Systemic Immune-Inflammation Index (SII) as a Predictor of Short-Term Mortality Risk in Sepsis-Associated Acute Kidney Injury: A Retrospective Cohort Study

Introduction

Sepsis, an imbalanced immune response to infection, leads to organ dysfunction and causes millions of deaths annually [1,2]. Sepsis-associated acute kidney injury (SA-AKI) is a prevalent complication documented in more than half of the cases. Pathophysiologically, these phenomena encompass systemic and renal inflammation, complement activation, dysregulation of the renin-angiotensin-aldosterone system (RAAS), mitochondrial dysfunction, metabolic reprogramming, microcirculatory insufficiency, and macrocirculatory anomalies [3]. The onset of acute kidney injury (AKI) concurrently exacerbates the progression and severity of sepsis, leading to poor prognoses, and this significantly increases mortality rates, predominantly in critically ill patients [4–6]. The management of SA-AKI currently focuses primarily on controlling infection, avoiding the use of nephrotoxic drugs, maintaining adequate perfusion and tissue oxygen delivery through the use of fluid and vasopressor therapy, and considering renal replacement therapy when necessary to remove toxins, maintain fluid and electrolyte balance, and acid-base equilibrium. Several studies have indicated that early anti-infective treatment can reduce mortality rates and improve renal outcomes. Additionally, fluid management differs in emphasis between early-stage SA-AKI and late-stage cases. While hemodynamic stability is a priority in early-stage SA-AKI, targeting fluid overload may be more relevant in late-stage SA-AKI [7,8]. Clinicians must promptly identify SA-AKI exacerbations, assess disease severity and prognosis based on clinical features, and intervene early to reduce mortality rates.

The systemic immune-inflammation index (SII) is a newly developed parameter that can potentially be used to assess the inflammatory and immune status of individuals. It utilizes lymphocyte, neutrophil, and platelet counts as a basis for cost-effectiveness and convenience [9]. Previous studies have reported the utility of SII in predicting survival in cardiovascular disease, depression, critical illness, and malignancies [10–12]. Although numerous studies have delved into the association between biomarkers (eg, osteopontin, antithrombin III, IL-6, and IL-8) and the prognostic outcomes of SA-AKI, their clinical utility remains limited, impeding timely prognostic evaluations [13–15]. Moreover, while prior investigations have explored the relationship between the neutrophil-to-lymphocyte ratio (NLR) or platelet-to-lymphocyte ratio (PLR) and the mortality rates of SA-AKI [16,17], no studies to date have combined these 2 indices to examine the correlation with short-term mortality in SA-AKI, particularly utilizing the Systemic Immune-Inflammation Index (SII). Given the straightforwardness and cost-effectiveness of the SII index, investigating its association with SA-AKI mortality rates holds significant research value. Thus, this study endeavored to address this lacuna by scrutinizing data sourced from the Medical Information Mart for Intensive Care IV (MIMIC) database, while accounting for confounding variables, to elucidate the relationship between SII and short-term mortality in SA-AKI.

Material and Methods

ETHICS STATEMENT:

The MIMIC-IV database has obtained approval from the institutional review boards of Beth Israel Deaconess Medical Center and Massachusetts Institute of Technology, ensuring adherence to legal and ethical guidelines. Rigorous measures, such as de-identification of personal and identifiable information, alongside the implementation of access controls, are employed to safeguard patient privacy and data integrity. Furthermore, techniques like temporal shifting are applied to bolster data security. All data collection procedures adhere to patient consent protocols, thus obviating the need for supplementary ethical approvals or informed consent documentation for research endeavors utilizing this resource.

DATA SOURCE AND STUDY DESIGN:

This retrospective cohort study utilized the MIMIC-IV (version 2.2). This is a comprehensive and freely accessible database containing the clinical data of patients admitted to the Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019. Access to the database was granted to author Jie Sun after successfully completing the accredited course on Protecting Human Research Participants (accreditation number: 54835759).

