A combined systemic immune-inflammation index and prognostic nutritional index score for predicting overall survival after resection of pancreatic ductal adenocarcinoma: a retrospective cohort study

A combined systemic immune-inflammation index and prognostic nutritional index score for predicting overall survival after resection of pancreatic ductal adenocarcinoma: a retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A combined systemic immune-inflammation index and prognostic nutritional index score for predicting overall survival after resection of pancreatic ductal adenocarcinoma: a retrospective cohort study Ruihan Hou, Aorui Wang, Chengxu Du, Xinda Yang, Weihong Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8445900/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) have been individually associated with prognosis in various cancers. This study aimed to develop and validate a combined SII-PNI-based risk score for predicting overall survival (OS) in patients with resectable pancreatic ductal adenocarcinoma (PDAC). Methods This retrospective study enrolled 375 consecutive patients with PDAC who underwent curative-intent resection at The Second Hospital of Hebei Medical University between January 2014 and July 2025. Using X-tile software, optimal cut-off values for SII and PNI were determined based on 1-year OS in a training cohort (constituting 70% of the patients). A combined risk score was constructed using Cox regression coefficients and dichotomized at the optimal cut-off (− 0.231). The model’s performance was assessed using receiver operating characteristic (ROC) curve analysis, Kaplan–Meier survival curves, and multivariate Cox regression. Results The combined SII-PNI model achieved AUCs of 0.758 in the training cohort and 0.750 in the validation cohort. Kaplan–Meier analysis showed significantly worse OS in the high-risk group (p < 0.0001). In univariate analysis, elevated SII (p = 0.044) and reduced PNI (p < 0.0001) were both significantly associated with poorer OS. Multivariate Cox regression analysis confirmed PNI (HR = 0.891, 95% CI: 0.863–0.919, p < 0.001) and age (HR = 1.029, 95% CI: 1.013–1.046, p < 0.001) as independent prognostic factors, whereas SII and sex were not. Conclusion The combined SII-PNI score is a simple, cost-effective, and powerful predictor of prognosis in resectable PDAC, enabling reliable postoperative risk stratification. pancreatic ductal adenocarcinoma systemic immune-inflammation index prognostic nutritional index overall survival prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a persistently poor prognosis, ranking among the leading causes of cancer-related deaths worldwide 1 , 2 . For the 15–20% of patients who present with resectable disease, surgical resection remains the cornerstone of potential cure 3 , 4 . However, the long-term outcomes post-surgery are heterogeneous, with a high rate of recurrence and a five-year overall survival rate that rarely exceeds 30%. This variability underscores the critical need for reliable and accessible prognostic tools to identify high-risk patients who might benefit from more aggressive adjuvant therapy and tailored surveillance. Beyond traditional tumor-node-metastasis (TNM) staging, the host's systemic condition is increasingly recognized as a pivotal determinant of cancer outcomes 5 – 8 . Systemic inflammation and nutritional status are two key host-derived factors that play a crucial role in the pathogenesis and progression of PDAC 9 . The systemic immune-inflammation index (SII), calculated as (platelet count × neutrophil count)/lymphocyte count, integrates these cellular components to quantify the balance between pro-tumor inflammation and anti-tumor immunity 10 – 12 . It has emerged as a promising prognostic marker in various cancers. Similarly, the prognostic nutritional index (PNI), defined as serum albumin (g/L) + 5 × lymphocyte count (×10⁹/L), reflects the patient's nutritional status and immune competence 12 , 13 . Low PNI is a well-documented indicator of cancer cachexia and is associated with poor survival in gastrointestinal malignancies 9 , 14 , 15 . Individually, both high SII and low PNI have been significantly associated with worse survival in PDAC 16 , 17 . However, inflammation and nutrition are biologically interconnected pathways that collectively shape the host-tumor interaction 18 , 19 . Relying on a single index may provide an incomplete prognostic picture. We hypothesize that a composite model integrating both indices would provide a more comprehensive assessment of the host's systemic status, potentially yielding superior prognostic accuracy compared to either marker alone. 20 , 21 Therefore, the primary objective of this study was to develop and validate a novel, combined risk score based on pre-operative SII and PNI for predicting overall survival in a large, homogeneous cohort of patients with resectable PDAC undergoing curative-intent surgery 22 , 23 . Methods Study Population In this single-center retrospective study,we retrospectively reviewed 375 consecutive patients who underwent curative-intent resection for PDAC at The Second Hospital of Hebei Medical University between January 2014 and July 2025. Exclusion criteria included incomplete laboratory data, perioperative mortality (within 30 days), or prior malignancy. Clinicopathological data were collected from the electronic medical record system. The study was approved by the Institutional Review Board of The Second Hospital of Hebei Medical University (Approval No. 2025-Y028). Given the retrospective nature of the study, the requirement for informed consent was waived. Laboratory Parameters Preoperative blood samples collected within 7 days before surgery were used for analysis. Risk Score Calculation The prognostic risk score was calculated for each patient using the following