Introduction

Acute kidney injury (AKI) is a common and severe complication in critically ill patients, with high incidence and mortality rates.1 Particularly among patients with sepsis, over 60% develop AKI.2–4 The pathogenesis of sepsis-associated acute kidney injury (S-AKI) is not yet fully understood, but evidence indicates that the inflammatory cascade triggered by sepsis increases the likelihood of AKI in critically ill patients.5 A retrospective study found that the mortality rate of S-AKI patients is significantly higher than that of non-sepsis AKI patients (71.7% vs 21.3%).6 Additionally, patients with critical S-AKI are closely associated with prolonged hospital stays and a great financial burden.7 Therefore, early identification of S-AKI is crucial for preventing complications, reducing mortality, and improving patient outcomes.

Inflammation plays a significant role in the occurrence and progression of S-AKI.8,9 Many biomarkers have limitations in predicting the prognosis of S-AKI. For example, the sensitivity of tissue inhibitor metalloproteinase 2 (TIMP-2), neutrophil gelatinase-associated lipocalin (NGAL), and insulin-like growth factor-binding protein-7 (IGFBP-7) in prognostic evaluation has not been validated in large-scale studies, and their high detection costs and long processing times limit their clinical application.3 Routine blood tests that reflect inflammatory markers are simple, rapid, cost-effective, and highly reproducible and have been used for prognostic evaluation in various diseases. Parameters from complete blood counts, such as platelet count, red cell distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), have all been reported for disease prognosis evaluation.10–13

The SII, calculated from neutrophil, lymphocyte, and platelet counts, is a novel inflammatory marker that reflects the balance of inflammation, immunity, and thrombosis pathways in the body.14 As a simple, economical, and convenient indicator, SII has been proven valuable in diagnosing and evaluating the prognosis of various diseases, including tumors, infections, and cardiovascular diseases.14–17 Studies have shown that high-SII levels are associated with poor prognosis in acute pancreatitis and severe outcomes in viral infections like COVID-19.18,19 Therefore, SII helps assess the severity and predict the prognosis of infectious diseases. However, there are limited studies on the prognostic value of SII in patients with critical S-AKI.This study aims to explore the relationship between SII and the prognosis of critically ill S-AKI patients and to construct a prediction model for the prognosis within 28-day after ICU admission.

Material and Methods Study Population

This study utilized a retrospective cross-sectional design. A total of 2096 ICU patients at first admission were recorded at Henan Provincial People’s Hospital between January 1, 2023, and December 31, 2023. The flowchart of this study and the number of patients are presented in Figure 1. The inclusion criteria were as follows: (1) diagnosed as S-AKI; (2) aged ≥18 years; (3) first admission ICU. The exclusion criteria were as follows: (1) history of chronic kidney disease; (2) hematologic disorders; (3) recent use of immunosuppressants or steroids within 3 months; (4) ICU length of stay ≤24 hours; (5) incomplete variables. This study was approved by the Ethics Committee of Henan Provincial People’s Hospital on October 2, 2023 [approval number: (2023) No. (145)]. All study procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional) and with the Helsinki Declaration of 1975. Written informed consent was obtained from all patients prior to study enrolment.

Figure 1 Flowchart of patient selection.

Subgroups and Outcome

Patients were divided into survival and death groups based on their 28-day outcome after ICU admission. Survival was defined as stabilization and discharge after 28 days in the ICU, while death was defined as mortality during the 28-day ICU treatment period or confirmed death during 28-day follow-up if ICU treatment period was less than 28 days or was abandoned. The outcome was mortality within 28-day of admission to ICU.

Definitions

S-AKI is generally defined as AKI in the presence of sepsis that cannot be explained by other important causes or meets both Sepsis 3.0 and Kidney Disease: Improving Global Outcomes (KDIGO) criteria.9 In accordance with the Sepsis 3.0 criterion,20 sepsis was defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, which can be represented by an increase in the sequential organ failure assessment (SOFA) score of 2 points or more. According to the KDIGO criterion,21 AKI was defined as an increase in creatinine level of at least 0.3 mg/dL (26.5 μmol/L) within the first 48 hours after sepsis diagnosis, an increase in creatinine level of at least 1.5 times baseline within the previous 7 days after sepsis diagnosis, or urine output ≤ 0.5 mL/kg/h for at least 6 hours. AKI is classified into 3 stages according to KDIGO guidelines.21

