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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39278977
72447
10.1038/s41598-024-72447-3
Article
Association between systemic immune inflammation index and short term prognosis of acute on chronic liver failure
Ma Yuanji
Xu Yan
Du Lingyao n.sync@163.com

Bai Lang pangbailang@163.com

Tang Hong
https://ror.org/007mrxy13 grid.412901.f 0000 0004 1770 1022 Center of Infectious Diseases, West China Hospital of Sichuan University, Chengdu, 610041 China
15 9 2024
15 9 2024
2024
14 2153524 4 2024
6 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The systemic immune-inflammatory index (SII) has been identified as an independent prognostic factor for multiple diseases. However, the impact of SII on outcome of acute-on-chronic liver failure (ACLF) is scant. A retrospective study enrolled patients with ACLF treated with artificial liver support system. Restricted cubic spline (RCS) (knots at the 10th, 50th, and 90th percentiles) and Cox proportional hazards models were applied to investigate the relationship between SII and 90-day transplant-free survival and overall survival in patients with ACLF. A total of 258 patients with ACLF were included. The 90-day transplant-free survival rate and overall survival rate were 58.5% and 66.3%. The SII was 465.5 (277.3–804.4). Adjusted RCS models showed linear exposure–response relationship between SII and 90-day transplant-free survival (P for overall < 0.001, P for nonlinear = 0.154) and 90-day overall survival (P for overall < 0.001, P for nonlinear = 0.103), and adjusted Cox models confirmed the positive relationship. Compared with patients with SII < 480, patients with ≥ 480 had more serious  condition, lower 90-day transplant-free survival rate (46.8% vs. 69.7%, adjusted HR (95% CI) for transplant or death: 2.13 (1.40–3.23), P < 0.001), and lower 90-day overall survival rate (56.3% vs. 75.8%; adjusted HR (95% CI) for death: 2.26 (1.42–3.61), P = 0.001). Stratified Cox models suggested no potential modifiers in the relationship between SII and 90-day transplant-free survival. Our findings suggested SII was positively associated with poor short-term prognosis of ACLF.

Keywords

Acute-on-chronic liver failure
Outcome
Risk factor
Systemic immune-inflammation index
Artificial liver support system
Subject terms

Prognostic markers
Viral hepatitis
National Key Research and Development Program of China2022YFC2304800 the 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan UniversityZYJC21014 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Acute-on-chronic liver failure (ACLF), characterized by acute decompensation of liver function superimposed on underlying chronic liver diseases, is one the most urgent end stage liver diseases1–3. Without proper treatment, most of the patients would end up with death. Although artificial liver support system (ALSS) therapy could improve the short-term prognosis of patients with liver failure, a remarkable portion of patients still failed to survive4–6. Early identifying these patients could help timely optimizing their therapeutic strategy, including heading for liver transplantation. However, effective predictors for poor prognosis are still lack.

The complicated networks between inflammation, endotoxemia, and coagulation exist in the pathogenesis of liver failure4. Hepatocytic debris acts as damage-associated molecular patterns to trigger the innate immune system, thus evoking the cytokine storm which accounts for systemic inflammatory response syndrome and multiorgan dysfunction. A previous study used to report the association between serum interleukin and the mortality in patients with end stage liver diseases (median (P25-P75) model for end-stage liver disease (MELD) score: 11.9 (8.7–18.0)), including liver failure, decompensated cirrhosis and hepatocellular carcinoma7. It turns out that serum interleukin 6 associate with 90-day and 1-year mortality with a predictive value comparable to that of MELD or MELD including serum sodium (MELD-Na) score7.

The inflammatory immune microenvironment plays a vital role in the release of proinflammatory factors8. Inflammation related indicators might help to reflect or predict prognosis. An easy to operative indicator for both local immune response and systemic inflammation, systemic immune-inflammatory index (SII), has been firstly reported in 2014 and well used now9. SII is calculated with peripheral lymphocyte (LYM), neutrophil (NEU) and platelet (PLT) counts, and has been identified as an independent prognostic factor for multiple diseases including liver, pancreatic and colorectal cancers10–13. However, the impact of SII on outcome of acute-on-chronic liver failure (ACLF) is scant.