formula derived from the Cox regression model: $$\:\begin{array}{c}RiskScore=(0.000134\times\:SII)-(0.116\times\:PNI)+(0.029\times\:Age)+(0.158\times\:Sex)\#\left(1\right)\end{array}$$ where: $$\:\begin{array}{c}\text{S}\text{I}\text{I}\text{}\text{=}\text{}\frac{\text{platelet\:count×platelet\:count}}{\text{\:lymphocyte\:count\:}}\#\left(2\right)\end{array}$$ $$\:\begin{array}{c}PNI=albumin(g/L)+5\times\:lymphocytecount(10⁹/L)\#\text{(}\text{3}\text{)}\end{array}$$ Age is the patient's age in years. Sex is assigned as 1 for Male and 0 for Female. Patients were classified into high-risk (Risk Score > − 0.231) or low-risk (Risk Score ≤ − 0.231) groups based on the optimal cut-off value determined by X-tile software. Cut-off Determination Optimal cut-offs for SII and PNI were determined using X-tile software (version 3.6.1, Yale University) based on 1-year OS in the training cohort (random 70% of the total cohort). A combined risk score was constructed using Cox regression coefficients and dichotomized at the optimal cut-off (− 0.231). Statistical Analysis ROC curve analysis, Kaplan–Meier survival analysis (log-rank test), and multivariate Cox proportional hazards models were performed using R software (version 4.3.0). A two-sided p-value < 0.05 was considered statistically significant. Results Patient Characteristics A total of 375 patients with resectable pancreatic ductal adenocarcinoma who underwent curative-intent resection between January 2014 and July 2025 were included in this study. The cohort was randomly divided into training (70%, n = 262) and validation (30%, n = 113) sets. The baseline characteristics were well-balanced between the two cohorts (Table 1). The median age was 64 years (range: 38–82), and 56.3% of patients were male. The median follow-up time was 28.5 months (range: 3–62 months). Characteristic Value Age, median (range), years 64 (38–82) Sex, n (%) Male 211(56.3) Female 164(43.7) BMI, median (IQR), kg m⁻² 23.1(21.2–25.0) Under-weight (< 18.5), n (%) 43(11.5) Pre-operative biliary drainage, n (%) 117(31.2) Neoadjuvant therapy, n (%) 69(18.4) Follow-up, median (range), months 28.5(3–62) Development of the Combined SII-PNI Risk Score Using Cox regression analysis in the training cohort, we developed a prognostic risk score based on preoperative systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) values, along with age and sex. The final risk score formula was derived as follows: Risk Score = (0.000134 × SII) − (0.116 × PNI) + (0.029 × Age) + (0.158 × Sex) where Sex was coded as 1 for male and 0 for female patients. Model Performance and Discrimination The combined SII-PNI model demonstrated excellent discriminative ability for predicting 1-year overall survival. In the training cohort, the area under the ROC curve (AUC) was 0.758 (95% CI: 0.702–0.814), while in the validation cohort, the AUC was 0.750 (95% CI: 0.671–0.829), indicating consistent performance across both datasets (Fig. 1 , 2 ). The concordance index (C-index) for the model was 0.724 (95% CI: 0.683–0.765) in the training set and 0.718 (95% CI: 0.662–0.774) in the validation set, further confirming the model's robust prognostic capability. Risk Stratification and Survival Analysis Using X-tile software, we identified − 0.231 as the optimal cut-off value for risk stratification based on 1-year overall survival. Applying this cut-off, 142 patients (37.9%) were classified as high-risk and 233 patients (62.1%) as low-risk. Kaplan-Meier survival analysis revealed significantly worse overall survival in the high-risk group compared to the low-risk group (log-rank test, p < 0.0001) (Fig. 3 ). The median overall survival was 18.3 months (95% CI: 15.2–21.4) in the high-risk group versus 32.7 months (95% CI: 28.9–36.5) in the low-risk group. Similar significant differences were observed when using median risk score stratification (log-rank test, p < 0.0001) (Fig. 4 ). Individual Biomarker Analysis When analyzed separately, both SII and PNI showed significant associations with overall survival in univariate analysis. Patients with high PNI (above median) demonstrated significantly better survival compared to those with low PNI (log-rank test, p < 0.0001) (Fig. 5 ). Similarly, patients with high SII (above median) showed a trend toward worse survival, though with borderline significance (log-rank test, p = 0.044) (Fig. 6 ). Univariate and Multivariate Cox Regression Analysis In univariate analysis, PNI (HR = 0.891, 95% CI: 0.863–0.919, p < 0.0001), SII (HR = 1.000, 95% CI: 1.000-1.001, p = 0.044), age (HR = 1.029, 95% CI: 1.013–1.046, p < 0.001), and sex (HR = 1.172, 95% CI: 0.872–1.576, p = 0.292) were evaluated for their association with overall survival. Multivariate Cox regression analysis, adjusting for all variables, identified PNI (HR = 0.891, 95% CI: 0.863–0.919, p < 0.001) and age (HR = 1.029, 95% CI: 1.013–1.046, p < 0.001) as independent prognostic factors. SII (HR = 1.000, 95% CI: 1.000-1.001, p = 0.521) and sex (HR = 1.158, 95% CI: 0.854–1.570, p = 0.348) did not retain independent prognostic significance in the multivariate model (Fig. 7 ). Risk Score Distribution The distribution of prognostic risk scores across the study population showed a bimodal pattern, with clear separation between the identified risk groups (Fig. 8 ). The optimal cut-off value of -0.231 effectively distinguished patients with divergent survival outcomes. The concordance index for the combined SII-PNI model was 0.724 (95% CI 0.683–0.765) in the training set and 0.718 (95% CI 0.662–0.774) in the validation set. Post-operative SII and PNI were available in 198 patients at POD7; however, their inclusion did not improve the C-index (0.724 vs 0.726, P = 0.81) and are therefore not reported further Discussion This study developed and validated a novel, combined risk score based on the Systemic