Data Collection

Patient data within the first 24 hours after ICU admission was collected as follows: 1) The basic information (gender, age, body mass index), mean arterial pressure (MAP), number of organ dysfunctions at ICU admission, length of stay in ICU, length of hospitalization, and AKI stage were extracted; 2) Comorbidities: hypertension, diabetes, coronary heart disease, stroke, respiratory failure, congestive heart failure, hepatic failure, malignant tumor); 3) Laboratory parameters: hemoglobin, white blood cell count, platelet count, neutrophil, lymphocyte, monocyte, albumin, glutamic pyruvic transaminase (ALT), glutamic oxaloacetic transaminase (AST), albumin, creatinine, serum bicarbonate, serum sodium, serum potassium, C-reactive protein, procalcitonin, and arterial blood gas analysis; 4) Scoring systems of severity-of-illness: SOFA score, Acute Physiology and Chronic Health Evaluation II (APACHEII); 5) Medical interventions: mechanical ventilation, use of vasopressors and renal replacement therapy. Clinical data were recorded as the worst value of the indicator within the first 24 hours of ICU admission. APACHE II and SOFA scores were calculated using the worst values recorded. Inflammatory markers were calculated as follows: NLR = neutrophil / lymphocyte; PLR = platelet count / lymphocyte; MLR = monocyte / lymphocyte; SII = platelet count × neutrophil / lymphocyte.22

Statistical Analysis

Statistical analysis were performed using SPSS software, version 26.0 and R software (version 4.3.3). Normally distributed and skewed continuous variables are presented as the mean ± standard deviation and medians (25th and 75th quartiles), respectively. Categorical variables are shown as frequency (%). Student’s t-test, Wilcoxon rank-sum test, and chi-squared test were used, as appropriate, to compare the two groups. Receiver operating characteristic (ROC) curves were used to determine the best cut-off values and prognostic ability of parameters. Patients were divided into high- and low-SII groups based on the best SII cut-off value. Kaplan-Meier curves describe the 28-day survival status of patients with different SII levels. Using stepwise backward regression analysis, we investigated the association between SII and 28-day mortality in the multivariate Cox proportional hazards regression model. A clinical prognostic prediction model was constructed, evaluated using the concordance index (C-index), and internally validated with the Bootstrap method. Decision curve analysis assessed the model’s predictive efficacy. P < 0.05 was considered statistically significant.

Results Patient Characteristics and ICU Admission Status of Patients

A total of 216 ICU patients with S-AKI were included (Figure 1), among whom 128 (59.3%) patients died. The median age was 66 years (range 18–91 years), of which 143 (66.2%) were males. Compared to the survival group, the death group had higher age, APACHE II scores, SOFA scores, the number of organ dysfunctions at ICU admission, and a higher proportion of mechanical ventilation within 24 hours of ICU admission (all P < 0.05). The death group also had higher proportions of comorbid coronary heart disease, respiratory failure, and malignant tumors (all P < 0.05). Baseline laboratory results showed higher white blood cell counts, NLR, PLR, MLR, and SII in the death group, while creatinine and procalcitonin levels were lower compared to the survival group (all P < 0.05) (Table 1).

Table 1 Comparison of the Variables Between the Survival Group and Death Group

Predictive Value of Inflammatory Markers, APACHE II, and SOFA Scores

ROC analysis indicated that SII had the highest predictive value for the 28-day mortality in S-AKI patients; the best cut-off value of SII was 3910; the sensitivity was 71.9%; the specificity was 85.2%; and the area under the ROC curve (AUC) was 0.867 (95% confidence interval [95% CI]: 0.821–0.914). Other parameters’ AUC values, sensitivities, and specificities are detailed in Table 2 and Figure 2.

Table 2 ROC Curves of Different Variables for Predicting S-AKI Prognosis

Figure 2 ROC analysis of different variables for the prediction of 28-day mortality.

According to the best SII cut-off value, patients were divided into high-SII (105 cases) and low-SII (111 cases) groups. Compared with the low-SII group, the high-SII group had a higher 28-day mortality rate (86.7% vs 32.4%, respectively, P <0.001). (Figure 3).

Figure 3 Kaplan-Meier curves for the description of survival between groups with different SII values (cut-off value: 3910) during the follow-up.

