实用医学杂志 ›› 2026, Vol. 42 ›› Issue (8): 1456-1462.doi: 10.3969/j.issn.1006-5725.2026.08.021

• 论著·临床实践 • 上一篇    下一篇

老年营养风险指数对红皮病患者感染风险的预测价值及分层管理策略

许素哗1,何伟2,郭丽娜1,林淑娴1,何盈犀1,许欣筑1,盛雯1,邱森玲3,4()   

  1. 1.广东省中医院,临床营养科,(广东 广州 510180 )
    2.广东省中医院,皮肤科,(广东 广州 510180 )
    3.广东省医学学术交流中心(广东省医学情报研究所) (广东 广州 510180 )
    4.广东省医学会医学创新与成果转化部 (广东 广州 510180 )
  • 收稿日期:2025-12-30 出版日期:2026-04-25 发布日期:2026-04-28
  • 通讯作者: 邱森玲 E-mail:157038752@qq.com
  • 基金资助:
    广东省医学科研基金项目(B2025457)

Predictive value of the geriatric nutritional risk index for infection risk and implications for stratified management in patients with erythroderma

Suhua XU1,Wei HE2,Lina GUO1,Shuxian LIN1,Yingxi HE1,Xinzhu XU1,Wen SHENG1,Senling QIU3,4()   

  1. 1.Department of Clinical Nutrition,Guangdong Provincial Hospital of Chinese Medicine,,Guangzhou 510180,Guangdong,China
    2.Department of Dermatology,Guangdong Provincial Hospital of Chinese Medicine,,Guangzhou 510180,Guangdong,China
    3.Guangdong Medical Academic Exchange Center (Guangdong Medical Information Research Institute),,Guangzhou 510180,Guangdong,China
    4.Department of Medical Innovation and Achievement Transformation,Guangdong Medical Association,Guangzhou 510180,Guangdong,China
  • Received:2025-12-30 Online:2026-04-25 Published:2026-04-28
  • Contact: Senling QIU E-mail:157038752@qq.com

摘要:

目的 探讨老年营养风险指数(geriatric nutritional risk index,GNRI)与红皮病患者预后的关联,评估其预测不良结局的效能,以提供基于营养风险分层管理的决策依据。 方法 纳入2017年8月至2024年10月期间于广东省中医院住院且年龄≥ 60岁的红皮病患者共183例,收集患者的临床资料并计算GNRI。根据GNRI将研究对象分为无营养风险组(n = 78)、轻度营养风险组(n = 39)和中重度营养风险组(n = 66)。采用广义线性模型分析GNRI与预后的关联,绘制受试者工作特征曲线评估GNRI对预后的预测效能。 结果 中重度营养风险组合并感染风险显著升高(OR = 4.81, 95% CI: 1.54 ~ 15.99,P = 0.007),尤其是肺部感染风险更高(OR = 8.00,95%CI:1.01 ~ 63.28,P = 0.049),住院时间显著延长(β = 2.53,95% CI:0.96 ~ 4.10,P = 0.002)。GNRI预测红皮病患者合并感染的曲线下面积(area under curve,AUC)为0.73(95%CI:0.63 ~ 0.83),预测肺部感染的AUC为0.81(95%CI:0.65 ~ 0.96)。 结论 GNRI是预测老年红皮病患者感染(尤其是肺部感染)的有效指标,中重度营养风险与感染增加及住院时间延长显著相关。因此,建议将GNRI作为老年红皮病的常规营养筛查工具,并依据其风险分层进行早期干预,以改善患者临床预后。

关键词: 红皮病, 老年营养风险指数, 预测价值, 广义线性模型, 受试者工作特征曲线

Abstract:

Objective To investigate the association between the Geriatric Nutritional Risk Index (GNRI) and the prognosis of patients with erythroderma, and to assess its effectiveness in predicting adverse outcomes, thus providing a basis for nutrition risk stratification and management. Methods A total of 183 hospitalized patients aged ≥ 60 years with erythroderma from the Guangdong Provincial Hospital of Chinese Medicine between August 2017 and October 2024 were recruited. Clinical data were gathered, and the GNRI was computed. The subjects were classified into a no nutritional risk group (n = 78), a mild nutritional risk group (n = 39), and a moderate-to-severe nutritional risk group (n = 66) according to their GNRI. A generalized linear model was employed to analyze the association between the GNRI and prognosis, and receiver operating characteristic (ROC) curves were drawn to assess the predictive performance of the GNRI for prognosis. Results The moderate-to-severe nutritional risk group exhibited a significantly elevated risk of infection (OR = 4.81, 95%CI: 1.54 - 15.99, P = 0.007). Specifically, there was a higher risk of pulmonary infection (OR = 8.00, 95%CI: 1.01-63.28, P = 0.049), and a significantly extended hospital stay (β = 2.53, 95%CI: 0.96 - 4.10, P = 0.002). The area under the curve (AUC) of GNRI for predicting infection in patients with erythroderma was 0.71 (95%CI: 0.61 - 0.81), and the AUC for predicting pulmonary infection was 0.81 (95%CI: 0.65 - 0.96). Conclusions The GNRI serves as an effective indicator for predicting infections, especially pulmonary infections, among elderly patients suffering from erythroderma. Moderate-to-severe nutritional risk is notably correlated with elevated infection rates and extended hospital stays. Consequently, it is advisable to adopt the GNRI as a routine nutritional screening tool for elderly erythroderma patients and to carry out early interventions based on risk stratification to enhance clinical outcomes.

Key words: erythroderma, geriatric nutritional risk index, predictive value, generalized linear model, receiver operating characteristic curve

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