For better predicting the prognosis of patient with ACLF treated with artificial liver support system, and the learn the potential applicability of SII in the management of patients with ACLF, we conducted this retrospective study to investigate the relationship between SII and short-term prognosis in patients with ACLF.

Methods

Study design

A secondary data analysis was conducted based on a cohort of previously studied patients with ACLF and their medical records14–16 at the Center of Infectious Diseases, West China Hospital of Sichuan University to assess the association between SII and short-term prognosis of patients with ACLF. The cohort was registered with ChiCTR2000035013 after acquiring ethical approval from the Biomedical Research Ethics Committee of West China Hospital of Sichuan University (2020-650). All study components were performed according to the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The data are anonymous, and the requirement for informed consent was therefore waived in this study.

Patients

The patients who received artificial liver support system (ALSS) therapy were initially screened (Fig. 1). The patients were excluded if they did not receive DPMAS plus PE therapy with regional citrate anticoagulation. The patients with liver cancer were also excluded. The remaining patients who fulfilled the diagnosis of HBV-ACLF were included. HBV-ACLF was diagnosed according to COSSH ACLF criteria1. The severity of HBV-ACLF was rated according to the COSSH ACLF score1.Fig. 1 Patients selection and follow-up. ALSS, artificial liver support system; DPMAS, double plasma molecular adsorption system; PE, plasma exchange; RCA, regional citrate anticoagulation; HBV, hepatitis B virus; PT-INR, international normalized ratio (INR) of prothrombin time (PT); ACLF, acute-on-chronic liver failure; SII, systemic immune-inflammation index.

All patients received standard medical treatment and ALSS therapy, which were described in the previously studies15,17. The standard medication included antiviral drugs, hepatoprotective agents, and drugs to treat complications and comorbidities.

All patients were enrolled on the day of the first session of ALSS therapy and were followed up to 90 days after enrollment or until death, or loss of follow-up.

SII and outcome

The SII is composed of peripheral NEU, LYM, and PLT counts prior to the first session of ALSS therapy, and is defined as follow: SII = PLT × NEU/LYM 9.

The primary outcome was 90-day transplant-free survival (TFS), the secondary outcomes were 90-day overall survival (OS), 28-day TFS and 28-day OS.

Statistical analysis

Quantitative data were represented as means ± standard deviation (SD) (normally distributed data) or medians (P25–P75) (non-normally distributed data) and compared by Mood's median test. Qualitative data were represented as frequencies (proportion) and compared by the chi-squared test. Restricted cubic spline (RCS) (knots at the 10th, 50th, and 90th percentiles) and Cox proportional hazards models were applied to investigate the relationship between SII and short-term prognosis in patients with ACLF. The optimal cut-off value of SII was identified based on the area under the receiver operating characteristic curve (AUC) to separate the patients into groups with a low-risk and a high-risk of poor short-term prognosis.

The Model 1 was a crude Cox model adjusted for none. The Model 2 was a partially adjusted Cox model adjusted for age (years), gender (female vs. male), liver cirrhosis (yes vs. no), HBV DNA (log10 IU/mL), other co-existing liver diseases (yes vs. no), comorbidities (yes vs. no). The Model 3 was a fully adjusted Cox model including model 2 covariates plus disease severity (COSSH ACLF score), sessions of artificial liver support system therapy (continuous values), and liver transplantation just for overall survival analysis (yes vs. no). Further stratified Cox models were performed with the same covariates in Model 3 to identify variables that modify the relationship between SII (continuous SII and stratified SII) and outcome of ACLF. The RCS model was a fully adjusted model including the same covariates in Model 3 too.

The RCS and survival curves were performed using R version 4.3.0 (2023-04-21), along with Storm Statistical Platform (http://www.medsta.cn/software). The other statistical tests were performed using SPSS v.24 (IBM Corp.). Statistical significance was set at P < 0.05.