Risk Factors Associated with 28-Day Mortality

Univariate Cox regression analysis showed that age, coronary heart disease, respiratory failure, malignant tumor, APACHE II scores, SOFA scores, number of organ dysfunctions, mechanical ventilation, white blood cell count, creatinine, blood urea nitrogen, NLR, PLR, MLR, and SII levels were associated with 28-day mortality in S-AKI patients (Table 3).

Table 3 Univariate and Multivariate Cox Proportional Hazards Regression Analysis for Risk Factors of 28-Day Mortality

Since SII is composed of white blood cells, platelets, and other components, in order to avoid the influence of collinearity on the stability of the regression model, Spearman correlation analysis was used to exclude factors significantly associated with SII. The results showed that SII was strongly correlated with leukocytes, platelets, NLR, PLR, and MLR (r = 0.378, 0.412, 0.729, 0.847, 0.362; P < 0.05) (Supplementary Table 1). Additionally, serum creatinine was excluded from the multivariate analysis due to its influence by various factors such as renal function and nutritional status.

The results of the Cox proportional hazards regression model are shown in Table 4. We constructed model I, which showed a positive correlation with short-term mortality (HR 2.603, 95% CI 1.705–3.937, P<0.001) after adjusting for age, coronary heart disease, respiratory failure, and malignancy. Further based on model 1, we constructed model II and adjusted SOFA scores, APACHE II scores, number of organ dysfunctions, mechanical ventilation use, and urea nitrogen. In a fully adjusted model, 28-day mortality was 2.505 times higher in the high-SII group compared to the low-SII group (95% CI 1.609–3.898, P<0.001). The relationship was consistent when adjusting for confounding factors in different models. Multivariate Cox regression analysis showed that age, respiratory failure, SII, number of organ dysfunction, SOFA scores, and APACHE II scores were independent risk factors for short-term poor prognosis in S-AKI patients. The detailed results of univariate and multivariate analysis are presented in Table 3.

Table 4 The Relationship Between SII and the Mortality of S-AKI Patients in a Cox Regression Model

Construction of the Nomogram Model

Based on the abovementioned variables, the nomogram was constructed for the prediction of 28-day mortality rates with S-AKI patients. For the nomogram, each predictive indicator’s scale value corresponded to a score, with the total score representing the predicted 28-day prognosis for S-AKI patients after ICU admission. A higher total score indicated a poorer prognosis and a higher mortality risk (Figure 4). Using the Bootstrap method (1000 samples) to assess the performance of the model, the C-index was 0.682 (95% CI 0.616–0.748). The calibration curve shows good agreement between the predicted and observed values in the nomogram model (Figure 5A). The day 28 ROC curve for the evaluation model showed an AUC of 0.916 (95% CI 0.892–0.938) (Figure 5B). Decision curve analysis demonstrated the model’s high clinical predictive efficacy (Figure 5C).

Figure 4 Nomogram model for estimating 28-day OS.

Figure 5 (A) Calibration curves of the predictive model for 28-day mortality. (B) ROC curves of prediction model for the day 28 mortality. (C) Decision curves of the predictive model for 28-day mortality.

Discussion

S-AKI has high incidence, mortality, and poor prognosis.8 Early identification and intervention are crucial for improving patient outcomes. This study compared the predictive power conventional risk factors and inflammatory markers for prognosis, revealing that the SII exhibited superior prognostic value for short-term outcomes in patients with S-AKI. A cutoff point of 3910 was established for SII to predict prognosis in S-AKI patients, with an AUC of 0.867 (sensitivity 0.719, specificity 0.852). The 28-day mortality rate in the ICU was significantly higher in the high-SII group than in the low-SII group (87.6% vs 32.4%, P < 0.001). Multivariate Cox regression analysis showed that SII as an independent predictor of prognosis for patients with S-AKI 28 days following their admission to ICU (HR 2.505, 95% CI 1.609–3.898, P < 0.001).

Previous studies have demonstrated that the SII is effective in predicting both the occurrence and prognostic outcomes of AKI across various settings.13,23,24 Jiang et al23 assessed the predictive capacity of SII in 4381 patients who underwent coronary angiography, among whom 786 developed AKI (AUC [95% CI] 0.625 [0.602–0.647]). In a study encompassing 305 patients with spontaneous cerebral hemorrhage after craniotomy, Wang et al24 demonstrated that SII exhibited predictive capability for AKI occurrence (AUC [95% CI] 0.669 [0.608–0.730]) and displayed favorable performance in prognosticating post-AKI outcomes (AUC: 0.888, sensitivity 0.789, specificity 0.874). Lan et al13 observed that there was a J-shaped correlation between SII and all-cause mortality in patients with severe AKI, especially at high levels of SII, which increased the mortality of patients. However, limited research has been conducted on the prognostic value of SII for short-term outcomes in patients with S-AKI admitted to the ICU. Therefore, we conducted an evaluation of the predictive performance of SII in predicting the prognosis of S-AKI and found that SII served as a reliable marker for short-term prognosis in these patients.

