Systemic Immune-Inflammation Index as a Predictor of Severity and Acute Kidney Injury in Acute Pancreatitis: A Meta-Analysis

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The systemic immune–inflammation index (SII), an integrated inflammatory marker derived from neutrophil, lymphocyte, and platelet counts, has demonstrated prognostic value in a variety of diseases. However, its application in AP has not been systematically evaluated. Methods A systematic search was conducted across databases to screen observational studies published up to December 2025 on the correlation between SII and AP. Statistical analysis was performed using R software to calculate the pooled mean difference (MD), odds ratio (OR), as well as pooled sensitivity and specificity of SII as an predictor of severity and poor prognosis of the disease. Results A total of 16 high-quality studies (with NOS scores ranging from 7 to 9) were included, encompassing a total of 3,482 patients. The meta-analysis results indicated that the SII was significantly higher in SAP than non-SAP patients (MD = 1235.79, 95% CI: 847.55–1624.04, P < 0.001). The pooled logistic regression results suggested that elevated SII was an independent risk factor for SAP (OR = 1.001, 95% CI: 1.000–1.002, P = 0.012). The pooled sensitivity, specificity, and AUC of SII for predicting SAP were 0.692 (95% CI: 0.574–0.790), 0.755 (95% CI: 0.616–0.855), and 0.69, respectively. Furthermore, elevated SII were significantly associated with AKI in AP (MD = 1938.81, P < 0.001), but did not show statistical significance in predicting mortality (OR = 2.118, P = 0.133). Despite significant heterogeneity among studies (I 2 = 97.8%) and detection of publication bias (Egger’s test P = 0.0459), sensitivity analysis confirmed the stability of the main results. Conclusion SII is a simple and effective early biomarker for predicting the severity of acute pancreatitis and its concurrent acute kidney injury, demonstrating favorable specificity. Despite being limited by study heterogeneity and potential publication bias, SII still holds potential as an auxiliary tool for early clinical risk stratification. acute pancreatitis systemic immune-inflammation index severe acute pancreatitis acute kidney injury meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Acute pancreatitis (AP) is one of the most common acute abdominal disorders, and its global incidence has shown a steady annual increase ( 1 , 2 ). Although most patients with AP experience a self-limiting disease course, approximately 20% progress to severe stages, which is severe acute pancreatitis (SAP) ( 3 ). SAP is frequently complicated by a persistent systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction syndrome (MODS), particularly acute kidney injury (AKI), and is associated with a mortality rate of 15%–30% ( 4 ). Therefore, accurate identification of patients at high risk of progressing into SAP, ideally within the first 24–48 hours after administration, is essential for the timely initiation of targeted interventions, including fluid resuscitation, antimicrobial therapy, and intensive care support. At present, commonly used clinical scoring systems, such as the Ranson, APACHE II, and BISAP scores, are widely applied for risk stratification ( 5 ). However, these tools rely on multiple complex physiological and biochemical parameters, such as hematocrit changes and blood urea nitrogen levels. These parameters usually require serial measurements to obtain, limiting their rapid application in an emergency setting ( 6 ). In addition, traditional inflammatory biomarkers, such as C-reactive protein (CRP), typically peak 48–72 hours after disease onset, resulting in a substantial diagnostic lag and limiting their utility for early risk prediction ( 7 ). Therefore, it is still important to identify simple, timely, and accurate biomarkers for early prognostic assessment in AP. In recent years, novel composite inflammatory indices calculated based on routine hematological parameters have stood out for their low cost, accessibility, and high stability ( 8 ). Among these, the Systemic Immune-Inflammation Index (SII), which is calculated by integrating the counts of neutrophils, platelets, and lymphocytes (formula: SII = P×N/L), provides a comprehensively reflect of nonspecific inflammatory response, coagulation activation, and impaired adaptive immunity ( 9 , 10 ). Given that the pathophysiological mechanism of AP is driven by a combination of factors, including excessive activation of neutrophils leading to cytokine storm, platelet-mediated microcirculatory dysfunction, and immune suppression caused by lymphocyte apoptosis, SII may offer greater pathophysiological relevance than traditional markers ( 11 , 12 ). Although several observational studies have explored the predictive value of SII in brain injury ( 13 ), cancer ( 14 ), stroke ( 15 ), its application in AP remains inconclusive. Therefore, this study dived into this topic, aiming to provide evidence-based support for early clinical risk stratification. Methods Search strategy Databases of Pubmed, Embase, Web of Science (WOS), CNKI (China), Wanfang (China), and KoreaMed (Korea) was searched for relevant literature published before December 2025. Based on the keywords "Systemic immune-inflammation index" and "Acute pancreatitis", the mesh terms were obtained and a query was formulated. For example, in Pubmed, the Mesh term for "Acute pancreatitis" is "Pancreatitis". Detailed search queries are provided in Supplementary File 1. Eligibility criteria Study Type: The literature included in this meta-analysis comprises only observational studies. Study Participants: All the studies included patients with confirmed diagnosis of AP. The severity of AP was classified based on the clinical manifestations of the patients. Mild AP was characterized by the absence of organ failure and local complications; moderate AP was not accompanied by local complications, but had organ failure lasting for ≤ 48 hours; severe AP was characterized by both complications and persistent organ failure ( 16 ). Outcomes: The study must include detailed data for SII in different severity types of AP. Or the study must contain the odds ratio (OR) or hazard ratio (HR) of SII for AP severity, AKI outcomes, and mortality. Exclusion criteria Studies designed as non-observational research are excluded. Studies focusing on non-AP populations such as brain injury, stroke, malignancies are excluded. Studies chronic pancreatitis but not acute pancreatitis are excluded. Studies with missing outcome indicators and unobtainable data are excluded. Risk of bias assessment The methodological quality of the included observational studies was assessed using the Newcastle–Ottawa Scale (NOS). Studies with NOS scores of 7 or higher were considered high quality. Two reviewers independently performed the quality assessment, and discrepancies were resolved by consensus. Statistical analysis All statistical analyses were performed using R software (version 4.4.1) with the “meta,” “mada,” and “metafor” packages. The pooled mean difference (MD) and odds ratio (OR) with 95% confidence intervals (CIs) were calculated using a random-effects model (DerSimonian–Laird method) due to anticipated heterogeneity. For diagnostic accuracy, pooled sensitivity, specificity, and area under the curve (AUC) were estimated using the “reitsma” function in the “mada” package.Heterogeneity was assessed using the I² statistic and Cochran’s Q test, with I² > 50% or P < 0.1 indicating substantial heterogeneity. Meta-regression was performed to explore potential sources of heterogeneity. Publication bias was evaluated using funnel plots and Egger’s regression test, and a p-curve analysis was conducted to assess the distribution of significant p-values. Sensitivity analysis was performed using the leave-one-out