Results

Patient characteristics

A total of 258 patients with HBV-ACLF were included and analyzed (Fig. 1). The mean age was 46.2 ± 11.7 years, 14.3% patients were female, and 78.3% patients had liver cirrhosis (Table 1). During the 90-day follow-up, 20 (7.8%) patients underwent liver transplantation, of which 15 (5.8) occurred within 28 days of enrollment. The 90-day TFS rate, 90-day OS rate, 28-day TFS rate and 28-day OS rate were 58.5%, 66.3%, 76.4%, and 82.2%, respectively (Supplementary Table S1).Table 1 Patients’ characteristics.

	All patients (N = 258)	90-day prognosis	
Transplant or death (N = 107)	Transplant-free survival (N = 151)	P	
Female	37 (14.3%)	22 (20.6%)	15 (9.9%)	0.016	
Age (years)	46.2 ± 11.7	49.2 ± 11.4	44.1 ± 11.5	0.058	
Liver cirrhosis	202 (78.3%)	95 (88.8%)	107 (70.9%)	0.001	
HBV DNA (log10 IU/mL)	4.76 (3.50–6.57)	4.68 (3.47–6.26)	4.80 (3.51–6.68)	0.800	
Causes of liver disease				0.874	
 HBV infection only	194 (75.2%)	81 (75.7%)	113 (74.8%)		
 HBV infection plus other causes	64 (24.8%)	26 (24.3%)	38 (25.2%)		
Comorbidities				0.006	
 No	215 (83.3%)	81 (75.7%)	134 (88.7%)		
 Yes	43 (16.7%)	26 (24.3%)	17 (11.3%)		
Disease severity assessment	
 COSSH ACLF score	6.5 ± 0.9	7.1 ± 0.9	6.1 ± 0.7	 < 0.001	
 COSSH ACLF II score	7.2 ± 0.8	7.7 ± 0.8	6.9 ± 0.7	 < 0.001	
 CLIF-C ACLF score	34.5 ± 7.2	38.1 ± 7.0	32.0 ± 6.2	 < 0.001	
 AARC score	9.9 ± 1.6	10.6 ± 1.4	9.4 ± 1.5	 < 0.001	
 MELD score	26.9 ± 4.8	29.3 ± 5.3	25.2 ± 3.6	 < 0.001	
Laboratory examination	
 PT-INR	2.12 (1.77–2.53)	2.33 (1.95–2.84)	2.02 (1.73–2.33)	0.002	
 Serum creatinine (× ULN)	0.80 (0.67–0.95)	0.88 (0.72–1.13)	0.77 (0.66–0.88)	0.001	
 Total bilirubin (μmol/L)	424.6 ± 124.6	479.4 ± 124.4	385.7 ± 109.5	 < 0.001	
 Direct bilirubin to total bilirubin ratio	0.78 ± 0.10	0.74 ± 0.09	0.80 ± 0.09	 < 0.001	
 Alanine aminotransferase (IU/L)	126 (62–261)	118 (58–232)	133 (66–282)	0.613	
 Aspartate aminotransferase (IU/L)	118 (83–198)	119 (85–234)	117 (79–191)	1.000	
 Aspartate aminotransferase to alanine aminotransferase ratio	1.08 (0.64–1.67)	1.21 (0.72–1.75)	0.97 (0.59–1.49)	0.077	
 Albumin (g/L)	31.8 ± 3.9	31.2 ± 3.5	32.2 ± 4.0	0.055	
 Albumin to globulin ratio	1.2 ± 0.4	1.3 ± 0.5	1.2 ± 0.3	0.800	
 Ammonia (mmol/L)	79.1 (60.0–111.3)	78.0 (60.0–119.0)	80.2 (60.0–108.0)	1.000	
 Lactate (mmol/L)	2.50 (1.90–3.33)	2.80 (2.20–3.87)	2.33 (1.80–3.00)	0.006	
 Serum sodium (mmol/L)	133.4 ± 9.4	132.6 ± 5.1	133.9 ± 11.4	0.002	
 Serum potassium (mmol/L)	3.45 ± 0.60	3.48 ± 0.61	3.43 ± 0.54	0.848	
 Serum chloride (mmol/L)	96.3 ± 5.0	94.9 ± 5.9	97.4 ± 4.0	0.002	
 Hemoglobin (g/L)	118.8 ± 20.4	114.5 ± 21.2	121.9 ± 19.3	0.129	
 Platelets (× 109/L)	87 (61–120)	77 (48–113)	93 (68–123)	0.070	
 White blood cells (× 109/L)	7.59 ± 3.66	8.59 ± 4.29	6.88 ± 2.96	0.002	
 Neutrophil (× 109/L)	5.82 ± 3.31	6.85 ± 3.87	5.09 ± 2.64	 < 0.001	
 Lymphocyte (× 109/L)	1.02 ± 0.51	0.89 ± 0.48	1.11 ± 0.51	 < 0.001	
SII	465.5 (277.3–804.4)	580.2 (291.4–926.0)	380.4 (265.1–643.7)	 < 0.001	
Sessions of ALSS therapy	4.0 (3.0–6.0)	4.0 (2.0–6.0)	4.0 (3.0–6.0)	0.959	
HBV, hepatitis B virus; ACLF, acute-on-chronic liver failure; COSSH, Chinese Group on the Study of Severe Hepatitis B; CLIF-C, European Association for the Study of the Liver—Chronic Liver Failure-Consortium; AARC, APASL ACLF Research Consortium; APASL, Asian Pacific Association for the Study of the Liver; MELD, Model for End-Stage Liver Disease; PT-INR, international normalized ratio (INR) of prothrombin time (PT); ULN, upper limit of normal; SII, systemic immune-inflammation index; ALSS, artificial liver support system.