Immune-Inflammation Index (SII) and Prognostic Nutritional Index (PNI) for predicting overall survival in patients with resectable pancreatic ductal adenocarcinoma (PDAC). The principal finding of our research is that this integrated SII-PNI model demonstrates robust and consistent discriminative ability, with AUCs approximating 0.75 in both training and validation cohorts. More importantly, it effectively stratified patients into distinct high-risk and low-risk groups with significantly different survival outcomes, underscoring its potential utility in postoperative risk assessment. The core of our findings reveals a fascinating hierarchy between the two biomarkers. While both SII and PNI were significant in univariate analysis, only PNI emerged as an independent prognostic factor in the multivariate Cox model 14 , 24 . This result strongly suggests that nutritional and immune competence, as captured by PNI, may be a more fundamental driver of prognosis in resectable PDAC than the systemic inflammation reflected by SII 25 , 26 . This observation can be interpreted through the lens of cancer cachexia, a hallmark of PDAC. PNI, which incorporates serum albumin and lymphocyte count, is a direct gauge of the patient's nutritional reserve and adaptive immune capacity 20 . A low PNI signifies a state of catabolism and impaired immune surveillance, creating a permissive environment for tumor recurrence and metastasis. On the other hand, SII, a ratio of platelets and neutrophils to lymphocytes, primarily reflects a pro-inflammatory and pro-thrombotic state. Our results imply that in the context of resected PDAC, the detrimental effects of malnutrition and lymphocytopenia may subsume or precede the prognostic information offered by systemic inflammation alone. Our findings regarding the supremacy of PNI align with a growing body of evidence. For instance, a study by also identified PNI, but not other inflammatory indices, as an independent predictor for survival in biliary tract cancers, highlighting the cross-cancer importance of nutritional status. Conversely, our result that SII lost its independent significance contrasts with studies in, where SII remained a powerful prognosticator. This discrepancy underscores the unique tumor microenvironment and pathophysiology of PDAC, where the profound metabolic dysregulation and cachexia might render nutritional status a more dominant prognostic axis than in other malignancies. Our study thereby contributes to a more nuanced understanding of which biomarker is most relevant in specific clinical contexts. Despite PNI's dominance, the combined SII-PNI model did not diminish in value. The integration of SII, even as a non-independent factor, likely captures complementary aspects of the host-tumor interaction, leading to a model with superior discriminative power (AUC 0.75) compared to using either marker alone. The principal advantage of our risk score is its pragmatism. Derived from routine, low-cost blood tests, it can be seamlessly integrated into clinical workflows without additional burden. This tool allows for the identification of high-risk patients who may benefit from more aggressive adjuvant therapy, intensified nutritional support, and closer post-operative surveillance. We acknowledge several limitations of our study. First, its single-center, retrospective design inherently introduces potential selection bias and limits the generalizability of our findings. Second, the study population was exclusively Chinese, and the applicability of our risk score to other ethnic groups requires verification. These limitations directly inform avenues for future research. Priority should be given to external validation in large, multi-center, and prospective cohorts. Furthermore, investigating the dynamic changes of the SII-PNI score during adjuvant therapy could reveal its value in monitoring treatment response. Finally, exploring the biological interplay between nutritional status (PNI) and systemic inflammation (SII) in the PDAC microenvironment through translational studies could provide mechanistic validation for our clinical findings. Conclusion The combined SII-PNI score is a reproducible, inexpensive, and powerful predictor of postoperative survival in PDAC. Prospective multicenter studies are needed to validate its clinical utility for individualized management. Authors’ Contributions Ruihan Hou contributed to data curation, formal analysis, and writing – original draft; Aorui Wang, Xinda Yang, Chengxu Du,Weihong Zhao,and Haitao Lv contributed to investigation and resources; Dongrui Li contributed to conceptualization, supervision, funding acquisition, writing – review & editing, and serves as the corresponding author. Declarations Conflict of Interest The authors declare that they have no competing interests. Ethics Approval This study was approved by the Institutional Review Board of The Second Hospital of Hebei Medical University (Approval No. 2025-Y028). Informed consent was waived due to the retrospective nature of the study. Ethics approval and consent to participate This study was approved by the Institutional Review Board of the Second Hospital of Hebei Medical University (Approval No. 2025-Y028). Written informed consent was waived due to the retrospective design. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Hengrui-Hebei Innovation and Development Medical Cooperation Program (Grant No. HR202502088). The funder had no role in study design, data collection, analysis, interpretation, or writing of the manuscript. Author Contribution Ruihan Hou contributed to data curation, formal analysis, and writing – original draft; Aorui Wang, Xinda Yang, Chengxu Du,Weihong Zhao,and Haitao Lv contributed to investigation and resources; Dongrui Li contributed to conceptualization, supervision, funding acquisition, writing – review & editing, and serves as the corresponding author. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660 . Siegel RL, Giaquinto AN, Jemal A, Cancer statistics. 2024. CA Cancer J Clin . 2024;74(1):12–49. 10.3322/caac.21820 Li X, Chen Y, Qiao G, et al. 5-Year survival rate over 20% in pancreatic ductal adenocarcinoma: A retrospective study from a Chinese high-volume center. 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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-8445900","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580233531,"identity":"a61378cf-3457-43d8-9fdb-3c48854566d6","order_by":0,"name":"Ruihan Hou","email":"","orcid":"","institution":"Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruihan","middleName":"","lastName":"Hou","suffix":""},{"id":580233532,"identity":"a6ae8174-89ae-4dab-9490-df53a5251b2d","order_by":1,"name":"Aorui Wang","email":"","orcid":"","institution":"Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aorui","middleName":"","lastName":"Wang","suffix":""},{"id":580233533,"identity":"6e1f0c4d-7efd-4934-8c64-512af8838f78","order_by":2,"name":"Chengxu Du","email":"","orcid":"","institution":"Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengxu","middleName":"","lastName":"Du","suffix":""},{"id":580233534,"identity":"f5b2bcf5-dea1-4e2a-b1b1-ebaac5d16a0a","order_by":3,"name":"Xinda Yang","email":"","orcid":"","institution":"Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinda","middleName":"","lastName":"Yang","suffix":""},{"id":580233535,"identity":"d02757b6-0ebc-49c7-99e2-6edfb61f6aad","order_by":4,"name":"Weihong Zhao","email":"","orcid":"","institution":"Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Zhao","suffix":""},{"id":580233536,"identity":"f622119c-6c95-43db-8f12-323cc9adbde6","order_by":5,"name":"Haitao Lv","email":"","orcid":"","institution":"Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Lv","suffix":""},{"id":580233537,"identity":"07dbd9c5-63b3-4c40-9936-70f26ad50a58","order_by":6,"name":"Dongrui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACPnYGAwkGBpsEBgkwn5mwFjZmsJY00rUcJkkL88bblXvO5/FLd6dJMFRYJzawnz1AQAtbseWZZ7eLJeec3SbBcCY9sYEnL4GAFh4zyYYDtxM33MjdJsHYdjixQYLHgBgt5xL3g7X8I17LgcQNEiAtDURpAfql4UBy4ow7ZzdbJBxLN27jycGvhZ+9eePNhgN2if2zezfe+FBjLdvPfga/FlSQALKXBPWjYBSMglEwCnAAAN0NQQorwTGXAAAAAElFTkSuQmCC","orcid":"","institution":"Second Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dongrui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-12-25 03:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8445900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8445900/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101274647,"identity":"b7976c99-d390-4e47-af84-cb9b5f44d6b9","added_by":"auto","created_at":"2026-01-28 03:12:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33207,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the combined systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) model for predicting 1-year overall survival in the training cohort.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/1d9f38958dec3578bc4b72a7.png"},{"id":101274648,"identity":"7d830aad-e62c-47c0-945d-f4a827afe58f","added_by":"auto","created_at":"2026-01-28 03:12:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45280,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the combined SII-PNI model for predicting 1-year overall survival in the validation cohort.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/9b34dbfbb3bb6a210b1d5e8b.png"},{"id":101274649,"identity":"ea0c978f-95d9-4bff-ab49-c24fd068ea59","added_by":"auto","created_at":"2026-01-28 03:12:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50587,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves stratified by the optimal prognostic risk score cut-off value. Patients were classified into high-risk (risk score \u0026gt; -0.231) and low-risk (risk score ≤ -0.231) groups based on the combined SII-PNI score.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/f34d84b0b37eda218ed3364d.png"},{"id":101297374,"identity":"cffc3e4d-3a9d-45be-99c1-761d60d9f467","added_by":"auto","created_at":"2026-01-28 09:26:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80222,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves stratified by the median prognostic risk score. Patients were classified into high-risk (risk score \u0026gt; median) and low-risk (risk score ≤ median) groups.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/fb37f9b96b8535042259fcbf.png"},{"id":101296884,"identity":"8c4454c5-1e7f-43a5-8a4c-6048b9c38022","added_by":"auto","created_at":"2026-01-28 09:22:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":62347,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves stratified by the prognostic nutritional index (PNI). Patients were dichotomized into high-PNI and low-PNI groups based on the median PNI value.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/25058153460629570e336933.png"},{"id":101274652,"identity":"22e47012-008f-409d-b952-9125fc042f3e","added_by":"auto","created_at":"2026-01-28 03:12:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62501,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves stratified by the systemic immune-inflammation index (SII). Patients were classified into high-SII and low-SII groups based on the median SII value.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/5e1c7e28c4f53423855a36bf.png"},{"id":101297143,"identity":"9c53dbb8-8414-47b1-963c-c9255e32b29b","added_by":"auto","created_at":"2026-01-28 09:25:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":39319,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariate Cox regression analysis for overall survival. Hazard ratios and 95\\% confidence intervals are shown for age, sex, SII, and PNI.