目的 探讨营养控制状态评分（controlling nutritional status， CONUT）联合预后营养指数（prognostic nutritional index， PNI）在老年结直肠癌患者贫血风险评估中的临床应用价值，构建贫血风险预测模型。 方法 回顾性收集2018年7月至2025年3月在新疆维吾尔自治区人民医院就诊的661例老年结直肠癌患者临床资料，依据是否合并贫血分组，并按7∶3比例随机分为训练集和验证集。采用XGBoost算法构建贫血风险预测模型，通过ROC曲线、SHAP值可视化等方法评估模型判别能力与可解释性。 结果 贫血组患者占比38.9%（257/661）。与对照组相比，贫血组患者PNI和白蛋白水平显著降低，CONUT评分和尿素氮水平显著升高，肿瘤最大径≥ 5 cm、低分化组织分级、Ⅲ—Ⅳ期TNM分期患者比例更高（P < 0.05）。XGBoost模型判别能力良好，AUC为0.897（95%CI：0.868 ~ 0.925）。SHAP值分析显示PNI、CONUT评分、白蛋白、尿素氮、TNM分期、组织分级、肿瘤最大径为模型主要贡献变量，其中PNI和白蛋白为保护因素，CONUT评分、尿素氮及肿瘤相关指标为危险因素。 结论 PNI、CONUT评分等营养指标及肿瘤特征可用于预测老年结直肠癌患者贫血风险。基于XGBoost构建的模型具备较高判别能力和良好解释性，有助于临床早期筛查高风险患者，指导个体化营养干预和贫血管理。

Objective This study aimed to assess the clinical utility of combining the Controlling Nutritional Status （CONUT） score with the Prognostic Nutritional Index （PNI） for evaluating anemia risk in elderly colorectal cancer patients and to establish a risk prediction model. Methods A total of 661 elderly colorectal cancer patients treated at Xinjiang Uygur Autonomous Region People's Hospital from July 2018 to March 2025 were included in this retrospective study. Patients were categorized into anemic and non-anemic groups and randomly assigned to a training set and validation set at a 7∶3 ratio. The XGBoost algorithm was applied to develop a predictive model for anemia risk， and its performance was assessed using the receiver operating characteristic （ROC） curve. SHAP value visualization， and other methods. Results Among the 661 patients， 257 （38.9%） were diagnosed with anemia. Compared with the non-anemic group， patients in the anemic group had significantly lower levels of PNI and albumin， but higher CONUT scores and blood urea nitrogen levels. Additionally， the anemic group had higher proportions of tumor diameter ≥ 5 cm， poorly differentiated tumors， and stage Ⅲ?Ⅳ disease （all P < 0.05）. The XGBoost model demonstrated good discriminatory ability， with an AUC of 0.897 （95%CI：0.868 ~ 0.925）. SHAP value analysis identified PNI， CONUT score， albumin， blood urea nitrogen， TNM stage， tumor differentiation， and tumor size as major contributing variables. PNI and albumin were protective factors， whereas CONUT score， blood urea nitrogen， and tumor-related features were risk factors. Conclusion Nutritional indicators such as PNI and CONUT score， along with tumor characteristics， can effectively predict the risk of anemia in elderly patients with colorectal cancer. The XGBoost-based predictive model demonstrates high discriminatory power and good interpretability， providing valuable support for early screening of high-risk patients and guiding individualized nutritional interventions and anemia management.