Quantitative data are represented as mean ± SD (normally distributed data) or median (P25–P75) (non-normally distributed data) and compared by Mood's median test. Qualitative data are represented as frequencies (proportion) and compared by chi-squared test.

The 90-day transplant-free survivors were less likely to have liver cirrhosis (70.9% vs. 88.8%, P = 0.001), and their disease severity (COSSH ACLF score: 6.1 ± 0.7 vs. 7.1 ± 0.9, P < 0.001) was milder than that in the transplant or death patients (Table 1). The median SII was 465.5 (277.3–804.4). The SII in 90-day transplant-free survivors was lower than that in transplant or death patients (380.4 (265.1–643.7) vs. 580.2 (291.4–926.0), P < 0.001).

Association of SII with outcome of ACLF

Adjusted RCS models showed linear exposure–response relationship between SII and 90-day TFS (P for overall < 0.001, P for nonlinear = 0.154; Fig. 2A), and 90-day OS (P for overall < 0.001, P for nonlinear = 0.103; Fig. 2B). The SII was positively associated with 90-day TFS (all hazard ratio (HR) (95% confidence interval (CI)) for transplant or death > 1.00, P < 0.001; Model 3: 1.00040 (1.00021–1.00059), P < 0.001), and 90-day OS (all HR (95% CI) for death > 1.00, P < 0.001; Model 3: 1.00044 (1.00025–1.00063), P < 0.001).Fig. 2 Relationship between SII and short-term prognosis of ACLF. SII, systemic immune-inflammation index; ACLF, acute-on-chronic liver failure; HR, hazard ratio; CI, confidence interval; TFS, transplant-free survival; OS, overall survival. Model 1 was a crude Cox model adjusted for none. Model 2 was a partially adjusted Cox model adjusted for age (years), gender (female vs. male), liver cirrhosis (yes vs. no), HBV DNA (log10 IU/mL), other co-existing liver diseases (yes vs. no), comorbidities (yes vs. no). Model 3 was a fully adjusted Cox model including model 2 covariates plus disease severity (COSSH ACLF score). Model 4 was a fully adjusted Cox model including model 3 covariates plus, sessions of artificial liver support system therapy (continuous values), and liver transplantation just for overall survival analysis (yes vs. no). The restricted cubic spline model (knots at the 10th, 50th, and 90th percentiles) was a fully adjusted model including the same covariates in Model 3.