It was previously thought that AKI caused by sepsis was acute tubular necrosis due to reduced renal perfusion.25 However, in recent years, further studies have revealed a multifaceted pathogenesis of S-AKI, including inflammation, oxidative stress, microcirculatory dysfunction, metabolic reprogramming, mitochondrial metabolism disorders, and cell cycle arrest.9 Sepsis triggers widespread activation of cytokines and chemokines, it would result in immune system dysfunction.26 Studies found that excessive inflammation induces toll-like receptor expression in tubular epithelial cells, which initiates the downstream signaling cascade and results in the injury of glomeruli and peripheral tubular cells.27,28 The imbalance of inflammatory cells caused by sepsis is mainly manifested as neutrophilia and lymphocytopenia. Therefore, NLR, calculated by a complete blood count, has been proposed as an indicator to reflect the body’s inflammatory and immune status.29 Xi et al30 found that NLR can be used to predict the prognosis of S-AKI, but its sensitivity and specificity are limited. The possible mechanism is that over-activated inflammation leads to platelet activation, abnormal clotting pathways, microcirculation dysfunction, and microthrombus formation.31 In addition, platelets can also express toll-like receptors and further aggravate tubular epithelial cell damage.32 Therefore, considering the close relationship between coagulation and immunity, both immune cells and platelets play key roles in the development of S-AKI.

SII, which combines neutrophil, lymphocyte, and platelet counts, can reflect the relationship between systemic inflammation, immunity, and coagulation disorders comprehensively.22 Hu et al14 first used it as an indicator to predict the prognosis of patients undergoing resection for hepatocellular carcinoma. Recent studies have shown that SII is associated with poor outcomes in various diseases and serve as a prognostic indicator for mortality in patients with AKI, sepsis, cardiovascular disease, and acute pancreatitis.13,16,17,33 Thus far, there are few studies on the value of SII in the prognosis of S-AKI. Previous studies based on the MIMIC-IV database have shown that a J-shaped association between SII and short-term mortality in patients, with both low and high SII being associated with increased mortality, particularly elevated SII levels; however, statistical significance was not reached for lower SII levels.34 In addition, Li et al35 reported a strong correlation between elevated SII and an increased incidence of AKI following sepsis. Therefore, further investigation is needed to determine the association between SII and short-term mortality risk in patients with S-AKI.

In our study, focusing on SII, we assessed the prognostic value of six biomarkers (SII, NLR, PLR, MLR, SOFA, and APACHE II). The study found that the high SII levels were significantly associated with the short-term risk of mortality in S-AKI patients. Additionally, two adjusted models were developed to account for confounding factors and further confirmed that SII levels were independently associated with the short-term risk of mortality in patients with S-AKI. To comprehensively improve the accuracy of prognostic assessment and develop a multi-parameter prognostic model, this study included various clinical data and serological markers to construct a prediction model for 28-day mortality in S-AKI patients after ICU admission, with a C-index of 0.682. The calibration curve showed good agreement between the predicted and actual risks of poor short-term prognosis in S-AKI patients, and the decision curve showed good clinical predictive efficacy and applicability. Therefore, SII serves as a potential novel marker for predicting the prognosis of S-AKI patients.

This study has certain limitations. First, it is a single-center retrospective study with a small sample size, and the results may have selection bias. The constructed nomogram model was only internally validated and lacked external or multicenter validation. Additionally, we used a single SII value rather than continuous measurements, not considering the evolution of the inflammatory state in sepsis patients. Therefore, future studies need to expand the sample sizes and conduct more in-depth research to validate the predictive value of inflammatory markers in critically ill patients.

Conclusion

In conclusion, this study showed that SII is independently associated with mortality in S-AKI patients and has a certain predictive value for prognosis within 28-day after ICU admission. A novel nomogram predictive model based on clinical data and the serum inflammatory marker SII was constructed, which can help assess the short-term mortality risk and prognosis of S-AKI patients. Routine blood tests are cost-effective, easily accessible, and highly reproducible, and the derived SII combined with clinical data can quickly and accurately quantify patient conditions, providing important implications for prognostic judgment and clinical application.

