The Study on the Prognostic Assessment Value of Pan-Immune-Inflammation Value (PIV) in Patients with Sepsis

The Study on the Prognostic Assessment Value of Pan-Immune-Inflammation Value (PIV) in Patients with Sepsis | 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 The Study on the Prognostic Assessment Value of Pan-Immune-Inflammation Value (PIV) in Patients with Sepsis Jieyu Liu, Yuxin Dong, Jingyuan Wang, Jiaxuan Sun, Songtao Shou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8726245/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Sepsis remains a leading cause of mortality in intensive care units worldwide. The pan-immune-inflammation value (PIV), a composite biomarker derived, has shown prognostic value in oncology, but its role in sepsis has not been well established. Objective To evaluate the prognostic significance of PIV for 28- and 90-day mortality in adult sepsis patients and to develop and externally validate PIV-based predictive models. The study also aimed to explore dynamic PIV trajectories and their associations with early organ dysfunction, including acute kidney injury (AKI) and acute respiratory distress syndrome (ARDS). Methods Adult sepsis patients from the MIMIC-IV and eICU databases were analyzed. PIV was assessed using survival analysis and multiple machine learning algorithms (Random Baseline, Logistic Regression, Gradient Boosting Classifier, AdaBoost, XGBoost, LightGBM) after LASSO regression–based feature selection and hyperparameter optimization. Model performance was evaluated with ROC curves and SHAP analyses. Group-based trajectory modeling (GBTM) was used to identify PIV dynamic patterns over the first 7 ICU days and assess their associations with AKI and ARDS. Results Elevated PIV values were significantly associated with higher 28-day and 90-day all-cause mortality (P < 0.05). LASSO regression confirmed PIV as a key prognostic feature. PIV-based models—especially Gradient Boosting Classifier and XGBoost—showed strong discrimination for short- and intermediate-term mortality. Four PIV trajectories (Traj-1 to Traj-4) were identified. Compared with Traj-1, Traj-3 exhibited a markedly increased risk of ARDS (AUC = 0.611) and AKI, followed by Traj-2, while Traj-4 showed no significant difference. Conclusions PIV is an effective immune-inflammatory biomarker for predicting sepsis outcomes. PIV-based machine learning models demonstrate promising accuracy for individualized mortality prediction. Moreover, dynamic PIV trajectories correlate with early organ dysfunction, supporting their use in early risk stratification and precision management in sepsis. Sepsis Pan-immune Inflammatory Value Trajectory Model Prognostic Assessment Machine Learning Full Text Additional Declarations No competing interests reported. Tables are available in the Supplementary Files section. Supplementary Files Table.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 Mar, 2026 Editor invited by journal 05 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 28 Jan, 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-8726245","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601302057,"identity":"3a2419f3-5c33-497f-be42-07ed6c8632f5","order_by":0,"name":"Jieyu Liu","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jieyu","middleName":"","lastName":"Liu","suffix":""},{"id":601302059,"identity":"92372221-30f6-4808-81e4-186f70a3deec","order_by":1,"name":"Yuxin Dong","email":"","orcid":"","institution":"Tianjin Medical 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Sepsis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, Pan-immune Inflammatory Value, Trajectory Model, Prognostic Assessment, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-8726245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8726245/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\u003eSepsis remains a leading cause of mortality in intensive care units worldwide. The pan-immune-inflammation value (PIV), a composite biomarker derived, has shown prognostic value in oncology, but its role in sepsis has not been well established.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the prognostic significance of PIV for 28- and 90-day mortality in adult sepsis patients and to develop and externally validate PIV-based predictive models. The study also aimed to explore dynamic PIV trajectories and their associations with early organ dysfunction, including acute kidney injury (AKI) and acute respiratory distress syndrome (ARDS).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAdult sepsis patients from the MIMIC-IV and eICU databases were analyzed. PIV was assessed using survival analysis and multiple machine learning algorithms (Random Baseline, Logistic Regression, Gradient Boosting Classifier, AdaBoost, XGBoost, LightGBM) after LASSO regression\u0026ndash;based feature selection and hyperparameter optimization. Model performance was evaluated with ROC curves and SHAP analyses. Group-based trajectory modeling (GBTM) was used to identify PIV dynamic patterns over the first 7 ICU days and assess their associations with AKI and ARDS.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eElevated PIV values were significantly associated with higher 28-day and 90-day all-cause mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). LASSO regression confirmed PIV as a key prognostic feature. PIV-based models\u0026mdash;especially Gradient Boosting Classifier and XGBoost\u0026mdash;showed strong discrimination for short- and intermediate-term mortality. Four PIV trajectories (Traj-1 to Traj-4) were identified. Compared with Traj-1, Traj-3 exhibited a markedly increased risk of ARDS (AUC\u0026thinsp;=\u0026thinsp;0.611) and AKI, followed by Traj-2, while Traj-4 showed no significant difference.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePIV is an effective immune-inflammatory biomarker for predicting sepsis outcomes. PIV-based machine learning models demonstrate promising accuracy for individualized mortality prediction. Moreover, dynamic PIV trajectories correlate with early organ dysfunction, supporting their use in early risk stratification and precision management in sepsis.\u003c/p\u003e","manuscriptTitle":"The Study on the Prognostic Assessment Value of Pan-Immune-Inflammation Value (PIV) in Patients with Sepsis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 22:56:20","doi":"10.21203/rs.3.rs-8726245/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-05T12:55:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T09:24:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T11:27:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T11:20:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2026-01-29T02:32:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9ac197d-d035-4644-9a76-77d0df98650f","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T22:56:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 22:56:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8726245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8726245","identity":"rs-8726245","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}