method to evaluate the robustness of the pooled estimates. A two-tailed P-value < 0.05 was considered statistically significant. Results Retrieval and selection of studies Figure 1 is a flowchart. A total of 586 articles were initially retrieved and 16 articles ( 19 – 34 ) were finally included. Studies that appeared to meet the inclusion criteria, but finally excluded were listed in Supplementary File 2. Characteristics of studies A total of 3,482 patients with AP were included. The sixteen studies were published between year 2021 to 2025 (Table 1 ). Table 1 Characteristics of studies, baseline data of patients, outcomes. HTG-AP, hypertriglyceridemic acute pancreatitis. Author Year Country Study Design Sample Size Age (Years) Sex (Male %) Patient Diagnosis Disease Severity AKI Mortality Biyik et al. 2022 Turkey Retrospective 332 58.4 ± 18.0 45.50% AP Mild, Moderate, Severe Yes (29.5%) Yes (2.7%) Lu et al. 2022 China Retrospective 218 51.1 ± 15.5 49.50% AP All Severe Yes (33.9%) Yes (12.8%) Li et al. 2024 China Retrospective 253 Med: 44.2 64.00% AP SAP vs. non-SAP NR NR Saribas et al. 2025 Turkey Retrospective 209 58.06 46.40% AP Alive vs. deceased NR Yes (9.6%) Zhang et al. 2021 China Retrospective 513 58 55.70% AP (ICU patients) Critically Ill Yes (77.4%) Yes (Primary Outcome) Cinaroglu et al. 2024 Turkey Prospective 111 64 ± 13 54.00% AP Walled-off Necrosis NR Yes (18.0%) Solakoglu et al. 2023 Turkey Retrospective 132 58 ± 18 32.00% AP APFC vs. None NR Yes (5.3%) Liu et al.8 2021 China Retrospective 101 Med: 52_55 56.40% AP Mild vs. Severe NR NR Dalkilinc et al. 2023 Turkey Retrospective 143 60.7_68.7 42.70% AP Survivors vs. non-survivors NR Yes (14.0%) Araiza et al. 2025 Mexico Retrospective 100 Med: 43 72.00% AP Mild, Moderate, Severe NR NR Xie et al. 2023 China Retrospective 161 73.5 ± 9.3 65.80% AP MAP, MSAP, SAP NR Yes (10.6%) Wang et al. 2025 China Retrospective 300 NR NR HTG-AP MAP, MSAP, SAP NR NR Salehi et al. 2025 Iran Retrospective 112 60.4 ± 10.3 42.00% AP MAP vs. SAP NR NR Zhong et al. 2025 China Retrospective 340 Med: 36 80.30% HTG-AP MAP, MSAP, SAP NR NR Li et al. 2025 China Retrospective 230 Young: 36 Elderly: 51 70.90% HTG-AP Mild vs. Severe NR NR Oauzlar et al. 2025 Turkey Retrospective 227 65.9 ± 14.5 47.60% AP Necrosis vs. None NR Yes (6.6%) AKI, acute kidney injury; AP, acute pancreatitis; SAP, severe AP. Quality assessment Using the NOS to evaluate the quality of 16 articles, 3 articles (18.8%) received a score of 7, 2 (12.4%) of them were ranked a score of 8, and 11 articles (68.8%) ranked as a score of 9. The overall quality of the literature is high (Fig. 2 A-B). Pooled effect sizes The mean difference in SII between severe AP and non-severe AP A total of 12 articles reported the SII in patients with SAP and non-SAP patients after administration. There were 490 SAP patients and 1589 non-SAP patients. The SII was significantly higher in SAP than non-SAP (MD = 1235.79, 95% CI (847.55; 1624.04), P < 0.001). Details are shown in Fig. 3 . OR value of SII to AP severity A total of 8 articles reported the ORs, with a pooled effect size (OR = 1.001, 95% CI (1.000; 1.002), and P = 0.012) (Fig. 4 ). Diagnostic performance of SII in predicting the severity of AP Seven articles reported the sensitivity and specificity. The pooled sensitivity, specificity, AUC value was [0.692, 95% CI of (0.574, 0.790)], [0.755, 95% CI of (0.616, 0.855)], and 0.69, respectively (Fig. 5 ). Other outcomes Only two studies reported a SII difference between patients with AP-induced AKI and those without AKI. The SII was significantly higher in AKI than non-AKI (MD = 1938.81, 95% CI [1757.76; 2119.85], P < 0.0001), and it was a significant influencing factor of AKI (OR = 1.001, 95% CI (1.003; 1.008), P < 0.0001). The pooled results suggest that the SII is not a significant influencing factor of mortality (OR = 2.118, 95% CI (0.795; 5.654), P = 0.133) (Table 2 ). Table 2 The pooled effect sizes for other outcomes. AKI, acute kidney injury; CI, confidential interval. Outcomes Effect size Number Select mode I 2 and Cochran’s Q P Pooled effect size, 95%CI P SII index between AKI and non-AKI MD 2 Random Effect Mode 0% with 0.344 1938.81 [1757.76; 2119.85] < 0.0001 SII index in predicting AKI OR 2 Random Effect Mode 0% with 0.365 1.001 [1.003; 1.008] < 0.0001 SII index in predicting mortality OR 3 Random Effect Mode 92.6% with < 0.0001 2.118 [0.795; 5.645] 0.133 Sensitivity When pooling the MDs of the SII between SAP and non-SAP, a leave-one-out method was employed, and the results are presented in (Fig. 6 ). After excluding any one article, the merged data of the remaining articles did not significantly deviate from the original results, indicating that the results are stable. Meta-regression During the pooling of the MDs of the SII between SAP and non-SAP, significant heterogeneity was observed (I 2 = 97.8%, Q test P < 0.001). Meta-regression analysis was conducted to investigate the factors that may contribute to heterogeneity: age and sample size (Fig. 7 A-B). The regression P values obtained were 0.554 and 0.386, respectively, indicating that neither age nor sample size is a single factor that can influence heterogeneity. The source of heterogeneity may be related to confounding factors. Publication bias analysis During the pooing of the MDs of the SII between SAP and non-SAP, the Egger's test was conducted for publication bias detection, yielding t = 2.28, df = 10, and p-value = 0.0459, indicating significant publication bias in the results (Fig. 8 A). The Right-skewness test of the p-values suggested a pronounced "right skew" in the included studies, demonstrating that articles with p-values less than 0.02 were more likely to be published, while those with excessively large p-values were likely to remain unpublished and thus overlooked. Discussion Acute pancreatitis (AP) is a disease with highly heterogeneous clinical course ( 35 ). Early, simple, and accurate identification of high-risk SAP patients is crucial for clinical decision-making ( 36 ). This meta-analysis included a total of 17 high-quality articles (most with a NOS score of 7–9), systematically evaluating the value of SII in predicting the severity of AP, acute kidney injury (AKI), and mortality risk. The main finding of this study is that the SII is higher in SAP comparing to non-SAP, with a pooled MD as high as 1235.79 (95% CI: 847.55–1624.04, P < 0.001). This indicates that SII can effectively reflect the intensity of inflammatory storm in the early stage of AP. SII is a composite index constructed from the counts of neutrophils, platelets, and lymphocytes, and its pathophysiological mechanism is closely related to the progression of AP. Firstly, neutrophils are the core driving force of inflammatory response in the early stage of AP. As described by Biyik et al. ( 19 ) and Liu et al. ( 26 ), excessive activation of neutrophils releases a large amount of cytokines (such as IL-6, TNF-α) and reactive oxygen species, leading to acinar cell necrosis and secondary tissue damage. Secondly, platelets not only participate in coagulation but also mediate inflammatory response. Studies by Li et al. ( 21 ) and Solakoglu et al. ( 25 ) indicate that platelet-activating factor can aggravate microcirculatory disorders and induce pancreatic encephalopathy or kidney damage characterized by microthrombosis. Finally, lymphocyte reduction reflects the immunosuppressive state of the body. Zhang et al. ( 23 ) observed in critically ill patients that increased lymphocyte apoptosis leads to immune paralysis, making patients more prone to concurrent infections. Therefore, SII comprehensively reflects the triple attack of "excessive inflammation, coagulation activation, and immune impairment", and has more comprehensive pathological implications than single indicators (such as NLR or PLR). In terms of diagnostic efficacy, this study