Statement of Ethics

This study was approved by the Ethics Committee of Henan Provincial People’s Hospital ((2023) No. (145) and was accordance with the principles of the Declaration of Helsinki.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was supported by the Henan Science and Technology Department (Project Number: 221111310800).

Disclosure

All authors report no conflicts of interest in this work.

References

1. Kellum JA, Prowle JR. Paradigms of acute kidney injury in the intensive care setting. Nat Rev Nephrol. 2018;14(4):217–230. doi:10.1038/nrneph.2017.184

2. Kellum JA, Chawla LS, Keener C, et al. The Effects of Alternative Resuscitation Strategies on Acute Kidney Injury in Patients with Septic Shock. Am J Respir Crit Care Med. 2016;193(3):281–287. doi:10.1164/rccm.201505-0995OC

3. Bellomo R, Kellum JA, Ronco C, et al. Acute kidney injury in sepsis. Intensive Care Med. 2017;43(6):816–828. doi:10.1007/s00134-017-4755-7

4. van der Slikke EC, Star BS, van Meurs M, et al. Sepsis is associated with mitochondrial DNA damage and a reduced mitochondrial mass in the kidney of patients with sepsis-AKI. Crit Care. 2021;25(1):36. doi:10.1186/s13054-020-03424-1

5. Morrell ED, Kellum JA, Pastor-Soler NM, Hallows KR. Septic acute kidney injury: molecular mechanisms and the importance of stratification and targeting therapy. Crit Care. 18(5):501.

6. Hoste EA, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med. 2015;41(8):1411–1423. doi:10.1007/s00134-015-3934-7

7. Hobson C, Ozrazgat-Baslanti T, Kuxhausen A, et al. Cost and Mortality Associated With Postoperative Acute Kidney Injury. Ann Surg. 2015;261(6):1207–1214. doi:10.1097/SLA.0000000000000732

8. Poston JT, Koyner JL. Sepsis associated acute kidney injury. BMJ. 2019;364:k4891. doi:10.1136/bmj.k4891

9. Peerapornratana S, Manrique-Caballero CL, Gómez H, Kellum JA. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int. 2019;96(5):1083–1099. doi:10.1016/j.kint.2019.05.026

10. Cato LD, Wearn CM, Bishop JRB, Stone MJ, Harrison P, Moiemen N. Platelet count: a predictor of sepsis and mortality in severe burns. Burns. 2018;44(2):288–297. doi:10.1016/j.burns.2017.08.015

11. Lai H, Wu G, Zhong Y, et al. Red blood cell distribution width improves the prediction of 28-day mortality for patients with sepsis-induced acute kidney injury: a retrospective analysis from MIMIC-IV database using propensity score matching. J Intensive Med. 2023;3(3):275–282. doi:10.1016/j.jointm.2023.02.005

12. Seyit M, Avci E, Nar R, et al. Neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio and platelet to lymphocyte ratio to predict the severity of COVID-19. Am J Emerg Med. 2021;40:110–114. doi:10.1016/j.ajem.2020.11.058

13. Jia L, Li C, Bi X, et al. Prognostic Value of Systemic Immune-Inflammation Index among Critically Ill Patients with Acute Kidney Injury: a Retrospective Cohort Study. J Clin Med. 2022;11(14):3978. doi:10.3390/jcm11143978

14. Hu B, Yang XR, Xu Y, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20(23):6212–6222. doi:10.1007/s40620-017-0452-4

15. He K, Si L, Pan X, et al. Preoperative Systemic Immune-Inflammation Index (SII) as a Superior Predictor of Long-Term Survival Outcome in Patients With Stage I-II Gastric Cancer After Radical Surgery. Front Oncol. 2022;12(829689).