POPULATION SELECTION CRITERIA:

A total of 66 239 patients who were admitted to the Intensive Care Unit (ICU) for the first time were identified from in the MIMIC database. The flow chart and number of patients in this study are shown in Figure 1. Only patients with a diagnosis of SA-AKI were included. We excluded patients who had a hospital stay in the ICU of less than 48 h, those with missing platelet, neutrophil count, or lymphocyte count data within 48 h of ICU admission, and those with a platelet, neutrophil count, or lymphocyte count of zero.

The study subjects conformed to the sepsis-3 criteria of from the Third International Consensus for Diagnosing Sepsis and Septic Shock [13]. AKI identification and classification was identified and classified on the basis of the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) guidelines [14]. AKI was defined as a serum creatinine (sCr) increase of ≥0.3 mg/dL (26.5 μmol/L) within 48 h, sCr elevation to ≥1.5 times baseline in the past 7 days, or urine output <0.5 mL/kg/h over a 6-hour period. If the baseline sCr level was not documented before ICU admission, the first recorded post-admission sCr level was used as the baseline reference.

VARIABLES EXTRACTION OF VARIABLES:

Using Structured Query Language (SQL), we extracted data from the MIMIC database within 48 h of ICU admission. The collected data were categorized as follows:

DEFINITION AND ENDPOINT:

SII was calculated using the following equation:

The primary endpoint of the study was 28-day mortality. Survival data were extracted from the MIMIC-IV database.

STATISTICAL ANALYSIS:

Statistical software packages R (http://www.Rproject.org, The R Foundation) and Free Statistics Software version 1.8 were used for all analyses. The patients’ baseline characteristics were stratified based on SII quartiles. Continuous variables were described using mean±standard deviation for normally distributed data and median with 25^th–75^th percentile range for non-normally distributed data. Categorical variables were presented as frequencies (percentages). One-way analysis of variance (ANOVA), Kruskal-Wallis H test, and chi-square tests were performed to assess statistical differences among the groups.

Multivariable Cox regression analyses were conducted to investigate the independent association between SII and 28-day mortality. The adjusted models included covariates, such as age, sex, comorbidities, laboratory indices, vital signs, treatment factors, and scoring systems. Covariates were selected based on the demographic data, literature on SA-AKI, and clinical experience. Missing values for variables were below 30%, and multiple imputations were used.

Restricted cubic spline (RCS) regression with 4 knots at percentiles was employed to evaluate the relationship between SII values and 28-day mortality in the adjusted model. A two-piecewise Cox regression model with smoothing, likelihood-ratio tests, and bootstrap resampling was used to determine inflection points. Kaplan-Meier survival curves were used to estimate patient survival status, and differences between curves were assessed using the log-rank test. Hazard ratios (HR) with 95% confidence intervals (CI) were calculated using a Cox proportional hazards model. Subgroup analyses were performed considering factors such as age, sex, hypertension, diabetes mellitus, malignant cancer, myocardial infarction, congestive heart failure, chronic kidney disease, and SOFA and SAPS II scores to ensure the robustness of the results. Statistical analyses were conducted using a two-tailed test, and statistical significance was defined as p<0.05.The Bonferroni correction threshold was used to account for multiple comparisons and define statistical significance (0.05/10=0.005 for the subgroup interaction tests).

Results

BASELINE CHARACTERISTICS:

A total of 7856 eligible patients (57.7% male, 42.3% female; mean age 64.7 years) were categorized into 4 SII (109/L) level groups: ≤653.6, 654.5–1472.5, 1473.0–3247.7, and ≥3248. Baseline characteristics revealed that higher SII values were associated with older age, female sex, and White ethnicity. These higher SII levels were also correlated with increased heart and respiratory rates, as well as elevated incidences of myocardial infarction, congestive heart failure, COPD, chronic kidney disease, and malignant cancer. Furthermore, the high SII group exhibited higher acute severity scores according to SAPS II and CCI, indicating a more severe condition. Increased vasopressor use and higher AKI stages were also observed in this group. In contrast, the low SII group displayed higher lymphocyte counts, blood lactate levels, and a higher prevalence of chronic liver disease. Additionally, these patients had higher rates of mechanical ventilation and RRT (Table 1).

