谷俊杰, 杨莹, 王晨宇, 高洋, 由婷婷, 白春梅, 赵林. 70岁及以上临床晚期胃癌患者化疗相关不良反应的列线图预测模型的构建与验证[J]. 中国肿瘤临床, 2022, 49(10): 507-511. DOI: 10.12354/j.issn.1000-8179.2022.20211824
引用本文: 谷俊杰, 杨莹, 王晨宇, 高洋, 由婷婷, 白春梅, 赵林. 70岁及以上临床晚期胃癌患者化疗相关不良反应的列线图预测模型的构建与验证[J]. 中国肿瘤临床, 2022, 49(10): 507-511. DOI: 10.12354/j.issn.1000-8179.2022.20211824
Junjie Gu, Ying Yang, Chenyu Wang, Yang Gao, Tingting You, Chunmei Bai, Lin Zhao. Construction and validation of a nomogram for predicting chemotherapy-related toxicities in advanced gastric cancer patients aged over 70 years[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2022, 49(10): 507-511. DOI: 10.12354/j.issn.1000-8179.2022.20211824
Citation: Junjie Gu, Ying Yang, Chenyu Wang, Yang Gao, Tingting You, Chunmei Bai, Lin Zhao. Construction and validation of a nomogram for predicting chemotherapy-related toxicities in advanced gastric cancer patients aged over 70 years[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2022, 49(10): 507-511. DOI: 10.12354/j.issn.1000-8179.2022.20211824

70岁及以上临床晚期胃癌患者化疗相关不良反应的列线图预测模型的构建与验证

Construction and validation of a nomogram for predicting chemotherapy-related toxicities in advanced gastric cancer patients aged over 70 years

  • 摘要:
      目的  本研究拟在≥70岁临床晚期胃癌患者中探索化疗相关毒性(chemotherapy related toxicities,CRT)相关危险因素并建立列线图预测CRT的风险。
      方法  选取2003年7月至2020年8月就诊于北京协和医院的≥70岁临床晚期胃癌患者,收集其临床资料,通过Logistic回归模型探索与CRT相关的危险因素,随后构建列线图模型来预测CRT发生的概率,通过自举重采样技术验证模型,采用校准曲线检验模型的一致性。
      结果  纳入研究的178例患者中CRT的发生率为41%。其中女性、ECOG≥1分、体质量减轻、化疗前血白蛋白<30 g/L、化疗前中性粒细胞与淋巴细胞比值(neutrophil and lymphocyte ratio,NLR)<4和化疗前血小板水平是CRT的独立危险因素。基于上述6个潜在危险因素构建列线图模型,该模型具有良好的诊断能力曲线下面积(AUC)=0.716,95%CI:0.677~0.755和良好的校准和拟合能力。
      结论  本研究构建了一个列线图模型预测≥70岁临床晚期胃癌患者CRT发生的概率。该模型可作为一种无创的、方便的模型早期预测这些患者CRT的发生。

     

    Abstract: Objective: To investigate the risk factors for chemotherapy related toxicities (CRT) and construct a nomogram for CRT risk estimation in patients aged over 70 years with advanced gastric cancer. Methods: Patients aged over 70 years with advanced gastric cancer who were admitted to Peking Union Medical College Hospital from July 2003 to August 2020 were enrolled in this retrospective study. Clinicopathological data of these patients were obtained. Logistic regression analyses were performed to investigate the relationship between variables and CRT. A nomogram for CRT was constructed and verified using bootstrap resampling. The calibration curve was applied to demonstrate the conformity between the model curve and ideal curve. Results: Among the 178 patients, the incidence of CRT was 41%. Female, ECOG ≥1, weight loss, prechemotherapy blood albumin <30g/L, prechemotherapy neutrophil and lymphocyte ratio (NLR) <4, and prechemotherapy platelet count were independent risk factors for CRT. A nomogram of CRT was developed based on these six risk factors. The nomogram showed good diagnostic performance area under the curve (AUC)=0.716, 95% confidence interval (CI)=0.677–0.755. This nomogram presented good fitting in the calibration curve. Conclusions: We constructed a nomogram for predicting CRT risk in patients with advanced gastric cancer aged over 70 years. This nomogram may be applied as a non-invasive and convenient model to detect early warning signs of CRT in these patients.

     

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