中文译文：癌症患者衰弱诊断列线图的建立和验证。
Development and validation of a diagnostic nomogram for frailty in cancer patients.
机构信息
Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China; Key Laboratory of Intelligent Clinical Nutrition and Transformation for Chongqing Municipal Health Commission, Chongqing, China.
Department of Clinical Nutrition, The Thirteenth People's Hospital, Chongqing, China; Key Laboratory of Intelligent Clinical Nutrition and Transformation for Chongqing Municipal Health Commission, Chongqing, China.
出版信息
Nutrition. 2024 Oct;126:112531. doi: 10.1016/j.nut.2024.112531. Epub 2024 Jul 14.
BACKGROUND
The presence of frailty decreases the overall survival of cancer patients. An accurate and operational diagnostic method is needed to help clinicians choose the most appropriate treatment to improve patient outcomes.
METHODS
Data were collected from 10 649 cancer patients who were prospectively enrolled in the Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers (INSCOC) project in China from July 2013 to August 2022. The training cohort and validation cohort were randomly divided at a ratio of 7:3. The multivariable logistic regression analysis, multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO) method were used to develop the nomogram. The concordance index and calibration curve were used to assess the diagnostic utility of the nomogram model.
RESULTS
The 10 risk factors associated with frailty in cancer patients were age, AJCC stage, liver cancer, hemoglobin, radiotherapy, surgery, hand grip strength (HGS), calf circumference (CC), PG-SGA score and QOL from the QLQ-C30. The diagnostic nomogram model achieved a good C index of 0.847 (95% CI, 0.832-0.862, P < 0.001) in the training cohort and 0.853 (95% CI, 0.83-0.876, P < 0.001) in the validation cohort. The prediction nomogram showed 1-, 3-, and 5-year mortality C indices in the training cohort of 0.708 (95% CI, 0.686-0.731), 0.655 (95% CI, 0.627-0.683), and 0.623 (95% CI, 0.568-0.678). The 1-, 3-, and 5-year C indices in the validation cohort were similarly 0.743 (95% CI, 0.711-0.777), 0.680 (95% CI, 0.639-0.722), and 0.629 (95% CI, 0.558-0.700). In addition, the calibration curves and decision curve analysis (DCA) were well-fitted for both the diagnostic model and prediction model.
CONCLUSIONS
The nomogram model provides an accurate method to diagnose frailty in cancer patients. Using this model could lead to the selection of more appropriate therapy and a better prognosis for cancer patients.
背景
虚弱的存在会降低癌症患者的总体生存率。需要一种准确且可行的诊断方法来帮助临床医生选择最合适的治疗方法，以改善患者的预后。
方法
数据来自于 2013 年 7 月至 2022 年 8 月期间在中国进行的“癌症患者营养状况及其临床结局调查（INSCOC）”项目前瞻性纳入的 10649 例癌症患者。训练队列和验证队列按 7:3 的比例随机划分。采用多变量逻辑回归分析、多变量 Cox 回归分析和最小绝对收缩和选择算子（LASSO）方法构建列线图。采用一致性指数和校准曲线评估列线图模型的诊断效能。
结果
与癌症患者虚弱相关的 10 个危险因素为年龄、AJCC 分期、肝癌、血红蛋白、放疗、手术、握力（HGS）、小腿围（CC）、PG-SGA 评分和 QLQ-C30 的 QOL。在训练队列中，诊断列线图模型的 C 指数为 0.847（95%CI，0.832-0.862，P<0.001），在验证队列中为 0.853（95%CI，0.83-0.876，P<0.001）。预测列线图在训练队列中显示出 1、3 和 5 年死亡率的 C 指数分别为 0.708（95%CI，0.686-0.731）、0.655（95%CI，0.627-0.683）和 0.623（95%CI，0.568-0.678）。验证队列中 1、3 和 5 年的 C 指数分别为 0.743（95%CI，0.711-0.777）、0.680（95%CI，0.639-0.722）和 0.629（95%CI，0.558-0.700）。此外，校准曲线和决策曲线分析（DCA）均适用于诊断模型和预测模型。
结论
列线图模型提供了一种准确的方法来诊断癌症患者的虚弱。使用该模型可以为癌症患者选择更合适的治疗方法，并获得更好的预后。
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