Abstract：
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.
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.
摘要：
背景：虚弱的存在会降低癌症患者的总体生存率。需要一种准确且可操作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：收集了2013年7月至2022年8月在中国前瞻性招募的10649名癌症患者的数据。训练队列和验证队列以7:3的比例随机划分。多变量Logistic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）方法用于开发列线图。使用一致性指数和校准曲线来评估列线图模型的诊断实用性。
结果：与癌症患者虚弱相关的10个危险因素是年龄，AJCC阶段，肝癌,血红蛋白,放射治疗,手术,手握力(HGS),小腿周长(CC),来自QLQ-C30的PG-SGA评分和QOL。诊断列线图模型在训练队列中达到良好的C指数0.847（95％CI，0.832-0.862，P<0.001），在验证队列中达到0.853（95％CI，0.83-0.876，P<0.001）。预测列线图显示1-，3-,训练队列中的5年死亡率C指数为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）。1-,3-,验证队列中的5年C指数为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,诊断模型和预测模型的校准曲线和决策曲线分析(DCA)均拟合良好.
结论：列线图模型提供了诊断癌症患者虚弱的准确方法。使用该模型可以为癌症患者选择更合适的治疗方法和更好的预后。
方法：收集了2013年7月至2022年8月在中国前瞻性招募的10649名癌症患者的数据。训练队列和验证队列以7:3的比例随机划分。多变量Logistic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）方法用于开发列线图。使用一致性指数和校准曲线来评估列线图模型的诊断实用性。
结果：与癌症患者虚弱相关的10个危险因素是年龄，AJCC阶段，肝癌,血红蛋白,放射治疗,手术,手握力(HGS),小腿周长(CC),来自QLQ-C30的PG-SGA评分和QOL。诊断列线图模型在训练队列中达到良好的C指数0.847（95％CI，0.832-0.862，P<0.001），在验证队列中达到0.853（95％CI，0.83-0.876，P<0.001）。预测列线图显示1-，3-,训练队列中的5年死亡率C指数为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）。1-,3-,验证队列中的5年C指数为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,诊断模型和预测模型的校准曲线和决策曲线分析(DCA)均拟合良好.
结论：列线图模型提供了诊断癌症患者虚弱的准确方法。使用该模型可以为癌症患者选择更合适的治疗方法和更好的预后。