NON-LINEAR RELATIONSHIP BETWEEN SII AND 28-DAY MORTALITY:

Using RCS, we visualized the relationship between the SII and 28-day mortality in patients with SA-AKI. After adjusting for all covariates, a J-shaped relationship (non-linear, p=0.025) was observed between the SII and 28-day mortality (Figure 2). The inflection point analysis indicated the lowest mortality near 760.08×109/L, beyond which it rapidly increased (Figures 2, 3, and Table 2). In the subsequent Cox regression analysis, the Q2 group (654.5–1472.5) served as a control group.

RELATIONSHIP BETWEEN SII AND 28-DAY MORTALITY:

The Kaplan-Meier curve in Figure 4 demonstrates that the survival rate was higher in the Q1 and Q2 groups than in the Q3 and Q4 groups, with the Q4 group exhibiting the lowest survival rate.

COX REGRESSION ANALYSIS:

Among all enrolled patients, the 28-day mortality rate was 24.7% (1943 deaths; Table 1). Cox regression analysis was performed to examine the relationship between the SII and 28-day mortality. The Q2 group served as a reference for RCS analysis.

After adjusting for sex and age in model 1, the Q3 and Q4 groups had higher hazard ratios (HRs) of 1.4 (95% CI: 1.22–1.6) and 1.77 (95% CI: 1.55–2.01), respectively, compared to the reference (Table 3). In model 4, further adjustment for confounders, including vital signs, scoring system, and laboratory data, there was a significant association between higher SII and increased 28-day mortality risk. The Q3 and Q4 groups had adjusted HRs of 1.33 (95% CI: 1.16–1.52) and 1.55 (95% CI: 1.36–1.77), respectively (Table 3). Although the Q1 group exhibited a gradual increase in the risk of death relative to the control group, the difference was not statistically significant.

SUBGROUP ANALYSES:

We performed a subgroup analysis to address baseline differences and explore the relationship between the SII and 28-day mortality (Table 4). The grouping variables include age, sex, laboratory test results, comorbidities (chronic kidney disease, congestive heart failure, myocardial infarction, diabetes, hypertension, malignant tumor), SOFA scores, and SAPS II scores. All covariates in model 4 were adjusted within each stratum, except for the stratification factor. All subgroups showed consistent trends in primary endpoints.

Discussion

STRENGTHS AND LIMITATIONS:

To the best of our knowledge, only 1 study has investigated use of SII and prognosis of combined acute kidney injury in critically ill patients. Notably, research on SII in the context of SA-AKI is lacking. Therefore, our extensive retrospective cohort study aimed to explore the relationship between the SII index and prognosis in patients with SA-AKI, revealing a J-shaped relationship. However, the existing literature primarily focuses on cardiovascular disease and patients with tumors, with limited investigation of their association with SA-AKI. Notably, our study is the first to reveal a J-shaped relationship between the SII and SA-AKI.

Nevertheless, our study has several limitations. First, potential residual confounding factors, which are common in observational studies, may exist. Second, as this was a single-center study, there may have been a risk of selection bias. Third, insufficient data prevented a comprehensive consideration of variables such as antibiotic usage, hormone administration, and immunosuppressant agent use. Lastly, relying on a single measurement of SII 48 h after ICU admission may not fully reflect the dynamic inflammatory immune status throughout hospitalization.

Conclusions

The mortality rate of SA-AKI varies with SII values, being the highest at both low and high levels. Our study demonstrated that an SII value of approximately 760.08×109/L was associated with the lowest mortality rate. The SII index is derived from the ratio of platelet count multiplied by the absolute neutrophil count to the absolute lymphocyte count. This index is readily obtainable in clinical practice and is more economical than other biomarkers such as NGAL, cystatin C, and IL-18. Therefore, SII holds promise as a potentially cost-effective and easily accessible prognostic biomarker for SA-AKI patients. However, additional extensive prospective studies are needed to validate these findings, incorporating variables such as antibiotic use, pathogenic microorganisms, and other potential confounding factors.