Adjusted RCS models showed linear exposure–response relationship between SII and 28-day TFS (P for overall = 0.006, P for nonlinear = 0.426; Fig. 2C), and 28-day OS (P for overall = 0.001, P for nonlinear = 0.199; Fig. 2D). The SII was positively associated with 28-day TFS (all HR (95% CI) for transplant or death > 1.00, P < 0.01; Model 3: 1.00034 (1.00012–1.00056), P = 0.002), and 90-day OS (all HR (95% CI) for death > 1.00, P ≤ 0.001; Model 3: 1.00038 (1.00017–1.00060), P = 0.001).

Association of stratified SII with outcome of ACLF

The optimal cutoff value of SII at 480.7 for 90-day TFS was determined by AUC (AUC for transplant or death: 0.617 (0.546 ~ 0.688), P = 0.001). Here, the SII was stratified at 480 with a sensitivity of 62.6% and a specificity of 60.9% for further analysis.

Compared with patients with SII < 480, patients with SII ≥ 480 had higher COSSH ACLF score (6.73 ± 0.98 vs. 6.33 ± 0.85, P = 0.001), lower 90-day TFS rate (46.8% vs. 69.7%; all HR (95% CI) for transplant or death > 1.00, all P < 0.001; Model 3: 2.13 (1.40–3.23), P < 0.001), lower 90-day OS rate (56.3% vs. 75.8%; all HR (95% CI) for death > 1.00, all P ≤ 0.001; Model 3: 2.26 (1.42–3.61), P = 0.001), lower 28-day TFS rate (70.6% vs. 81.8%; all HR (95% CI) for transplant or death > 1.00, all P < 0.05; Model 3: 2.06 (1.67–3.65), P = 0.013), and lower 28-day OS rate (77.0% vs. 87.1%; all HR (95% CI) for death > 1.00, all P < 0.05; Model 3: 2.58 (1.26–5.25), P = 0.009) (Supplementary Table S1, Fig. 3).Fig. 3 Survival curves of patients with ACLF and different stratified SII. SII, systemic immune-inflammation index; ACLF, acute-on-chronic liver failure; HR, hazard ratio; CI, confidence interval; TFS, transplant-free survival; OS, overall survival. Model 1 was a crude Cox model adjusted for none. Model 2 was a partially adjusted Cox model adjusted for age (years), gender (female vs. male), liver cirrhosis (yes vs. no), HBV DNA (log10 IU/mL), other co-existing liver diseases (yes vs. no), comorbidities (yes vs. no). Model 3 was a fully adjusted Cox model including model 2 covariates plus disease severity (COSSH ACLF score), sessions of artificial liver support system therapy (continuous values), and liver transplantation just for overall survival analysis (yes vs. no).

Potential factors modifying the association of SII with outcome of ACLF

The stratified Cox models performed with the same covariates in Model 3 suggested that age (≥ 50 vs. < 50 years), gender (female vs. male), liver cirrhosis (yes vs. no), HBV DNA (≥ 4 vs. < 4 log10 IU/mL), other co-existing liver diseases (yes vs. no), comorbidities (yes vs. no), sessions of artificial liver support system therapy (≥ 6 vs. 3–5 vs. 1–2 sessions) were not potential factors to modify the relationship between SII (continuous SII and stratified SII) and 90-day TFS of ACLF (Figs. 4 and 5).Fig. 4 Stratified Cox regression analysis to identify variables that modify the relationship between SII and 90-day transplant-free survival of ACLF. SII, systemic immune-inflammation index; ACLF, acute-on-chronic liver failure; ALSS, artificial liver support system; HR, hazard ratio; CI, confidence interval. Adjusted HR&: multivariable Cox regression analysis (Model 3) includes SII (continuous SII), age (years), gender (female vs. male), liver cirrhosis (yes vs. no), HBV DNA (log10 IU/mL), other co-existing liver diseases (yes vs. no), comorbidities (yes vs. no), disease severity (COSSH ACLF score) and sessions of artificial liver support system therapy (continuous values).

