Wei Zhang , Xueyan Li , Yuanyuan Yin , Min Liu , Li Xu
{"title":"神经外科围手术期营养不良影响因素分析——横断面调查。","authors":"Wei Zhang , Xueyan Li , Yuanyuan Yin , Min Liu , Li Xu","doi":"10.1016/j.clnesp.2025.07.1113","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to analyze the factors influencing malnutrition in perioperative neurosurgery patients using the random forest algorithm and logistic regression.</div></div><div><h3>Methods</h3><div>A cross-sectional survey was conducted among 330 perioperative neurosurgery patients from eight Class A tertiary general hospitals in Shanghai. The random forest algorithm was employed to rank the importance of independent variables potentially affecting malnutrition, and the top eight variables were subsequently included in a logistic regression model.</div></div><div><h3>Results</h3><div>The random forest algorithm identified total protein level, prealbumin level, hematocrit, hemoglobin level, CRP level, glycosylated hemoglobin level, leg circumference, and neutrophil count as the primary factors influencing nutritional status. Logistic regression analysis showed that HBA1c level was a risk factor for malnutritional (<em>OR</em> = 1.316, 95%CI:1.066–1.624, <em>P</em> < 0.05), while total protein level (<em>OR</em> = 0.869, 95%CI:0.826–0.915, <em>P</em> < 0.001), hematocrit volume (<em>OR</em> = 0.848, 95%CI: 0.740–0.972, <em>P</em> = 0.019), and calf circumference (<em>OR</em> = 0.887, 95%CI: 0.789–0.997, <em>P</em> = 0.045) were protective factors.</div></div><div><h3>Conclusion</h3><div>It is recommended to enhance nutrition support and diabetes management, along with implement early nutrition intervention for high-risk groups, to mitigate the risk of malnutrition.</div></div>","PeriodicalId":10352,"journal":{"name":"Clinical nutrition ESPEN","volume":"69 ","pages":"Pages 358-366"},"PeriodicalIF":2.6000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of influencing factors of malnutrition in perioperative neurosurgery patients — A cross-sectional survey\",\"authors\":\"Wei Zhang , Xueyan Li , Yuanyuan Yin , Min Liu , Li Xu\",\"doi\":\"10.1016/j.clnesp.2025.07.1113\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>This study aimed to analyze the factors influencing malnutrition in perioperative neurosurgery patients using the random forest algorithm and logistic regression.</div></div><div><h3>Methods</h3><div>A cross-sectional survey was conducted among 330 perioperative neurosurgery patients from eight Class A tertiary general hospitals in Shanghai. The random forest algorithm was employed to rank the importance of independent variables potentially affecting malnutrition, and the top eight variables were subsequently included in a logistic regression model.</div></div><div><h3>Results</h3><div>The random forest algorithm identified total protein level, prealbumin level, hematocrit, hemoglobin level, CRP level, glycosylated hemoglobin level, leg circumference, and neutrophil count as the primary factors influencing nutritional status. Logistic regression analysis showed that HBA1c level was a risk factor for malnutritional (<em>OR</em> = 1.316, 95%CI:1.066–1.624, <em>P</em> < 0.05), while total protein level (<em>OR</em> = 0.869, 95%CI:0.826–0.915, <em>P</em> < 0.001), hematocrit volume (<em>OR</em> = 0.848, 95%CI: 0.740–0.972, <em>P</em> = 0.019), and calf circumference (<em>OR</em> = 0.887, 95%CI: 0.789–0.997, <em>P</em> = 0.045) were protective factors.</div></div><div><h3>Conclusion</h3><div>It is recommended to enhance nutrition support and diabetes management, along with implement early nutrition intervention for high-risk groups, to mitigate the risk of malnutrition.</div></div>\",\"PeriodicalId\":10352,\"journal\":{\"name\":\"Clinical nutrition ESPEN\",\"volume\":\"69 \",\"pages\":\"Pages 358-366\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical nutrition ESPEN\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2405457725028645\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NUTRITION & DIETETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical nutrition ESPEN","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405457725028645","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
Analysis of influencing factors of malnutrition in perioperative neurosurgery patients — A cross-sectional survey
Objective
This study aimed to analyze the factors influencing malnutrition in perioperative neurosurgery patients using the random forest algorithm and logistic regression.
Methods
A cross-sectional survey was conducted among 330 perioperative neurosurgery patients from eight Class A tertiary general hospitals in Shanghai. The random forest algorithm was employed to rank the importance of independent variables potentially affecting malnutrition, and the top eight variables were subsequently included in a logistic regression model.
Results
The random forest algorithm identified total protein level, prealbumin level, hematocrit, hemoglobin level, CRP level, glycosylated hemoglobin level, leg circumference, and neutrophil count as the primary factors influencing nutritional status. Logistic regression analysis showed that HBA1c level was a risk factor for malnutritional (OR = 1.316, 95%CI:1.066–1.624, P < 0.05), while total protein level (OR = 0.869, 95%CI:0.826–0.915, P < 0.001), hematocrit volume (OR = 0.848, 95%CI: 0.740–0.972, P = 0.019), and calf circumference (OR = 0.887, 95%CI: 0.789–0.997, P = 0.045) were protective factors.
Conclusion
It is recommended to enhance nutrition support and diabetes management, along with implement early nutrition intervention for high-risk groups, to mitigate the risk of malnutrition.
期刊介绍:
Clinical Nutrition ESPEN is an electronic-only journal and is an official publication of the European Society for Clinical Nutrition and Metabolism (ESPEN). Nutrition and nutritional care have gained wide clinical and scientific interest during the past decades. The increasing knowledge of metabolic disturbances and nutritional assessment in chronic and acute diseases has stimulated rapid advances in design, development and clinical application of nutritional support. The aims of ESPEN are to encourage the rapid diffusion of knowledge and its application in the field of clinical nutrition and metabolism. Published bimonthly, Clinical Nutrition ESPEN focuses on publishing articles on the relationship between nutrition and disease in the setting of basic science and clinical practice. Clinical Nutrition ESPEN is available to all members of ESPEN and to all subscribers of Clinical Nutrition.