII) index, integrating neutrophils, platelets, and lymphocytes, has shown prognostic value in various diseases but remains understudied in sepsis.</p> Methods <p>We conducted a retrospective cohort study including 4,001 sepsis patients from the MIMIC-IV database. SII was calculated as (neutrophil × platelet)/lymphocyte and log-transformed due to non-normal distribution. Tertiles of log-SII were evaluated. Multivariable Cox proportional hazards models, restricted cubic splines (RCS), and Kaplan-Meier analyses were used to assess the relationship between log-SII and 30-/90-day mortality. Five machine learning (ML) models, including random forest (RF), logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), and multilayer perception (MLP) were constructed for mortality prediction. Feature contributions were further interpreted using Shapley additive explanation (SHAP) values.</p> Results <p>Patients in the low log-SII (Q1) had significantly higher 30-day (hazard ratio [HR] 1.36, 95% confidence interval [CI] 1.11–1.67) and 90-day mortality (HR 1.35, 95% CI 1.10–1.65) compared to Q2. An initial non-linear association between log-SII and mortality was observed in unadjusted and minimally adjusted models; however, this non-linear relationship was attenuated and no longer significant in the fully adjusted model (Model 3), suggesting that the apparent non-linearity may be explained by confounding factors. Subgroup analyses confirmed consistent results across most strata. Among ML models, LR demonstrated the highest discriminative performance (area under the receiver operating characteristic curve [AUROC] = 0.770, 95%CI: 0.752–0.788). SHAP analysis identified SII as a key predictor of mortality risk.</p> Conclusion <p>Low SII on ICU admission is independently associated with increased short- and long-term mortality in sepsis. As a simple and cost-effective biomarker, SII may support early risk stratification. Prospective validation and investigation of its dynamic changes are warranted.</p> Clinical trial number <p>Not applicable.</p>

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Systemic immune-inflammation (SII) index as a novel prognostic biomarker in critically ill patients with sepsis: analysis of the MIMIC-IV cohort and predictive modeling based on machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月7日

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  • Xudong Zhang,
  • Yiquan Xu,
  • Yu Lei,
  • Miaomiao Tang,
  • Yanqing Wang,
  • Jianghui Luo,
  • Shuying Zhu

摘要

Background

Sepsis remains a leading cause of mortality in intensive care units (ICUs), highlighting the need for reliable and accessible prognostic biomarkers. The systemic immune-inflammation (SII) index, integrating neutrophils, platelets, and lymphocytes, has shown prognostic value in various diseases but remains understudied in sepsis.

Methods

We conducted a retrospective cohort study including 4,001 sepsis patients from the MIMIC-IV database. SII was calculated as (neutrophil × platelet)/lymphocyte and log-transformed due to non-normal distribution. Tertiles of log-SII were evaluated. Multivariable Cox proportional hazards models, restricted cubic splines (RCS), and Kaplan-Meier analyses were used to assess the relationship between log-SII and 30-/90-day mortality. Five machine learning (ML) models, including random forest (RF), logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), and multilayer perception (MLP) were constructed for mortality prediction. Feature contributions were further interpreted using Shapley additive explanation (SHAP) values.

Results

Patients in the low log-SII (Q1) had significantly higher 30-day (hazard ratio [HR] 1.36, 95% confidence interval [CI] 1.11–1.67) and 90-day mortality (HR 1.35, 95% CI 1.10–1.65) compared to Q2. An initial non-linear association between log-SII and mortality was observed in unadjusted and minimally adjusted models; however, this non-linear relationship was attenuated and no longer significant in the fully adjusted model (Model 3), suggesting that the apparent non-linearity may be explained by confounding factors. Subgroup analyses confirmed consistent results across most strata. Among ML models, LR demonstrated the highest discriminative performance (area under the receiver operating characteristic curve [AUROC] = 0.770, 95%CI: 0.752–0.788). SHAP analysis identified SII as a key predictor of mortality risk.

Conclusion

Low SII on ICU admission is independently associated with increased short- and long-term mortality in sepsis. As a simple and cost-effective biomarker, SII may support early risk stratification. Prospective validation and investigation of its dynamic changes are warranted.

Clinical trial number

Not applicable.