Abstract：
OBJECTIVE: The key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients\' historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information.
METHODS: This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance.
RESULTS: NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/).
CONCLUSIONS: Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.
METHODS: This multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n = 3365) and a validation cohort (n = 1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance.
RESULTS: NRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/).
CONCLUSIONS: Nutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.
摘要：
目标：全球领导营养不良倡议（GLIM）的关键步骤是营养风险筛查，而结直肠癌（CRC）患者最合适的筛查工具尚不清楚。GLIM诊断依赖于减肥信息，偏见甚至无法回忆起患者的历史体重可能会导致对营养不良的错误估计。我们旨在比较几种筛查工具在GLIM诊断中的适用性，并建立机器学习（ML）模型来预测没有体重减轻信息的CRC患者的营养不良。
方法：这项多中心队列研究纳入了4487例CRC患者。通过Kaplan-Meier曲线比较了GLIM诊断与四种筛查工具相结合预测生存概率的能力。并选择最准确的一个作为营养不良参考标准。参与者被随机分配到一个训练队列（n=3365）和一个验证队列（n=1122）。采用了几种ML方法来建立没有体重减轻数据的营养不良预测模型。我们估计了特征重要性，并保留了前30%的变量用于重新训练简化模型。接收器工作特性曲线下的面积(AUC),准确度,灵敏度,并计算特异性以评估和比较模型性能.
结果：NRS-2002是CRC患者GLIM诊断的最合适的筛查工具，风险比最高(1.59；95%CI，1.43-1.77)。GLIM联合NRS-2002诊断为营养不良的患者共有2076例（46.3％）。简化的随机森林（RF）模型优于其他模型，AUC为0.830（95％CI，0.805-0.854），和准确性，敏感性和特异性分别为0.775、0.835和0.742。我们基于简化的RF模型部署了一个在线应用程序，以准确估计没有体重减轻信息的CRC患者的营养不良概率（https://zzuwt1998。shinyapps.io/dynomapp/)。
结论：2002年营养风险筛查是GLIM过程中最佳的初始营养风险筛查工具。RF模型优于其他模型，并开发了一种在线预测工具来正确识别营养不良高危患者。
方法：这项多中心队列研究纳入了4487例CRC患者。通过Kaplan-Meier曲线比较了GLIM诊断与四种筛查工具相结合预测生存概率的能力。并选择最准确的一个作为营养不良参考标准。参与者被随机分配到一个训练队列（n=3365）和一个验证队列（n=1122）。采用了几种ML方法来建立没有体重减轻数据的营养不良预测模型。我们估计了特征重要性，并保留了前30%的变量用于重新训练简化模型。接收器工作特性曲线下的面积(AUC),准确度,灵敏度,并计算特异性以评估和比较模型性能.
结果：NRS-2002是CRC患者GLIM诊断的最合适的筛查工具，风险比最高(1.59；95%CI，1.43-1.77)。GLIM联合NRS-2002诊断为营养不良的患者共有2076例（46.3％）。简化的随机森林（RF）模型优于其他模型，AUC为0.830（95％CI，0.805-0.854），和准确性，敏感性和特异性分别为0.775、0.835和0.742。我们基于简化的RF模型部署了一个在线应用程序，以准确估计没有体重减轻信息的CRC患者的营养不良概率（https://zzuwt1998。shinyapps.io/dynomapp/)。
结论：2002年营养风险筛查是GLIM过程中最佳的初始营养风险筛查工具。RF模型优于其他模型，并开发了一种在线预测工具来正确识别营养不良高危患者。