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/ec9c95e1430d3a2633ac2a5b.png"},{"id":101274651,"identity":"8e33a0e2-27f5-4869-9966-dba502d1cfec","added_by":"auto","created_at":"2026-01-28 03:12:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":77352,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of prognostic risk scores. The optimal cut-off value of -0.231 (dashed line) was determined using X-tile software.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/2ff3ff1b3b679975550fe41b.png"},{"id":101397763,"identity":"c01ca852-72cf-4b2d-b553-4bc11fa9822f","added_by":"auto","created_at":"2026-01-29 09:36:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":954183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8445900/v1/8303cb83-84b7-4f73-bf66-5b77ab8b200a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A combined systemic immune-inflammation index and prognostic nutritional index score for predicting overall survival after resection of pancreatic ductal adenocarcinoma: a retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a persistently poor prognosis, ranking among the leading causes of cancer-related deaths worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For the 15\u0026ndash;20% of patients who present with resectable disease, surgical resection remains the cornerstone of potential cure\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, the long-term outcomes post-surgery are heterogeneous, with a high rate of recurrence and a five-year overall survival rate that rarely exceeds 30%. This variability underscores the critical need for reliable and accessible prognostic tools to identify high-risk patients who might benefit from more aggressive adjuvant therapy and tailored surveillance.\u003c/p\u003e \u003cp\u003eBeyond traditional tumor-node-metastasis (TNM) staging, the host's systemic condition is increasingly recognized as a pivotal determinant of cancer outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Systemic inflammation and nutritional status are two key host-derived factors that play a crucial role in the pathogenesis and progression of PDAC\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The systemic immune-inflammation index (SII), calculated as (platelet count \u0026times; neutrophil count)/lymphocyte count, integrates these cellular components to quantify the balance between pro-tumor inflammation and anti-tumor immunity\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. It has emerged as a promising prognostic marker in various cancers. Similarly, the prognostic nutritional index (PNI), defined as serum albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte count (\u0026times;10⁹/L), reflects the patient's nutritional status and immune competence\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Low PNI is a well-documented indicator of cancer cachexia and is associated with poor survival in gastrointestinal malignancies\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIndividually, both high SII and low PNI have been significantly associated with worse survival in PDAC\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, inflammation and nutrition are biologically interconnected pathways that collectively shape the host-tumor interaction\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Relying on a single index may provide an incomplete prognostic picture. We hypothesize that a composite model integrating both indices would provide a more comprehensive assessment of the host's systemic status, potentially yielding superior prognostic accuracy compared to either marker alone.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTherefore, the primary objective of this study was to develop and validate a novel, combined risk score based on pre-operative SII and PNI for predicting overall survival in a large, homogeneous cohort of patients with resectable PDAC undergoing curative-intent surgery\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003e In this single-center retrospective study,we retrospectively reviewed 375 consecutive patients who underwent curative-intent resection for PDAC at The Second Hospital of Hebei Medical University between January 2014 and July 2025. Exclusion criteria included incomplete laboratory data, perioperative mortality (within 30 days), or prior malignancy. Clinicopathological data were collected from the electronic medical record system. The study was approved by the Institutional Review Board of The Second Hospital of Hebei Medical University (Approval No. 2025-Y028). Given the retrospective nature of the study, the requirement for informed consent was waived.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory Parameters\u003c/h3\u003e\n\u003cp\u003ePreoperative blood samples collected within 7 days before surgery were used for analysis.\u003c/p\u003e\n\u003ch3\u003eRisk Score Calculation\u003c/h3\u003e\n\u003cp\u003eThe prognostic risk score was calculated for each patient using the following formula derived from the Cox regression model:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}RiskScore=(0.000134\\times\\:SII)-(0.116\\times\\:PNI)+(0.029\\times\\:Age)+(0.158\\times\\:Sex)\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\text{S}\\text{I}\\text{I}\\text{}\\text{=}\\text{}\\frac{\\text{platelet\\:count\u0026times;platelet\\:count}}{\\text{\\:lymphocyte\\:count\\:}}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}PNI=albumin(g/L)+5\\times\\:lymphocytecount(10⁹/L)\\#\\text{(}\\text{3}\\text{)}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAge is the patient's age in years. Sex is assigned as 1 for Male and 0 for Female. Patients were classified into high-risk (Risk Score\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;0.231) or low-risk (Risk Score\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.231) groups based on the optimal cut-off value determined by X-tile software.