李蕾, 陈燕, 邵松, 郑权, 施学芝. 创伤骨折术后病人营养不良风险预测模型的构建[J]. 蚌埠医科大学学报, 2023, 48(7): 967-970, 975. DOI: 10.13898/j.cnki.issn.1000-2200.2023.07.026
    引用本文: 李蕾, 陈燕, 邵松, 郑权, 施学芝. 创伤骨折术后病人营养不良风险预测模型的构建[J]. 蚌埠医科大学学报, 2023, 48(7): 967-970, 975. DOI: 10.13898/j.cnki.issn.1000-2200.2023.07.026
    LI Lei, CHEN Yan, SHAO Song, ZHENG Quan, SHI Xue-zhi. Construction of a risk prediction model for malnutrition in patients after traumatic fracture surgery[J]. Journal of Bengbu Medical University, 2023, 48(7): 967-970, 975. DOI: 10.13898/j.cnki.issn.1000-2200.2023.07.026
    Citation: LI Lei, CHEN Yan, SHAO Song, ZHENG Quan, SHI Xue-zhi. Construction of a risk prediction model for malnutrition in patients after traumatic fracture surgery[J]. Journal of Bengbu Medical University, 2023, 48(7): 967-970, 975. DOI: 10.13898/j.cnki.issn.1000-2200.2023.07.026

    创伤骨折术后病人营养不良风险预测模型的构建

    Construction of a risk prediction model for malnutrition in patients after traumatic fracture surgery

    • 摘要:
      目的探讨创伤骨折术后病人营养状态和发生营养不良的影响因素,构建预测创伤骨折术后病人营养不良的列线图模型。
      方法选取400例创伤骨折手术病人为研究对象,采用简易营养评价量表(MNA)评估病人营养状况,按MNA评分分为营养不良组110例和非营养不良组290例,采用单因素分析和logistic回归分析评价创伤骨折术后病人营养不良的风险因素,并建立预测创伤骨折术后病人发生营养不良的列线图模型。
      结果400例创伤骨折手术病人中,110例(27.50%)病人存在营养不良。单因素分析结果显示,2组病人年龄、体质量指数、吸烟史、高血压、糖尿病、慢性胃炎、骨折类型、血红蛋白、白蛋白、出血量差异均有统计学意义(P < 0.05~P < 0.01)。多因素logistic回归分析结果显示,年龄、糖尿病、慢性胃炎、骨折类型、血红蛋白、白蛋白是创伤骨折术后病人营养不良的独立影响因素(P < 0.05~P < 0.01)。利用上述指标构建列线图模型,该模型预测创伤骨折术后营养不良发生的曲线下面积为0.865(95%CI:0.825~0.905),敏感度为81.51%,特异度为79.82%。
      结论创伤骨折术后病人营养不良或存在营养不良风险的发生率较高,年龄、糖尿病、慢性胃炎、骨折类型、血红蛋白、白蛋白是创伤骨折术后营养不良的独立影响因素。列线图模型能直观、简洁地为创伤骨折术后病人提供个体化的营养不良风险预测。

       

      Abstract:
      ObjectiveTo explore the nutritional status and risk factors of malnutrition in patients after traumatic fracture surgery, and to construct a nomograph model to predict malnutrition in patients after traumatic fracture surgery.
      MethodsA total of 400 patients undergoing traumatic fracture surgery were selected as the study subjects, and their nutritional status was evaluated using the mini-nutritional assessment (MNA).The patients were divided into malnutrition group (n=110) and non-malnutrition group (n=290) according to the MVA scores.Univariate analysis and logistic regression analysis were applied to evaluate the risk factors of malnutrition in patients after traumatic fracture surgery, and a nomograph model was constructed to predict the incidence of malnutrition in patients after traumatic fracture surgery.
      ResultsAmong the 400 patients undergoing trauma fracture surgery, 110 cases (27.50%) had malnutrition.The results of univariate analysis showed that there were significant differences in age, body mass index, smoking history, hypertension, diabetes mellitus, chronic gastritis, fracture type, hemoglobin, albumin, and blood loss between the two groups (P < 0.05 to P < 0.01).Multivariate logistic regression analysis showed that age, diabetes mellitus, chronic gastritis, fracture type, hemoglobin and albumin were independent influencing factors for malnutrition in patients after traumatic fracture surgery (P < 0.05 to P < 0.01).Using the above indicators to construct a nomograph model, the area under the curve for predicting malnutrition after traumatic fracture surgery was 0.865 (95%CI: 0.825-0.905), with a sensitivity of 81.51% and a specificity of 79.82%.
      ConclusionsThe incidence of malnutrition or malnutrition risk in patients after traumatic fracture surgery is high.Age, diabetes mellitus, chronic gastritis, fracture type, hemoglobin and albumin are independent influencing factors of malnutrition after traumatic fracture surgery.The nomograph model can directly and concisely provide personalized malnutrition risk prediction for patients after traumatic fracture surgery.

       

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