Fig. 5 Stratified Cox regression analysis to identify variables that modify the relationship between stratified SII and 90-day transplant-free survival of ACLF. SII, systemic immune-inflammation index; ACLF, acute-on-chronic liver failure; ALSS, artificial liver support system; HR, hazard ratio; CI, confidence interval. Adjusted HR&: multivariable Cox regression analysis (Model 3) includes SII (stratified SII), age (years), gender (female vs. male), liver cirrhosis (yes vs. no), HBV DNA (log10 IU/mL), other co-existing liver diseases (yes vs. no), comorbidities (yes vs. no), disease severity (COSSH ACLF score) and sessions of artificial liver support system therapy (continuous values).

Discussion

SII has been identified as an independent prognostic factor for multiple diseases9–11,13. However, the impact of SII on outcome of ACLF is scant. In this retrospective study that enrolled patients with ACLF treated with ALSS, we found SII was positively associated with poor short-term prognosis of ACLF.

SII integrated PLT, NEU and LYM counts to reflect the local immune response and systemic inflammation in the whole human body9. High SII acts as a poor prognostic factor in multiple cancers. A meta-analysis enrolled 22 studies with 7657 patients with cancers showed that a higher level of SII was correlated with poor overall survival (HR (95% CI): 1.69 (1.42–2.01), P < 0.001) and poor recurrence-free survival (HR (95% CI): 1.66 (1.07–2.59), P = 0.025) in patients with multiple kinds of cancers18. However, whether SII influences the prognosis of patients with liver diseases and its correlation to liver dysfunction remains be to elucidated, not mentioning the ACLF. A very interesting study enrolled patients with liver dysfunction and solid tumor other than liver cancer, including colorectal cancers, breast cancers, pancreatic carcinoma, and stomach cancers. Receiver operating characteristic curve (ROC) analysis showed SII ≥ 626.28 predicted mortality, with the sensitivity of 78.7%, and specificity of 100% (P = 0.013), consistent with MELD score19. It seems that the relationship between SII and mortality is more related to liver function regardless of the underlying cancers.

Several studies have investigated the association between SII and liver diseases. SII acts as a good predictor for both prognosis and therapeutic response. In patients with hepatocellular carcinoma (HCC), high SII exhibited its excellently prognostic value9. Vascular invasion, large tumors, or early recurrence were more often when the SII ≥ 3309. Also, circulating tumor cell was significantly higher in the SII ≥ 330 group (P = 0.029)9. In patients with high MELD score (≥ 30) after deceased donor liver transplantation, a preoperative SII ≥ 870 may be a significant risk factor for early posttransplant mortality20. Besides urgent appeal that systematic infection needs to be monitored to avoid futile liver transplantation mortality, the study proves more that high SII is related to liver dysfunction and poor prognosis20. Another study evaluated the prognostic value of inflammatory markers in patients with radically resectable HCC post-hepatectomy. SII shows potentially incremental prognostic value for predicting post-hepatectomy liver failure as well as procalcitonin21. Nevertheless, elevated SII (SII ≥ 340) is also associated with poor prognosis in HCC patients receiving biotherapy22. The elevated SII was an independent risk factor for overall survival in patients with sequential therapy with sorafenib and regorafenib (adjusted HR (95% CI): 2.21 (1.09–4.49), P = 0.028)22. These studies all imply a positive relationship between SII and poor prognosis of patients with liver diseases.

Limited studies have explored the predictive value of SII for prognosis related to liver failure. A study to investigate the relationship between SII and early allograft dysfunction (EAD) and 90-day mortality after liver transplantation in ACLF showed that SII could predict the occurrence of EAD and it was an independent risk factor for 90-day mortality after liver transplantation23. However, the liver transplantation altered the immunological internal environment of patients. Likewise, our study found patients with SII ≥ 480 had a much worse short-term prognosis, and could be an independent risk factor. None of the age, gender, liver stiffness, HBV viral load, other co-existing liver diseases/comorbidities, even sessions of ALSS therapy would modify the relationship between SII and short-term prognosis of ACLF. It has further demonstrated the independently prognostic value of SII for prognosis in patients with ACLF and suggested that SII could be applied to stratified patients and optimized therapeutic strategy.