\u003c/p\u003e\n\u003ch3\u003eCut-off Determination\u003c/h3\u003e\n\u003cp\u003eOptimal cut-offs for SII and PNI were determined using X-tile software (version 3.6.1, Yale University) based on 1-year OS in the training cohort (random 70% of the total cohort). A combined risk score was constructed using Cox regression coefficients and dichotomized at the optimal cut-off (\u0026minus;\u0026thinsp;0.231).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eROC curve analysis, Kaplan\u0026ndash;Meier survival analysis (log-rank test), and multivariate Cox proportional hazards models were performed using R software (version 4.3.0). A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient Characteristics\u003c/p\u003e \u003cp\u003eA total of 375 patients with resectable pancreatic ductal adenocarcinoma who underwent curative-intent resection between January 2014 and July 2025 were included in this study. The cohort was randomly divided into training (70%, n\u0026thinsp;=\u0026thinsp;262) and validation (30%, n\u0026thinsp;=\u0026thinsp;113) sets. The baseline characteristics were well-balanced between the two cohorts (Table\u0026nbsp;1). The median age was 64 years (range: 38\u0026ndash;82), and 56.3% of patients were male. The median follow-up time was 28.5 months (range: 3\u0026ndash;62 months).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (range), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (38\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211(56.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164(43.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, median (IQR), kg m⁻\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.1(21.2\u0026ndash;25.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder-weight (\u0026lt;\u0026thinsp;18.5), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-operative biliary drainage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117(31.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoadjuvant therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up, median (range), months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5(3\u0026ndash;62)\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\u003eDevelopment of the Combined SII-PNI Risk Score\u003c/p\u003e \u003cp\u003eUsing Cox regression analysis in the training cohort, we developed a prognostic risk score based on preoperative systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) values, along with age and sex. The final risk score formula was derived as follows:\u003c/p\u003e \u003cp\u003eRisk Score = (0.000134 \u0026times; SII) \u0026minus; (0.116 \u0026times; PNI) + (0.029 \u0026times; Age) + (0.158 \u0026times; Sex)\u003c/p\u003e \u003cp\u003ewhere Sex was coded as 1 for male and 0 for female patients.\u003c/p\u003e \u003cp\u003eModel Performance and Discrimination\u003c/p\u003e \u003cp\u003eThe combined SII-PNI model demonstrated excellent discriminative ability for predicting 1-year overall survival. In the training cohort, the area under the ROC curve (AUC) was 0.758 (95% CI: 0.702\u0026ndash;0.814), while in the validation cohort, the AUC was 0.750 (95% CI: 0.671\u0026ndash;0.829), indicating consistent performance across both datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concordance index (C-index) for the model was 0.724 (95% CI: 0.683\u0026ndash;0.765) in the training set and 0.718 (95% CI: 0.662\u0026ndash;0.774) in the validation set, further confirming the model's robust prognostic capability.\u003c/p\u003e \u003cp\u003eRisk Stratification and Survival Analysis\u003c/p\u003e \u003cp\u003eUsing X-tile software, we identified \u0026minus;\u0026thinsp;0.231 as the optimal cut-off value for risk stratification based on 1-year overall survival. Applying this cut-off, 142 patients (37.9%) were classified as high-risk and 233 patients (62.1%) as low-risk.\u003c/p\u003e \u003cp\u003eKaplan-Meier survival analysis revealed significantly worse overall survival in the high-risk group compared to the low-risk group (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The median overall survival was 18.3 months (95% CI: 15.2\u0026ndash;21.4) in the high-risk group versus 32.7 months (95% CI: 28.9\u0026ndash;36.5) in the low-risk group. Similar significant differences were observed when using median risk score stratification (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIndividual Biomarker Analysis\u003c/p\u003e \u003cp\u003eWhen analyzed separately, both SII and PNI showed significant associations with overall survival in univariate analysis. Patients with high PNI (above median) demonstrated significantly better survival compared to those with low PNI (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, patients with high SII (above median) showed a trend toward worse survival, though with borderline significance (log-rank test, p\u0026thinsp;=\u0026thinsp;0.044) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnivariate and Multivariate Cox Regression Analysis\u003c/p\u003e \u003cp\u003eIn univariate analysis, PNI (HR\u0026thinsp;=\u0026thinsp;0.891, 95% CI: 0.863\u0026ndash;0.919, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), SII (HR\u0026thinsp;=\u0026thinsp;1.000, 95% CI: 1.000-1.001, p\u0026thinsp;=\u0026thinsp;0.044), age (HR\u0026thinsp;=\u0026thinsp;1.029, 95% CI: 1.013\u0026ndash;1.046, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and sex (HR\u0026thinsp;=\u0026thinsp;1.172, 95% CI: 0.872\u0026ndash;1.576, p\u0026thinsp;=\u0026thinsp;0.292) were evaluated for their association with overall survival.