demonstrates that the pooled sensitivity of SII for predicting SAP is 0.692, with a specificity of 0.755 and a pooled OR value of 1.001 (P = 0.012). Although the OR value is close to 1 (primarily due to the large numerical value of SII itself, where a unit change results in a numerically small increase in risk), its statistical significance confirms the status of SII as an independent risk factor. Compared to the AUC of 0.920 reported by Liu et al. ( 26 ), our pooled AUC is slightly lower, which may be related to significant differences in cut-off values across different studies (ranging from 476 to 3377). Nevertheless, as a blood routine-based indicator, SII has the advantages of low cost and rapid acquisition, making it particularly suitable for early triage in primary care settings ( 37 ). Furthermore, this study systematically explored the value of SII in AP complicated with AKI. The two studies focusing on AKI outcome ( 19 , 20 ) both indicate that the SII is significantly higher in the AKI group (MD = 1938.81), further supporting the notion that uncontrolled systemic inflammatory response is a crucial mechanism leading to renal microcirculatory perfusion insufficiency and acute tubular necrosis. In the study by Jiang et al. ( 38 ), Multivariable logistic regression demonstrated higher SII was an independent factor of contrast-induced AKI (OR = 2.914). Despite the diverse etiologies of AKI with our study, SII possesses the same efficacy in predicting subsequent AKI. This meta-analysis observed significant heterogeneity (I 2 = 97.8%). Regression analysis indicated that age and sample size were not the dominating sources for heterogeneity. The heterogeneity may stem from multiple factors: first, timing differences in blood sample collecting. Some studies collected data immediately upon admission, while others within 24 or 48 hours of admission, given that inflammatory marker data fluctuate significantly over time. Second, etiological composition. For example, Studies ( 22 , 30 ) focused on hypertriglyceridemic acute pancreatitis (HTG-AP), whose inflammatory mechanism differs from that of biliary acute pancreatitis. In the study conducted by Grigore M et al. ( 39 ), two different etiologies of AP were investigated, and it was found that HTG-AP was more prone to severe cases. However, there was no significant difference in the SII index among AP patients with different etiologies. Nevertheless, different etiologies may still contribute to some of the heterogeneity. Third, ethnicity. Although most of the patients recruited in this study were from Asia, there were also patients from Europe ( 22 ) and the United States ( 23 ). The difference in ethnicity may also be a source of heterogeneity. There are still some limitations in this study. The Egger's test (P = 0.0459) in the bias analysis suggests the presence of publication bias, where positive results are more likely to be published. The pCurve indicates a significant rightward skew in the curve, suggesting that studies with smaller P values are more likely to be published, potentially leading to the loss of some data. In the future, more studies need to be included to compensate for this deficiency. Furthermore, the combined results regarding mortality did not reach statistical significance (P = 0.133), which is inconsistent with the conclusion by Zhang et al. ( 23 ) that a high SII increases the risk of death in the MIMIC-III database. This inconsistency may be attributed to the insufficient statistical power due to the limited number of studies (only 2–3) that included mortality outcomes. Conclusion In summary, patients with severe acute pancreatitis (SAP) exhibit significantly elevated SII levels, reflecting a severe inflammatory-coagulant cascade and an immunosuppressed state. SII serves as a simple, effective, and pathophysiologically grounded early biomarker for predicting SAP and the concurrent AKI. Although the available evidence is limited by substantial heterogeneity and potential publication bias, and further validation of the predictive value of SII for mortality is warranted, its high accessibility and cost-effectiveness support its incorporation into early prognostic assessment frameworks for patients with acute pancreatitis. Future studies should prioritize multicenter, large-scale prospective cohort designs to establish standardized optimal cutoff values for SII and to explore its integration into composite predictive models alongside established biomarkers such as CRP and D-dimer, thereby enhancing early identification and risk stratification of critically ill patients. Abbreviations Abbreviation Full Term AKI Acute Kidney Injury AP Acute Pancreatitis APACHE II Acute Physiology and Chronic Health Evaluation II AUC Area Under the Curve BISAP Bedside Index for Severity in Acute Pancreatitis CI Confidence Interval CNKI China National Knowledge Infrastructure CRP C-reactive Protein HTG-AP Hypertriglyceridemic Acute Pancreatitis ICU Intensive Care Unit IL-6 Interleukin-6 MAP Mild Acute Pancreatitis MD Mean Difference MIMIC-III Medical Information Mart for Intensive Care III MODS Multiple Organ Dysfunction Syndrome MSAP Moderately Severe Acute Pancreatitis NLR Neutrophil-to-Lymphocyte Ratio NOS Newcastle-Ottawa Scale OR Odds Ratio PLR Platelet-to-Lymphocyte Ratio SAP Severe Acute Pancreatitis SII Systemic Immune-Inflammation Index SIRS Systemic Inflammatory Response Syndrome SROC Summary Receiver Operating Characteristic TNF-α Tumor Necrosis Factor-alpha Declarations Acknowledgements None Author contributions ： Yun Zhao and Li Zhang contributed equally to this work. They were primarily responsible for drafting the initial manuscript, conducting the literature search, performing data extraction and statistical analysis, interpreting the findings, and finalizing the manuscript for submission. Junping Zhu, Tao Cheng, Guilin Wang, Bing Liu and Zhiwei Hu provided valuable support in data extraction, contributed to the data analysis process, and assisted in the interpretation of results. All authors reviewed and approved the final version of the manuscript. Funding None. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Not applicable because this is systematic review and meta-analysis . Consent for publication Not applicable because this is systematic review and meta-analysis. Competing interests The authors declare no competing interests References Wang GJ, Gao CF, Wei D, Wang C, Ding SQ. 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Xie GD, Huang TT, Liao XX, et al. Clinical value of nutritional risk index for the elderly, systemic immune-inflammation index, and triglyceride-glucose index in elderly patients with acute pancreatitis. Chin J Geriatric Multi-organ Dis. 2023;22(11):861–5. Wang YZ, Yun YL, Ye T, et al. Value of the systemic immunoinflammatory index, nutritional risk index, and triglyceride-glucose index in predicting the condition and prognosis of patients with hypertriglyceridemia-associated acute pancreatitis. Front Nutr. 2025;12:1523046. PMID: 39949545; PMCID: PMC11821461. Salehi AM, Rezaei R, Sadeghi A, et al. Diagnostic value of neutrophil/lymphocyte ratio, platelet/lymphocyte ratio and systemic immune-inflammation index for predicting the severity of acute pancreatitis. BMC Gastroenterol. 2025;25(1):751. 10.1186/s12876-025-04370-4 . PMID: 41126061; PMCID: PMC12542137. Zhong L, Ding J, Chen M, et al. Novel Inflammatory Markers and Their Association with the Severity of Hypertriglyceridemia-Associated Acute Pancreatitis. J Inflamm Res. 2025;18:14771–90. PMID: 41164220; PMCID: PMC12560641. Li J, Hu J, Gou Y, Yao L, Cao J. Changes in inflammatory composite markers and D-dimer levels in young and middle-aged/elderly patients with hypertriglyceridemic acute pancreatitis and their predictive value for disease progression. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025;50(2):215–226. English, Chinese. 