In addition to being analyzed alone, there were also studies trying to combine SII with other scores to predict the prognosis. The albumin-bilirubin (ALBI) score/grade, which was originally developed to measure liver function in HCC patients, has the capability to predict the in-hospital mortality in cirrhotic patients with ACLF24. A scoring system consisting of baseline ALBI grade I (adjusted HR (95% CI) for progression-free survival: 0.73 (0.50–0.90), P = 0.040; adjusted HR (95% CI) for overall survival: 0.38 (0.18–0.81), P = 0.012) and SII ≤ 330 (adjusted HR (95% CI) for progression-free survival: 0.34 (0.14–0.83), P = 0.017; adjusted HR (95% CI) for overall survival: 0.49 (0.23–0.82), P = 0.037) could better predict the prognosis of regorafenib after sorafenib-refractory treatment in unresectable HCC patients25. Another study found there was a positive correlation between neutrophil-to-lymphocyte ratio (NLR), SII and MELD score, and SII combined with NLR showed good performance in predicting the prognosis within 90 days after admission in patients with ACLF26. It gives us the idea to develop a more comprehensive scoring system in future study, which not only predict the occurrence or recurrence of HCC but also the mortality of liver failure.

This study had limitations. First, the patients were originated from a single geographic region and fulfilled the COSSH ACLF criteria1. The results that were derived from this subset with a small number of patients may not be applicable to patients who are diagnosed according to other ACLF criteria, such as CLIF-C ACLF and AARC ACLF2,3. Second, the cutoff value of SII (480) should be validated by large-scale, prospective, cohort studies. Third, it is thought that ALSS therapy could improve the short-term prognosis of patients with ACLF4–6. That all patients received ALSS therapy in our study may also have effect on the results.

In conclusion, our findings suggested SII was positively associated with poor outcome of ACLF. More attention should be paid to patients with ACLF and high SII. Further large-scale, multi-center, prospective, cohort studies are warranted to validated the performance of SII. A validated model with SII would help to assess disease severity and predict outcomes in patients with ACLF and guide clinical management.

Supplementary Information

Supplementary Table S1.

Abbreviations

AARC APASL ACLF Research Consortium

ACLF Acute-on-chronic liver failure

ALSS Artificial liver support system

APASL Asian Pacific Association for the Study of the Liver

AUC Area under the receiver operating characteristic curve

CI Confidence interval

COSSH Chinese Group on the Study of Severe Hepatitis B

CLIF-C European Association for the Study of the Liver—Chronic Liver Failure-Consortium

DPMAS Double plasma molecular adsorption system

EAD Early allograft dysfunction

HBV Hepatitis B virus

HCC Hepatocellular carcinoma

HR Hazard ratio

LYM Lymphocyte

MELD Model for end-stage liver disease

NEU Neutrophil

OS Overall survival

PE Plasma exchange

PLT Platelet

PT-INR International normalized ratio of prothrombin time

RCA Regional citrate anticoagulation

RCS Restricted cubic spline

ROC Receiver operating characteristic curve

SII Systemic immune-inflammatory index

TFS Transplant-free survival

ULN Upper limit of normal

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72447-3.

Acknowledgements

We thank all patients participating in this study for their understanding and recognition of our work.

Author contributions

MYJ and DLY: statistical analysis, drafting of the manuscript, and interpretation of data. MYJ and BL had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. MYJ, DLY, and XY: acquisition of data. BL and TH: study concept and design, and critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2022YFC2304800), and the 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC21014).

Data availability

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Competing interests

The authors declare no competing interests.

Ethics statement

Approval for the cohort used was obtained from the Biomedical Research Ethics Committee of West China Hospital of Sichuan University. All study components were performed according to the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The data are anonymous, and the requirement for informed consent was therefore waived in this study.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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