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This retrospective observational study assessed whether adding inflammatory indices, the Systemic Immune-Inflammation Index (SII) and Systemic Inflammation Response Index (SIRI), improves DCI prediction. Among 624 adult aSAH patients, multivariable logistic regression produced a bedside score (VASS-DCI). The full model outperformed VASOGRADE in discrimination and decision-curve clinical benefit. The VASS-DCI score stratified patients into low, moderate, and high DCI risk groups.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-composite-model-vass-dci-for-delayed-cerebral-ischemia-following-aneurysmal-subarachnoid-hemorrhage-role-of-vasograde-and-inflammatory-biomarkers/449097/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-composite-model-vass-dci-for-delayed-cerebral-ischemia-following-aneurysmal-subarachnoid-hemorrhage-role-of-vasograde-and-inflammatory-biomarkers/449097.png","ImageObject",300,407,{"name":92,"@type":93},"eBook King","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address in DCI prediction after aSAH?","Question",{"text":113,"@type":114},"Existing clinical prediction tools for delayed cerebral ischemia do not integrate systemic inflammatory status, despite inflammation being recognized as a contributor to DCI.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the VASS-DCI bedside score developed?",{"text":118,"@type":114},"A retrospective cohort of 624 adult aSAH patients was analyzed using multivariable logistic regression to identify independent predictors, which were then used to construct the VASS-DCI score.",{"name":120,"@type":111,"acceptedAnswer":121},"Which factors were independent predictors of delayed cerebral ischemia?",{"text":122,"@type":114},"Increasing age, VASOGRADE category (yellow/red), SII ≥ 2,056,309, and SIRI ≥ 5,568 were independent predictors of DCI.",{"name":124,"@type":111,"acceptedAnswer":125},"How does VASS-DCI perform compared with the VASOGRADE score?",{"text":126,"@type":114},"The full multivariable model significantly outperformed VASOGRADE (AUC 0.774 vs 0.637) and showed higher net clinical benefit across threshold probabilities from 10% to 55%.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},449097,1790817715,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":46,"preview_page_count":4},962088006270,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","ORIGINAL ARTICLE  \nA Composite Model – “VASS-DCI” – for Delayed Cerebral Ischemia Following Aneurysmal Subarachnoid Hemorrhage: Role of VASOGRADE and Inflammatory Biomarkers  \nPrachi Sharma1, Radhakrishnan Muthuchellappan2, Thomas Francis3, Prithu Sharma4, Shilpa H Mallesha5, Soundarya Nagaraja6  \nReceived on: 23 September 2025; Accepted on: 07 November 2025; Published on: 19 December 2025  \n Visual Abstract  \n Supplement Material  \nAbstrAct  \nBackground and aims: Existing clinical prediction tools for delayed cerebral ischemia (DCI) in aneurysmal subarachnoid hemorrhage (aSAH) patients do not integrate systemic inflammatory status, which is increasingly recognized as a contributor to DCI. We evaluated whether adding inflammatory indices, Systemic Immune-Inflammation Index (SII) and Systemic Inflammation Response Index (SIRI), to existing tools improves DCI prediction.  \nPatients and methods: This retrospective observational study included 624 adult patients with aSAH who underwent clipping or coiling over a 2-year period. Patients were stratified into DCI and non-DCI groups. Multivariable logistic regression identified independent predictors of DCI, which were used to construct a bedside score (VASS-DCI) based on adjusted odds ratios. Receiver operating characteristic (ROC) analysis, DeLong’s test, and decision curve analysis (DCA) were used to assess model performance and clinical utility.  \nResults: A total of 179 patients (28.7%) developed DCI. Increasing age, VASOGRADE (yellow/red), SII ≥ 2,056,309, and SIRI ≥5,568 were independent predictors of DCI. A multivariable model developed using these variables significantly outperformed theVASOGRADE score (AUC 0.774 vs 0.637; p \u003C 0.0001) . DCA demonstrated a higher net clinical benefit of the full model across a threshold probability range of 10–55% compared to VASOGRADE. A clinical risk score, theVASS-DCI (range 0–6), was developed using these predictors. The score stratified patients into low (0–2), moderate (3–4), and high-risk (5–6) groups, with corresponding DCI incidences of 11.8, 48.8, and 64.3%, respectively.  \nConclusion: TheVASS-DCI score integrates inflammatory, clinical, and radiological parameters into a practical bedside tool for early prediction of DCI in aSAH patients.  \nKeywords: Biomarkers, Delayed cerebral ischemia, Inflammation, Prediction tools, Subarachnoid hemorrhage.  \nIndian Journal of Critical Care Medicine (2025): 10.5005/jp-journals-10071-25098  \nIntroductIon  \nDelayed cerebral ischemia (DCI) following ruptured aneurysmal subarachnoid hemorrhage (aSAH) occurs in approximately 20–30% of patients and significantly contributes to poorer outcomes.1, 2 This underscores the need for early identification of patients at risk of developing DCI during the acute phase of aSAH. The pathogenesis of DCI is multifactorial and includes vasospasm, cortical spreading depolarizations, microcirculatory disturbances, thrombosis, and neuroinflammation.3 Inflammatory cell infiltration is a key pathological feature of aneurysmal wall degeneration and rupture.4,5 Following aneurysmal rupture, the inflammatory response gets exaggerated, further injuring the surrounding brain tissue.6 Given these mechanisms, there is growing interest in using simple, bedside-accessible inflammatory biomarkers for early DCI prediction.7  \nStudies in the past have evaluated systemic inflammatory markers such as leukocyte count, C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and recently, pan-immune inflammation value (PIV), as predictors of DCI and to prognosticate patients after aSAH.8–13The SII combines neutrophils, lymphocytes, and platelets, while SIRI incorporates neutrophils, monocytes,  \n1–3,5,6Department of Neuroanesthesia and Neurocritical Care, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India  \n4Department of Econom","cbCaipBXUQmcvZSl","https://ap.wps.com/l/cbCaipBXUQmcvZSl","pdf",1133659,"English","# Background and aims\n# Patients and methods\n# Results\n# Conclusion\n# Introduction\n## Pathogenesis and rationale\n## Inflammatory biomarkers in prior studies","[{\"question\":\"What problem does the study address in DCI prediction after aSAH?\",\"answer\":\"Existing clinical prediction tools for delayed cerebral ischemia do not integrate systemic inflammatory status, despite inflammation being recognized as a contributor to DCI.\"},{\"question\":\"How was the VASS-DCI bedside score developed?\",\"answer\":\"A retrospective cohort of 624 adult aSAH patients was analyzed using multivariable logistic regression to identify independent predictors, which were then used to construct the VASS-DCI score.\"},{\"question\":\"Which factors were independent predictors of delayed cerebral ischemia?\",\"answer\":\"Increasing age, VASOGRADE category (yellow/red), SII ≥ 2,056,309, and SIRI ≥ 5,568 were independent predictors of DCI.\"},{\"question\":\"How does VASS-DCI perform compared with the VASOGRADE score?\",\"answer\":\"The full multivariable model significantly outperformed VASOGRADE (AUC 0.774 vs 0.637) and showed higher net clinical benefit across threshold probabilities from 10% to 55%.\"}]","A Composite Model - “VASS-DCI” - for Delayed Cerebral Ischemia Following Aneurysmal Subarachnoid Hemorrhage - Role of VASOGRADE and Inflammatory Biomarkers | PDF",1790729358]