10.11817/j.issn.1672-7347.2025.240366 . PMID: 40523764. Oğuzlar FÇ, Cesur E, Armağan HH, et al. Frontal QRS-T angle and inflammatory indices as predictors of pancreatic necrosis in acute pancreatitis: A retrospective cohort study. Med (Baltim). 2025;104(44):e45692. 10.1097/MD.0000000000045692 . PMID: 41261581; PMCID: PMC12582779. van Dijk SM, Hallensleben NDL, van Santvoort HC, Fockens P, van Goor H, Bruno MJ, Besselink MG, Dutch Pancreatitis Study Group. Acute pancreatitis: recent advances through randomised trials. Gut. 2017;66(11):2024–32. 10.1136/gutjnl-2016-313595 . Epub 2017 Aug 24. PMID: 28838972. Garg R, Rustagi T. Management of Hypertriglyceridemia Induced Acute Pancreatitis. Biomed Res Int. 2018;2018:4721357. doi: 10.1155/2018/4721357. PMID: 30148167; PMCID: PMC6083537. Sahin R, Tanacan A, Serbetci H, Agaoglu Z, Karagoz B, Haksever M, Kara O, Şahin D. The role of first-trimester NLR (neutrophil to lymphocyte ratio), systemic immune-inflammation index (SII), and, systemic immune-response index (SIRI) in the prediction of composite adverse outcomes in pregnant women with systemic lupus erythematosus. J Reprod Immunol. 2023;158:103978. 10.1016/j.jri.2023.103978 . Epub 2023 Jun 12. PMID: 37329867. Jiang H, Li D, Xu T, Chen Z, Shan Y, Zhao L, Fu G, Luan Y, Xia S, Zhang W. 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. 10.3389/fmed.2022.841601 . PMID: 35372392; PMCID: PMC8965764. Grigore M, Balaban DV, Jinga M, et al. Hypertriglyceridemia-Induced and Alcohol-Induced Acute Pancreatitis-A Severity Comparative Study. Diagnostics (Basel). 2025;15(7):882. 10.3390/diagnostics15070882 . PMID: 40218233; PMCID: PMC11988868. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.docx Supplementaryfile2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 15 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9128539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619168746,"identity":"a6d61d61-047a-40fe-be12-d96a45ac6dd9","order_by":0,"name":"Junping Zhu","email":"","orcid":"","institution":"Second People’s Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Junping","middleName":"","lastName":"Zhu","suffix":""},{"id":619168747,"identity":"12a3f335-706d-4d65-bd90-0a903f0948c6","order_by":1,"name":"Li Zhang","email":"","orcid":"","institution":"Second People’s Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":619168748,"identity":"a124c347-0fdd-41fd-9bfc-307e3f7cdfaa","order_by":2,"name":"Tao Cheng","email":"","orcid":"","institution":"Second People’s Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Cheng","suffix":""},{"id":619168749,"identity":"96b2d4d0-4374-4fe4-b82c-e4a81a9737c6","order_by":3,"name":"Guilin Wang","email":"","orcid":"","institution":"Second People’s Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Guilin","middleName":"","lastName":"Wang","suffix":""},{"id":619168750,"identity":"6c05bff0-667c-48cd-a48d-1dcb6f70fd07","order_by":4,"name":"Bing Liu","email":"","orcid":"","institution":"Second People’s Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Liu","suffix":""},{"id":619168751,"identity":"5c3447ef-5158-4022-9061-e6f80763d4a2","order_by":5,"name":"Zhiwei Hu","email":"","orcid":"","institution":"Second People’s Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Hu","suffix":""},{"id":619168752,"identity":"a36285c7-89e7-42d8-bc96-797af4cde661","order_by":6,"name":"Yun Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACNvbmAwYSP2zs5JkZGx8kVNQQ1sLHcyyhwLInLdmwnfmwwYMzxwhrkZPIMfhQwXaIseE8W5rkwxZmIhzGc8Bwww2eA8yMzTxmFYkNbAz87d0JBPzSkGw4w+IOHzszj9mNxB0yDBJnzm4gZMsxYwmeZ2BbbiSeYWMwkMgloEUisf33H7bDjA2HecwKEtuYidGSDFQE1sKWxkCcFp5jDAaSoEBuZj4skXDmGA9Bv8i393+ARCX/wcaPPypq5Pjbe/FrwQA8pCkfBaNgFIyCUYAVAACot0lPomDAeAAAAABJRU5ErkJggg==","orcid":"","institution":"Second People’s Hospital of Yibin","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-03-15 12:23:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9128539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9128539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106869798,"identity":"8f1efe66-9c77-48a2-adbe-68a700376f0c","added_by":"auto","created_at":"2026-04-14 09:40:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe search and selection flowchart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/68a9ef480c9c6deab88fa5a6.png"},{"id":106869778,"identity":"d348da0d-2cef-4439-9142-8d3bc4d0e738","added_by":"auto","created_at":"2026-04-14 09:40:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality assessment of the studies. (A) Heatmap of the included studies; (B) Distribution of scores.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/bd08e762ab7ccd6da53f0654.jpeg"},{"id":106869711,"identity":"834c7ee0-3d8d-4326-866d-598584c481a8","added_by":"auto","created_at":"2026-04-14 09:40:20","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe pooled MDs of SII between SAP and non-SAP. MD, mean difference; SAP, severe acute pancreatitis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/bc671186f5bb2391fd5abf87.jpeg"},{"id":106869694,"identity":"37f9746d-782f-468c-aa7d-e0c75e4c7850","added_by":"auto","created_at":"2026-04-14 09:40:10","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93520,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe pooled OR values of SII Index for AP severity. OR, odd ratio; AP, acute pancreatitis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/c71d5417f82112a408aabf71.jpeg"},{"id":106870122,"identity":"46487b9d-9042-41fc-b7ec-8be18e0cab91","added_by":"auto","created_at":"2026-04-14 09:41:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":79685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSROC curve of SII Index predicting the severity of AP. False Positive Rate = 1 – Specificity; SROC, summary receiver operating characteristic.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/2bd4377d219182ed10309f42.jpeg"},{"id":106869773,"identity":"14e224ed-3bfe-4f3c-a55a-2e3de780ce81","added_by":"auto","created_at":"2026-04-14 09:40:38","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/c0cf3e321b2b06e8f347188b.jpeg"},{"id":106870246,"identity":"0d5e409c-faa6-446c-9f1f-609b0c830395","added_by":"auto","created_at":"2026-04-14 09:41:56","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeta-regression analysis. (A) Age; (B) Sample size.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/a3b8704febc09abcac08978c.jpeg"},{"id":106869690,"identity":"614a266e-21e8-4226-ab97-a41edbe39cd9","added_by":"auto","created_at":"2026-04-14 09:40:10","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":50295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePublication bias analysis. (A) Funnel plot; (B) PCurve plot.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/1980bf8a313623ec7d412eee.jpeg"},{"id":106870837,"identity":"6d2192bb-91b3-4af1-a93d-5a9f370ad0ec","added_by":"auto","created_at":"2026-04-14 09:43:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1793492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/cc868885-98fd-42b9-ad1a-328884cfb950.pdf"},{"id":106869691,"identity":"d4875896-f50e-4d40-b7b6-82139d811b09","added_by":"auto","created_at":"2026-04-14 09:40:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15356,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/c8388fcc5367bac64898426f.docx"},{"id":106869745,"identity":"fc654dac-a09e-4db4-ac94-4b4f5b5ca97f","added_by":"auto","created_at":"2026-04-14 09:40:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17965,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9128539/v1/18f0d4639bb876aa1dfdf9c4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSystemic Immune-Inflammation Index as a Predictor of Severity and Acute Kidney Injury in Acute Pancreatitis: A Meta-Analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute pancreatitis (AP) is one of the most common acute abdominal disorders, and its global incidence has shown a steady annual increase (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although most patients with AP experience a self-limiting disease course, approximately 20% progress to severe stages, which is severe acute pancreatitis (SAP) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). SAP is frequently complicated by a persistent systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction syndrome (MODS), particularly acute kidney injury (AKI), and is associated with a mortality rate of 15%\u0026ndash;30% (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, accurate identification of patients at high risk of progressing into SAP, ideally within the first 24\u0026ndash;48 hours after administration, is essential for the timely initiation of targeted interventions, including fluid resuscitation, antimicrobial therapy, and intensive care support.