\u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis, adjusting for all variables, identified PNI (HR\u0026thinsp;=\u0026thinsp;0.891, 95% CI: 0.863\u0026ndash;0.919, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and age (HR\u0026thinsp;=\u0026thinsp;1.029, 95% CI: 1.013\u0026ndash;1.046, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as independent prognostic factors. SII (HR\u0026thinsp;=\u0026thinsp;1.000, 95% CI: 1.000-1.001, p\u0026thinsp;=\u0026thinsp;0.521) and sex (HR\u0026thinsp;=\u0026thinsp;1.158, 95% CI: 0.854\u0026ndash;1.570, p\u0026thinsp;=\u0026thinsp;0.348) did not retain independent prognostic significance in the multivariate model (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRisk Score Distribution\u003c/p\u003e \u003cp\u003eThe distribution of prognostic risk scores across the study population showed a bimodal pattern, with clear separation between the identified risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The optimal cut-off value of -0.231 effectively distinguished patients with divergent survival outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concordance index for the combined SII-PNI model was 0.724 (95% CI 0.683\u0026ndash;0.765) in the training set and 0.718 (95% CI 0.662\u0026ndash;0.774) in the validation set.\u003c/p\u003e \u003cp\u003ePost-operative SII and PNI were available in 198 patients at POD7; however, their inclusion did not improve the C-index (0.724 vs 0.726, P\u0026thinsp;=\u0026thinsp;0.81) and are therefore not reported further\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed and validated a novel, combined risk score based on the Systemic Immune-Inflammation Index (SII) and Prognostic Nutritional Index (PNI) for predicting overall survival in patients with resectable pancreatic ductal adenocarcinoma (PDAC). The principal finding of our research is that this integrated SII-PNI model demonstrates robust and consistent discriminative ability, with AUCs approximating 0.75 in both training and validation cohorts. More importantly, it effectively stratified patients into distinct high-risk and low-risk groups with significantly different survival outcomes, underscoring its potential utility in postoperative risk assessment.\u003c/p\u003e \u003cp\u003eThe core of our findings reveals a fascinating hierarchy between the two biomarkers. While both SII and PNI were significant in univariate analysis, only PNI emerged as an independent prognostic factor in the multivariate Cox model\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This result strongly suggests that nutritional and immune competence, as captured by PNI, may be a more fundamental driver of prognosis in resectable PDAC than the systemic inflammation reflected by SII\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis observation can be interpreted through the lens of cancer cachexia, a hallmark of PDAC. PNI, which incorporates serum albumin and lymphocyte count, is a direct gauge of the patient's nutritional reserve and adaptive immune capacity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A low PNI signifies a state of catabolism and impaired immune surveillance, creating a permissive environment for tumor recurrence and metastasis. On the other hand, SII, a ratio of platelets and neutrophils to lymphocytes, primarily reflects a pro-inflammatory and pro-thrombotic state. Our results imply that in the context of resected PDAC, the detrimental effects of malnutrition and lymphocytopenia may subsume or precede the prognostic information offered by systemic inflammation alone.\u003c/p\u003e \u003cp\u003eOur findings regarding the supremacy of PNI align with a growing body of evidence. For instance, a study by also identified PNI, but not other inflammatory indices, as an independent predictor for survival in biliary tract cancers, highlighting the cross-cancer importance of nutritional status. Conversely, our result that SII lost its independent significance contrasts with studies in, where SII remained a powerful prognosticator. This discrepancy underscores the unique tumor microenvironment and pathophysiology of PDAC, where the profound metabolic dysregulation and cachexia might render nutritional status a more dominant prognostic axis than in other malignancies. Our study thereby contributes to a more nuanced understanding of which biomarker is most relevant in specific clinical contexts.\u003c/p\u003e \u003cp\u003eDespite PNI's dominance, the combined SII-PNI model did not diminish in value. The integration of SII, even as a non-independent factor, likely captures complementary aspects of the host-tumor interaction, leading to a model with superior discriminative power (AUC 0.75) compared to using either marker alone. The principal advantage of our risk score is its pragmatism. Derived from routine, low-cost blood tests, it can be seamlessly integrated into clinical workflows without additional burden. This tool allows for the identification of high-risk patients who may benefit from more aggressive adjuvant therapy, intensified nutritional support, and closer post-operative surveillance.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations of our study. First, its single-center, retrospective design inherently introduces potential selection bias and limits the generalizability of our findings. Second, the study population was exclusively Chinese, and the applicability of our risk score to other ethnic groups requires verification.\u003c/p\u003e \u003cp\u003eThese limitations directly inform avenues for future research. Priority should be given to external validation in large, multi-center, and prospective cohorts. Furthermore, investigating the dynamic changes of the SII-PNI score during adjuvant therapy could reveal its value in monitoring treatment response. Finally, exploring the biological interplay between nutritional status (PNI) and systemic inflammation (SII) in the PDAC microenvironment through translational studies could provide mechanistic validation for our clinical findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe combined SII-PNI score is a reproducible, inexpensive, and powerful predictor of postoperative survival in PDAC. Prospective multicenter studies are needed to validate its clinical utility for individualized management.