16. Jiang D, Bian T, Shen Y, Huang Z. Association between admission systemic immune-inflammation index and mortality in critically ill patients with sepsis: a retrospective cohort study based on MIMIC-IV database. Clin Exp Med. 2023;23(7):3641–3650. doi:10.1007/s10238-023-01029-w

17. Yang YL, Wu CH, Hsu PF, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest. 2020;50(5):e13230. doi:10.1111/eci.13230

18. Zhang D, Wang T, Dong X, et al. Systemic Immune-Inflammation Index for Predicting the Prognosis of Critically Ill Patients with Acute Pancreatitis. Int J Gen Med. 2021;14:4491–4498. doi:10.2147/IJGM.S314393

19. Fernandes NF, Costa IF, Pereira KN, de Carvalho JAM, Paniz C. Hematological ratios in coronavirus disease 2019 patients with and without invasive mechanical ventilation. J Investig Med. 2023;71(4):321–328. doi:10.1177/10815589221149189

20. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–810. doi:10.1001/jama.2016.0287

21. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–184. doi:10.1159/000339789

22. Kosidło JW, Wolszczak-Biedrzycka B, Matowicka-Karna J, Dymicka-Piekarska V, Dorf J. Clinical Significance and Diagnostic Utility of NLR, LMR, PLR and SII in the Course of COVID-19: a Literature Review. J Inflamm Res. 2023;16:539–562. doi:10.2147/JIR.S395331

23. Jiang H, Li D, Xu T, et al. Systemic Immune-Inflammation Index Predicts Contrast-Induced Acute Kidney Injury in Patients Undergoing Coronary Angiography: a Cross-Sectional Study. Front Med Lausanne. 2022;9(841601).

24. Wang Q, Li S, Sun M, Ma J, Sun J, Fan M. Systemic immune-inflammation index may predict the acute kidney injury and prognosis in patients with spontaneous cerebral hemorrhage undergoing craniotomy: a single-center retrospective study. BMC Nephrol. 2023;24(1):73. doi:10.1186/s12882-023-03124-2

25. Uchino S. Acute Renal Failure in Critically Ill Patients A Multinational, Multicenter Study. JAMA. 2005;294(7):813–818. doi:10.1001/jama.294.7.813

26. Liu D, Huang SY, Sun JH, et al. Sepsis-induced immunosuppression: mechanisms, diagnosis and current treatment options. Mil Med Res. 2022;9(1):56. doi:10.1186/s40779-022-00422-y

27. Dellepiane S, Marengo M, Cantaluppi V. Detrimental cross-talk between sepsis and acute kidney injury: new pathogenic mechanisms, early biomarkers and targeted therapies. Crit Care. 2016;20(61). doi:10.1186/s13054-016-1219-3

28. Cantaluppi V, Quercia AD, Dellepiane S, Ferrario S, Camussi G, Biancone L. Interaction between systemic inflammation and renal tubular epithelial cells. Nephrol Dial Transplant. 2014;29(11):2004–2011. doi:10.1093/ndt/gfu046

29. Wei W, Huang X, Yang L, et al. Neutrophil-to-Lymphocyte ratio as a prognostic marker of mortality and disease severity in septic Acute kidney injury Patients: a retrospective study. Int Immunopharmacol. 2023;116(109778):109778. doi:10.1016/j.intimp.2023.109778

30. Bu X, Zhang L, Chen P, Wu X. Relation of neutrophil-to-lymphocyte ratio to acute kidney injury in patients with sepsis and septic shock: a retrospective study. Int Immunopharmacol. 2019;70:372–377. doi:10.1016/j.intimp.2019.02.043

31. Fani F, Regolisti G, Delsante M, et al. Recent advances in the pathogenetic mechanisms of sepsis-associated acute kidney injury. J Nephrol. 2018;31(3):351–359.

32. Mandel J, Casari M, Stepanyan M, Martyanov A, Deppermann C. Beyond Hemostasis: platelet Innate Immune Interactions and Thromboinflammation. Int J Mol Sci. 2022;23(7). doi:10.3390/ijms23073868

33. Liu X, Guan G, Cui X, Liu Y, Liu Y, Luo F. Systemic Immune-Inflammation Index (SII) Can Be an Early Indicator for Predicting the Severity of Acute Pancreatitis: a Retrospective Study. Int J Gen Med. 2021;14:9483–9489. doi:10.2147/IJGM.S343110

34. Sun J, Qi Y, Wang W, Meng P, Han C, Chen B. Systemic Immune-Inflammation Index (SII) as a Predictor of Short-Term Mortality Risk in Sepsis-Associated Acute Kidney Injury: a Retrospective Cohort Study. Med Sci Monit. 2024;30:e943414. doi:10.12659/MSM.943414

35. Li D, Zhu Y. Predictive Value of Systemic Immune Inflammation Index in Septic Patients with Acute Kidney Injury. Arch Esp Urol. 2024;77(1):67–71. doi:10.56434/j.arch.esp.urol.20247701.9