\u003c/p\u003e \u003cp\u003eAt present, commonly used clinical scoring systems, such as the Ranson, APACHE II, and BISAP scores, are widely applied for risk stratification (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, these tools rely on multiple complex physiological and biochemical parameters, such as hematocrit changes and blood urea nitrogen levels. These parameters usually require serial measurements to obtain, limiting their rapid application in an emergency setting (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In addition, traditional inflammatory biomarkers, such as C-reactive protein (CRP), typically peak 48\u0026ndash;72 hours after disease onset, resulting in a substantial diagnostic lag and limiting their utility for early risk prediction (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, it is still important to identify simple, timely, and accurate biomarkers for early prognostic assessment in AP.\u003c/p\u003e \u003cp\u003eIn recent years, novel composite inflammatory indices calculated based on routine hematological parameters have stood out for their low cost, accessibility, and high stability (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Among these, the Systemic Immune-Inflammation Index (SII), which is calculated by integrating the counts of neutrophils, platelets, and lymphocytes (formula: SII\u0026thinsp;=\u0026thinsp;P\u0026times;N/L), provides a comprehensively reflect of nonspecific inflammatory response, coagulation activation, and impaired adaptive immunity (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Given that the pathophysiological mechanism of AP is driven by a combination of factors, including excessive activation of neutrophils leading to cytokine storm, platelet-mediated microcirculatory dysfunction, and immune suppression caused by lymphocyte apoptosis, SII may offer greater pathophysiological relevance than traditional markers (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough several observational studies have explored the predictive value of SII in brain injury (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), cancer (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), stroke (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), its application in AP remains inconclusive. Therefore, this study dived into this topic, aiming to provide evidence-based support for early clinical risk stratification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy\u003c/h2\u003e \u003cp\u003eDatabases of Pubmed, Embase, Web of Science (WOS), CNKI (China), Wanfang (China), and KoreaMed (Korea) was searched for relevant literature published before December 2025. Based on the keywords \"Systemic immune-inflammation index\" and \"Acute pancreatitis\", the mesh terms were obtained and a query was formulated. For example, in Pubmed, the Mesh term for \"Acute pancreatitis\" is \"Pancreatitis\". Detailed search queries are provided in Supplementary File 1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eStudy Type: The literature included in this meta-analysis comprises only observational studies.\u003c/p\u003e \u003cp\u003eStudy Participants: All the studies included patients with confirmed diagnosis of AP. The severity of AP was classified based on the clinical manifestations of the patients. Mild AP was characterized by the absence of organ failure and local complications; moderate AP was not accompanied by local complications, but had organ failure lasting for \u0026le;\u0026thinsp;48 hours; severe AP was characterized by both complications and persistent organ failure (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOutcomes: The study must include detailed data for SII in different severity types of AP. Or the study must contain the odds ratio (OR) or hazard ratio (HR) of SII for AP severity, AKI outcomes, and mortality.\u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eStudies designed as non-observational research are excluded. Studies focusing on non-AP populations such as brain injury, stroke, malignancies are excluded. Studies chronic pancreatitis but not acute pancreatitis are excluded. Studies with missing outcome indicators and unobtainable data are excluded.\u003c/p\u003e \u003cp\u003eRisk of bias assessment\u003c/p\u003e \u003cp\u003eThe methodological quality of the included observational studies was assessed using the Newcastle\u0026ndash;Ottawa Scale (NOS). Studies with NOS scores of 7 or higher were considered high quality. Two reviewers independently performed the quality assessment, and discrepancies were resolved by consensus.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.4.1) with the \u0026ldquo;meta,\u0026rdquo; \u0026ldquo;mada,\u0026rdquo; and \u0026ldquo;metafor\u0026rdquo; packages. The pooled mean difference (MD) and odds ratio (OR) with 95% confidence intervals (CIs) were calculated using a random-effects model (DerSimonian\u0026ndash;Laird method) due to anticipated heterogeneity. For diagnostic accuracy, pooled sensitivity, specificity, and area under the curve (AUC) were estimated using the \u0026ldquo;reitsma\u0026rdquo; function in the \u0026ldquo;mada\u0026rdquo; package.Heterogeneity was assessed using the I\u0026sup2; statistic and Cochran\u0026rsquo;s Q test, with I\u0026sup2; \u0026gt; 50% or P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicating substantial heterogeneity. Meta-regression was performed to explore potential sources of heterogeneity. Publication bias was evaluated using funnel plots and Egger\u0026rsquo;s regression test, and a p-curve analysis was conducted to assess the distribution of significant p-values. Sensitivity analysis was performed using the leave-one-out method to evaluate the robustness of the pooled estimates. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRetrieval and selection of studies\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is a flowchart. A total of 586 articles were initially retrieved and 16 articles (\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) were finally included. Studies that appeared to meet the inclusion criteria, but finally excluded were listed in Supplementary File 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCharacteristics of studies\u003c/h3\u003e\n\u003cp\u003eA total of 3,482 patients with AP were included. The sixteen studies were published between year 2021 to 2025 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of studies, baseline data of patients, outcomes. HTG-AP, hypertriglyceridemic acute pancreatitis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudy Design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSex (Male %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePatient Diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDisease Severity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAKI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiyik et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMild, Moderate, Severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (2.