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAuthors\u0026rsquo; Contributions\u003c/h2\u003e \u003cp\u003eRuihan Hou contributed to data curation, formal analysis, and writing \u0026ndash; original draft; Aorui Wang, Xinda Yang, Chengxu Du,Weihong Zhao,and Haitao Lv contributed to investigation and resources; Dongrui Li contributed to conceptualization, supervision, funding acquisition, writing \u0026ndash; review \u0026amp; editing, and serves as the corresponding author.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003e This study was approved by the Institutional Review Board of The Second Hospital of Hebei Medical University (Approval No. 2025-Y028). Informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was approved by the Institutional Review Board of the Second Hospital of Hebei Medical University (Approval No. 2025-Y028). Written informed consent was waived due to the retrospective design.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Hengrui-Hebei Innovation and Development Medical Cooperation Program (Grant No. HR202502088). The funder had no role in study design, data collection, analysis, interpretation, or writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRuihan Hou contributed to data curation, formal analysis, and writing \u0026ndash; original draft; Aorui Wang, Xinda Yang, Chengxu Du,Weihong Zhao,and Haitao Lv contributed to investigation and resources; Dongrui Li contributed to conceptualization, supervision, funding acquisition, writing \u0026ndash; review \u0026amp;amp; editing, and serves as the corresponding author.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. 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Front Nutr. 2025;12:1595119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2025.1595119\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2025.1595119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pancreatic ductal adenocarcinoma, systemic immune-inflammation index, prognostic nutritional index, overall survival, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8445900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8445900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) have been individually associated with prognosis in various cancers. This study aimed to develop and validate a combined SII-PNI-based risk score for predicting overall survival (OS) in patients with resectable pancreatic ductal adenocarcinoma (PDAC).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis retrospective study enrolled 375 consecutive patients with PDAC who underwent curative-intent resection at The Second Hospital of Hebei Medical University between January 2014 and July 2025. Using X-tile software, optimal cut-off values for SII and PNI were determined based on 1-year OS in a training cohort (constituting 70% of the patients). A combined risk score was constructed using Cox regression coefficients and dichotomized at the optimal cut-off (\u0026minus;\u0026thinsp;0.231). The model\u0026rsquo;s performance was assessed using receiver operating characteristic (ROC) curve analysis, Kaplan\u0026ndash;Meier survival curves, and multivariate Cox regression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe combined SII-PNI model achieved AUCs of 0.758 in the training cohort and 0.750 in the validation cohort. Kaplan\u0026ndash;Meier analysis showed significantly worse OS in the high-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In univariate analysis, elevated SII (p\u0026thinsp;=\u0026thinsp;0.044) and reduced PNI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were both significantly associated with poorer OS. Multivariate Cox regression analysis confirmed PNI (HR\u0026thinsp;=\u0026thinsp;0.891, 95% CI: 0.863\u0026ndash;0.919, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and age (HR\u0026thinsp;=\u0026thinsp;1.029, 95% CI: 1.013\u0026ndash;1.046, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as independent prognostic factors, whereas SII and sex were not.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe combined SII-PNI score is a simple, cost-effective, and powerful predictor of prognosis in resectable PDAC, enabling reliable postoperative risk stratification.\u003c/p\u003e","manuscriptTitle":"A combined systemic immune-inflammation index and prognostic nutritional index score for predicting overall survival after resection of pancreatic ductal adenocarcinoma: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 03:12:08","doi":"10.21203/rs.3.rs-8445900/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-11T17:53:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-29T08:28:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21398306519715597449502893331967177473","date":"2026-01-29T06:15:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T02:32:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120104999460340561175415926742882766137","date":"2026-01-26T02:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T06:07:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-30T10:18:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T23:45:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T23:43:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-12-25T03:00:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66cfeba7-216c-4c9c-8bdc-4cf4d02cb7b8","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T10:01:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 03:12:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8445900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8445900","identity":"rs-8445900","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}