[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128396-en":3,"doc-seo-128396-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128396,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Multicenter Evaluation of Prognostic Nutritional Index and Systemic Immune-Inflammation Index in Predicting Mortality Among Critically Ill Cardiovascular and Cerebrovascular Patients With Varied Glucose Metabolism - A Machine Learning-Based Cohort Study","Critically ill patients with cardiovascular and cerebrovascular diseases face high mortality risk, yet existing prognostic models inadequately address glucose-metabolic subgroups. A retrospective, multicenter, machine learning cohort study evaluates whether combining the Prognostic Nutritional Index (PNI) and the Systemic Immune-Inflammation Index (SII) improves mortality prediction across glucose regulation categories. Cox regression, Kaplan–Meier, and ROC analyses assess associations, discrimination, and external generalizability using an independent Chinese cohort.","OPEN ACCESS  \nEDITED BY  \nRicardo Cleto Marinho,  \nSanto António Local Health Unit, Portugal  \nREVIEWED BY  \nLaura Avila-Jimenez,  \nMexican Social Security Institute, Mexico Xiao Dong Song,  \nFirst Affiliated Hospital of Chongqing Medical University, China  \n*CORRESPONDENCE  \nShujie Huang  \n [huangsj3.thaa@vip.163.com](huangsj3.thaa@vip.163.com)[ ](huangsj3.thaa@vip.163.com)Jianhua Cheng  \n [cjh19940317@163.com](cjh19940317@163.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 11 September 2025  \nREVISED 17 December 2025  \nACCEPTED 13 January 2026  \nPUBLISHED 03 February 2026  \nCITATION  \nLi Z, Xie M, Wu H, Wang T, Huang S and Cheng J (2026) Multicenter evaluation of prognostic nutritional index and systemic immune-inflammation index in predicting mortality among critically ill cardiovascular and cerebrovascular patients with varied glucose metabolism: a machine learning-based cohort study.  \nFront. Nutr. 13:1703589.  \ndoi: 10.3389/fnut.2026.1703589  \nCOPYRIGHT  \n© 2026 Li, Xie, Wu, Wang, Huang and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 03 February 2026 DOI 10.3389/fnut.2026.1703589  \nMulticenter evaluation of prognostic nutritional index and systemic immune-inflammation index in predicting mortality among critically ill cardiovascular and cerebrovascular patients with varied glucose metabolism: a machine learning-based cohort study  \nZhimin Li 1, 2†, Mingchen Xie 1†, Haitao Wu 1, Tingxuan Wang 1, Shujie Huang3* and Jianhua Cheng 1*  \n1Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China, 2Department of Critical Care Medicine, Jishuitan Hospital, Beijing, China, 3Department of Vascular Surgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, China  \nBackground: Critically ill patients with cardiovascular and cerebrovascular diseases face high mortality risks, necessitating precise prognostic tools. Current models lack granularity in assessing glucose metabolic subgroups, while isolated use of the Prognostic Nutritional Index (PNI) and Systemic ImmuneInflammation Index (SII) has limitations. This study evaluates their combined predictive value for mortality across glucose metabolic profiles using machine learning.  \nMethods: We conducted a retrospective cohort study of 1,698 patients from the MIMIC-IV database (2008–2019), stratified by glucose metabolic status: normal glucose regulation (NGR), prediabetes (Pre-DM), and diabetes mellitus (DM) . Prognostic associations and discrimination performance were evaluated using Cox regression, Kaplan–Meier analysis, and ROC curves. Machine learning models—including logistic regression, decision tree, random forest, XGBoost, and LightGBM—were developed based on Boruta-selected features to predict 28-day and 90-day all-cause mortality. Model performance was assessed using AUC, accuracy, and F1-score. To externally validate the machine learning models, we incorporated an independent cohort of critically ill cardiovascular and cerebrovascular patients (n = 1,194) from two tertiary hospitals in China: The Affiliated Hospital of Qingdao University and Qingdao Municipal Hospital. Results: Higher PNI was associated with reduced mortality, whereas elevated SII predicted higher mortality risk. The combined PNI-SII model outperformed individual indices across glucose subgroups, showing the best performance in Pre-DM patients (AUC = 0.775 for 28-day mortality) . PNI’s protective effect was atte","cbCaityDudjXr7Ur","https://ap.wps.com/l/cbCaityDudjXr7Ur","pdf",3530942,2,1,17,"English","en",105,"# Introduction\n# Methods\n# Results\n## Mortality prediction performance\n# Discussion\n# Conclusion","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"It targets the need for more precise mortality prediction in critically ill cardiovascular and cerebrovascular patients, particularly across different glucose metabolism profiles.\"},{\"question\":\"How are glucose metabolic subgroups defined for analysis?\",\"answer\":\"Patients are stratified into normal glucose regulation (NGR), prediabetes (Pre-DM), and diabetes mellitus (DM).\"},{\"question\":\"What is the study’s main finding about PNI and SII?\",\"answer\":\"Higher PNI is associated with lower mortality, while higher SII predicts higher mortality risk; the combined PNI-SII model outperforms individual indices, with the best performance in the Pre-DM group.\"}]","Multicenter Evaluation of Prognostic Nutritional Index and Systemic Immune-Inflammation Index in Predicting Mortality Among Critically Ill Cardiovascular and Cerebrovascular Patients With Varied Glucose Metabolism - A Machine Learning-Based Cohort Study | PDF",1785947286,43,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"multicenter-evaluation-of-prognostic-nutritional-index-and-systemic-immune-inflammation-index-in-predicting-mortality-among-critically-ill-cardiovascular-and-cerebrovascular-patients-with-varied-glucose-metabolism-a-machine-learning-based-cohort-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/multicenter-evaluation-of-prognostic-nutritional-index-and-systemic-immune-inflammation-index-in-predicting-mortality-among-critically-ill-cardiovascular-and-cerebrovascular-patients-with-varied-glucose-metabolism-a-machine-learning-based-cohort-study/128396/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What clinical problem does the study address?","Question",{"text":76,"@type":77},"It targets the need for more precise mortality prediction in critically ill cardiovascular and cerebrovascular patients, particularly across different glucose metabolism profiles.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are glucose metabolic subgroups defined for analysis?",{"text":81,"@type":77},"Patients are stratified into normal glucose regulation (NGR), prediabetes (Pre-DM), and diabetes mellitus (DM).",{"name":83,"@type":74,"acceptedAnswer":84},"What is the study’s main finding about PNI and SII?",{"text":85,"@type":77},"Higher PNI is associated with lower mortality, while higher SII predicts higher mortality risk; 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