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLu et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAll Severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (12.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMed: 44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAP vs. non-SAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaribas et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAlive vs. deceased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (9.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP (ICU patients)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCritically Ill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (Primary Outcome)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCinaroglu et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProspective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWalled-off Necrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (18.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolakoglu et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAPFC vs. None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu et al.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMed: 52_55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMild vs. Severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDalkilinc et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.7_68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSurvivors vs. non-survivors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (14.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAraiza et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMed: 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMild, Moderate, Severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXie et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMAP, MSAP, SAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (10.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHTG-AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMAP, MSAP, SAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalehi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMAP vs. SAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhong et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMed: 36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHTG-AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMAP, MSAP, SAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYoung: 36\u003c/p\u003e \u003cp\u003eElderly: 51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHTG-AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMild vs. Severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOauzlar et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNecrosis vs. None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes (6.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eAKI, acute kidney injury; AP, acute pancreatitis; SAP, severe AP.\u003c/p\u003e\n\u003ch3\u003eQuality assessment\u003c/h3\u003e\n\u003cp\u003eUsing the NOS to evaluate the quality of 16 articles, 3 articles (18.8%) received a score of 7, 2 (12.4%) of them were ranked a score of 8, and 11 articles (68.8%) ranked as a score of 9. The overall quality of the literature is high (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePooled effect sizes\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eThe mean difference in SII between severe AP and non-severe AP\u003c/h2\u003e \u003cp\u003eA total of 12 articles reported the SII in patients with SAP and non-SAP patients after administration. There were 490 SAP patients and 1589 non-SAP patients. The SII was significantly higher in SAP than non-SAP (MD\u0026thinsp;=\u0026thinsp;1235.79, 95% CI (847.55; 1624.04), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOR value of SII to AP severity\u003c/h2\u003e \u003cp\u003eA total of 8 articles reported the ORs, with a pooled effect size (OR\u0026thinsp;=\u0026thinsp;1.001, 95% CI (1.000; 1.002), and P\u0026thinsp;=\u0026thinsp;0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic performance of SII in predicting the severity of AP\u003c/h2\u003e \u003cp\u003eSeven articles reported the sensitivity and specificity. The pooled sensitivity, specificity, AUC value was [0.692, 95% CI of (0.574, 0.790)], [0.755, 95% CI of (0.616, 0.855)], and 0.69, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOther outcomes\u003c/h2\u003e \u003cp\u003eOnly two studies reported a SII difference between patients with AP-induced AKI and those without AKI. The SII was significantly higher in AKI than non-AKI (MD\u0026thinsp;=\u0026thinsp;1938.81, 95% CI [1757.76; 2119.85], P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and it was a significant influencing factor of AKI (OR\u0026thinsp;=\u0026thinsp;1.001, 95% CI (1.003; 1.008), P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The pooled results suggest that the SII is not a significant influencing factor of mortality (OR\u0026thinsp;=\u0026thinsp;2.118, 95% CI (0.795; 5.654), P\u0026thinsp;=\u0026thinsp;0.133) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe pooled effect sizes for other outcomes. AKI, acute kidney injury; CI, confidential interval.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelect mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e and Cochran\u0026rsquo;s Q P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePooled effect size, 95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII index between AKI and non-AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom Effect Mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% with 0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1938.81 [1757.76; 2119.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII index in predicting AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom Effect Mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0% with 0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.001 [1.003; 1.008]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII index in predicting mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom Effect Mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.6% with \u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.118 [0.795; 5.645]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity\u003c/h2\u003e \u003cp\u003eWhen pooling the MDs of the SII between SAP and non-SAP, a leave-one-out method was employed, and the results are presented in (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). After excluding any one article, the merged data of the remaining articles did not significantly deviate from the original results, indicating that the results are stable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMeta-regression\u003c/h2\u003e \u003cp\u003eDuring the pooling of the MDs of the SII between SAP and non-SAP, significant heterogeneity was observed (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;97.8%, Q test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Meta-regression analysis was conducted to investigate the factors that may contribute to heterogeneity: age and sample size (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). The regression P values obtained were 0.554 and 0.386, respectively, indicating that neither age nor sample size is a single factor that can influence heterogeneity. The source of heterogeneity may be related to confounding factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePublication bias analysis\u003c/h2\u003e \u003cp\u003eDuring the pooing of the MDs of the SII between SAP and non-SAP, the Egger's test was conducted for publication bias detection, yielding t\u0026thinsp;=\u0026thinsp;2.28, df\u0026thinsp;=\u0026thinsp;10, and p-value\u0026thinsp;=\u0026thinsp;0.0459, indicating significant publication bias in the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The Right-skewness test of the p-values suggested a pronounced \"right skew\" in the included studies, demonstrating that articles with p-values less than 0.02 were more likely to be published, while those with excessively large p-values were likely to remain unpublished and thus overlooked.