目的 基于营养炎症代谢相关指标构建Ⅱ～Ⅲ期胃癌根治术后患者3年肿瘤特异性死亡风险的列线图模型,并探讨其潜在临床价值,为胃癌患者的个体化预后评估与管理提供循证依据。方法 回顾性选取2018年1月至2022年12月于首都医科大学附属北京友谊医院诊治的363例Ⅱ～Ⅲ期胃癌根治术患者为研究对象,按7∶3比例随机分为训练集(254例)和验证集(109例),训练集根据预后结局分为死亡组(91例)和生存组(163例)。收集患者临床基线资料及营养、炎症、代谢相关实验室指标,采用Cox比例风险回归分析筛选胃癌患者肿瘤特异性死亡的独立影响因素,基于筛选结果构建预后列线图模型;通过一致性指数(C-index)、时间依赖受试者操作特征(ROC)曲线、Gronnesby-Borgan检验、Bootstrap自举重抽样及决策曲线分析(DCA),验证模型的区分度、拟合优度与临床应用价值。结果 Cox比例风险回归模型显示,美国东部肿瘤协作组体力状况评分(ECOG-PS评分)2～3分(HR=2.074,95%CI=1.351～3.186,P=0.001)、中性粒细胞与淋巴细胞比值(NLR)≥2.66(HR=1.753,95%CI=1.132～2.714,P=0.012)、血小板与淋巴细胞比值(PLR)≥151.72(HR=2.258,95%CI=1.456～3.502,P<0.001)、白蛋白-胆红素(ALBI)2级(HR=2.057,95%CI=1.342～3.152,P=0.001)、预后免疫-炎症-营养指数(PIIN)≥31.49(HR=2.120,95%CI=1.366～3.291,P=0.001)是胃癌患者肿瘤特异性死亡的独立危险因素;白蛋白/球蛋白比值(AGR)≥1.46(HR=0.415,95%CI=0.263～0.654,P<0.001)、预后营养指数(PNI)≥45(HR=0.571,95%CI=0.357～0.912,P=0.019)是胃癌患者肿瘤特异性死亡的独立保护因素。基于上述指标构建的预后列线图模型,预测胃癌患者肿瘤特异性死亡的C-index为0.850(95%CI=0.799～0.901);训练集1年生存、2年生存、3年生存的曲线下面积(AUC)分别为0.729、0.783、0.829,验证集分别为0.590、0.839、0.829;Gronnesby-Borgan检验显示训练集(χ2=8.762,P=0.271)与验证集(χ2=9.125,P=0.244)拟合优度良好;Bootstrap自举重抽样内部验证与DCA结果显示,模型预测胃癌患者肿瘤特异性死亡的净获益区间均较大,有助于临床决策。结论 基于营养、炎症、代谢多维指标构建的胃癌患者预后列线图模型具有良好的预测效能与临床实用性,可实现胃癌患者的预后风险分层,同时为胃癌患者围手术期营养支持、抗炎及代谢调节等个体化干预策略的制订提供科学依据。
Abstract
Objective To construct a nomogram model for predicting the 3-year tumor-specific mortality risk of patients with stage II-III gastric cancer after radical resection based on nutritional inflammation metabolism-related indicators, and to explore its potential clinical value, in order to provide evidence-based basis for individualized prognosis assessment and management of gastric cancer patients. Method A total of 363 patients with stage II-III gastric cancer who underwent radical resection at Beijing Friendship Hospital, Capital Medical University from January 2018 to December 2022 were retrospectively selected as the research subjects. They were randomly divided into a training set (254 cases) and a validation set (109 cases) at a ratio of 7:3. The training set was divided into a death group (91 cases) and a survival group (163 cases) based on the prognosis outcome. Clinical baseline data and nutritional, inflammatory, and metabolic-related laboratory indicators of the patients were collected. Cox proportional hazards regression analysis was used to screen the independent influencing factors of tumor-specific death in gastric cancer patients. Based on the screening results, a prognostic nomogram model was constructed. The discrimination, goodness-of-fit, and clinical application value of the model were verified through consistency index (C-index), time-dependent ROC curve, Gronnesby-Borgan test, Bootstrap self-repeated sampling, and decision curve analysis (DCA). Result Cox proportional hazards regression analysis showed that Eastern Cooperative Oncology Group performance status (ECOG-PS)of 2-3 (HR=2.074, 95%CI=1.351-3.186,P=0.001), neutrophil-to-lymphocyte ratio (NLR)≥2.66 (HR=1.753,95%CI=1.132-2.714,P=0.012), platelet-to-lymphocyte ratio (PLR) ≥ 151.72 (HR=2.258,95%CI=1.456-3.502,P<0.001), albumin-bilirubin (ALBI) grade 2 (HR=2.057, 95%CI=1.342-3.152,P=0.001), and prognostic immune-inflammation-nutrition index (PIIN) ≥ 31.49 (HR=2.120, 95%CI=1.366-3.291, P=0.001) were independent risk factors for tumor-specific death in gastric cancer patients; albumin/globulin ratio (AGR) ≥ 1.46 (HR=0.415,95%CI=0.263-0.654,P<0.001), and prognostic nutrition index (PNI) ≥ 45 (HR=0.571, 95%CI=0.357-0.912, P=0.019) were independent protective factors for tumor-specific death in gastric cancer patients. The C-index of the prognostic nomogram model based on the above indicators for predicting tumor-specific death in gastric cancer patients was 0.850 (95%CI=0.799-0.901); the AUC of 1-year, 2-year, and 3-year survival in the training set was 0.729, 0.783, and 0.829, respectively, and in the validation set were 0.590, 0.839, and 0.829; the Gronnesby-Borgan test showed that the fitting goodness of the training set (χ2=8.762,P=0.271) and the validation set (χ2=9.125,P=0.244) was good; the internal validation and decision curve of the Bootstrap method showed that the net benefit interval of the model for predicting tumor-specific death in gastric cancer patients was large, which was helpful for clinical decision-making. Conclusion The prognostic nomogram model constructed based on multiple-dimensional indicators of nutrition, inflammation, and metabolism in gastric cancer patients has good predictive efficacy and clinical practicability, can stratify the prognosis risk of gastric cancer patients, and provides scientific basis for the formulation of individualized intervention strategies such as perioperative nutritional support, anti-inflammation, and metabolic regulation for gastric cancer patients.
关键词
胃癌 /
根治性切除术 /
营养状态 /
炎症反应 /
肿瘤代谢 /
预后 /
列线图 /
危险因素
Key words
Gastric cancer /
Radical resection /
Nutritional status /
Inflammatory response /
Tumor metabolism /
Prognosis /
Nomogram /
Risk factors
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