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含营养指标的肝癌放化疗患者不良反应预测模型构建 分析
马东波,王仲
连云港市第一人民医院,徐州医科大学附属连云港医院临床营养科,江苏连云港222000
Construction and analysis of adverse reaction prediction model for liver cancer patients with radiotherapy and chemotherapy containing nutritional indicators
Ma Dongbo, Wang Zhong
Department of Clinical Nutrition, Lianyungang First People's Hospital, Lianyungang Hospital Affiliated to Xuzhou Medical University, Lianyungang 222000, Jiangsu, China
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摘要 目的探讨含营养指标的肝癌放化疗患者不良反应预测模型的构建。方法前瞻性选取2019年1月至2021年3月连云 港市第一人民医院收治的231例肝癌放化疗患者为研究对象,采用简单随机分组法选取70%(162例)作为建模集,30%(69例)作 为测试集,比较建模集放化疗后的相关资料,并应用多因素Logistic回归筛选放化疗不良反应的相关因素,构建预测模型,采用一 致性指数(C‐index)量化和校正曲线,评价模型效能,绘制决策曲线分析评估含营养指标模型的临床净受益,同时在测试集对模 型进行外部验证。结果放化疗1个疗程后,建模集74例(45.68%)发生不良反应归为不良反应组,88例(54.32%)未发生不良反 应归为无不良反应组。建模集两组肿瘤直径、控制营养状况(CONUT)评分、甲胎蛋白(AFP)、碱性磷酸酶(ALP)、γ‐谷氨酰基转 移酶(GGT)、脱‐γ‐羧基凝血酶原(DCP)和预后营养指数(PNI),差异均有统计学意义(均P<0.05);多因素Logistic回归分析显示, 肿瘤直径(OR=1.699,95%CI=1.117~2.583)、CONUT评分(OR=2.396,95%CI=1.205~4.763)、AFP(OR=1.068,95%CI=1.020~1.118)、 DCP(OR=1.013,95%CI=1.000~1.025)、GGT(OR=1.090,95%CI=1.037~1.144)和ALP(OR=1.013,95%CI=1.003~1.023)为放化疗 不良反应发生的独立危险因素,PNI(OR=0.913,95%CI=0.875~0.953)为不良反应发生的保护因素(均P<0.05)。Nomogram模型 预测不良反应发生的C‐index为0.867(95%CI=0.815~0.920),决策曲线显示,当含CONUT评分、PNI营养相关指标模型预测值在 (0~0.6)区间时,可提供附加临床受益。外部验证显示,测试集69例患者中,31例(44.93%)发生不良反应归为不良反应组,38例 (55.07%)未发生不良反应归为无不良反应组,该模型预测敏感度为90.32%,特异度为91.67%。结论含CONUT评分、PNI营养 指标构建的预测模型可提高预测肝癌放化疗患者不良反应发生的准确性。
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马东波
王仲
关键词 肝癌放化疗预后营养指数控制营养状态评分预测模型    
Abstract:Objective To explore the construction of a predictive model for adverse reactions of liver cancer patients with radiotherapy and chemotherapy containing nutritional indicators. Method Prospectively selected 231 patients with liver cancer radiotherapy and chemotherapy admitted to Lianyungang First People's Hospital from January 2019 to March 2021 as the research objects. Randomly selected 70% (162 cases) of cases as the modeling set, and 30% (69 cases) as the test set. Compared the relevant data of the modeling set after radiotherapy and chemotherapy, and apply multi⁃factor Logistic regression to screen the relevant factors, construct the prediction model, use the consistency index (C⁃index) to quantify and calibrate the curve, evaluate the performance of the model, draw the decision curve to analyze and evaluate the clinical net of the nutritional index model. At the same time, external verification of the model is performed on the test set. Result After one course of radiotherapy and chemotherapy, 74 cases (45.68%) in the modeling set had adverse reactions and were classified as the adverse reaction group, and 88 cases (54.32%) without adverse reactions were classified as the non⁃adverse reaction group. In modeling set two groups, tumor diameter, controlling nutritional status (CONUT) score, alpha⁃fetoprotein (AFP), alkaline phosphatase (ALP), γ⁃glutamyl transferase (GGT), des⁃γ⁃carboxy prothrombin (DCP), and prognostic nutritional index (PNI) had statistically significant differences (P<0.05). Multivariate Logistic analysis showed that tumor diameter (OR=1.699, 95%CI=1.117-2.583), CONUT score (OR=2.396, 95%CI=1.205-4.763), AFP (OR=1.068, 95%CI=1.020-1.118), DCP (OR=1.013, 95%CI=1.000-1.025), GGT (OR=1.090, 95%CI=1.037-1.144), ALP (OR=1.013, 95%CI=1.003-1.023) in liver cancer are independent risk factors for adverse reactions and chemotherapy. PNI (OR=0.913, 95%CI=0.875-0.953) is a protective factor for adverse reactions (all P<0.05). The Nomogram model predicts the occurrence of adverse reactions with a C⁃index of 0.867 (95%CI=0.815-0.920). The decision curve shows that when the predicted value of the model with CONUT score and PNI nutrition⁃related index is in the interval (0-0.6), additional clinical benefits can be provided. External validation showed that among the 69 patients in the validation set, 31 (44.93%) had adverse reactions and were classified as the adverse reaction group, and 38 (55.07%) without adverse reactions were classified as the non⁃adverse reaction group. The prediction sensitivity of the model was 90.32% and the specificity was 91.67%. Conclusion The prediction model constructed with CONUT score and PNI nutritional indicators can improve the accuracy of predicting the occurrence of adverse reactions in patients with liver cancer radiotherapy and chemotherapy.
Key wordsLiver cancer    Radiotherapy and chemotherapy    Prognostic nutritional index    Control nutritional status score; Predictive model
收稿日期: 2021-09-27     
通讯作者: 马东波,电子邮箱:mdpdoc@163.com   
引用本文:   
马东波,王仲. 含营养指标的肝癌放化疗患者不良反应预测模型构建 分析[J]. 肿瘤代谢与营养电子杂志, 2022, 9(2): 200-206.
Ma Dongbo, Wang Zhong. Construction and analysis of adverse reaction prediction model for liver cancer patients with radiotherapy and chemotherapy containing nutritional indicators. Electron J Metab Nutr Cancer, 2022, 9(2): 200-206.