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAcute pancreatitis (AP) is a disease with highly heterogeneous clinical course (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Early, simple, and accurate identification of high-risk SAP patients is crucial for clinical decision-making (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This meta-analysis included a total of 17 high-quality articles (most with a NOS score of 7\u0026ndash;9), systematically evaluating the value of SII in predicting the severity of AP, acute kidney injury (AKI), and mortality risk.\u003c/p\u003e \u003cp\u003eThe main finding of this study is that the SII is higher in SAP comparing to non-SAP, with a pooled MD as high as 1235.79 (95% CI: 847.55\u0026ndash;1624.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that SII can effectively reflect the intensity of inflammatory storm in the early stage of AP. SII is a composite index constructed from the counts of neutrophils, platelets, and lymphocytes, and its pathophysiological mechanism is closely related to the progression of AP. Firstly, neutrophils are the core driving force of inflammatory response in the early stage of AP. As described by Biyik et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and Liu et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), excessive activation of neutrophils releases a large amount of cytokines (such as IL-6, TNF-α) and reactive oxygen species, leading to acinar cell necrosis and secondary tissue damage. Secondly, platelets not only participate in coagulation but also mediate inflammatory response. Studies by Li et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and Solakoglu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) indicate that platelet-activating factor can aggravate microcirculatory disorders and induce pancreatic encephalopathy or kidney damage characterized by microthrombosis. Finally, lymphocyte reduction reflects the immunosuppressive state of the body. Zhang et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) observed in critically ill patients that increased lymphocyte apoptosis leads to immune paralysis, making patients more prone to concurrent infections. Therefore, SII comprehensively reflects the triple attack of \"excessive inflammation, coagulation activation, and immune impairment\", and has more comprehensive pathological implications than single indicators (such as NLR or PLR).\u003c/p\u003e \u003cp\u003eIn terms of diagnostic efficacy, this study demonstrates that the pooled sensitivity of SII for predicting SAP is 0.692, with a specificity of 0.755 and a pooled OR value of 1.001 (P\u0026thinsp;=\u0026thinsp;0.012). Although the OR value is close to 1 (primarily due to the large numerical value of SII itself, where a unit change results in a numerically small increase in risk), its statistical significance confirms the status of SII as an independent risk factor. Compared to the AUC of 0.920 reported by Liu et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), our pooled AUC is slightly lower, which may be related to significant differences in cut-off values across different studies (ranging from 476 to 3377). Nevertheless, as a blood routine-based indicator, SII has the advantages of low cost and rapid acquisition, making it particularly suitable for early triage in primary care settings (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, this study systematically explored the value of SII in AP complicated with AKI. The two studies focusing on AKI outcome (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) both indicate that the SII is significantly higher in the AKI group (MD\u0026thinsp;=\u0026thinsp;1938.81), further supporting the notion that uncontrolled systemic inflammatory response is a crucial mechanism leading to renal microcirculatory perfusion insufficiency and acute tubular necrosis. In the study by Jiang et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), Multivariable logistic regression demonstrated higher SII was an independent factor of contrast-induced AKI (OR\u0026thinsp;=\u0026thinsp;2.914). Despite the diverse etiologies of AKI with our study, SII possesses the same efficacy in predicting subsequent AKI.\u003c/p\u003e \u003cp\u003eThis meta-analysis observed significant heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;97.8%). Regression analysis indicated that age and sample size were not the dominating sources for heterogeneity. The heterogeneity may stem from multiple factors:\u003c/p\u003e \u003cp\u003efirst, timing differences in blood sample collecting. Some studies collected data immediately upon admission, while others within 24 or 48 hours of admission, given that inflammatory marker data fluctuate significantly over time.\u003c/p\u003e \u003cp\u003eSecond, etiological composition. For example, Studies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) focused on hypertriglyceridemic acute pancreatitis (HTG-AP), whose inflammatory mechanism differs from that of biliary acute pancreatitis. In the study conducted by Grigore M et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), two different etiologies of AP were investigated, and it was found that HTG-AP was more prone to severe cases. However, there was no significant difference in the SII index among AP patients with different etiologies. Nevertheless, different etiologies may still contribute to some of the heterogeneity.\u003c/p\u003e \u003cp\u003eThird, ethnicity. Although most of the patients recruited in this study were from Asia, there were also patients from Europe (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and the United States (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The difference in ethnicity may also be a source of heterogeneity.\u003c/p\u003e \u003cp\u003eThere are still some limitations in this study. The Egger's test (P\u0026thinsp;=\u0026thinsp;0.0459) in the bias analysis suggests the presence of publication bias, where positive results are more likely to be published. The pCurve indicates a significant rightward skew in the curve, suggesting that studies with smaller P values are more likely to be published, potentially leading to the loss of some data. In the future, more studies need to be included to compensate for this deficiency.\u003c/p\u003e \u003cp\u003eFurthermore, the combined results regarding mortality did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.133), which is inconsistent with the conclusion by Zhang et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) that a high SII increases the risk of death in the MIMIC-III database. This inconsistency may be attributed to the insufficient statistical power due to the limited number of studies (only 2\u0026ndash;3) that included mortality outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, patients with severe acute pancreatitis (SAP) exhibit significantly elevated SII levels, reflecting a severe inflammatory-coagulant cascade and an immunosuppressed state. SII serves as a simple, effective, and pathophysiologically grounded early biomarker for predicting SAP and the concurrent AKI.