鼻咽癌放疗患者显著体重丢失的影响因素及预测模型

1,2陈利平,2夏 乐,1史 蕾

肿瘤代谢与营养电子杂志 ›› 2023, Vol. 10 ›› Issue (1) : 133-139.

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肿瘤代谢与营养电子杂志 ›› 2023, Vol. 10 ›› Issue (1) : 133-139.
论著

鼻咽癌放疗患者显著体重丢失的影响因素及预测模型

  • 1,2陈利平,2夏 乐,1史 蕾
作者信息 +

Critical weight loss among nasopharyngeal carcinoma patients undergoing intensity-modulated radiation therapy influencing factors analysis and prediction model establishment

  • 1,2Chen Liping ,2Xia Le,1Shi Lei
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摘要

目的 明确鼻咽癌患者调强放射治疗(IMRT)期间显著体重丢失的发生情况,探索影响患者显著体重丢失的关键因 素,建立预测模型。 方法 前瞻性收集 377 例接受 IMRT 鼻咽癌患者的一般人口学资料、临床特征、营养状况,以放疗期间体重 丢失≥10%作为判断显著体重丢失的标准,将其分为显著体重丢失组(CWL 组,154 例)和无显著体重丢失组( non-CWL 组, 233 例)。 单因素分析用于候选因子的初步选择,选取有统计学意义的变量进行多因素 Logistic 回归分析,建立预测模型。 绘 制受试者操作特征(ROC)曲线检测模型的预测效能,采用 Hosmer-Lemeshow(H-L)检验预测模型的拟合度。 结果 377 例鼻咽 癌患者中,有 154 例(40. 8%)出现了显著体重丢失。 两组患者的肿瘤家族史、合并心脑血管疾病、N 分期(UICC)、颈部放疗剂 量、体质指数(BMI)、放疗期间是否使用靶向药物、放疗期间是否使用化疗药物、Epstein-Barr(EB)病毒拷贝数、工作情况之间 的比较,差异有统计学意义(P<0. 10)。 多因素 Logistic 回归分析结果显示,无肿瘤家族史、高 N 分期、颈部放疗剂量高、高 BMI 值、放疗期间使用化疗药、放疗期间不使用靶向药是鼻咽癌患者放疗期间显著体重丢失的独立危险因素(P<0. 05)。 基于以上 6 个因素建立预测模型,ROC 曲线下面积为 72. 5%,灵敏度为 73. 9%,特异度为 57. 8%,H-L 检验结果为 P= 0. 25,提示模型有 较好地拟合效果及鉴别效能。 结论 所构建的预测模型能较好地预测鼻咽癌患者放疗期间显著体重丢失的发生风险,为临床 医护人员及时、针对性对高风险患者给予预防性营养治疗提供参考。

Abstract

Objective To analyze the influencing factors of critical weight loss among patients with nasopharyngeal carcinoma undergoing intensity-modulated radiation therapy IMRT and establish a prediction model. Method The general demographic data clinical characteristics and nutritional status of 377 patients with nasopharyngeal carcinoma NPC patients undergoing IMRT were prospectively collected. Taking weight loss ≥10% as the criteria for judging critical weight loss patients were divided into critical weight loss CWL group 154 cases and non-critical weight loss Non-CWL group 233 cases . Univariate analysis was performed first to select significant variables for multiple factor logistic regression analysis. And a prediction model was constructed based on the analysis results. Then use the area under the receiver operating characteristic ROC curve and the Hosmer-Lemeshow fit curve to evaluate the effectiveness and discrimination of the model. Result Among 377 nasopharyngeal carcinoma patients 154 patients were divided into CWL group. Univariate analysis showed that family history of tumor combined cardiovascular and cerebrovascular diseases N stage UICC the IMRT dose of neck BMI use of chemotherapy during radiotherapy use of targeted drugs during radiotherapy Epstein-Barr EB virus DNA copy number and working conditions were significant differences between two groups P< 0. 10 . The multivariate Logistic regression showed that no family history of tumor high N stage UICC high IMRT dose of neck high BMI use of chemotherapy during radiotherapy and no use of targeted drugs during radiotherapy were independent risk factors for CWL P<0. 05 . According to the results of multivariate Logistic regression analysis a prediction model for CWL in NPC patients undergoing IMRT was constructed and evaluated. The area under the ROC curve was 72. 5% the sensitivity was 73. 9% and the specificity was 57. 8% . The H - L fit curve results showed that P = 0. 25 suggested that the model has good fitting effect and discrimination efficiency. Conclusion The constructed prediction model has good predictive value and can be used as a tool to screen the risk of CWL in nasopharyngeal carcinoma patients undergoing IMRT. The model provides reference for clinical medical staff to ·133· 肿瘤代谢与营养电子杂志 2023 年 2 月 9 日第 10 卷第 1 期 Electron J Metab Nutr Cancer, Feb. 9, 2023, Vol. 10, No. 1 timely and targeted preventive nutritional support for high CWL risk patients with nasopharyngeal carcinoma during radiotherapy.

关键词

鼻咽癌 / 放射治疗 / 体重丢失 / 预测模型

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1,2陈利平,2夏 乐,1史 蕾. 鼻咽癌放疗患者显著体重丢失的影响因素及预测模型[J]. 肿瘤代谢与营养电子杂志. 2023, 10(1): 133-139
1,2Chen Liping,2Xia Le,1Shi Lei. Critical weight loss among nasopharyngeal carcinoma patients undergoing intensity-modulated radiation therapy influencing factors analysis and prediction model establishment[J]. Electronic Journal of Metabolism and Nutrition of Cancer. 2023, 10(1): 133-139

基金

广州市科技计划项目(202201011758) 深圳市“医疗卫生三名工程”项目资助(SZZYSM202108013)

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