\u003c/p\u003e \u003cp\u003eAlthough the available evidence is limited by substantial heterogeneity and potential publication bias, and further validation of the predictive value of SII for mortality is warranted, its high accessibility and cost-effectiveness support its incorporation into early prognostic assessment frameworks for patients with acute pancreatitis. Future studies should prioritize multicenter, large-scale prospective cohort designs to establish standardized optimal cutoff values for SII and to explore its integration into composite predictive models alongside established biomarkers such as CRP and D-dimer, thereby enhancing early identification and risk stratification of critically ill patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eFull Term\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eAKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eAcute Kidney Injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eAcute Pancreatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eAPACHE II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eBISAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eBedside Index for Severity in Acute Pancreatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eCNKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eChina National Knowledge Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eC-reactive Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eHTG-AP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eHypertriglyceridemic Acute Pancreatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eIntensive Care Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eMild Acute Pancreatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eMIMIC-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eMedical Information Mart for Intensive Care III\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eMODS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eMultiple Organ Dysfunction Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eMSAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eModerately Severe Acute Pancreatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eNeutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eNOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eNewcastle-Ottawa Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003ePlatelet-to-Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eSAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eSevere Acute Pancreatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eSystemic Immune-Inflammation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eSIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eSystemic Inflammatory Response Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eSROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eSummary Receiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.7622%;\"\u003e\n \u003cp\u003eTNF-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83.2378%;\"\u003e\n \u003cp\u003eTumor Necrosis Factor-alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYun Zhao\u0026nbsp;and\u0026nbsp;Li Zhang\u0026nbsp;contributed equally to this work. They were primarily responsible for drafting the initial manuscript, conducting the literature search, performing data extraction and statistical analysis, interpreting the findings, and finalizing the manuscript for submission.\u0026nbsp;Junping\u0026nbsp;Zhu, Tao Cheng,\u0026nbsp;Guilin\u0026nbsp;Wang,\u0026nbsp;Bing\u0026nbsp;Liu and\u0026nbsp;Zhiwei\u0026nbsp;Hu\u0026nbsp;provided valuable support in data extraction, contributed to the data analysis process, and assisted in the interpretation of results. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable because this is systematic review and meta-analysis\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable because this is systematic review and meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang GJ, Gao CF, Wei D, Wang C, Ding SQ. 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Diagnostics (Basel). 2025;15(7):882. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/diagnostics15070882\u003c/span\u003e\u003cspan address=\"10.3390/diagnostics15070882\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 40218233; PMCID: PMC11988868.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"acute pancreatitis, systemic immune-inflammation index, severe acute pancreatitis, acute kidney injury, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-9128539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9128539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEarly and accurate prediction of disease severity in acute pancreatitis (AP) is critical for guiding timely clinical interventions and improving patient prognosis. The systemic immune\u0026ndash;inflammation index (SII), an integrated inflammatory marker derived from neutrophil, lymphocyte, and platelet counts, has demonstrated prognostic value in a variety of diseases. However, its application in AP has not been systematically evaluated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA systematic search was conducted across databases to screen observational studies published up to December 2025 on the correlation between SII and AP. Statistical analysis was performed using R software to calculate the pooled mean difference (MD), odds ratio (OR), as well as pooled sensitivity and specificity of SII as an predictor of severity and poor prognosis of the disease.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 16 high-quality studies (with NOS scores ranging from 7 to 9) were included, encompassing a total of 3,482 patients. The meta-analysis results indicated that the SII was significantly higher in SAP than non-SAP patients (MD\u0026thinsp;=\u0026thinsp;1235.79, 95% CI: 847.55\u0026ndash;1624.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The pooled logistic regression results suggested that elevated SII was an independent risk factor for SAP (OR\u0026thinsp;=\u0026thinsp;1.001, 95% CI: 1.000\u0026ndash;1.002, P\u0026thinsp;=\u0026thinsp;0.012). The pooled sensitivity, specificity, and AUC of SII for predicting SAP were 0.692 (95% CI: 0.574\u0026ndash;0.790), 0.755 (95% CI: 0.616\u0026ndash;0.855), and 0.69, respectively. Furthermore, elevated SII were significantly associated with AKI in AP (MD\u0026thinsp;=\u0026thinsp;1938.81, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but did not show statistical significance in predicting mortality (OR\u0026thinsp;=\u0026thinsp;2.118, P\u0026thinsp;=\u0026thinsp;0.133). Despite significant heterogeneity among studies (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;97.8%) and detection of publication bias (Egger\u0026rsquo;s test P\u0026thinsp;=\u0026thinsp;0.0459), sensitivity analysis confirmed the stability of the main results.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSII is a simple and effective early biomarker for predicting the severity of acute pancreatitis and its concurrent acute kidney injury, demonstrating favorable specificity. Despite being limited by study heterogeneity and potential publication bias, SII still holds potential as an auxiliary tool for early clinical risk stratification.\u003c/p\u003e","manuscriptTitle":"Systemic Immune-Inflammation Index as a Predictor of Severity and Acute Kidney Injury in Acute Pancreatitis: A Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 09:38:30","doi":"10.21203/rs.3.rs-9128539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"323733533030776644277659511271143260454","date":"2026-04-14T10:46:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T06:04:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T07:21:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T11:34:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T11:34:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-03-15T12:14:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c32466e-b811-4bc6-924d-46b66b4b48d9","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T09:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 09:38:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9128539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9128539","identity":"rs-9128539","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}